I like the idea of AI taking care of boring tasks, including sorting through bills and financial paperwork. I can even see the appeal of it helping with shopping. But asking an AI assistant for financial advice and simply handing over the keys to your bank account are different, and this distinction is becoming a problem for some of America's biggest banks.

According to a new Axios report, financial institutions are encountering AI agents while they access customer accounts and seem to be doing more than requested by users. JPMorgan, Chase, Bank of America, and Morgan Stanley are all taking steps to protect their customers, but it's a sobering aspect of AI that raises real questions about how much to trust the tech in the world of finance.

While there's no confirmed epidemic of AI assistants emptying savings accounts and the reports don't establish any financial losses, it still makes me nervous to think that we've reached the point where banks are having to worry about whether an AI assistant has decided to improvise with somebody's money.

There's been a real push to encourage the use of ever-more independent AI agents. Every new demonstration seems to involve an assistant that can accomplish increasingly complicated tasks while its owner sits back and watches. That's impressive when the task is organizing a vacation itinerary, but less appealing when the assistant has access to the money that would pay for it.

AI experiments with money

It's notable that it isn't like AI agents are hacking their way into accounts, exactly. Customers might voluntarily share their login credentials with the AI to give the software access to financial information that banks have spent decades trying to protect. I've done it for my own tests, but it's always been simply a way for the AI to look at very specific elements of my finances, never to touch them.

Banking websites can be frustrating, financial paperwork is tedious, and managing money requires more attention than most of us would prefer to give it. An assistant that promises to handle the irritating parts sounds wonderful. But AI agents have a habit of interpreting instructions rather creatively. And while asking it to reduce your monthly expenses and getting a list of subscriptions you might want to cancel is great, giving that same assistant permission to make financial decisions means you're trusting its interpretation of a vague instruction with money you actually need.

That should make people uncomfortable. These systems can misunderstand requests and make incorrect judgments. We've all seen chatbots produce an answer that sounds perfectly reasonable until you discover they've invented half of it, and adding the ability to take real-world actions makes those mistakes considerably more consequential. The fact that banks are already encountering agents whose behavior raises questions about authorization suggests that those limits deserve much more attention.

Keeping AI outside the vault

I wouldn't go so far as to say banks should block AI assistants altogether. There are plenty of good reasons to let people connect these tools to their accounts, and I don't particularly like the idea of banks deciding which services customers are allowed to use.

But I'd be much less comfortable letting that same assistant actually do anything with my money I don't ask for. I'd want it to ask permission every single time, with no room for interpreting something I said last Tuesday as authorization to make a transfer today.

That might defeat some of the appeal of autonomous AI agents, but I'm perfectly fine with that. Saving a few seconds isn't worth the possibility of discovering that an AI assistant has made an expensive decision on my behalf. Most importantly, someone needs to be responsible when an agent makes a mistake, because telling customers to take it up with the chatbot isn't going to cut it.

The frustrating thing is that none of this should come as a surprise. AI companies are racing to give their agents more independence, often before they've worked out how to prevent them from making mistakes. We're supposed to be impressed that an assistant can complete a task without asking for help, but that becomes much less impressive when it does something the user never wanted.

CISOs and CIOs should not just chase the latest hype around AI that can escape sandboxes and morphing security threats, but bet on tried and true ROI of identity security.

It’s demonstrably true that the information security scene is transforming as agentic AI accelerates operational speed and potentially exposes new attack surfaces. Most AI providers have reported their latest generation models are so powerful that they are capable of escaping sandboxes and hacking organizations. Regardless if you believe this is hype, truth or genius marketing, what matters is this: information security leaders should rigorously check the basics of their discipline.

Yes, CISOs need to be ahead of the curve but this can’t be at the cost of dropping the ball on security and identity essentials. I say this because, all too often, even in sophisticated and otherwise mature enterprises, the latest breaches are still abusing the lack of essential controls, via credentials and permissions the agents should never have been able to reach.

And sometimes, thought leaders and experts attempting to understand and communicate rapid technology changes, are creating more confusion and distracting from first principles.

‘AI washing’ – the practice of overstating the importance of AI in a product, project or strategy – is real. We need to do more to demystify data security and separate hype from reality and place a higher value on specificity when we talk about risks.

The identity challenge

The industry's instinct to frame this as an "AI security" problem is understandable, but it is not actionable. For CISOs trying to reduce real risk, the more precise frame is identity.

For an example of what I’m talking about, consider onboarding. When people join an organization, they are granted privileges based on their needs, seniority and roles. A major part of contemporary information security is about staying on top of identity management, but that’s not easy.

In hypergrowth, companies hire very quickly. But IT leaders also need to stay abreast of many other people who need access to systems. These include freelancers, consultants, suppliers, auditors and work-experience temporary staff. That gap (unrevoked, over-permissioned, forgotten access) is exactly what autonomous AI agents exploited in the Hugging Face breach earlier this year.

The attack was novel. The underlying vulnerability was not. NHIs and human identities share the same failure mode: access granted, never reviewed, never revoked. That is the identity challenge, and it predates AI by decades.

A complex puzzle

IT today is complicated. CIOs stay in their roles for between three and five years. CISOs (and security staff generally) are in high demand, and organizations understandably turn to outsourcers for help. It’s easy for basic management disciplines like maintaining identity hygiene to get lost in the mix, such are the comings and goings of personnel.

And things are getting harder. Who owns an AI identity? Not every enterprise employs an identity specialist, so who is in charge when the identity is non-human? The CIO? What about the CISO? HR? How does the chief risk officer fit in?

It’s easy for tasks to fall between gaps. And even if onboarding is immaculate, who is running offboarding and the impacts on identity? The consequences of that question going unanswered are well documented. Verizon's 2026 Data Breach Investigations Report found that exploitation of vulnerabilities has overtaken stolen credentials as the top initial access vector for the first time in 19 years.

But credentials still drove the privilege escalation and lateral movement that followed. A vulnerability opens the door; an unmanaged identity walks through the building unchallenged.

And in the agentic AI era, who will be in charge of managing those NHIs that often have extraordinarily high permissions and license to roam? The Hugging Face breach answered that question in practice: nobody was. One over-privileged, unmonitored non-human identity provided cluster-admin access across two environments the moment it was compromised.

The AI was the intruder. Vulnerabilities have surged as an entry point precisely because AI agents are now extraordinary zero-day discovery engines, finding in hours what took human attackers weeks. But identity remains the common denominator: had proper least-privilege, just-in-time access and NHI governance been in place, the breach would have been contained even after the vulnerability was exploited. You cannot patch your way out of an identity problem.

Back to basics (and learning from the past)

If I think about a lot of the breaches I’ve seen, a lot of them relate to these seemingly obvious gaps that become chasms. It’s the left- behind orphan credentials, the ‘puppies with two owners’ and other grey areas that cause problems. ‘Hey. I thought you had this?’, people hear… when it’s too late.

To improve, we need to learn from the past, because we all know the famous quote (of uncertain authorship) about those who ignore history being condemned to repeat mistakes.

A relevant analogy here is cloud computing. Some of us lived through the datacenter wave, then the cloud wave hit. The Capital One breach of 2019 (over 100 million records exposed through a misconfigured AWS role) became the defining lesson of that era: cloud created vast new entitlement sprawl, and nobody was governing it. That breach, and others like it, drove the creation of Cloud Infrastructure Entitlement Management (CIEM) to bring least-privilege principles to cloud permissions at scale. AI is the current wave, and the pattern is similar.

Agents are being deployed with extraordinarily broad permissions, credentials are being embedded in pipelines without rotation or scoping, and ownership of who governs those entitlements is unclear. We will need the same focused response we built for the cloud. Not new acronyms, but the same rigorous application of least-privilege, just-in-time access, and entitlement governance, adapted to the new environment. And whatever wave follows AI will present the same challenge again. Identity is the constant.

Every wave reshapes the technology landscape, but the question of who (human or machine) has access to what, under what conditions, and for how long, never goes away. We just keep failing to answer it before the damage is done. If you weren't there, speak to a cloud veteran because so much of security today has to be about assembling teams of all talents. So use them and build consensus with clear allocation of who is responsible for what and a handover plan for when people leave or swap roles.

The current environment is replete with risks. Consider what AI has done to the act of building software: a non-coder like me can now spin up an app or deploy an agent in an afternoon. That is genuinely transformative, and dangerous. The population of people creating software that handles credentials, calls APIs, and interacts with production systems has exploded overnight, and most of them have never thought about identity hygiene in their lives.

Every time you use an LLM, you are storing credentials somewhere. As that population scales, so does the attack surface. We will see an avalanche of badly architected agents and apps built fast, deployed faster, and governed by nobody. It’s easy to offer up credentials to the world just as misconfigured AWS accounts led to major problems a couple of decades ago.

Today, everybody is excited about the abuses of agents running wild. These are hot stories, and yes, we should be concerned. But really, hackers are going to hack, just as they always have, and they will exploit any loopholes we offer up. The Verizon 2026 DBIR showed vulnerabilities are now the number one initial access vector, but identity is what converts a foothold into a catastrophe.

So many foundational security elements remain neglected like storing static credentials in plain sight. We can still do a lot more with Zero-Trust principles and enforcing least privilege to mitigate risks and reduce impacts. The tools exist and the discipline exists. CIEM was built precisely because the cloud created entitlement problems nobody had anticipated, and it worked, for organizations that adopted it seriously.

The same rigor applied to NHI governance and agentic AI today will determine whether the next wave of breach headlines features your organization or your competitor's. Let’s get the basics right and ensure we can walk at the same time as we try to run.

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AI has moved from pilot projects to the core of the finance function. Deloitte’s CFO Signals shows that close to 93% of CFOs now use AI across key operations, a dramatic shift from the tentative experimentation of just two years ago. The pace of adoption is undeniable but the organizations making the most meaningful progress won’t be the ones automating for the sake of it, they’re being deliberate in their choices.

Finance is full of tasks that look automatable but rely on context the data doesn’t capture. A spike in supplier costs might resemble a contract issue until you know it reflects a strategic shift agreed in a leadership meeting.

A variance might look like a warning sign until you understand it’s tied to a planned operational change. AI can highlight anomalies, but it cannot intuit the organizational nuance behind them. That gap between what the data shows and what the business knows is where trust can break down.

Finance teams need to know not only that AI can identify an issue, but when they can trust its interpretation and when human judgement needs to step in. This will be central to the next phase of AI adoption.

AI isn’t replacing judgement, it’s relocating it

I should be clear, the shift happening in finance isn’t about completely removing human judgement from the process, but slightly shifting where that judgement is used. Rather than having people spend their time checking every transaction or working through every variance, more of that initial analysis can be handled by AI, leaving finance teams to set the parameters, decide what needs attention and step in when something doesn’t quite fit.

That change is already reflected in how CFOs are using the technology. FTI Consulting’s Global CFO Survey found that nearly 70% of CFOs now rely on AI-driven insight alongside experience-based judgement rather than in place of it. So this is already a process that’s happening. In practice, judgement is still very much part of the role, but it is being applied further up the process, where the bigger decisions are made.

Trust shouldn’t be an afterthought

As we allow AI to take on more responsibility in finance, the question is no longer whether it can complete a task but whether finance teams are truly comfortable relying on the result.

That matters when dealing with sensitive data and decisions where a small error can have real financial or regulatory consequences. An AI tool that drafts a report is one thing. An agent that recommends a supplier, flags unusual spending or takes action on a payment is another.

Trust needs to be built into how these systems work, rather than added later through a policy document or training session. Finance teams need enough visibility into how an output was reached to challenge it when necessary, alongside clear boundaries around what an AI system can do independently and when it should hand a decision back to the team.

The model isn’t the bottleneck, the data environment is

The biggest obstacle to AI in finance is rarely the model. It’s the data underneath it. Finance teams have spent years stitching together information from accounting systems, expense platforms, procurement tools and spreadsheets. If those systems contain duplicate suppliers, inconsistent coding or outdated information, AI won’t fix the problem, it will simply accelerate the consequences.

Data quality needs to come before more ambitious AI projects. Clean, consistent and accessible financial data gives AI something reliable to work with. Fragmented data makes even sophisticated systems harder to trust. Much of the most important AI work happens before deployment: connecting systems, cleaning data and establishing a single, accurate view of financial information.

Once that foundation is in place, AI can do more than automate administration. It can help identify anomalies, understand spending patterns and give finance teams better information when decisions need to be made.

The real value sits in the unglamorous work: speeding up invoice processing, tightening expense categorization, spotting spend anomalies early or making planning data easier to surface. Starting with focused, practical use cases lets teams see how AI behaves in their own environment, where it genuinely helps, and what needs to change before expanding its role.

It’s a more realistic route than trying to transform the entire finance function at once. The organizations making progress are solving specific problems well, then scaling once they know the system works in the messy reality of their own data and workflows.

The finance team becomes more strategic

The real opportunity with AI is not to create a finance function that does less. It is to create one that spends less time on administration and more time on decisions that matter.

As AI takes on repetitive work, finance teams can focus more on analyzing the business, identifying risks and opportunities, and challenging assumptions. Instead of waiting for month-end to understand what happened, they can move towards more continuous analysis and decision-making.

AI does not remove the need for financial expertise. It makes that expertise more valuable, because people still need to decide what questions to ask, whether an answer makes sense and what action should follow.

The goal isn’t to automate everything

AI adoption in finance shouldn’t be measured by how many processes can be handed to an agent, but by whether teams can make better decisions with less administrative effort.

The key for CFO’s is knowing where AI can act independently, where it should make a recommendation and where human judgement is still needed. The finance teams that get this balance right will move beyond AI experimentation and turn it into genuine gains.

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UK organizations are already feeling AI's effect on the bottom line: a recent study found that 66% of UK enterprises are experiencing significant AI-driven productivity improvements, with leaders reporting that AI is freeing up time specifically for strategic decision-making, not just automating routine tasks.

That shift matters as, despite advances, analytics remained a retrospective or heavy human driven insight tool for long – reactive and slow to respond. With deterministic rule-based architecture, hard code, and static workflows. It worked well enough in predictable use-cases, but not in in dynamic, data-intensive scenarios.

This is changing rapidly, as AI re-imagines traditional analytics into decision intelligence, and autonomous AI goes even further, making decisions, scenario planning, and activating context aware actions with limited or no human instruction based on autonomy dial. Where traditional automation executed predefined workflows, agentic AI systems working in squads analyze data from varied sources to make a chain of complex decisions.

For instance, an AI underwriting agent typically estimates a prospective borrower’s creditworthiness, tweaks loan pricing, routes paperwork, alerts supervisors to compliance concerns. decisions once made by people, now made by systems working in squads.

The scale of the opportunity is significant. The decision intelligence market in Europe, estimated to be $4.35 billion in 2025, is steadily advancing towards a projected value of $20.61 billion by 2035. UK organizations could be well placed to capture a meaningful share of that value if they move deliberately.

Drawing the line between AI and human judgement

The central challenge for any organization adopting decision intelligence is not the technology itself, it's deciding who gets to decide what and when. Several organizations lack a formal mechanism for deciding the extent of autonomy they should allow their systems. This frequently leads to ad-hoc practices, without any limit’s enforcement or governance of autonomy.

The fix is to re-invent or re-imagine clear business decision flow rights into the system from the outset - what an agent may act on independently, and what it must escalate to a person. Going back to underwriting agent example, it can reasonably price a loan or route paperwork on its own, but if it flags a compliance concern or encounters an applicant whose profile falls outside its constraints data, that decision should stop with a human, not get resolved by the agent guessing its way through an edge case.

The future enterprise is not about replacing human judgment but augmenting it through intelligent delegation. Enterprise Decision Intelligence systems, fueled by domain aware foundational models and machine-consumable context, can autonomously handle high-volume, full explainability, data-intensive decisions across supply chains, customer operations, pricing, and activation ecosystems.

Human expertise, meanwhile, remains indispensable for governance, ethical oversight, exception handling, and decisions where business context outweighs algorithmic certainty.

Profound potential, profound risks

The potential benefits are substantial. For instance, when beauty and fragrance retailers have implemented an AI decision engine that determined the right time and proposition for engaging each customer, it earned a double-digit ROI through strategic AI collaboration.

However, AI-based decision intelligence systems come with concerns such as cybersecurity risk, compliance failure, lack of transparency and accountability, and occasionally, improper outcomes.

Even the entry cost, especially when agentic AI is involved, can be quite high and can be optimized through enterprise intelligence foundation models. Above all, full implementation needs to be iterative, involving not only a technology upgrade, but also operating model changes, deep integrations, specialized talent, and a robust ethical and governance framework to build transparency, accountability, and trust in these systems.

As organizations build experience and confidence, they will be able to progressively transition from reactive analytics to proactive decision intelligence. The ones that get the human-AI division of labor right first, not just the ones that move fastest, will be the ones that capture the advantage.

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Quantum computing still feels like uncharted territory, yet the threat it poses is significant. On the one hand, it promises advancements in areas such as financial modelling. On the other, it has the potential to undermine the encryption standards that protect governments, businesses, and critical infrastructure around the world.

People often draw parallels with AI, but the comparison is imperfect. AI is transforming how organizations operate. Quantum computing could fundamentally challenge the cryptographic foundations that protect sensitive data and digital communications. While the timeline remains uncertain, organizations need to prepare now. The earlier they start to assess their cryptographic exposure and planning for post-quantum security, the better prepared they will be when the threat materializes.

Quantum is speeding up

Investment in quantum computing is growing, with major economies such as the US already directing significant funding in the technology. The UK has also previously committed up to £2 billion in quantum innovation and aims to become the first country in the world to manufacture and deploy quantum computers at scale.

This is driving the development of quantum computing, which could bring significant benefits. By tackling complex calculations that would take traditional computers years to complete, the technology could optimize energy and transport networks or streamline global supply chains. Yet, like AI, it also poses a significant cybersecurity risk.

Most of the today’s public-key cryptography, including RSA and elliptic curve cryptography (ECC), relies on mathematical problems that would take conventional computers a significant amount of time to solve. A sufficiently powerful quantum computer could solve these at an exponentially faster rate, potentially undermining the encryption used to protect sensitive information.

While there is no definitive date for when quantum computing will be able to break widely used encryption – often referred to as ‘Q-Day’ – the cybersecurity threat posed by the technology is already here.

The threat is already here

Threat actors are deploying a strategy known as ‘harvest now, decrypt later’, collecting and storing encrypted information today with the intention of decrypting it once quantum computers reach sufficient capability. This means information does not need to be vulnerable today to be at risk.

This is particularly concerning for sensitive, long-lived data that needs to remain confidential, including government and critical infrastructure data, intellectual property and confidential business information. Data intercepted now could remain secure, only to become exposed once quantum technology advances later.

Ultimately, organizations – regardless of industry – cannot afford to wait until Q-Day arrives. Now is the time to transition to quantum-safe security. Yet, it can be difficult to know where to start.

Protection starts now

The first step for organizations is to understand where they are currently exposed. In practice, that means identifying the information that needs long-term protection and understanding the cryptography used to secure it. A cryptographic audit can help bring these dependencies to light, enabling teams to decide which systems and data need attention first.

Those findings can guide the transition towards quantum-resistant security, in which post-quantum cryptography (PQC) will play a central role. The National Institute of Standards and Technology (NIST)’s Post-Quantum Cryptography standards provide a clear starting point for organizations planning to migrate their existing environments.

Other approaches have more specific applications. Quantum key distribution (QKD) uses quantum mechanics to share encryption keys in a way that can reveal interception attempts. It requires specialized infrastructure, making it more suited to high value uses than to widespread deployment. The right approach will depend on what an organization needs to protect, how long that information must remain confidential and the systems it relies on.

AI has demonstrated just how quickly emerging technologies can transform cybersecurity, and quantum computing could be next. Organizations cannot afford to wait for the threat to fully materialize, and should begin the transition today by mapping exposure, testing alternatives and establishing migration priorities. The sooner they do so, the more time they will have to manage the transition before their information becomes at risk.

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As the AI buildout continues and models improve from generation to generation, fears persist over how useful this technology will actually be in practice and whether AI will reach a ceiling before it makes a meaningful impact.

Although roles and responsibilities, and jobs, are being replaced in the name of AI, does that mean it's changing things for the better?

Energy scarcity

Microsoft CEO Satya Nadella was speaking at the World Economic Forum in Davos in 2026 about the impact of AI on the global economy and the business world.

In his remarks, Nadella acknowledged the tensions at the heart of the AI expansionism that has captured not only big tech but the vast majority of the corporate world.

For AI to continue growing at the pace it has been – not just in terms of the compute required to train the models but also for inference as usage grows – we'll need plenty of data centers to churn through tokens. And for these tokens to be created, the AI pioneers will need plenty of energy.

Encountering bottlenecks

Burning as much electricity as AI companies plan on doing so for AI has caused alarm among the public – particularly as much of this has demanded infrastructure built close to populated areas in towns and cities.

Nadella acknowledged there has been a productivity gap between the hype around AI and the meaningful impact on people's lives – warning that without something to point to, unrest will bubble up and boil over into something more obstructive.

As time passed, we're seeing more tensions rise as people – and even legislators – object to new data center projects due to concerns over the impact on the energy grid, the environmental damage, and local communities.

Mini-LED vs OLED has been a battle in the TV world for a few years now. While mini-LED always provided more brightness and better bright room viewing and OLED delivered better black levels, thanks to its self-dimming pixels, that has changed in recent times, with both TV techs managing to cover more bases.

Both LG and TCL make some of the best TVs you can get, with the former delivering some of the best OLED TVs and the latter offering up some of the best mini-LED TVs. Both also deliver great value for the price you pay, especially TCL.

The LG B6 is one of the best OLEDs I’ve tested this year, offering great performance for a more affordable price. Now, the TCL C7L (this is the UK / AU model — the TCL QM7L is the US equivalent) has arrived in our testing lab so I thought I’d compare the C7L, the brand’s mid-range SQD mini-LED for 2026, against LG’s most entry-level OLED, as they’re available for similar prices.

Can the mini-LED hold its own against OLED? A quick note that we’re using the LG B6E, a cheaper B6 that performs the same other than missing some AI picture modes, and with a different back panel.

Brightness

Flagship and mid-range OLED TVs have seen a brightness boost over the years, with one example being the near 1,000-nit peak HDR brightness increase between the LG G4 (1,489 nits) from 2024, and its 2026 successor the LG G6 (2,475 nits). It’s taken until this year for entry-level OLED to get a brightness boost: and it’s not as significant as that.

The LG B6E measured 808 nits HDR peak brightness (10% window pattern) in Filmmaker Mode in my tests: a nice boost over the B5’s 668 nits. However, this 808 nits pales in comparison to the 2,083 nits peak HDR brightness achieved by the C7L in its Filmmaker Mode. Mini-LED has always been the brighter tech, but do these results reflect real-life?

In practically every scene I tested, the answer was a resounding yes. With both TVs set to Dolby Vision Filmmaker, in a scene where Lawrence and his guide are by a well in the desert in Lawrence of Arabia, the white sands and blue skies were much more impactful on the C7L, showing off the brightness advantage. The B6E did a good job with the picture, but next to the C7L it looked dim in comparison. Switching to the B6E’s Cinema Home mode did give it a boost in brightness, but still the C7L’s picture dazzled in comparison.

This was also apparent in HDR highlights in other movies. In Speed Racer, the brilliant white of the Mach 5 was much punchier on the C7L than the B6E, though the gap in luminance wasn’t quite as obvious as it had been in Lawrence of Arabia.

For bright-room viewing, neither TV was perfect, with both showing mirror-like reflections. The C7L did a better job in brighter conditions, thanks to that added brightness — it's the clear winner for pure brightness, and for visibility in strongly lit rooms

Contrast and dark scenes

OLED’s advantage when it comes to darker, more contrast-y scenes is its self-dimming pixels. As it doesn’t rely on a backlight and it can turn off individual pixels, this produces much deeper black tones that most mini-LEDs can achieve, and with precise borders between light and dark. However, more mini-LED TVs are demonstrating better backlight control and more impressive black tones.

In my time testing the C7L, I’ve been impressed with its black levels: especially for a mid-range mini-LED. Yes, there’s still some blooming going on, but it’s greatly reduced from the previous generations of TCL’s TVs I’ve tested before — not that long ago, this would've been premium performance. Still, when it comes to black handling, OLED is king.

Watching any space scenes from Project Hail Mary, the B6E clearly produces richer, deeper black tones that are consistently strong throughout. The C7L, while rendering strong black tones of its own, don’t quite have the same consistency, as some shots of the stars in space demonstrate blooming, a common occurrence with backlit TVs.

When it comes to contrast and shadows however, there’s a bit of give-and-take. Watching The Batman, with its array of contrast-rich scenes, the B6E clearly produces a more balanced image thanks to its deeper dark tones and well-handled bright tones. The TCL’s picture however is more impactful thanks to those punchier highlights. As Bruce works at his desk in the Batcave, the dark crevices and wood of the desk blend well with the lamps and monitors on both TVs.

In fact, some black tones can actually look crushed on the B6E, leading to a loss of shadow detail, such as the wall at the rear of a subway platform in The Batman. The same surface is much more legible on the C7L. Switching from Filmmaker to Cinema Home on the B6E does rectify this, without compromising significantly on dark tones.

The Batman has proven to be a challenge for backlit TVs in my time testing, due to its low brightness. While The C7L did a great job overall, occasional blooming would appear behind bright objects, which was more noticeable when viewed in dimmed conditions. For dark scenes and dark rooms, the B6E proved to be the better choice.

Color

Color is probably the most contentious subject when it comes to mini-LED vs OLED, as both tech can deliver great colors in their own way, and I found this to be true putting these two TVs side-by-side.

Watching The Super Mario Galaxy Movie, both TVs deliver vibrant, bold colors that accurately delivered the pop you’d want from this movie. The C7L’s colors were punchier overall, thanks to that higher brightness, but the B6E’s colors looked richer, with more depth. This was obvious whenever the Lumas were on screen, where they had more of a 3D-look on the B6E, but were much brighter on the C7L. Mario and Luigi’s overalls showed that both TVs delivered great colors, but the B6E’s looked that bit more 3D and solid.

Switching to Speed Racer, both TVs again captured the cartoon-ish, candy-colored aesthetic of the movie well. The C7L had the more shimmering colors, but the B6E’s were more true-to-life, as shown by the grass in Trixie’s flashback early in the movie. I also found that different colors looked better on different TVs, with reds and greens standing out more on the B6E, but yellow and orange looked better on the C7L.

One advantage that the C7L did have was in its color temperature accuracy. The B6E demonstrated a green tint in comparison, which can be an issue for some OLEDs. In isolated viewing this isn't so obvious, but next to the C7L, it was noticeable. In the subway scene in The Batman, the surroundings had a green hue to them on the B6E visible above, but the C7L’s cooler color temperature helped render more accurate gray tones.

This scene was the most obvious case of the B6E’s green tint I found during my testing, and it wasn’t legible in other movies such as Project Hail Mary and Speed Racer.

In terms of which TV delivers the better color, they both have their strengths and both have great color reproduction overall, but if I had to pick one, it would be the B6E purely because I prefer the richer color profile. But the C7L’s colors were very good.

The best buy

When it comes to value for money, both these TVs tick plenty of boxes. Both TVs have a feature set up there with the best gaming TVs, with 4K 120Hz, variable refresh rate, auto low latency mode and Dolby Vision gaming supported, although the B6E has four HDMI 2.1 ports to the C7L’s two — but they both have razor-sharp performance.

Picture quality comes down to preference, and both deliver adequate built-in sound, though both could benefit from one of the best soundbars.

When it comes to price, it's close in most places. For the 55-inch models I tested, you’ll be looking to pay $799 for both in the US, with the QM7L going for $100 cheaper in the larger 65-inch size. In the UK and Australia, the C7L is better value, with the 55-inch costing £899 / AU$1,495 while the 55-inch B6E costs roughly £1,099 in the UK (though it has dropped to £999 before) and the standard B6 costs £1,199 / AU$2,495.

Honestly, you can’t really go wrong with either TV. The TCL is the much better pick for bright rooms, while the B6E is the better choice for darker, cinematic viewing and I really am torn on which is the better pick, as this was such a close contest.

Purely from a price standpoint, the TCL is the winner (especially when you go beyond 65 inches — mini-LED's price advantage grows as sizes do), and is the more practical choice for a bright room.

But if you can get the B6E for a good price and you'll watch in a room with controlled lighting, then the better contrast and color depth make it my pick.

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The late Apple co-founder Steve Jobs had a reputation for being ahead of the curve. At the time, Microsoft was heavily pushing touchscreen computing in an attempt to innovate and embrace new possibilities when interfacing with machines; Jobs dug his heels in and refused to take Apple down that same path.

Swimming against the tide

Steve Jobs was speaking during the 'Back to the Mac' media event at Apple's corporate headquarters while introducing Mac OS X 10.7 Lion.

Instead of adding a touchscreen to the MacBook, which Jobs saw as something of a gimmick, Apple focused on multi-touch gestures using the trackpad and mice instead of adding a touchscreen into the fabric of the display. The reason, according to Jobs, was simple: using this technology would quickly lead to strain, fatigue, and the loss of precision the longer a user was navigating using touch.

He put his foot down despite immense industry pressure at the time in the form of the emergence of new kinds of devices such as the HP Touchsmart family.

Times are changing

With Windows continuing to introduce new touchscreen devices and 2-in-1s to the market, Apple stood firm on its resolve over the next few years – but the manufacturer experimented with bridge technologies like the touchbar, which was eventually phased out.

There has since been renewed pressure on Apple to embrace touchscreen and embed this in its devices – with the company now expected to break its stance for the first time and introduce a new touchscreen MacBook Pro with an OLED panel, according to rumors.

By nearly all accounts, we’re inching closer to a passwordless society. The FIDO Alliance, which develops new passwordless authentication standards and whose tagline is quite literally “reducing reliance on passwords”, now estimates that more than 5 billion passkeys are in use worldwide. In fact, more than three-quarters of global consumers have already enabled a passkey on at least one account.

The technology is a boon for the financial industry because it finally removes the sector’s entrenched dependency on knowledge-based credentials – reusable “secrets” that criminals can steal, guess, or extract through social engineering. Suddenly, being able to quote a user’s mother’s maiden name, the town they grew up in, or their several-digit password, isn’t enough – and that makes user access instantly more secure.

Passkeys also give customers a much easier ride when it comes to accessing their accounts, speeding up the process by allowing them to use a key biometric like a fingerprint rather than hopping through two or three screens and entering their “memorable information” each time they log on.

So far so good. However, according to research by Javelin, account takeovers still impacted some 6 million US consumers in 2025 – an increase of 18% on the previous year – with losses exceeding $15 billion. Countless other stats and analyses also support this trend, including The Federal Reserve, which reported a 36% increase in account takeovers between 2023-4 – and those are just the incidents that were brought to its attention.

A surface read of these figures suggests that passkeys are failing outright, but dive a little deeper and it’s clear that the problem isn’t with the technology itself, but how it’s implemented and safeguarded. Securing the front door doesn’t work if a fraudster can manipulate the locksmith into handing them a new key.

While a successful passkey authentication establishes control of a device-linked credential, it’s still on financial institutions to prove how that credential came to be enrolled and whether the session it grants access to remains trustworthy. Did the account holder genuinely intend to add that payee, recover that account, or move a huge sum of money? Passkeys are useful, but they were never designed to answer all of these questions on their own.

In finance, scale doesn’t always equate to risk

Back in August, researchers at Palo Alto Networks’ Unit 42 published details of three proof-of-concept attacks against passkey-protected accounts. They demonstrated how malware on one compromised device could authenticate itself without the account holder even being present. It could even interfere with the registration of a new passkey, or even expose the passkeys synchronized to that user’s environment. Seemingly, another blow for passkeys.

This is an alarming piece of research for sure, but best taken with a grain of salt. Delve deeper into the report, and it’s clear that every one of these scenarios began with a “poisoned” device, where malware was already present. The most serious cases also depended on a very particular combination of browsers and operating systems with preset security hardware.

Reassuringly, none of the cases had an “Enigma moment” where they broke the cryptography behind passkeys, nor did they uncover a “master vault” of credentials that could be ransacked in a single raid. That means attacks like these, where customers are pursued individually, would be far harder to scale than the kind of breaches we frequently read about in the headlines.

But “hard to scale” isn’t really much of a defense in finance. A criminal doesn’t need to steal access to thousands of accounts if one “whale” justifies the effort, and if successful, that single breach can spell doom for a bank’s reputation or regulatory standing – so warnings, while taken in context, should nevertheless be heeded.

Account recovery was, and remains, the weakest link

One criticism users often have of passkeys is, “what happens when I lose or switch my device?” It’s an inconvenience for customers, and it's a real headache for banks. Of course, banks want to make the experience of switching devices as quick and painless as possible, but issuing a new passkey isn’t like resetting a password. It will only be as trustworthy as the processes used to issue it.

If passkey recovery falls back on an email, a text, or a PIN sitting in a notebook somewhere, all an attacker has to do is clear that relatively weak hurdle. How can a finance enterprise know if the person claiming to have a new device isn’t just an attacker who has come across this information by chance or while targeting a specific individual? It relegates passkeys to basic, knowledge-based security again, undermining the very purpose they were created for.

A bank simply cannot recover confidence in a customer’s identity if it never established that confidence in the first place. During enrollment, when the bank verifies who an individual is and binds their identity to a trusted record, it’s usually done through a server-held biometric reference – something that is preserved even when the original device is lost.

If a situation arises where a customer needs to recover an account, the bank should always return to that same source of truth rather than create new “evidence” by asking for it through channels that may have already been compromised. So while recovery may not be needed months or years after onboarding, the efficacy of it is really decided on day one.

Authentication shouldn’t be confused with authorization

The line between authentication and authorization may seem invisible at times, but there’s a canyon between the two – particularly when it comes to moving money. Authentication establishes access, but authorization is what determines less tangible things like permission and intent. A passkey might prove the presence of a credential on the device, while a biometric or PIN check “releases” it and opens it for the user.

That confirms that the authenticator approved the login, but it doesn’t mean that everything that follows on that session is legitimate. Proving a user logged in legitimately at 10:19 doesn’t also prove that they meant to transfer $5,000 to a new payee at 10:26 – and that’s the grand canyon between authentication and authorization.

How the finance industry crosses that canyon depends on their ability to establish user intent in the moment. Of course, they can’t simply add gateways at every single point in the user’s journey – that kind of friction would be enough of an inconvenience for customers to start considering switching to a competitor.

But they can engage in step-up authentication and do what they can to establish contextual baselines. If a user logs on at 9:50 and checks their balance, like they have done countless times before at roughly the same time every few days, the passkey alone is enough. If they want to move money, add a new payee, or – perish the thought – recover their account, they need to reestablish trust by going back to the server stored source of truth outlined above.

Each request should warrant its own process for establishing truth depending on its level of sensitivity and what is at stake.

Putting passkeys in their place

When it comes to modern banking, passkeys absolutely deserve their seat at the head of the table. They strip away the traditional dependencies on knowledge that can be easily stolen, and replace it with device-based credentials that are far harder to crack from a social engineering perspective. On paper, passkeys should banish account takeovers, but in practice, there’s still work to be done.

Finance enterprises still need to know who was originally bound to a device-based credential, and they need to be able to separate access from intent. Passkeys can carry some of that burden, but not all of it. Increasingly, finance is on the front lines of the new war on trust, and they must do what they can to establish it, nurture it, and preserve it.

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Ask most technology leaders anecdotally how their customer relationships are doing, and the answer sounds solid. Renewal rates hold up, and people are continuing to invest in tech to stay competitive. However, there’s a danger in getting too comfortable.

Recent research among UK IT decision-makers found that while the large majority rate their vendor relationships positively overall, almost half say slow response times are a persistent frustration, and 40% flag integration issues without a proper fix.

Leaders who only track the headline number miss the friction clearly sitting beneath it. This matters for two reasons: how customers find technology providers has changed, and so has what they need from them.

AI is reshaping discovery

Many consumers now use AI when making a purchase. A simple search of ‘best budget sofas’ will generate a list of pros and cons from Google’s Gemini. Whether it's a reseller weighing up a new supplier or an enterprise buyer choosing between platforms, the same is largely true for tech buyers. Somewhere between deciding they have a problem and picking up the phone, they stop and ask an AI tool what it thinks.

Forrester's State of Business Buying report recently found that AI tools are the single most meaningful source of information buyers use, ahead of vendor websites, sales reps and product experts combined. Gartner's research backs this up, finding that 45% of B2B buyers used generative AI during a recent purchase, mainly to gather information on vendors and products before any human contact. AI is the first step.

People still want a conversation before they sign anything, but if your reputation isn't holding up in the places AI tools are reading, you don't get considered for the conversation at all.

What tech firms can sometimes forget is that AI discovery matters to both current and prospective customers. It only takes a quick search from a so-called ‘happy’ customer to see a competitor's name where yours should be and start wondering whether their loyalty is misplaced.

To show up in the answers AI tools give, tech companies need quality owned channels like website copy, blogs and case studies, alongside third-party endorsements from reviews, press coverage, forum threads and comparison sites. AI often gives as much credit to what other people have said about a business as to what it says about itself. Marketing efforts need to be multi-channel and shaped around the specific questions a buyer is asking.

With GEO (generative engine optimization) still so new, the rules are shifting fast. Tech providers must know what’s being said about their brand in the places that matter, and keep closing the gap between that and what buyers need to hear.

Complexity is the real retention risk

Picture a UCaaS provider that's worked with a mid-sized accountancy firm since the pandemic, keeping partners and clients connected through voice, video and messaging as the firm went hybrid.

The provider does discovery well: strong owned content, credible reviews, regular mentions in the trade press, the kind of presence that shows up when an AI tool is asked to compare providers. Over the years, it's added feature after feature to the platform, each one an improvement on paper. Then, with no real warning, the firm gives notice.

The reason is rarely one dramatic failure. The firm grew, added a second office, and needed its comms platform to integrate with the existing practice management software. Instead, every new feature meant another login, another dashboard, another thing for a firm with no in-house IT team to configure.

Nobody at the provider connected this to billable hours, client responsiveness, compliance – outcomes the firm cares about. The technology hadn't got worse, but the firm was overwhelmed and felt its provider didn’t understand the business behind the invoice.

The accountancy firm's problem was never about willingness. It had no in-house IT team, and each new feature landed on the same handful of people already juggling billing, compliance and client work, which is typical for SMEs.

The Government’s 2026 skills assessment found that the skills needed for digital roles are changing 66% faster than in most other jobs, while smaller businesses have disproportionately less access to the training needed to keep pace. Firms are finding it hard to keep up, which is exactly why the burden of translating new technology falls to whoever supplies it.

Customers value providers who act as strategic advisors, simplify integration and focus on real business outcomes. In practice, that means:

• Treating integration as an ongoing part of the relationship, not a box ticked during onboarding.
• Translating new capability into the numbers a customer already tracks.
• Flagging complexity early, while there's still time to plan around it.

The winning supplier is the one who takes work off a stretched customer's plate. Get that right, and skills shortages become the reason a customer needs you more. Get it wrong, and the accountancy firm's story repeats: a customer who looked comfortable, right up until the moment they weren't.

Never get too comfortable

The suppliers who hold onto customers over the next few years won't be the ones with the most features. They'll be the ones who show up accurately wherever a buyer is looking, and who make the day-to-day easier for a customer that has less capacity than they used to.

That means auditing what AI tools already say about the brand, treating integration as ongoing work and checking in on the customers who look most settled, since they're often the ones closest to leaving.

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On 12 June, thousands of organizations discovered an uncomfortable truth about the AI tools they had started building into their businesses. They didn’t have control over it.

The US government issued an export-control directive affecting two Anthropic models. Anthropic responded by disabling them for every customer. The restrictions were later lifted, but that is hardly reassuring. Access disappeared and then reappeared because of decisions customers neither made nor controlled.

It’s a wake-up call that many claimed that they had after the Cloudflare outage… or the CrowdStrike outage… or Meta’s… or AWS’… or so on… And yet we were met with predictable surprise amongst an industry that has been so afraid of having potentially uncomfortable conversations around digital sovereignty.

We often talk about data-center locations, regional availability and compliance certificates. We discuss whether the server sits in York or New York, then congratulate ourselves for making a sovereign choice.

But if a foreign government can remove your service, an outage can take down your entire business, a vendor can change your operating model or a proprietary API makes migration economically impossible, the location of the server is almost beside the point.

Data residency is a comfort blanket

Putting a server in Paris does not magically make your architecture French. The server is only one part of the system and it may not even be the most important part. Sovereignty is about control over the complete operational chain. That includes the infrastructure, of course, but also the tools used to provision it, access it, deploy software to it and keep it running.

A business can satisfy its data-residency requirements while remaining completely dependent on foreign technology. A compliance box is checked and the organization believes it has reduced its risk because the data has moved, but the levers governing that data remain in somebody else’s hands.

The control plane matters. The developer tooling matters. The source-code platform, identity provider, orchestrator and AI assistant all matter. If any one of them becomes an immovable dependency, the organization is less sovereign than it thinks.

As an industry, we have focused on the physical location of servers because it is easy to understand. Technical autonomy is much harder, and it requires organizations to examine dependencies they have spent years pretending are somebody else’s problem.

Enterprises are stuck in a legal sandwich

If technical lock-in wasn’t bad enough, the legal situation makes it worse. Modern enterprises increasingly find themselves trapped in a legal sandwich, caught between rules that apply simultaneously but do not always point in the same direction.

Weave Intelligence demonstrates the example of the friction between the US CLOUD Act and the EU’s GDPR.

The CLOUD Act can require US-based technology companies to disclose data under their control, even when that data is stored outside the United States. GDPR, meanwhile, places strict conditions on transfers of European personal data to third countries and access by foreign authorities.

This creates an obvious problem for a European company using a US-owned technology provider. The data might be sitting in Frankfurt, but the company operating the service can still be subject to US law. If it receives a valid demand from the US authorities, the provider may face obligations in one jurisdiction that create serious legal and regulatory risks in another.

This is not just a US-EU issue of course, but rather an example of where residency is often second to complex legal definitions of data ownership. And whilst enterprises cannot solve this international law through architecture, they can stop making the problem worse by building every critical workflow around one provider, one jurisdiction and one set of proprietary services.

The legal sandwich becomes much harder to swallow when there is no realistic way to change what is inside it.

If you cannot leave, you are not sovereign

Digital sovereignty means retaining the practical ability to change provider, jurisdiction or deployment model without bringing the business to a halt.

Managed services allow teams to build quickly without worrying about every part of the underlying infrastructure. But each provider-specific service creates another dependency, and each dependency makes the exit door slightly smaller. This is the genius, and the true cost of the hyperscaler.

The test is very straightforward. If your provider disappeared tomorrow, could your teams continue building and operating software?

If the answer exists only in a 40-page exit-strategy document that nobody has tested, the answer is no. A genuine exit strategy must be executable and the business must be able to shift workloads, change providers or bring critical capabilities on premises without redesigning how every development team works.

Own the layer between developers and infrastructure

To achieve genuine technical autonomy, organizations must decouple software delivery from the infrastructure underneath it. This is the strategic role of the Internal Developer Platform.

An IDP provides a consistent layer between development teams and the different environments where their software runs. Developers follow the same golden paths to build, test and deploy, whether the destination is a local cloud provider, a global hyperscaler or an on-premise environment.

The infrastructure can change without forcing every developer to change how they work. More importantly, the organization retains control over the workflows that keep software moving. Deployment logic, policies, configurations and operational knowledge are not welded to one infrastructure vendor.

This turns portability from a paragraph in a risk document into a capability that can be exercised and tested. Organizations can move workloads between environments during normal operations, identify the dependencies that remain and address them before a crisis arrives.

Stop claiming sovereignty. Prove it.

Real sovereignty has to be designed, tested and maintained. Organizations should regularly move workloads between environments, test whether critical tools can run independently and expose the dependencies hiding behind their architecture diagrams.

Because the first time you test your sovereignty should not be during an outage, a legal dispute or a geopolitical crisis.

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The growing use of enterprise AI is reshaping decision-making, accountability and executive roles, forcing CEOs to rethink how their organizations operate from the top down. The result: the C-suite, traditionally the upholder of continuity and stability, is being redesigned.

Confidence in AI is increasing, but unlocking its full value requires organizations to overcome barriers around governance, skills and workforce readiness. By establishing clear guardrails, investing in talent and embracing new operating models, leaders can accelerate adoption while building trust across the organization.

Looking beyond resilience

Chief executives are no strangers to navigating persistent economic, geopolitical and regulatory pressures. However, the most progressive leaders are looking beyond mere resilience, they are starting to harness the power of AI to fundamentally reshape their leadership, business model and the organization that supports it.

A transformation is underway that is rewriting how global executives approach organizational design. Forward-thinking, "AI-first" leaders – those already realizing the performance gains of integrated technology, are no longer attaching AI to legacy structures. Instead, they are leveraging these tools to fundamentally redesign executive decision-making, decentralize accountability and pioneer entirely new paths for value creation.

AI is a shared responsibility

Consequently, AI is no longer a niche, back-office function. It has expanded to become a top priority for CEOs around the globe, triggering a strategic reconfiguration of the C-suite and forcing leaders to constructively reshape corporate identities and strategies. The challenge now facing these leaders is that progress here sits with organizational and behavioral change, not simply technology. This change is harder to achieve and softer to measure in the short term.

To accelerate operations, executives are shifting accountability deeper into the organization. This redistribution of power is coupled with a call for broader technological expertise; CEOs increasingly agree that all business leaders must become technology literate in their own domains.

AI competency is no longer optional. To navigate this rapid shift, organizations are elevating the role of the Chief AI Officer (CAIO) to a business-critical requirement.

Trust vs caution

Executive confidence in the strategic potential of AI is significantly rising, with leadership becoming increasingly comfortable making business decisions based on AI-generated insights. This trust is particularly pronounced among those spearheading technological integration who anticipate a substantial shift toward automation, expecting AI tools to eventually guide a major portion of daily operational decisions.

However, this executive optimism stands in stark contrast to the reality of daily operations. A distinct gap remains in widespread workforce adoption, as only a minority of employees routinely use these tools in their day-to-day roles.

This lag is not driven by a lack of confidence in staff capabilities – leadership largely believes teams possess the necessary skills. Instead, it points to deeper organizational hurdles, which involves a large cultural shift in the way organizations work and how employees behave.

This measured implementation is primarily driven by critical concerns surrounding governance and risk management. For modern business leaders, AI sovereignty, the capability to control exactly where enterprise data is stored, processed and accessed, is non-negotiable. This highlights the absolute necessity of establishing robust internal controls and strict operational guardrails before expanding deployment.

Success starts with people

The most successful technology strategies are human-centric. Modern enterprise leaders widely agree that long-term success hinges far more on widespread employee adoption than on the sophistication of the technology itself. Tools alone cannot serve as a silver bullet for business transformation.

Importantly, employees are not resisting change due to a lack of access to advanced tools. Adoption is stalled because daily workflows, performance incentives and organizational trust have not evolved at the same pace. Unlocking value at scale requires behavioral change, redesigned operational processes and a collaborative culture.

This shift demands a transformation of talent strategy. Over the coming years, a significant portion of the workforce will require comprehensive retraining to work alongside advanced systems in their current roles, while others must be completely reskilled to transition into entirely new functions.

Managing this shift requires tight integration between talent management and technology leadership, ensuring that human capital and technology strategy are developed in tandem.

Writing a new playbook

The AI era requires a new playbook. Organizations that have redesigned their core workflows and operating models around AI are already reporting greater revenue growth than their peers. For leaders aiming to replicate this success, here are five strategic moves:

1. Redesign the C-Suite – Restructure leadership for agility and clarity, formalizing roles (including the CAIO) and establishing AI competency as a baseline expectation for all functional leaders.
2. Embed AI across workflows – Move beyond isolated pilots to optimize end-to-end efficiency and effectiveness with embedded AI agents.
3. Customize the AI mix – Prioritize long-term differentiation over short-term ROI by developing a hybrid strategy that combines custom, foundation and specialized AI models.
4. Orchestrate intelligence – Focus heavily on the human element by aligning daily workflows, building new skills and actively fostering a workplace culture of trust.
5. Expect the unpredictable – View an "AI-first" operating model not as a final destination, but as a foundation for future disruption. The C-suite must remain agile and prepared for what comes next, whether it is quantum computing or another unforeseen technological leap.

The transition to an AI-powered enterprise is an organizational challenge that extends far beyond the IT department. It involves a corporate disruption that requires a new kind of leadership – one that is agile and focused on integrating people and technology to unlock growth and new value.

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Real-time has become something of a benchmark in IT. Teams see a problem, diagnose it quickly and, ideally, remediate it before the employee has time to raise a ticket.

Clearly, that's better than waiting for an employee to report a problem to the service desk. But real-time isn't necessarily preventative - if an issue occurs and we respond in milliseconds, we have still reacted to an issue.

And if the same thing happens again tomorrow? An automated remediation may mean the employee barely notices and an engineer never gets involved, which is valuable. But we're still fixing something we've fixed before.

Recurring problems carry a cost even when IT becomes very efficient at dealing with them. The cost shows up on both sides: employees lose time when technology doesn't work as expected, while IT spends time investigating and remediating familiar conditions. This is where Site Reliability Engineering (SRE) offers a way to break that cycle.

Bringing SRE to the endpoint

SRE came out of Google's approach to running large-scale production systems, applying software engineering principles to operational problems. At its simplest, it means defining the reliability a service needs, measuring it and reducing the repetitive work involved in keeping it there.

A Service Level Objective (SLO) puts a measurable target around that reliability, while error budgets recognize that no service will be perfect all of the time. These are established ideas in software and infrastructure, but they translate increasingly well to the digital employee experience.

The device, applications and services an employee relies on are now part of how the organization operates, and their reliability has a direct bearing on whether people can get work done.

A recent conversation with a partner put the challenge neatly. They said: “Tell me about emerging risk whilst it’s still at 0–2% and give me the evidence to prioritize. At 5–10% beyond baseline, the service desk will have already lit up.”

They were talking about deviation from their normal baseline, not the percentage of employees affected. They wanted enough evidence to spot deterioration early and decide whether it warranted attention before the consequences showed up in support demand. A millisecond reaction is good, but prevention is always better.

That depends on being able to see problems before they become obvious. Digital employee experience data can expose issues that conventional operational measures miss, giving IT evidence of what is happening before the service desk becomes the signal.

At a large Fortune 100 insurance organization, for example, the team discovered that approximately 25% of call-center downtime was effectively invisible to IT. By combining endpoint telemetry with proactive automation, the organization reduced ticket volume by 35% in a year and avoided more than 45,000 tickets through a single self-service automation.

Learn from what keeps coming back

Anyone who has worked in IT operations knows how much time can disappear into familiar problems. A large hospital network found that a small number of recurring issues were generating a disproportionate amount of service desk activity. They included problems with file synchronization, security services, CPU utilization, network services and collaboration tools.

By using endpoint data to identify and address those recurring causes, the organization reduced annual ticket volume by roughly 50,000 incidents and estimated savings of approximately $1.6 million.

This is where the SRE idea of toil maps particularly well to workplace IT. Toil is the repetitive operational work involved in keeping a service running, and automation can take a lot of that work away from engineers. But if the same remediation keeps being required, that pattern is worth paying attention to.

Sometimes automation will remain the sensible answer. Not every repeated condition needs an engineering project behind it. In other cases, the evidence may point towards an application, configuration, update, or another underlying problem that can be tackled more permanently.

Global investment management firm PIMCO provides another example of what changes when teams have better evidence to work with. Its infrastructure team used endpoint evidence and change correlation to reduce root-cause investigations that had previously taken days to minutes.

That speed matters, but so does what the team can do with the evidence: understand what changed, isolate the cause and get closer to eliminating a recurring issue rather than repeatedly treating its symptoms.

Knowing what happened after the fix

We have plenty of evidence that tells us an IT action happened. A script completed, an update installed, a device restarted or a workflow reached its final step. What matters next is whether it worked. Did the issue disappear? Did the affected experience return to its expected range? Was the improvement still there a day or a week later?

Subsequent telemetry can provide that answer. Without it, IT knows an intervention took place but has less evidence of the outcome or whether the improvement lasted. SLOs help by giving teams an expected level of reliability against which to assess recovery. A completed action is one data point, while the state of the service afterward is another.

AI makes this feedback increasingly important because more diagnosis and remediation will happen without an engineer examining every signal or action individually. If AI identifies a problem and initiates a response, IT still needs evidence of the outcome.

The workflow from insight to outcome matters here. As more of it becomes autonomous, validation and accountability need to remain part of it. Otherwise, IT could automate more activity without necessarily making the environment more reliable.

None of this argues against real-time observability, automation or an efficient service desk. We need them. How quickly IT reacts still matters, as does how much manual work automation removes.

For CIOs, the bigger gain comes when those capabilities lead to fewer problems requiring attention in the first place. That means spotting deterioration earlier, learning from recurring issues and checking whether interventions have actually held. We've become very good at fixing problems quickly, and now SRE gives us a chance to spend less time fixing the ones we've seen before.

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On September 14th, 2026, AI frontier lab OpenAI called on UK lawmakers to impose regulations to tackle the security threats posed by the rapid development of artificial intelligence. This follows an unsolicited attack from an OpenAI agent that broke the firewalls of startup Hugging Face earlier this year.

Cybersecurity isn’t a new issue for business leaders, but its significance is increasing in the age of AI. Clients trust them with information, employees don’t want to disclose their salaries or investors don’t want to make their investments public.

Without clear governance and processes on the use of AI, these are all at risk of being leaked. Doing nothing poses an existential risk for firms.

A new class of security risks

AI presents great opportunities to speed up repetitive tasks, like data reporting, leaving more time for employees to produce more strategic, quality work for clients. For an agent to become useful, it needs as much context and information access as possible, but this is where there is the greatest potential for something to go wrong if not managed correctly.

For instance, agents may have access to bookkeeping, reporting, document management and CRM systems, meaning a successful attack could expose highly sensitive client information.

The main issue is LLMs have difficulty distinguishing data from instructions. An agent can interpret instructions from an email, a document or a webpage, even if those are malicious. These are called prompt injection attacks. Basically hackers manipulate generative AI systems by feeding them with malicious inputs disguised as legitimate user prompts.

Another threat is data leaking. An agent can visit a website or perform a web search and potentially release confidential information, for example by incorporating company data into a URL or a search query. This could expose sensitive data to the Internet without the company or the agent noticing.

The risk becomes particularly acute when three things come together: an agent receives untrusted inputs, accesses sensitive information and takes action in external systems – the so-called Lethal Trifecta. A malicious instruction hidden in an email, document or webpage can potentially trigger actions at a later point using the agent's legitimate permissions.

Therefore, firms need to set strict guardrails around each agent to limit what data it can access, what capabilities it has and what actions it is allowed to take. This is essential to avoid information leaks or an AI agent going rogue.

The threat sitting inside a company

AI adoption is scaling across all levels of a business, from finance to marketing, sales to product development. New research from Turbotic shows 51% of business leaders fear employees are feeding sensitive company data into unauthorized AI tools and 36% are concerned about employees using AI without oversight. This is called Shadow AI.

When an employee prompts a public AI platform, it leaks sensitive client information and receives output that looks confident but could be inaccurate. This could lead to inconsistent tone, misaligned advice or data breaches. The problem isn’t the technology itself, but it’s the lack of traceability and human review. Placing too much confidence in AI tools without the right guardrails is dangerous.

Most of the time, employees don’t even realize the security issues that pertain to AI usage.

They need to understand that experimentation outside any approved workflow or data safeguard is a security risk. Sharing sensitive information with a public model or using content generated by AI without reviewing it risks greater exposure to compliance breaches, AI slop and reputational damage.

Most importantly, employees become responsible for output they cannot explain, verify or trace.

To remove this risk, leaders need to democratize AI by giving employees access and clear guidance on the tools they’re allowed to use, how to provide the right context to AI agents, set up processes to review AI output and establish any guardrails to prevent data breaches. This approach that we call Organizational AI will make a firm more capable from security and capacity perspectives.

Managing the risks of AI

Despite increasing warnings about AI security threats, fewer than 1% of organizations have fully operationalized responsible AI, according to a report from the World Economic Forum and Accenture.

Given the risks associated with free AI usage, having a responsible AI policy is now obligatory.

Without restrictions on basic access controls, employees may be able to use AI to surface information they shouldn’t have access to, from colleagues’ salaries or confidential investor data. Once an AI agent is connected to an organization's data, weak permissions are a liability and it can more easily surface accidental access.

Anything that relates to company data, client communications, or that influences professional advice needs to be controlled by clear governance. It’s the responsibility of leaders to provide employees with auditable and transparent boundaries, and step one involves providing clear guidelines on what, when and how AI tools can be used. Then comes explainability combined with governance and oversight to generate trust.

The critical need for Organizational AI

Firms need to get the fundamentals right: What data can AI access? Where does the input come from? What systems can it connect to? What actions is it allowed to take? When must a human review or approve the output? All of these should be clearly communicated to employees and baked into processes.

Organizational AI is implementing a system that runs across the whole firm that the organization can grow into for a better client experience, team development and security management.

Companies should start now. The existential risk of doing nothing is bigger than the risk of doing it badly.

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Facial recognition technology (FRT) is fast becoming part of everyday life in the UK.

In recent months, much of the media attention around FRT has focused on the police, as forces up and down the country roll out both retrospective and live facial recognition (LFR).

As deployment increases, so too does scrutiny. Concerns around the accuracy, privacy and efficacy, alongside debates over proportionality and whether widespread use is creating a “surveillance society”, all deserve serious consideration.

But while so much of the conversation around FRT focuses on police use, we risk overlooking the growing use of facial recognition by private organizations.

Public attitudes change depending on who is behind the camera

Public attitudes towards facial recognition are rarely determined by the technology alone. Where it is used, why it is deployed, and, crucially, who is operating it can all influence public levels of support.

There is an important distinction between FRT being used by the police to identify a wanted suspect – often for violent or sexual offences – and it being deployed by a retailer to identify someone suspected of previously shoplifting. Questions around accuracy, privacy, and the handling of biometric data are valid in both cases, but the purpose and context are very different.

This disparity is reflected in public attitudes. Earlier this year, Face Int commissioned an independent survey of 2,000 UK adults which found that almost three quarters (73%) support police use of FRT, compared with 54% who support its use in retail environments.

This does not mean that police use should evade scrutiny, nor does it necessarily mean that people oppose private sector deployment. What it does show is that public acceptance of facial recognition cannot be separated from the circumstances in which it is used, or from the organization operating it.

When police use LFR, its presence is typically made clear through marked vehicles and signage, often accompanied by advance announcements about when and where it will operate, and how data will be processed.

In retail spaces, its use may not always be as obvious, nor will the public necessarily understand what happens to their data. This is particularly sensitive: 57% of respondents to Face Int’s survey said they were concerned about how images of their faces are stored.

Moving beyond accuracy

Facial recognition is often discussed in terms of its accuracy, with many reports of individuals being falsely identified. Ensuring that the technology is accurate, robust and appropriately tested is essential.

But accuracy alone will not secure public trust. How facial data is collected, processed and stored, and governance surrounding its use, matter just as much. Indeed, 43% of the UK public stated that clear laws governing FRT would increase their trust in the technology.

In the UK, FRT is currently regulated through a patchwork of legislation, including the Data Protection Act and the Human Rights Act. This means that those deploying the technology must interpret several laws to ensure they are compliant, leading to inconsistent standards across the sector. In contrast, the EU AI Act specifically addresses biometric identification, with additional requirements to protect the public and their data due to come into effect in December 2027.

Earlier this year, the Government completed a consultation on a new framework for law enforcement use of FRT, but notably this did not extend to private sector deployments.

More recently, the Independent Commissioner’s Office (ICO) published findings from its work examining police use of FRT, stating that it was working with forces and police oversight bodies to give the public confidence in its governance and oversight.

What responsible use should look like

Greater scrutiny does not necessarily mean more restrictions. It should provide clearer expectations for how organizations capture, process and store biometric data.

Organizations must be transparent about when and why FRT is being used. Information about the purpose of a deployment, what happens to people’s data and who is responsible for it, should be readily available before the data is captured.

Privacy should also be built into FRT systems from the outset, rather than added after sensitive data has already been collected. Techniques such as hashing and other forms of biometric template protection can remove the need to capture facial images. This privacy-by-design approach should be a minimum expectation for those investing in FRT.

Additionally, organizations should only capture and retain data they genuinely need. Data relating to individuals who do not match a legitimate watchlist should not be retained, while clear retention period should apply to information that does need to be stored.

Robust security is also essential. Access should be restricted, with clear records of why, when and by whom data has been accessed. Anyone handling biometric data should receive appropriate training, and organizations must have processes in place for responding quickly and transparently to breaches.

Finally, technology should support human decision making, not replace it. While the technology is improving rapidly, facial recognition matches must still be treated as an alert requiring human review.

An opportunity for the new Government

The recent change in government and dissolution of the Department for Science, Innovation and Technology (DSIT) makes this debate particularly timely.

With DSIT’s responsibilities being spread across various departments and embedded across the Government’s wider economic and social agenda, there is an opportunity to develop a more coherent approach to facial recognition. After all, the implications of facial recognition extend far beyond the technology itself, spanning policing, the high street, data protection, security and economic growth.

The Government must ensure its response to the proliferation of FRT is cohesive, with departments working alongside regulators and the private sector.

The ICO’s examination of police use of FRT is a useful model for how scrutiny of the private sector could work. This could identify good practice and areas of concern, and inform clearer guidance or regulation.

Responsible businesses should welcome that scrutiny. Clearer standards give businesses greater certainty about what good deployment looks like, and give the public greater confidence that their biometric data will be handled appropriately.

The debate over facial recognition is not a binary choice between embracing the technology or restricting it. The question is how we can realize its benefits while ensuring its use is transparent, proportionate, accurate, and secure.

Police deployments will rightly remain part of the conversation, but as facial recognition becomes commonplace across the private sector, scrutiny must follow. Public trust will depend not only on what the technology can do, but on whether people have confidence in how it is used and how their data is protected.

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We understand what it means to own a house, a car, a share in a company, a patent or a trademark. In each case, there is an established legal framework that defines the asset, establishes who holds it and enables it to be sold, transferred, financed or protected.

Data is more complicated. A business may have spent decades building a valuable body of data that it protects and depends upon, yet establishing precisely what constitutes that asset, and what rights are attached to it, is far less straightforward.

That uncertainty has commercial consequences. It can make data harder to value, license or share with confidence, leaving transactions heavily dependent on contracts and trust between the parties rather than an independently verifiable understanding of what the data is and how it may be used.

This is a challenge we have been exploring in the Isle of Man through the development of the Data Asset Foundations framework, which establishes the statutory basis for defined, governed data to be recognized as an asset.

With the framework now moving into implementation, a much broader question remains relevant for businesses: if we are serious about treating data as an asset, what does that actually require?

What makes data an asset?

The first point is that a data asset cannot simply mean any dataset.

For a body of data to function as a distinct asset, four fundamental questions need to be answered. What exactly is it? Not simply "our customer records", but a precise boundary: which information, covering which period, from which sources and, if the data has been worked on rather than simply collected, how it was produced.

Who may use it? What is the holder actually permitted to do, and what limits came attached to the data when it was gathered? Those limits do not disappear on registration; they travel with the asset as registration does not displace existing contractual, intellectual property or data-protection rights; those obligations continue to apply.

Who is responsible for it? There needs to be a named party accountable for keeping the asset in good order and maintaining its governance. How can others have confidence in it? A means needs to be established by which a third party can see that the asset has been independently assessed and understand its assurance status, without needing direct access to the underlying data itself.

These are relatively simple questions, but many organizations would struggle to answer all four consistently across the data they hold. Doing so requires businesses to move beyond broad descriptions of data and identify distinct bodies of information with defined boundaries, rights, responsibilities and potential uses.

That distinction has practical commercial value. Once there is greater certainty around what the asset is and the rights attached to it, there is a stronger basis from which it can be valued, licensed, shared or potentially used in financing.

Why the rules matter as much as the data

Perhaps the most important part of this shift is recognizing that a data asset is more than its underlying information. Its governance matters too. Typically, businesses hold data in one place while information about how that data should be governed sits elsewhere.

Its provenance, permitted uses, quality requirements and restrictions may be spread across policies, contracts, spreadsheets and other internal records. The relationship between the data and those rules therefore depends heavily on the organization maintaining it.

The Isle of Man's Data Asset Foundations framework provides the statutory basis for bringing the two together. The Data Asset Register, which is now being implemented as part of the framework's supporting operational arrangements, will record information defining the asset and its governance, including its identity, provenance, rights, permissions, responsibilities and means of verification.

Crucially, the underlying dataset itself will not sit on the Register. Instead, the information recorded will provide a consistent way of establishing what the asset is and the governance and rights attached to it.

There is a useful comparison with land registration. A land register does not determine whether a property is valuable or well managed. Instead, it provides certainty about what the property consists of, where its boundaries lie and who owns it, thereby reducing friction when conducting transactions based on the property.

The same principle has commercial relevance for data. Greater certainty will not create value where none exists, but it can make existing value easier to recognize and use, whether through licensing, sharing, financing or the development of new data-driven products.

For a potential investor, lender or licensee, that could make a material difference. Today, assessing a dataset may mean piecing together its provenance, permissions and governance from contracts, policies and internal systems. A defined and governed data asset can provide a clearer evidential starting point for that assessment, reducing some of the friction involved without determining the asset's value or guaranteeing a particular commercial outcome.

Making data an asset in more than name

For years, businesses have described data as one of their most valuable assets. But there is a difference between recognizing that data has value and creating the conditions that allow organizations to act on that value. Much of that challenge comes down to certainty: knowing what the asset is, where it came from, what rights attach to it and how those claims can be verified.

The Isle of Man's Data Asset Foundations framework is intended to provide that greater clarity. By creating a clear bedrock of uniform understanding, organizations' self-described most valuable asset can move from an internal resource to a defined, registered and governable asset, providing a much stronger basis for valuation, financing and commercial use.

As AI increases both the demand for high-quality data and the scrutiny applied to its use, the organizations best placed to benefit will be those that can demonstrate not simply that they possess data, but that they understand what it is, where it came from, what rights attach to it and how it is governed. Providing greater certainty around those questions is ultimately what the Data Asset Foundations framework is seeking to achieve.

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If the rumors are true, next week could mark the introduction of the first-ever touchscreen MacBooks. That's a Mac laptop with a touchscreen. People will probably embrace them and, no, I don't think their arms will fall off.

Steve Jobs famously hated the concept of a touchscreen MacBook. During an October 2010 product launch, he famously dismantled the notion:

"You know, the first thing you think about is this [he gestured toward the image of someone reading out to touch a MacBook screen]. Right, we thought about this years ago. We've done tons of user testing on this, and it turns out it doesn't work. Touch surfaces don't want to be vertical. It gives a great demo, but after a short period of time you start to fatigue, and after an extended period of time your arm wants to fall off; it doesn't work. It's ergonomically terrible. Touch surfaces want to be horizontal, hence touchpads for a notebook. That's why we've perfected our multi-touch trackpads over the years."

In short:

• It doesn't work
• Touch demands horizontal screens
• Your arm gets tired
• Your arm falls off

Jobs' comments came just 10 months after he unveiled the original iPad, which did have a sort of oddball keyboard accessory that put your hands way too close to the display, and two years before Microsoft introduced its first Surface Pro computers, which were basically tablets with kickstands and detachable keyboards.

Still, Jobs drew a line in the sand, and for more than a decade, Apple toed that line. Even when Apple seemed to split the difference with the Touch Bar on the 2016 MacBook Pro, Apple executives insisted a touchscreen was not the right move for Apple's popular line of MacBooks.

As former Global Head of Marketing Phil Schiller told Steve Levy back then,

"Our instincts were that it didn’t [make sense to do a touchscreen], but, what the heck, we could be wrong—so our teams worked on that for a number of times over the years. We’ve absolutely come away with the belief that it isn’t the right thing to do. Our instincts were correct.”

But unless the rumor mill is very, very wrong, Apple is set to prove Jobs and the rest of the old guard wrong and introduce its first touch-screen MacBook Pro next week in Los Angeles.

It will be, perhaps, a recognition that all those Windows OEMs who build 2-in-1 convertibles where the screen can fold all the way onto the bottom of the laptop or even detach to become a stand-alone tablet aren't wrong. There is a market for touchscreen computers.

What would Steve say?

What, though, would Steve Jobs make of this? The iconoclastic tech maverick, who died in 2011, would surely be appalled. Apple devotees might claim, "This never would've happened on his watch." Maybe they'll unfairly blame new CEO John Ternus, an actual engineer and 25-year company veteran who must've had a hand in the development of the new, alleged, touchscreen MacBook Pro. Surely Ternus was somehow involved over what was likely a 2-to-5-year development process, but the credit or blame should lie with now Executive Chairman Tim Cook, who probably greenlit the project.

If Steve Jobs were alive today, what might he say to Cook and Ternus about this? "Well, you better sell a line of arm sling accessories for all those aching muscles!"

No. In fact, I'm certain Steve Jobs would applaud the innovation. You see, Jobs was often adamant about something until he was not. He would insist Apple had no interest in making a phone, until the company delivered the iPhone.

There was a thing called the Steve Jobs "reality distortion field" for a reason. Steve Jobs didn't think an App Store and third-party apps were needed (or at least he claimed as much) before the App Store was unveiled. Video on the iPod was a bad idea until there was the technology to support it. Then it was a great one.

Steve Jobs believed in changing his mind. In an interview Jobs gave to Playboy as he turned 30, he said, "If you want to live your life in a creative way, as an artist, you have to not look back too much. You have to be willing to take whatever you’ve done and whoever you were and throw them away."

Jobs was more a marketer than engineer and, perhaps, more artist than tactician. In order for him to change his mind, the technology had to inspire him, or in order for him to be satisfied, he'd change the technology and specs (see the last-minute swap of the original iPhone's plastic screen cover to glass) until he was satisfied.

Sure, Jobs could say one thing on stage, and the company leadership would parrot it for years until, like Jobs, the innovation met the moment. And then, as Jobs was wont to do, change their minds.

So, if we do see our first touchscreen MacBook Pro next week, don't shed tears for Steve Jobs' discarded design principles. I'm sure he would say that this is as it should be and that a touchscreen MacBook Pro is, in fact, "magical."

Businesses invest heavily in attracting, developing and retaining talent. At the same time, many continue to view workplace technology primarily through the lens of procurement cycles, standardization and cost. Yet the technology employees use every day shapes how they experience the business and how effectively they can contribute.

As AI becomes embedded in everyday work, that connection becomes even more important. The organizations that realize the greatest value from AI will not simply introduce new tools. They will ensure employees have the technology, support and confidence to use them effectively.

The employee experience starts with technology

When workplace technology works well, it is largely invisible. When it does not, the effects are felt in everyday moments: a video call that starts late because something will not connect, a laptop that struggles through a day of meetings, or a workspace setup that is inconsistent from one location to the next. Individually, these are minor frustrations. Collectively, they influence productivity, engagement and perceptions of the workplace over time.

Employees do not judge technology by specifications or refresh cycles. Increasingly, they judge employers by the quality of the digital experience they provide and whether they can work without friction. As a result, organizations are increasingly looking beyond individual devices and considering the broader technology environment.

The PC, display, peripherals and collaboration tools all contribute to the employee experience, shaping how effectively people can collaborate, stay productive and adapt to changing business needs.

From access to enablement

This conversation is becoming more significant as AI moves from experimentation into everyday workflows. As these capabilities become more commonplace, organizations are recognizing that successful AI adoption depends on more than simply providing access to new tools. It requires the surrounding systems, processes and technology environments that enable employees to use them effectively.

This challenge is reflected in Microsoft's 2026 Work Trend Index, which highlights a growing "Transformation Paradox": employees are increasingly ready to work with AI tools, while many organizations are still adapting their systems, processes and operating models to fully support them.

While AI is often discussed in terms of applications and platforms, employees experience it through the devices they use every day. For most employees, the PC remains the primary workspace: the place where meetings happen, content is created, decisions are made and work gets done. As AI is embedded in everyday business applications, the role of the PC is evolving.

It is no longer simply a gateway to information or a communication tool. Increasingly, it is becoming a platform through which employees interact with AI-powered capabilities as part of their daily work.

As these capabilities become embedded into everyday workflows, employees can spend less time summarizing meetings, searching for information or completing repetitive administrative tasks and more time on work that requires judgement, creativity and human interaction. The outcome is not simply productivity in the traditional sense. It is a better day-to-day experience of work.

The technology behind workforce readiness

Workforce readiness increasingly depends on whether employees have the tools and support required to make effective use of emerging capabilities. This is where device strategy becomes increasingly important. Ageing technology rarely creates a single catastrophic failure. More often, it limits potential through delays, workarounds and interruptions that make it harder for employees to take full advantage of new ways of working.

At the same time, AI is raising expectations about what employees should be able to accomplish. If organizations want people to embrace new ways of working, they need to consider whether the technology environment supporting them is fit for that purpose. That means looking beyond refresh schedules alone and considering the broader role workplace technology plays in enabling productivity and workforce readiness.

Why device strategy is becoming talent strategy

Organizations do not build successful workplaces through technology alone. Yet it is becoming increasingly difficult to separate workforce outcomes from the technology environment that supports them.

That is why the conversation around AI PCs is expanding beyond IT and procurement to include HR and business leaders. As organizations look for ways to improve employee experience, strengthen productivity and prepare people for AI-enabled work, device strategy is becoming part of a broader talent conversation.

In the past, technology was viewed as infrastructure that supported work. In the AI era, it is increasingly becoming infrastructure that supports talent. Organizations that recognize technology as part of their talent infrastructure will be better positioned to attract, empower and retain the people they need to succeed.

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Psychologist Abraham Maslow once warned: “It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail.” Agentic AI is becoming the enterprise version of that hammer.

Once people understand what agentic AI can do, the list of possible use cases grows quickly.

While that enthusiasm should be applauded, it can lead to strategies that are back to front “If AI is the answer, what’s the question?” By the time it comes to looking at results and whether the investment in AI has been fruitful, it’s hard to identify what’s been achieved and why achieving it was important in the first place.

Start with the value, not the agent

In the rush that followed ChatGPT, many organizations deployed AI tools to their workforces at large with no defined value or end goal. People started working differently almost immediately, but with no baseline captured, there was no way to know what had actually improved. Who knows how long it takes to write a coherent email and who knows what time is saved by having ChatGPT rephrase it for you!

This is what McKinsey calls the “gen AI paradox”. Its 2025 survey found just 39% of organizations reported any enterprise-level financial impact from their AI investment, despite mass adoption.

Organizations need to be precise about what they want to measure. There are three layers to this and most only capture the first.

• Adoption: are people using it?
• Behavior: are people doing their jobs differently?
• Outcome: revenue per salesperson, tickets closed per engineer, cycle time on a review gate.

Adoption and outcome together show a correlation; for example, people who use AI sell more. But it’s the, often missed, behavior layer in the middle that turns that correlation into causation, i.e. they sell more because they are now able to use AI to fill in more client reports, leaving more time to hit the phones.

Know your workflow

If organizations can’t see how work is really being done, two things happen. The baseline measures the wrong process, making any claimed improvement meaningless and an agent given the wrong steps cannot complete the job.

One procurement process at Atos looked like a four-step workflow on paper, but when we looked carefully and asked those doing it, it was closer to 10. People moved between Enterprise Resource Planning (ERP), Outlook and spreadsheets, copying information, waiting for replies and re-entering data manually.

A baseline on four steps would have measured the wrong process and an agent given four steps couldn’t have completed a job that takes ten.

Not every problem needs an agent

Understanding the workflow also shows whether agentic AI is the right technology at all. If tasks follow fixed rules “if this happens, do that” the answer may be Robotic Process Automation (RPA), workflow automation or a process change.

Agentic AI is better suited to workflows where the system needs to interpret context, coordinate across tools, make recommendations or act within defined boundaries. Even then, it needs a clear measure of success and a credible business case. The economics of deploying Agentic AI are still in infancy, but this will act as a natural filter for only the most valuable cases.

Prove the value before building

Value is where the tough work of a clear business case comes in. Proving the value of Agentifying a process before any code is written.

This requires access to good data sets. Evidence on cost and business value before introducing Agentic AI is needed before budget is committed. Where the benefits are measurable and enough open data exists, it’s possible to build something real in a week. Where the data still requires work, it is always worth refining the data you have before attempting to justify a business case.

Work managed upfront will save cost and time and give you evidence of value.

Build a portfolio, not a queue

Once there is a shortlist of use cases, organizations can prioritize against financial impact, feasibility, process impact, technology requirements, human impact and risk. Scoring across multiple factors rather than ranking on ROI alone creates a portfolio instead of a queue. A lower-value use case with fast impact might prove value early and build credibility for agents that follow. A high-value use case with a two-year runway may need to wait.

Budget for a new mortgage

Even once the right case has been selected, many organizations still struggle to see impact because they focus on the build, then under-resource the work needed to make the change land.

Gartner calls this a “transition mortgage”, where AI adoption demands more support than traditional technology, often requiring 25% more training effort and up to 200% additional change management.

In our own organization, we were guilty of building independent tools in different regions and teams to solve the same business problem. We have now established clearer oversight of our use cases. The issue was not the quality of the tools, but duplicated effort, cost and resources.

Not every workflow needs a hammer

C-suite leaders should start with fewer use cases where the value and workflows are clear, the baseline is measurable, the data is available and the role of the agent is well defined. Then they should budget for communications, adoption, measurement and scale.

The value from agentic AI is not measured by the number of agents an organization deploys. It is measured by whether the work changed and whether the organization can prove it. Agentic AI is a powerful hammer, but success lies in understanding which problems are nails and which are not.

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The most fascinating thing about Apple's new iPhone Duo – and others like it – may not be that it folds. It may be the innovation required to keep it cool. Because at a time when performance demands from edge AI continue to increase, we’ve now taken available smartphone real estate and – though not exactly halved it – constrained it more than ever. And that impacts heat. Which impacts performance.

What makes the Duo interesting isn't simply the engineering required to create a folding phone. It's that Apple is confronting the same challenge every edge device manufacturer now faces: AI capability is increasing faster than our ability to manage the heat it generates.

That’s why I believe we’re entering a new phase of device innovation, with thermal design increasingly shaping what’s possible. In the edge AI era, managing heat helps determine what a device can do, how long it can sustain performance, and how small or thin it can be.

The iPhone Duo is just the latest reminder that smartphone manufacturers have run into a stubborn engineering problem: How to keep mobile processors cool when there’s almost nowhere left to put a cooling system.

For its part, with the Duo, Apple has packed its A20 Pro and other heat-generating electronics into an exceptionally thin, foldable design. It’s also created a custom vapor chamber to help dissipate heat and sustain performance in the new form factor.

The latter is more than an engineering footnote. It’s a glimpse at a challenge the edge AI industry is starting to confront. Thermal design and industrial design are inseparable. And new solutions to thermal challenges must be considered.

Foldables Make the Challenge Obvious

Foldable phones make the thermal management challenge easy to perceive. Conventional smartphone design gives engineers a relatively predictable enclosure in which to arrange the processor, battery, cameras, and thermal management system. A foldable must accommodate a hinge and two display sections while remaining thin enough to fold comfortably and fit in a pocket.

At the same time, the processor and other electronics still generate heat – more, even, when we consider how people are starting to use mobile devices. Which means foldable designs necessarily concentrate significant heat-generating components into smaller and thinner portions of the device, precisely where space for thermal hardware is already constrained.

In general, vapor chambers remain one of the most effective ways to manage heat in a compact electronic device. They work by moving heat away from a concentrated source, such as a mobile processor, and spreading it across a larger area. But vapor chambers ultimately spread heat rather than remove it.

Sure, as processors become more powerful, manufacturers can increase the size and effectiveness of the vapor chamber. But eventually that thermal component itself becomes a design constraint, especially in foldable phones.

Agentic AI Makes the Challenge Urgent

In foldables, innovating around thermal management is itself an admirable design goal – if folding were the only technological advancement engineers had to tackle. But they’re pushing the envelope on physical design at the exact moment when smartphones are being asked to do much more. Namely, to process agentic AI workloads.

As agentic AI moves from the cloud to edge devices – AI-capable smartphones, wearables, glasses, and more – thermal management becomes the industry's most important challenge. Imagine a new foldable smartphone with its own specialized thermal management system, designed to accommodate an innovative new form factor. Now imagine asking that foldable to process vast amounts of data differently than before.

Unlike traditional mobile apps that perform discrete tasks, AI agents operate continuously. They sense their environment, preserve context, analyze streams of data, and act in real time.

In my view, the industry is still underestimating how significant this transition will be. Historically, a smartphone's thermal profile was defined by short bursts of activity: launching an app, streaming a video, capturing a photo. Agentic AI at the edge changes that model entirely. Devices are moving toward persistent intelligence, continuously sensing, processing, reasoning, and responding. With these sustained workloads, a device’s ability to manage heat becomes a defining constraint on its performance.

Passive thermal technologies remain indispensable. Vapor chambers, graphite, heat spreaders, and advanced materials will continue to play a critical role in future devices. The challenge is that Edge AI introduces thermal loads that increasingly benefit from more than heat spreading alone. As devices become thinner and workloads become more persistent, designers will need additional ways to not just to move heat away from sensitive components, but to move heat out of the system entirely.

Micro-Cooling Complements the Vapor Chamber

So here we are, with foldable smartphones capable of agentic AI processing. It’s a perfect storm of thermal design challenges. Engineers can continue enlarging or customizing the thermal systems they integrate into new and different form factors. Or they can find other ways to extract more thermal performance from available space.

The latter approach is where active, micro-cooling technology becomes a viable and important solution.

Unlike miniature rotary fans, which exist today and take up precious real estate of their own, MEMS-based piezoelectric actuators, fabricated in silicon, generate airflow inside extremely compact packages. Basically, it’s a fan on a chip, capable of precise active cooling that fits where conventional solutions cannot.

With micro cooling (µCooling), heat isn’t just spread out, it’s removed from the device entirely before it can sink into the case, delaying or eliminating thermal throttling. This hybrid approach to cooling in highly constrained spaces delivers better thermal performance in today’s AI-enabled smart devices.

It also creates greater design flexibility – a must as devices themselves become more physically flexible. If active µCooling can provide additional thermal performance, manufacturers may not need to maximize the physical dimensions of vapor chambers. A more appropriately sized, passive thermal system could be paired with localized active µCooling and thereby free up internal volume for other components and give designers more flexibility in how a device is assembled.

Put simply, with µCooling, thermal hardware wouldn’t have to consume as much of a device’s design envelope.

µCooling As Part of the Compute Architecture

The implications extend beyond folding smartphones. AI glasses, headphones, watches, robots, and other edge devices face the same fundamental problem, often with even tighter constraints on size, weight, and power.

The industry wants to put more intelligence into mobile devices while simultaneously making them smaller and more comfortable. That creates a physical contradiction. More compute generates more heat. Smaller devices provide less room to manage it.

µCooling offers a way to change the equation. Not by eliminating vapor chambers and other passive solutions, but by supplementing them with localized active cooling that provides additional thermal headroom while giving product designers more freedom.

As AI runs more continuously in the background, edge devices will spend more time computing and less time idle. Sustaining performance will matter as much as reaching peak capability. Foldable phones make this challenge acute, but it extends across the edge AI market as more intelligence and local processing place greater demands on compact hardware.

That intelligence has a thermal cost. Over the next decade, progress will depend as much on what physical devices can sustain as on what AI models can do. That’s why I believe thermal management is becoming a foundational technology of the edge AI era. The companies that solve this challenge will help define the next generation of intelligent devices.

Manufacturers already know this, which is why thermal management is so essential to product design. µCooling is the innovation behind the innovation – like smartphones that fold.

Check out our list list of the best product management software.

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The "enterprise brain" is a term featuring more and more prominently in the enterprise technology market and at the C-suite level. Technology vendors of all varieties are scrambling to connect internal documents, knowledge bases and structured data systems into a unified architecture.

The compelling goal is to provide AI agents with as deep organizational context as possible so they can reason, automate and assist with scarcely believable accuracy.

But as businesses pour resources into linking their databases, policy documents and CRM entries, a significant blind spot remains.

Efforts to build an enterprise brain often overlook the intelligence contained in the millions of conversations employees are already having with customers, colleagues and candidates across phone calls and messaging channels.

Documents tell you what your policies are and CRM records can tell you what happened, but business conversations tell you why an incident happened.

For any company that is serious about building a true enterprise brain, conversations cannot remain a missing layer of organizational intelligence.

Beyond the confines of structured data

Standard business tools are excellent at tracking outcomes but terrible at preserving nuances. Structured systems may record instances of an enterprise deal stalled, an account at risk of churning or a candidate withdrawing from an interview process. But it of course can’t put its finger on the actual trigger.

A deal doesn’t just stall, there will always be a key factor that changed the buyer's mind during a late-stage call. Similarly a candidate doesn’t just decline an offer, maybe a competitor introduced a work-from-home policy during a quick negotiation chat.

When organizations rely solely on static databases to train AI models or inform strategy, they are feeding their enterprise brain an incomplete diet. They receive the post-mortem without the symptoms, and AI needs the sometimes messy, real-time context found in human dialogue to understand the operational reality of a company.

Actionable context

Artificial intelligence has reached a level of maturity where analysing high-volume voice and chat interactions is no longer a nice to have or a bonus. But just recording every interaction and dumping raw transcripts into a data lake, which effectively hoards unstructured audio, solves absolutely nothing. To make conversational context truly useful, businesses must focus on a few core operational pillars.

First, conversational insights must automatically sync with existing enterprise systems rather than living solely within a VoIP dashboard, allowing real-time data from a support call to enrich the CRM and trigger automated alerts. Second, the extracted intelligence needs to be routed immediately to the teams equipped to act on it. For example, if sales calls routinely show that prospect friction stems from a specific pricing tier, that signal should flow directly to product strategy and marketing rather than remaining trapped in the revenue organization.

Finally, while AI should perform the heavy lifting of summarization, sentiment analysis and pattern recognition, human judgment must always remain the final filter for important decision-making. While this may seem obvious, it is amazing how overly reliant on AI many employees have quickly become, including using it for critical business judgement calls.

Breaking down knowledge silos

Knowledge silos continue to be huge challenges for many organizations. Sales teams learn what buyers want, support teams learn where the product fails, and recruitment learn how the market perceives employer branding. All is highly valuable information, but all too often these groups don’t share a unified intelligence pool.

When conversational context is ingested into the enterprise brain, these walls dissolve across every department. For instance, when a prospect mentions a niche feature gap during a sales demo, that insight can prompt product teams to prioritize the feature while sales updates its outreach messaging.

Similarly, if a customer casually mentions an upcoming corporate restructuring on a support call, account management can initiate proactive outreach to protect the account before contract renewal.

By connecting these conversational dots across departments, organizations shift from reactive firefighting to proactive alignment.

Value over volume

Now this is not to say that trying to capture, transcribe, and analyze every single internal interaction is the way forward. Because it goes without saying, this is borderline impossible. If attempted, it can lead to sky-high compute costs and even legitimate anxiety regarding employee monitoring.

To build a functional, trusted enterprise brain, organizations must prioritize value over volume. The focus should be on high-value workflows where conversation directly drives business outcomes, such as customer-facing revenue calls, critical support interactions or key talent acquisition touchpoints.

Focusing on these targeted areas minimizes data noise while delivering immediate ROI. Organizations must build trust by maintaining clear boundaries. Conversational analysis should focus on market trends, product feedback and operational blockers instead of micromanaging employee tone or enforcing rigid scripts.

The complete enterprise brain

A truly intelligent enterprise cannot run on paperwork and revenue tables alone. It requires the context and nuance that only human interactions provide.

As enterprise AI continues to evolve at rapid pace, having the largest static document libraries will count for nothing. The organizations that thrive will be the ones who successfully pair their structured business data with the rich, dynamic intelligence hidden inside their everyday conversations.

Checkout our list of the best business intelligence platforms.

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AI development and expansion is rocketing across our society, sparkling and bright like a Roman candle, but with its tail on fire. The disruptions are coming so fast, and the concerns are rising so quickly, that no one, not even those running the biggest AI companies, knows what to do. Instead of addressing issues like AI slop, job loss, self-replicating and developing AI running amok, and data centers, these AI bosses have come up with a better solution: cold, hard cash.

On Monday, Elon Musk casually posted on his X platform that he would give one of his SpaceX shares to every child in America, with the promise of more to come. Over at Anthropic, Dario Amodei proposed a public fund built on the value of 5% of his company's shares when it goes public next month.

Of the two offers, Anthropic has the most obvious value. The company might be worth a trillion dollars when it goes public in November, which means the fund will be close to $50 billion. That's a lot of money.

Whether it's $150 (the approximate value of a SpaceX share) or $5 billion, neither offer actually addresses AI's myriad issues. None of this equals regulation, oversight, a pause, or a concrete global discussion about a technology that may in 12 months' time be able to do any of our jobs, access most of our systems, and start making decisions on our behalf. None address the real-time, growing cybersecurity threats posed by AI that can crack any encryption and set off Denial of Service attacks at unimaginable scales.

But how?

Let’s leave aside all that for a moment and talk about the nuts and bolts of these proposals.

First of all, there’s the question of how Elon Musk is going to give every one of the 72 million children in America a stock grant (assuming — a big assumption — he follows through). There are ways of doing this (custodial accounts, transfer to a brokerage, even platforms like GiveaShare that support stock gifts for children). Trump Account recently did something similar (sort of tiny Individual Retirement Accounts for pint-sized people). However, when those stocks go in there, they're not something the children will access now or in the near future.

What would be the value of $150? A couple of toys, a few outfits? More likely, what’s going to happen here is those SpaceX shares will sit and maybe accumulate value (maybe not), and then, when the child is an adult looking for one of the few jobs AI has not taken over, they’ll use it to help support themselves for a little while. Maybe buy a meal.

As for Amodei's Anthropic offer, well, it is a lot of money, but once you set up the fund, you’d figure out how to manage it and allocate it. Who is going to control that fund, and how will it be parceled out? Will it, for instance, go to every individual state? Will it go to schools, grade schools, maybe high schools, maybe universities? Will it be used to set up AI curriculum? If so, what curriculum built now will be useful a decade from now? Those billions could be used, but they’ll have to be sort of churned and replenished so that new curriculum could be built (probably by AI).

I’m not saying that’s what’s going to happen because it could just be for job training centers, retraining for new jobs that people have never done before, but that AI cannot reasonably do. Maybe Anthropic will be funding trade schools. I have yet to see an AI plumber or electrician, though I guess eventually the robots will be skilled enough to run a pipe or fix a circuit.

Of the two plans, I have far less confidence in Musk to hand over those 72 million shares because he’s made promises like this before and has not carried through. It becomes a bit of a show, a flurry of hands and a digital wink to convince people that he actually cares, but there’s no indication that he really does beyond the growth of his company and personal wealth.

Money for something

Now, these things are not mutually exclusive. I do believe we need money to help retrain vast workforces in this AI age, and that should happen sooner as opposed to later. And sure, you know, giving a child a share is not a terrible gesture, a little something to put aside for later, for a rainy day, or a day when they realize that the AI is in control and the only way they can buy food is directly from another person because the AI is running the grocery store.

Maybe though, Anthropic's Fund could be used to figure out a better, more environmentally and community-friendly way to build data centres, perhaps in places where both people and wildlife don’t need to live (space, underground). I don’t know what the answer is to that, but that research needs to happen.

It’s unclear if either one of these monetary plans will actually come through, but one thing I can guarantee is that AI is not slowing down. Yes, there’s probably a coming correction, and there may be more consolidation, but the trajectory is set. It will continue, and if we don’t get people together to actually talk about how we’re managing this revolution, no amount of money is going to solve our AI problems.

Amazon has found another way to make itself part of our everyday lives with its new Bee AI devices. The Bees can record conversations and are pitched as a way to remember important details and translate plans into actions. Naturally, those actions can include buying things from Amazon, making every casual conversation a potential shopping cart. You can pay $75 or $100 for the privilege, depending on whether you choose the Bee Frontier, which clips to a wrist strap or clothes, or the Bee Eclipse, a magnetic smartphone attachment.

There are practical applications for something that remembers conversation details you might otherwise forget. An assistant that recalls an appointment, prepares a follow-up email, or reminds you about a promise could save considerable time. But Amazon's announcement also promises personalized shopping recommendations based on what Bee learns about you, which strikes me as a depressing, if inevitable destination for AI tools.

Of course, you might argue plenty of people have invited Amazon into their homes through Alexa devices. Plus, Amazon already has a potentially huge amount of customer data from shopping histories, which lets it recommend products so well that it's a cliché joke to feel like you only have to mention a topic out loud for related items to show up in your recommendations.

That joke is an explicit feature of the new Bees. Turning spoken conversations into another source of personalized assistance is supposed to appeal to potential customers. The question is whether this is a bridge too far when it comes to allowing Amazon to eavesdrop on our lives to shape our shopping.

Idle chat becomes a shopping list

The idea of an AI assistant remembering what you say and not letting anything important slip through the cracks is enticing for the convenience it promises. But imagine casually mentioning that your back hurts, your car is making a strange noise, or your child has developed an interest in dinosaurs. Those aren't just setups for a reminder to make a doctor's appointment, bring in your car, and buy tickets to the museum you'll get. They're also data points for potential purchasing interests that might otherwise never have reached a retailer.

Amazon hasn't said Bee will automatically turn every overheard complaint into a product recommendation, yet the shopping integration creates an obvious path to do so. But it might be too much personalization. Adding information from spoken conversations to what Amazon already knows could paint a much more intimate picture of someone's life, good and bad, regardless of Amazon's promises to keep data under the user's control.

And there are real privacy protections in the system, according to the company. Bee's AI processes audio in real time and deletes it rather than retaining recordings, while conversation transcripts are encrypted and users can delete them. The devices also include physical controls and lights that indicate when they're listening. Plus, the Amazon shopping connection is optional and opt-in.

But deleting the original audio doesn't eliminate every privacy concern when a device is specifically designed to remember and interpret what people say. The information extracted from a conversation can be just as revealing as the recording itself.

Eavesdropping Bees

The privacy problem becomes even more complicated when other people enter the conversation. Someone wearing a Bee device might be perfectly comfortable having their conversations analyzed, but their friends and colleagues haven't necessarily agreed to participate. A small recording indicator is hardly a substitute for meaningful consent, especially when people may not recognize the device or understand what it does.

We've already seen this controversy unfold with Meta's Ray-Ban smart glasses. People have reported unexpected recordings, and even filming. Bee doesn't have the same camera-related concerns, but recording conversations introduces its own problems. A friend discussing a medical diagnosis or workplace dispute in confidence might not feel so open if they know you're wearing a Bee. They haven't necessarily agreed to have an AI system connected to the world's largest online retailer convert their comments into searchable data.

There are also practical questions about where recording is appropriate. Private conversations can involve confidential information, and recording laws can be wildly different depending on location and circumstances. Anyone using Bee needs to consider the consent of the people talking around them, regardless of whether Amazon deletes the original audio.

The fact that personalized shopping is part of the product's ambitions says something revealing about how the company imagines the future of personal AI. Amazon and Meta seem convinced that the future involves devices observing more of our lives so they can anticipate what we need next. And it's easy to think of reasons to develop these capabilities. People with memory difficulties, busy professionals, and anyone struggling to manage complicated schedules could benefit from reliable reminders and searchable conversations. But making recording devices an ordinary accessory could have far-reaching consequences, not all pleasant.

For me, the shopping integration is where Amazon's vision becomes particularly unappealing. I can appreciate an assistant remembering that I promised to call someone or reminding me about an appointment. I have considerably less enthusiasm for a device that might turn a casual remark about needing new shoes into another opportunity for Amazon to suggest something I should purchase.

We've spent years teaching technology companies about our interests through searches, purchases, clicks, and browsing histories. Wearable AI assistants like the Bee could extend that relationship into our conversations with other people. I don't particularly want my conversations becoming another source of shopping recommendations, though. If it's that important, maybe just write it down.

• The Bing Wallpaper app has seen an annoying feature reintroduced
• This means those clicking on a blank space of the Windows 11 desktop will get a Bing search fired up in their default browser
• Unlike the last time Microsoft tried this stunt, there's no way to disable the ability this time around

Microsoft is up to its old tricks again, except even worse this time — with a scheme whereby Windows 11 users with the Bing Wallpaper app installed are sent to its search engine with a mere stray click on the desktop.

Windows Latest reports that an old, potentially off-putting shortcut has been reintroduced to the Bing Wallpaper app, whereby if you click anywhere on the Windows 11 desktop, a search in the (separate) Bing engine is fired up (in your default web browser).

If you recall, this actually happened a year ago, but Microsoft reversed course, although now appears to be trying to reintroduce this feature. The difference is that this time around, Windows Latest notes that there's no option to turn off the functionality (which was present last year, in the app's settings).

However, there are some points to note here: firstly that you must choose to install the Bing Wallpaper app (which cycles Bing images daily for your desktop background). It's not a default Windows 11 app, so this isn't something Microsoft is piping directly to Windows users.

Secondly, the Bing search doesn't trigger every time you click on a blank piece of desktop (a space without a file, folder or icon). It does this once, and then the Bing search won't be triggered for another 24 hours. You'll only experience this once per day, in other words.

Analysis: Microsoft must do better

Just because this app isn't part of Windows 11 by default doesn't give Microsoft the excuse to push functionality like this. It's just going to annoy or even confuse people if they accidentally click the desktop and end up with a Bing search being loaded.

The search relates to the wallpaper image, in case you're curious, and fair enough – maybe some folks might be inquisitive and actually want to know more about the picture by clicking on it.

However, this should be an ability that's optional and can be turned on, not something that's enabled by default. What's worse still is that in this new incarnation of the click-to-Bing farce, you can't even turn it off if you want to do so.

Why is Microsoft engaging with this kind of nonsense to promote its own services? I really can't begin to guess given that 2026 is the year of fixing Windows 11 and curing all the ills around the OS, and while this app may not be part of the operating system, it's still a piece of official Microsoft software – and there shouldn't be any place for this cynical Bing traffic grab or any other remotely anti-consumer practices.

Whether it's a part of Windows by default, or not – or whether you have to be 'foolish' to install the Bing Wallpaper app, as some online commenters argue – is besides the point. Microsoft is supposed to be better than this now, but another episode with a recent Harry Potter advert in this Bing app – one that provoked a considerable rebellion – suggests that it very much needs to try harder.

Organizations can be under huge pressure to make changes to their IT estates. In particular, there are two drivers of this change.

Firstly, there’s the pressure to innovate. The rapid advancement of technology and AI coupled with shifting business priorities can put organizations under pressure to adopt the latest tools, with vendor messaging advocating for the speed of adoption. Simultaneously, ongoing inflation and slower economic growth can mean there is a need to find more cost-effective solutions.

Secondly, there’s the change that is forced. Operating systems might be reaching their end of life and need upgrading, or there are outages and cyber threats to defend against. There have also been several recent examples of software vendor prices rising significantly due to ownership changes, like with VMware, and companies therefore needing to find new solutions for their budget and reduce vendor dependency.

However, in dealing with these pressures, the trap companies can fall into is trying to deliver all of this change at once and within a small timeframe. Yet in doing so, this change becomes harder to manage and the risk of outages and system failures increases. So, how can they deliver any change that is needed safely?

The dangers of too much change at once

The main challenge in changing too much too quickly is visibility. Companies have to oversee complex IT estates – on-prem systems, the cloud, servers, end-user computing, networks – where change is constant and security updates are largely automated. As this change happens, there are more interactions between older and newer systems, different operating systems, different products, and this means the risk of failure can increase.

Configuration drift, for example, occurs when changes, which haven’t been recorded or authorized, cause a system to drift away from its intended baseline (of how it should perform). Without the right monitoring software, this drift can remain invisible, creating added risk to every subsequent change – and that’s when an outage or cyber incident happens.

In May, for example, Google Cloud suspended Railway’s production account without warning because Railway couldn’t see that the platform had a technical dependency on GCP that caused it to fail, leading to an eight hour outage.

What’s more, as we’ve seen with the adoption of AI tools, change can happen faster than training and processes can keep up with, further increasing the risk of disjointed workflows, security vulnerabilities and data breaches. But even when teams can see the changes that need to take place, they can also be faced with the challenge of deciding how to modernize their existing applications.

Finding alternatives to the buy-or-rebuild dilemma

Of course, trying to slow down change isn’t always possible. A company might have been priced out of its VM software or needs to upgrade its applications for security purposes. In this position, traditionally, IT teams have had to either buy new software or rebuild existing apps, which can both be expensive and time-consuming.

This can lead to vendor lock-in, as IT doesn’t see a viable alternative and doesn’t have the engineering capacity to rewrite applications tied to their original operating systems.

To overcome this dependency, as agentic coding matures, organizations may increasingly opt for a third option: using AI agents to build their own replacements for SaaS tools. But while this can appear to remove vendor lock-in and license costs, it also transfers responsibility for maintenance, security, configuration and ongoing support in-house, making it even harder to oversee change without additional resources or capacity.

However, there is another route. Packaging and migrating existing applications can allow teams to modernize without taking on the burden of building from scratch. By packaging an app’s or virtual machine’s existing operating system dependencies, teams can move them into containers and modern desktop infrastructure without needing to rewrite the app.

The app maintains its existing behavior, but can now work on the latest operating systems and in workspaces like AWS.

Delivering change at scale

With a modernization strategy formed, how do teams gain ongoing visibility across their entire estate and know when apps need to be packaged?

With so much change taking place, manual monitoring and reconciliation processes will struggle to keep up with the demands of infrastructure modernization. Instead, teams should consider using configuration monitoring software to gain continuous visibility into change across their entire estate.

Drift detection tools, for example, can automatically measure change across an estate against established configuration baselines and alert IT teams whenever a change triggers a system to move away from its baseline. This means that an automated patch by an external provider or a manual intervention by a user gets detected and flagged to IT, changes that might otherwise go unnoticed and trigger an outage further down the line.

Imperatively, this estate-wide visibility means teams can see and understand the impact of any unauthorized changes taking place and intervene before they lead to failure.

It also gives them the information they need to understand how a transformation project might interact with the systems currently running in their estate, helping to inform the feasibility and implementation of the proposed changes and deliver more targeted, impactful and safer change.

Alongside configuration visibility, it’s also possible for teams to automate the packaging and delivery of apps, saving them a large amount of time and cost in manually performing a typically laborious task. This subsequently creates a repeatable and scalable process that enables apps to be regularly deployed and made available to users.

Escaping the trap

With so much pressure to change, above all, escaping the transformation trap requires a mindset shift. It involves, one, seeing change as an ongoing problem to manage, and two, not attempting to carry out too many innovation projects at once.

Small, incremental changes can deliver more meaningful impact and ensure systems are properly deployed or integrated, while this targeted approach also provides more capacity for IT teams to deal with any forced change that might surface.

Yet by prioritizing change visibility, IT teams can mitigate forced change by addressing risks earlier and seeing when upgrades are needed in good time. And by automating processes like packaging apps, they gain more capacity to prioritize innovation projects as routine changes take place faster and more securely.

When these processes are joined together, instead of grappling with an overwhelming and uncontrolled level of change that increases risk, teams can ensure change remains a driver of security, resilience and innovation.

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There has been an explosion of AI-powered products released over the past four years, with most built on top of leading foundation models. Startups, enterprise software vendors, and even large incumbents route their core intelligence through APIs from the leading AI labs, and a growing but still narrow set of open-weight alternatives.

This arrangement has most certainly enabled a genuine burst of AI products, but it has also created a structural fragility which can be remedied through the democratization of model creation and training.

Defining a wrapper

In its simplest form, an AI wrapper is a product whose core function is an interface sitting on top of a third-party foundation model's API, with little to no proprietary technology beneath that layer.

That approach can have a short lifespan and Jasper can illustrate that. Once the standout of the AI writing boom with a $1.5 billion peak valuation, Jasper saw its core market vaporized when ChatGPT introduced free, native writing features. Its revenue fell from approximately $120 million in 2023 to approximately $55 million in 2024. Jasper did survive, by redirecting its efforts toward enterprise workflows and brand-specific training.

Generally speaking, analysis shows that, among AI startups that have wound down in the US in 2025, 81% were "wrappers and apps". Copilots and assistants layered on foundation models, AI productivity tools, content generators, and vertical SaaS products whose core differentiation was an LLM front-end rather than proprietary data or infrastructure.

In Jasper’s case, and in many others, what pressured the company wasn't a competitor out-executing them. It was the platform they built on top of simply absorbing the feature they'd built their company around.

Not all wrappers are equal

It would be a gross generalization to say that all AI wrappers are the same. A thin prompt-and-display wrapper is different from a product that does heavy retrieval, tool orchestration, agentic workflows, or domain-specific fine-tuning. The latter can have real defensibility even while sitting on top of a third-party base model. The model call is one component, but certainly not the whole product.

Some companies also use multiple foundation models or fine-tune/distill their own smaller models for specific tasks after prototyping on a frontier API. Open-weight models add variety too as not all wrappers call a closed-source frontier model.

The wrapper problem

Regardless of where an AI product sits on the wrapper complexity scale, they all share the same problem. When a company's core value proposition depends entirely on an API call to a third party, that company does not fully control its own product.

It cannot control pricing changes, rate limits, deprecations, shifts in model behavior, or the possibility that the platform provider decides to compete directly in the same vertical.

This is not a theoretical risk, though how much it affects any given company likely depends on how much of its value sits above the model layer.

The economics of the wrapper

So what is the defensibility of an AI wrapper in practice?

The short answer is that it comes down to intellectual property.

Where a company benefits from an existing distribution advantage, established customer relationships, regulatory licensing, network effects, or years of accumulated brand trust, model-layer differentiation may not matter as much. But if what makes a wrapper distinctive is observable from outside the organization and replicable by a competent competitor within a limited period, it seems reasonable to expect it will eventually be replicated.

That is the unfortunate economic truth beneath the wrapper model. The cost of replication is near zero for the platform that controls the underlying model, and near total for the startup that doesn't. A wrapper's moat, if it has one, has to come from something other than the model call itself. Absent those, a wrapper is unlikely to have a lasting competitive advantage.

The barrier to the solution

At this point, you might be inclined to agree that model ownership is one solution to this problem. But that's easier said than done.

The reason so few companies own their models is not a lack of desire. It’s, more plausibly, financial. Training a frontier-competitive model requires enormous compute budgets, specialized talent, proprietary or expensively licensed data pipelines, and the infrastructure to run reinforcement learning at scale. These costs put model creation out of reach for all but the best-funded companies.

For companies to build on their own models as a norm, this barrier has to fall meaningfully. Several forces are already pushing in this direction, open-weight models being one of them. But even then, hosting and fine-tuning an open-weight model is no small task. It still demands technical talent and compute infrastructure most startups don't have access to.

True democratization of model ownership

Democratization, in this context, doesn't mean every company training a frontier-scale model from scratch. That goal is neither realistic nor necessary. It means democratization reaching the training layer the same way access to AI has been democratized for end users.

Democratizing model training and ownership could be what gives founders a lasting chance at turning an idea into a business that doesn't eventually get absorbed by a model provider as a simple feature. And if that happens, the stakes may go beyond individual business durability.

As things stand, access to models is cheap. Access to training is not. This needs to change. The ability to train an AI model should be fully democratized so that the value, the control, and the responsibility can be distributed rather than centralized.

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You don't need to understand the mechanics of AI tools to understand what's happening to traditional technology pricing. AI is breaking the link between effort, cost and value that technology services have relied on for decades.

Imagine two managed service providers responding to the same production incident. One assigns an engineer who spends five hours tracing logs, checking dependencies and isolating the root cause.

The other uses AI-enabled observability to identify the issue in minutes, trigger the appropriate remediation and restore service before users notice. The second provider used fewer hours but delivered more value. Should they be paid less?

Enterprise technology services are running into the same pricing challenge, but on a larger scale. The old pricing model equated more hours with higher cost and, in theory, greater value. But today, AI is enabling organizations to move from reactive support to self-healing operations. In our own client engagements, we're seeing auto-generated tickets fall by as much as 70%, roughly one in five operational events resolved autonomously, and up to 45% fewer major outages where predictive models are in place.

That should translate into straightforward cost savings, but the reality that CIOs and CFOs are grappling with is more complicated. AI can deliver the same or greater value, with significantly less effort, which undercuts the assumptions behind what they are paying for. Managed services providers are facing the inverse of this same problem: How do you price services when the traditional relationship between effort, cost and value no longer holds?

Increasingly, predictability is replacing capability as the bigger challenge to address.

What senior leaders are saying about AI

The challenges of identifying the right AI use cases are coming up in conversation after conversation with CXOs and increasingly with business leaders beyond IT. Leaders of all kinds are recognizing that a successful AI program begins with an operational problem, and the sophistication of the AI needs to be matched to the scale of that problem.

That also means organizations must be willing to redesign their operations around what AI makes possible. Only then can clients and providers have a meaningful conversation about value and how to price it. For example, one enterprise client's finance team was struggling to get a single, accurate view of its cash position across dozens of markets and currencies.

Each region had recorded the same transactions differently for years, and leadership had spent more than five years trying to standardize the process without success. Before this, there was no credible baseline for measuring improvement, making it nearly impossible to agree on the value AI was expected to create.

A client’s business operations are often just as unsettled as the technology helping run them. The contracts holding the two together were built for a past world with more predictable staffing, fixed scope and stable demand.

There is no one right pricing model

The natural response to this uncertainty is to look for a new pricing model to replace the old one. But no single model solves the problem. Outcome-based, consumption-based, and AI-enabled pricing approaches each have advantages in the right circumstances and introduce new trade-offs. The challenge lies in creating enough flexibility to operate when effort, cost and demand no longer move predictably.

Outcome-based pricing works well when a result can be clearly defined and attributed. That gets much harder once AI-driven workflows start feeding into one another, making it difficult to trace which system or step contributed to a business result several automated stages downstream.

Consumption-based pricing ties cost to usage, which sounds fair until you run into the token paradox. While the price of a single AI query keeps falling, total spend keeps climbing anyway, because usage is no longer limited to one person typing one prompt. It's chains of AI agents running continuously in the background, each step triggering the next and including its own line item.

Take a lesson from a banking client running a 20-developer modernization program. Token costs per developer had climbed to roughly $1,000 a month. The team reduced that to $200 by routing simple work to lower-cost models, identifying usage patterns where token consumption was four times higher than the team average, and using deterministic automation for repetitive tasks where AI wasn't needed.

Gain-sharing models sound intriguing because the provider takes on more risk in exchange for a share of the outcome. However, they can become difficult to manage when both parties disagree about which improvements resulted from the provider’s work and how much credit each side deserves.

Why pricing flexibility is vital

These pricing models each assume either effort, cost or demand will remain predictable enough to price against. Increasingly, that is not the case.

While the market has not settled on a single approach, we believe hybrid structures have the greatest potential to become the preferred commercial model for AI-enabled managed services. A hybrid model combines a predictable base fee for core services, a variable component tied to AI consumption and outcome-based incentives where results can be accurately measured.

For CIOs, this approach provides greater budget predictability while offering some protection against consumption spikes. For providers, it helps manage AI consumption volatility while creating incentives for efficiency, continuous improvement and value creation.

Designing partnerships for uncertainty

The goal was never to find the perfect pricing model for AI, but rather to build commercial structures that can flex when conditions change again. That starts with determining clear baselines before any gain-share or outcome-based conversation begins.

It continues by intentionally building in scheduled points to recalibrate scope and pricing instead of being locked into the traditional three-year contract. Transparency around performance, costs, governance and AI outcomes also needs to be prioritized so that both sides trust the numbers.

We are still in the early innings of figuring this out. But it’s clear that AI is changing the role of the technology partner, from delivering capacity to helping enterprises manage uncertainty and accelerate business outcomes. Enterprises must build enough flexibility into their pricing models to adapt as the technology, demand and value equation changes.

While no one can perfectly price the future, we can design commercial models that evolve alongside the technology and the value it creates.

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Five degrees might not sound like much when you look at the weather forecast. But for the networks our everyday lives depend on, it can mean the difference between equipment running reliably and being pushed past the limits it was built for.

That matters more every year as extreme heat becomes more common. Heatwaves, wildfires, severe storms, and prolonged periods of high temperatures are putting IT infrastructure under pressure in places that were never designed for these conditions.

At the same time, the hardware inside our networks is becoming more powerful and densely packed, making it even harder to keep cool.

Traditionally, outdoor wireless equipment has been designed to operate at temperatures of up to 60°C, taking into account both the heat outside the equipment and the heat generated by the hardware itself. But some vendors are now lowering that ceiling to around 55°C.

As temperatures rise and access points generate more heat of their own, that five-degree difference could be critical.

Our networks are getting hotter

The pressure is building as wireless technology evolves. Access points, for example, have gone from housing two radios to three or four, and each extra radio adds more heat that has to be dissipated. As a result, vendors are having to think carefully about how they balance heat and power as they pack in more performance benefits.

Wi-Fi 8 could also raise the stakes even further. Future wireless platforms are expected to incorporate more AI-assisted processing at the edge, increasing demand for compute resources, memory and power. But that extra processing power and memory will also need more power and will generate more heat, creating another challenge for vendors trying to keep their equipment reliable in demanding environments.

Solar load is an increasingly critical factor too. Direct solar exposure can raise equipment temperatures well beyond ambient conditions, making solar load a critical factor in outdoor network design. It’s important to consider both how hot the air around the equipment is becoming and how much additional heat the hardware is absorbing from the sun. Together, this extra heat can push already hot equipment much closer to its limits.

There is a cost to resilience

Designing hardware to operate reliably across a wider temperature range is more complicated than it might seem. Temperatures are rising and are becoming more unpredictable, and the conditions outdoor access points face today could look very different in just a few years.

Every component has its own operating limits, so each part needs to be selected based on the temperatures it’s expected to encounter. However, building access points capable of operating at temperatures closer to 70°C might need industrial or automotive-grade parts. These can be significantly more expensive and aren’t always as widely available globally, creating their own supply chain challenges.

Cooling increasingly powerful access points brings its own trade-offs, too. Fans help dissipate heat, but they add more moving parts and another potential point of failure. Larger heat sinks and liquid cooling can be helpful alternatives, but they also make the equipment bigger, heavier, and more expensive.

At the same time, customers want more powerful and capable equipment at competitive prices. For vendors, however, building more powerful outdoor access points that can withstand higher temperatures increases hardware cost and complexity.

Lowering the maximum operating temperature can therefore help keep costs down. But that five-degree reduction matters, since it can limit the countries, climates and outdoor environments where an access point can be reliably deployed.

Is your network ready for the heat?

As extreme weather becomes more common, organizations need to think carefully about the conditions their network infrastructure may need to operate in over the coming years. That means looking beyond day-one performance specifications and considering how equipment will cope under sustained heat, solar load and other environmental stresses throughout its expected lifespan.

The concern isn’t that an access point will suddenly fail the moment temperatures rise. However, as equipment becomes more powerful and densely packed, thermal management becomes an increasingly important part of maintaining reliable performance over the long term. Designing hardware to operate reliably in demanding conditions can help protect performance, reliability and the lifespan of the investment.

For networking vendors, that means balancing power, performance, cost and thermal design, while ensuring equipment is appropriately designed and tested for the environments in which it will be deployed. For organizations investing in their networks, it means taking a more forward-looking approach and considering not just the conditions equipment faces today, but what it may need to withstand several years from now.

That starts with asking the right questions before deployment. How much environmental headroom does the equipment have? What conditions has it genuinely been designed and tested to withstand? How does it perform under solar load and other real-world stresses? And will that resilience continue to support the network throughout its expected lifespan?

The goal is to build networks for the future, with the resilience to keep performing as both technology and the environment around them change.

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There’s never been so much change happening across an IT estate. And it’s constant. On a day-to-day basis, IT teams might be creating and monitoring user permissions, deploying any scheduled operating system (OS) updates or integrating new third-party software tools. This is not to mention all the change carried out by external providers, outsourced teams and employees.

Change itself is not a problem. Indeed, it’s essential for ongoing security, resilience and modernization. Change that isn’t actively managed, however, is a different story. This could be change that goes unnoticed, like an external patch that automatically updates a system without IT oversight, or change that is forced, like rushing an OS upgrade to meet an impending end-of-life deadline.

The trouble is many organizations have to monitor incredibly large and complex IT estates that can contain hundreds or thousands of business-critical endpoints. And so much change is now automated or carried out by external parties. Consequently, without the right software and processes, this means that IT teams can have limited visibility into many changes taking place.

These changes might seem small or innocuous on their own. But left unmanaged, they mean systems are drifting away from their intended baseline, which adds risk to each subsequent change – and that’s when systems can fail and damaging outages occur.

Change that is not managed effectively also increases vulnerability to cyber attacks. Misconfigurations or gaps in security coverage materially enabled more than 90% of cyber incidents investigated in recent research, showing how seemingly minor changes or lapses in oversight can create openings for attackers.

So, what are some of the ways IT teams can effectively control change across their estate?

Here are three key steps:

1. Understand

The first step is to gain greater visibility into change across the IT estate, so teams can identify potential risks and understand their impact before they cause disruption. Achieving this requires the use of automation tools that enable continuous drift detection. With this in place, IT teams can monitor their entire estate – servers, end-user computing, networks, on-prem and cloud – for configuration change in real time.

To do this, the technology needs to scan every layer in the infrastructure to establish a known-good configuration baseline to use. The platform can then flag any time a change causes a device to drift from this intended baseline, while also allowing teams to gain visibility on the gap between the change that was expected and approved and what is actually running in the estate. This means a manual fix or external patch gets flagged, rather than flying under the radar until it triggers an outage or failure.

2. Respond swiftly

One of the key challenges IT teams face is reconciling and approving change. Inevitably, with so much change taking place, trying to manually perform this process leaves room for errors and can delay approvals. So, to reduce manual effort, the next step is to automate change approval processes, using software to automatically reconcile every detected change against approved baselines and validate what steps were taken against those planned in the change request.

With this, teams will then be able to rapidly respond to unapproved changes and use their time to take action on issues before they escalate and impact operations. More broadly, this also speeds up the delivery of new upgrades or projects and gives IT teams greater freedom to innovate safely.

3. Protect

There’s always a risk of a cyber incident taking place. But when teams understand what’s happening in their estate and accelerate their approval processes, they can also detect and remediate issues faster and reduce their mean time to resolution. This can help to mitigate a failure escalating into an outage, or minimize the cost of an incident, while also strengthening overall resilience.

Protecting systems also means ensuring they are compliant and that teams can export defensible evidence on demand whenever a regulator requests to see it. This also highlights why continuous compliance that generates clear and auditable trails is so important. Otherwise, audits are often accompanied by a time-consuming, manual search through spreadsheets and screenshots for evidence, and this also means information can be out-of-date by the time it’s collected.

Controlled change unlocks innovation

IT change is constant, and with the rapid development of AI and cloud tools, it’s also only going to increase. This provides more opportunities for companies to modernize, but it also creates more risk when it happens without control.

To maintain control and prevent damaging incidents from taking place, IT teams need to understand when configuration change happens across their estate, accelerate the rate at which change can be approved and reconciled, and protect their systems through faster remediation and establishing a real-time audit trail.

While it can be easy to put off resilience projects in favor of innovation, the threats facing IT are becoming stronger and more varied, so controlling change has to be a key priority. If anything, it provides the foundation to innovate successfully and safely.

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Not everyone owns a Ferrari, but I’m sure those who do rarely use it to pick their children up from school. It is less energy-efficient, not as safe or spacious and may garner unwanted attention. The analogy is simple: in AI, you do not always need the most powerful tool for everyday tasks.

It is a trap many businesses have fallen into since the first wave of AI, when early adopters were trying to prove they could access the cutting edge of the technology. Everyone wanted to have the most powerful AI model, the most sought-after accelerators and the fastest route from the pilot stage to implementation.

But as AI moves from pilot into production, new costs are emerging, largely associated with usage. A proof-of-concept used by a handful of people can look cheap, but running the same application thousands of times a day, or by agents calling upon multiple models for one task, is an entirely different proposition.

The mistake is not owning a “Ferrari”, but using the same model, depending on the same hardware choices or taking the same deployment path for every project. That is where the next phase of enterprise AI is getting interesting, with new efficiencies coming from matching each workload to the most suitable model and accelerator, rather than defaulting to your biggest “engine”.

Every model has a different role to play

There is a popular idea among some enterprises that AI is a ranking exercise, in which you should standardize as much as possible on the smartest model available. In theory it sounds tidy, but it ignores the complex differences between each AI task and workload.

For instance, a customer service copilot needs to respond quickly enough that the user never notices the machinery behind it. A large document summarization job can wait longer, with the user expecting the AI to “think” much more carefully. Government applications of AI are likely to instead prioritize sovereignty and ownership, making where and how workloads run just as important as performance.

This logic applies from the size of the model to the infrastructure underneath it. Smaller models can often handle routine classification, extraction and routing tasks quicker and at lower cost, while mid-sized models support a great deal of everyday reasoning work. The largest, most powerful models are important but may be used more selectively, when their capability and cost are justified – for example, in complex reasoning, deep strategic planning and high-stakes tasks where accuracy and nuance are critical.

Equally, to maximize token efficiency, enterprises should consider how different workloads behave and why they do not all require the same type of accelerator. With a diversity of silicon to draw on, different combinations of models and hardware can be selected to suit different requirements, whether the priority is speed, throughput, cost or control.

As such, rather than fitting every workload around an assumed preference, enterprise teams should start with the desired outcome. A retailer running a customer service assistant will have very different priorities from a government department handling sensitive information.

The former is likely to prioritize response time, user experience and cost at scale, while the latter places greater weight on sovereignty, security and where the workload runs. The infrastructure should leave enough choice to match different workloads and changing requirements without being locked into one route.

Building on a diversity of silicon

If an organization invests heavily in one type of accelerator, it can be tempting to run every workload through it. This is where the wrong assumptions, made at enterprise scale, can increase costs rapidly. Using the same piece of hardware for every task can mean overpaying for performance with minimal or no benefit to the user.

Low-latency applications need hardware optimized for responsiveness, while high-volume workloads may benefit more from throughput and cost efficiency. Running both on the same infrastructure can mean paying for performance that one of them never uses.

In most cases, building on a diversity of silicon – heterogeneous accelerators that can be aligned to each workload – will enable enterprises to make smarter infrastructure decisions. In some scenarios, organizations may even prefer to combine hardware from different providers that complement each other. NVIDIA GPUs will make sense for many enterprise scenarios, but choice matters.

Being able to select across providers such as AMD, Qualcomm, Microsoft, Cerebras and others lets enterprises align infrastructure to their specific needs and improve efficiency. Cerebras, for example, has run OpenAI's gpt-oss-120b at around 3,000 tokens per second – a level of speed that is especially valuable for applications where response time is critical.

The deployment environment also matters, particularly in large-scale enterprises or at the government level, where a great deal of sensitive information needs to stay within a specific jurisdiction. In Europe, there is strong demand for infrastructure developed in-market and operated around local requirements.

For enterprises operating across markets, that means infrastructure choices cannot be separated from questions of data residency and local control. Increasingly, the answer is infrastructure that is sovereign enabled – with in-country accelerators, consumption-based pricing and no upfront capital commitment – rather than a single hardware decision made once and lived with for years. Being locked into one route leaves little room to respond when those requirements change.

Understanding AI tokens and efficiency

Another consideration is that AI spending behaves differently from traditional enterprise software, where a license has a relatively predictable cost. Businesses will face a larger AI bill when an agent takes more steps, or a powerful model is pulled into more tasks than necessary, which makes usage itself a poor proxy for value.

Enterprises are already starting to rethink how freely employees can consume tokens, particularly in coding roles. Uber, for example, reportedly used its entire 2026 AI budget in four months, largely on Claude Code. The lesson from this type of example is that giving more people more tokens does not necessarily equate to greater value.

Agentic AI provides another example. Unlike an employee who occasionally asks a routine standalone question, an AI agent can work through a long sequence of steps, read files, generate code, test it and review the output for mistakes. Agentic AI, especially in coding, can be uniquely expensive compared to reasoning and chat, with highly variable token usage where increased consumption does not always improve accuracy.

As counting tokens does not provide the complete picture, enterprises should consider measuring success on outcomes rather than on performance or capability alone. It can be useful to use faster AI models, more powerful hardware or longer-running agents, but only when the outcome justifies what they consume.

For a customer service agent that could be the cost of resolving a case or, for an employee copilot, whether human employees are making faster or more complete progress on meaningful work.

However, the aim should not be to cut token use altogether. IT leaders should seek to understand exactly what they’re paying for and whether it has a clear business case. That is the definition of AI efficiency we should be moving towards. Rather than seeking maximum usage or minimum cost, CIOs and their teams should ensure they return enough value from each workload to justify the investment that goes into running it.

Saving the Ferrari for the racetrack

There will always be AI workloads that require the largest, most powerful model, the fastest accelerator or the most expensive deployment option. The point is to stop making those choices by default. The next phase of widespread AI production requires a more deliberate approach to every workload.

Start with the task, choose a model that’s capable enough to do it well, run it on the accelerator suited to its performance needs, and make sure the deployment environment meets the security and sovereignty requirements around it. Then measure whether the result is worth what it costs.

This approach may be less headline-grabbing, but it is what will separate resilient, production-scale enterprise and government deployments from experimentation. As AI becomes part of everyday workflows, efficiency will increasingly stem from making smarter choices about where each capability is needed – and from having the diversity of models, silicon and deployment options to act on them.

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AI coding assistants are no longer experimental. In organizations that have adopted them, AI is responsible for roughly half of committed code. The question is, therefore, no longer whether developers will use AI, but whether security programs can govern what those systems produce. AI-generated code is remarkably good at producing syntactically correct code, with a 99.9% pass rate. In contrast, security performance tells a very different story.

As it stands, the average security pass rate is only 56%, meaning roughly 44% of tested code-generation tasks introduce a known vulnerability. As the volume of AI-generated code increases while security performance remains largely unchanged, organizations risk introducing more vulnerable code into their development environments. As a result, software vulnerabilities are now the top breach entry point at 31%, overtaking stolen credentials.

Closing that gap means rethinking how security fits into AI-assisted development. Organizations need to make security part of the development process from the start, choose models with risk in mind and verify code in context before it reaches production. The goal is not to slow developers down; it’s to make sure security can operate at the same speed and scale as the software being created.

Model choice is a security decision

AI models learn from enormous volumes of historical and publicly available code, including examples from open-source projects and developer communities. That training data contains secure implementations, but it also contains outdated libraries, weak validation and insecure coding patterns. While models are designed to produce useful, functional code, without the right controls they can reproduce those insecure patterns at scale.

Organizations cannot control every source used to train a model, but they can control which models enter development. The strongest model tested achieved a 68% security pass rate, while six of the 11 models clustered between 50% and 53%. Model selection is, therefore, not simply a productivity decision but a risk consideration.

Choosing a stronger model can reduce exposure, but it does not make the output trustworthy by default. AI-generated code should still be tested throughout the software development lifecycle using capabilities like static analysis and enforceable security policies. Security must be part of the delivery process itself.

Security needs application context

AI-generated code can appear to work as intended, compiling successfully and passing functional tests while still containing security vulnerabilities. Under deadline pressure, that can create misplaced confidence, particularly when developers accept output without fully understanding how it behaves inside the wider application.

The difference in security performance across vulnerability types shows why context matters. While models achieved pass rates of 83% for SQL injection and 87% for insecure cryptographic algorithms, performance fell to 15% for cross-site scripting and 12% for log injection. Some weaknesses can be identified through a recognizable coding pattern, but others often depend on data flows, authorization, trust boundaries, and how different parts of an application interact.

That’s why verification needs to happen in the workflow and in the context of the application. AI-native analysis can add contextual risk detection alongside established static analysis, surfacing issues such as logic flaws and broken authentication directly in pull requests. The key is to combine contextual analysis with independent testing before code is merged and risk has the potential to spread.

Prioritize risk, not just findings

As AI increases the volume and speed of software development, it also increases the amount of risk security teams have to assess, prioritize and remediate. That adds additional pressure to organizations who are already carrying significant backlogs of unresolved vulnerabilities.

That backlog enables security debt to build as flaws are identified but remain unfixed, meaning known risk sits in applications for longer. The more that debt accumulates, the harder and more expensive it becomes to reduce. Today, security debt affects 82% of organizations, while critical security debt affects 60%.

Those figures point to a growing capacity problem. Organizations are generating and discovering risk faster than they can reduce it. Treating every finding as equally urgent will only make that problem worse. Teams, therefore, need to focus on exploitability, application criticality and business impact so they can direct limited remediation capacity towards the vulnerabilities most likely to cause real harm.

AI-powered remediation can help close the gap between discovery and action by proposing fixes inside the developer workflow and reducing repetitive manual work. But remediation still needs to be governed. A generated fix must be tested, validated and checked for regressions. The objective should be to reduce meaningful exposure, not simply increase the number of findings or fixes moving through the system.

What leaders need to do now

AI-assisted code has become embedded into software development. As a result, leaders should treat its security implications as a change to the software operating model.

That means knowing where AI-generated code enters the organization, evaluating models against relevant security requirements, building verification into developer workflows, and focusing remediation on exploitable business risk. It also means retaining evidence of what was tested, what was fixed, and what risk was knowingly accepted.

The organizations that succeed will not be those that slow AI adoption, but those that evolve their development practices so security can keep pace with software creation. AI is making it easier to generate working code, but the real test is whether businesses can prove it is secure before it reaches production.

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There is no shortage of published thought leadership on the potential of artificial intelligence. But what often gets overlooked is the gap between theoretical potential and the reality of deploying it successfully inside a global enterprise. Gallup’s 2026 State of the Workplace report reveals a stark disconnect: while 65% of employees say AI has boosted their individual productivity, only 12% strongly agree the technology has actually transformed how work gets done in their organization.

Having led tech transformations from both sides — as an executive driving innovation at Canadian National Rail and as a partner accelerating AI outcomes for customers with Tenarai — I’ve learned that successful digital transformation thrives on people. In the agentic era, the unlock for enterprise AI outcomes is empowering employees to unlearn the tools and habits they have relied on for years, helping them embrace new capabilities with confidence.

As the industry shifts from passive AI assistants to autonomous AI agents, traditional software delivery models fall short because they overlook the human-AI connection. Bridging this gap requires elevating change management into a foundational pillar of enterprise AI transformation, aligning tech with the context and culture of the enterprise.

From assistants to active agents

To understand why legacy engineering structures fall short on scaling AI outcomes, let’s look at how technology changes human workflows. Generative AI serves primarily as a task-based drafting tool, keeping humans comfortably in the driver’s seat to review and approve.

Agentic AI, in contrast, is goal-driven, context-aware, and collaborative; acting more like an autonomous digital co-worker. This shift is already reshaping complex enterprise operations, but its success hinges on people’s ability to adapt.

A recent data migration we led for a global logistics leader illustrates this challenge. While moving its data pipeline from a legacy SAS architecture to the cloud presented significant engineering complexity, the greater challenge was actually guiding its long-tenured business analysts in unlearning many years of established workflows.

By pairing technical migration with hands-on change management initiatives, we helped these analysts understand the clear value and benefits of change. Ultimately, winning over their belief system did far more to accelerate our deployment timeline than the underlying technical migration.

This tension reveals the fundamental flaw in traditional AI adoption. When employees transition from controlling task-based tools to supervising autonomous agents, a vulnerability gap is exposed. Legacy IT structures, often operating in silos, are inherently built to fail in this environment, leaving frontline staff stranded between old habits and new autonomous capabilities. Bridging this divide requires installing a modern delivery model engineered around human-centered collaboration.

Architects of trust: FDEs and agentic design leads

To close this adoption gap and scale agentic AI from proof-of-concept experiments to real business outcomes, organizations must deploy a dual-role framework. This model embeds change management, domain expertise, and operational empathy directly into the technical pipeline through two key roles: the Forward Deployed Engineer and the Agentic Design Lead.

Traditional software teams frequently write code in silos based on slow-moving specifications. The Forward Deployed Engineer (FDE) model eliminates this bottleneck. FDEs operate as adaptable technical architects embedded directly at the intersection of business processes, corporate context, and live production code.

Working within cross-functional units, FDEs collaborate closely with specialized AI agents to compress build cycles from months into weeks. By working alongside business users, FDEs rapidly translate operational goals into scalable, production-ready systems while resolving friction in real time.

While the FDE drives technical execution, the Agentic Design Lead focuses entirely on the human-AI connection and organizational alignment. This specialized role reshapes business workflows around agentic capabilities before development begins, securing active adoption at every stage. By analyzing user behavior, the Agentic Design Lead remodels the design process with the actual users in mind, ensuring a seamless human-AI connection is pulled all the way through the final solution.

Managing the risk of unlearning

Guardrails and governance are essential because unlearning decades of defined software habits can be highly uncomfortable for an organization. To scale agentic workflows safely, leaders must establish a clear map of where unlearning is encouraged and where it is not, dividing enterprise operations into two distinct risk categories: deterministic and probabilistic.

Deterministic controls govern mission-critical environments where errors risk physical operations or human safety; such as warehouse operations, heavy rail dispatching, air traffic management, or industrial manufacturing. These high-consequence domains demand rigid, non-autonomous safeguards.

Conversely, probabilistic frameworks support domains where autonomous agents excel, including demand forecasting, targeted marketing, and automated document analysis. For these adaptive environments, leaders should establish secure sandboxes where cross-functional teams can safely experiment, evaluate performance logs, and scale proven use cases incrementally.

Change management as an internal marketing campaign

Even the most advanced AI architecture fails if the workforce resists adoption. Enterprise transformation cannot succeed through executive mandates or static dashboards. Leaders must execute it like an internal marketing campaign, earning employee buy-in across the organization and purposely guiding teams through grassroots engagement.

Organizations can build momentum through interactive workshops, innovation days, tailored training, and immersive boot camps that demystify new tools through hands-on exposure. Meanwhile, low-pressure forums such as team connection meetings or informal brown-bag sessions provide safe spaces to address concerns, establish trust, and cultivate creative confidence across teams. We’ve done all of these activities and though some may seem simple, they work.

Winning in the agentic era isn't about outspending competitors on models, it's about understanding people and navigating them through change. Operational success belongs to the organizations that view change not as a technical hurdle, but as their most important product. By guiding their employees in the art of unlearning, leaders transform workforce friction into adoption, ensuring that as agents take on the heavy lifting, human potential remains the true engine of innovation.

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It's been a theory for quite some time now that Nvidia is — what's the polite way to put this? — let's say deprioritizing PC gamers, perhaps, or more specifically, its gaming graphics cards. But this week has added more evidence to the collective weight that GeForce GPUs are increasingly at risk of being sidelined further by Team Green.

I'm talking first about the story that emerged a few days ago, where a leaker claimed Nvidia is diverting all GB202 (Blackwell) chips to make AI (RTX Pro) graphics cards. That's the graphics processor used in the RTX 5090, but apparently not anymore; it's for AI boards only now, we're told.

That makes some sense as the RTX 5090 is already disappearing from shelves, certainly in the US, where you can't buy one for less than the best part of 10 grand (from gouging third-party sellers, with no major retailers actually having stock anymore). So arguably this effective discontinuation of the RTX 5090 is already happening.

On top of that, the latest developments are that Micro Center is reportedly listing the Blackwell flagship as 'not carried', and Nvidia itself has removed all mention of the RTX 5090 from its own GPU marketplace (though it is still mentioned elsewhere on the site). Search for the RTX 5090 on that marketplace, and you won't find it any longer — the highest-end graphics card listed is now the RTX 5080 (hat tip to Wccftech for spotting this being flagged up on Reddit).

Aside from an actual announcement from Nvidia (which probably wouldn't ever happen anyway), that seems like about as clear an indication you can get that the RTX 5090 has been shoved out of the exit door, never to return, because Nvidia needs those GB202 chips where they are most profitable. RTX Pro GPUs offer a much heftier premium that Team Green can heap on top.

A new Blackwell flagship?

So, what does this mean? For starters, if it is true that the RTX 5090 has been scrapped, the RTX 5080 becomes the flagship Nvidia GeForce GPU. Once the existing stock of RTX 5090s has been depleted, this could mean the price of the RTX 5080 is inflated. When I say inflated, I mean jacked up even more, because it has already seen some nasty price hikes. Currently, the RTX 5080 is at around $1,800 in the US on Newegg, or £1,450 on Overclockers in the UK – and those are the lowest prices; it goes up from there. It's a very similar story in many other regions.

This is clearly a concern for gamers as you can see from the Reddit thread I pointed out above, with comments such as: "If you want a 5080 better buy one while you can still afford one." Really, it's debatable if the card is anything like 'affordable' relative to its graphical frame rate muscle even now.

Here's where another rumor comes into play. Elsewhere on the grapevine, there's talk that Nvidia may actually release the RTX 5080 Super variant which has long been speculated about. This would be a 24GB graphics card, and it'd essentially replace the discontinued RTX 5090 – with the theory being that Nvidia would also ditch the vanilla RTX 5080 (16GB). This would leave the rumored RTX 5080 Super as the sole high-end GeForce graphics card, quite some distance above the RTX 5070 Ti.

If that happens, imagine the pricing concerns here – what MSRP would Nvidia attach to the RTX 5080 Super? And then consider the cost inflation that will be added when you take into account that this card will also be sought after for AI usage (as seen with the RTX 5090).

If an RTX 5080 Super is indeed inbound for early 2027, that would also suggest that the next-gen RTX 6000 family is further away than some rumors believe – as this Super version would be a stopgap GPU to keep the Blackwell range ticking over. The RAM crisis may have a fair bit to do with this strategy also, given that most predictions foresee it getting worse next year (and the misery continuing into 2028, and maybe beyond). Of course, launching a new GeForce range into such memory headwinds wouldn't be easy.

Software focus and streaming dreams

All in all, it feels to me like there's a stream of subtle – and not so subtle – hints and rumors that Nvidia is very much slowing down the development of gaming GPUs (while not wanting to give that impression, naturally). The other part of the equation here is the increased focus on the software side, of course, rather than the hardware. DLSS 5 is out and Nvidia is already pushing hard with honing and refining that neural rendering tech, and something else that happened earlier this week was that Team Green highlighted some big leaps in performance here. Pretty huge strides forward, in fact.

Of course, you could argue that all the development resources being ploughed into DLSS – including frame generation, which got a hefty boost alongside DLSS 5 – shows Nvidia isn't abandoning gaming GPUs for PC gamers. And I concede that's true to an extent, but there is very much a vibe of a major slowdown leading towards the feared deprioritization of GeForce GPUs – and there's another angle to consider here too: cloud gaming.

Don't forget that GeForce Now also benefits from DLSS. Just this week, Nvidia added DLSS 4.5 override controls to the streaming service to help push up frame rates – those on the RTX 5080 (Ultimate) plan can use Dynamic Multi Frame Generation of up to 4x now. Ray Reconstruction for bolstering ray-traced visuals is available, too, among other options.

Would Nvidia rather have you subscribed to its cloud gaming service, making monthly payments, than buying a GeForce GPU for a one-off sum? It seems an obvious direction for the company to be heading in, and with increasing warning signs around Team Green's gaming GPUs, I'm becoming more convinced that this is where Nvidia's priorities are shifting for the future of PC gaming.

This isn't to say GeForce graphics cards are definitely on their way out, or that an RTX 6000 line-up won't happen – it's already well underway after all – but beyond the next generation, I'm increasingly dubious about where GeForce GPUs might fit into the big PC gaming picture. Between AI demand and cloud gaming (plus increasingly faster internet connections), there's a squeeze underway that could prove painful for the GeForce line-up – and the ongoing RAM crisis is hardly helping, either.

AI has changed many aspects of the professional world, especially the practice of software development and engineering. A swathe of tools and platforms now enable programmers to use AI assistants and copilots – as well as swarms of agents – to work on building systems autonomously, based on instructions. And for one key figure in the AI space, this was something of an inevitability.

Going viral

In a viral post on the social media platform X, one of the key figures behind the conception of OpenAI, Andrej Karpathy, remarked on the future of programming, provoking a reaction from many in the industry at the time.

ChatGPT had launched a few months prior to that, and developers were already familiar with the basic level of coding that the chatbot could perform, especially in popular languages such as Python and HTML. Much of this was also based on simple text-based instructions – meaning that the AI models could understand and process natural language and churn out complex programming in a way that hadn't been seen before.

Karpathy, who in 2024 co-founded Eureka Labs, an AI education platform, perfectly encapsulated the feeling, with the tech industry in the process of understanding the full implications of this technology.

Good vibes only

Karpathy added in a follow-up post that many examples out there in scientific literature showed that natural language was capable of wielding AI to engage in complex programming. These findings served as a collective precursor to many enterprise features that we see in widespread use today, such as GitHub Copilot, Claude Code, and others.

However, the rise of vibe coding and instructions-based programming also means that the nature of the work is changing for many software developers. Many now simply review code instead of writing it themselves, engaging in validation and bug-squashing.

Thus, the role of software development is becoming something that requires fewer and fewer juniors. This, however, may lead to other unintended consequences – including the fact that the traditional career pipeline is permanently changed.

There’s a phrase I often used to use when referring to Elon Musk: Don’t bet against Elon.

This stemmed from long before he became a supervillain, and was instead no more than an oddball tech-sector genius primed to become a real-life Tony Stark. It was at the tail-end of this honeymoon, in 2017, that Musk unveiled the Tesla Roadster 2020, a luxury model EV that promised insane specs and performance (0-to-60 mph in 1.9 seconds) and maybe even something we’d only dreamed of: rockets that might let it fly.

Now, for chronology sticklers, Musk did not promise rockets during Tesla's November 2017 event; it was primarily devoted to the Tesla Semi. That tease came months later on his favorite platform, Twitter, where Musk wrote:

“SpaceX option package for new Tesla Roadster will include ~10 small rocket thrusters arranged seamlessly around car. These rocket engines dramatically improve acceleration, top speed, braking & cornering. Maybe they will even allow a Tesla to fly …”

I posted my skepticism with a Tom Hanks GIF of him saying “Really?”

This morning, my X feed has been filled with people replying to that old post with “Yes, really” and wild videos of the fully redesigned Tesla Roadster flying with the aid of what appears to be 10 thrusters (SpaceX rocket engines).

In the videos shared from Musk’s launch event, the Roadster inexplicably rides up a vertical incline and then flies off it, flips over, and lands on its wheels.

Yes, a car can fly. We may not have affordable healthcare or housing, world peace, safe social media, or know if AI will save us or kill us, but we have a car that can fly.

I’m not discounting the impressive engineering or sheer audacity of the flying Tesla Roadster, but it’s been a long time since Musk crashed his own launch event with a “one-more-thing” Roadster update, and I feel different about, well, just about everything.

Back then, it had been five years since I had interviewed Musk and gotten him to playfully whistle Fly Me to the Moon. The busy entrepreneur’s personality had been shifting slowly to a more petulant and unpredictable place. I’d recently sat through a Tesla Model 3 launch where he abruptly ended a press conference without taking any real questions from journalists, and at the Semi event, he showed up incredibly late, though he did deliver the goods in the end.

I see things differently now

Now, when I look at this more geometric and rocket-filled Tesla Roadster (it looks nothing like the one I saw in 2017), it’s through the prism of everything else Musk has done in the last five or so years.

He purchased Twitter, smothered the brand, and rebooted it as the darker and often angrier X. To be fair, some now report that X has started to rekindle some of its older, kinder, and more inclusive community (I’ve experienced a bit of this myself), but it’s still a far cry from the place Musk so enjoyed when he touted all his latest Tesla and SpaceX advancements back in 2017.

Musk became not just the face of government cost-cutting; he became President Donald Trump’s weapon of choice, with the Musk-led Doge taking an axe to life-saving programs around the world.

It’s hard to reconcile the excitement of a flying car with the man who now guides it.

Or is it?

The fact is that no one needs this car, and anyone who understands even the tiniest bit of physics (and rocket technology) knows that the power and effort to lift and fly a one-ton car is extraordinary.

Its thrusters have nothing to do with the EV motors or batteries — they’re cold-gas thrusters, which use massively compressed gas stored in a 200-liter tank and a 400-liter tank. Musk was clear that this car is not expected to be road legal, which is good, because I’m frankly terrified of the idea of something crashing into it and rupturing those tanks.

But even if he delivers multiple of these cars for the invite-only crowd it’s aimed at, and it doesn’t turn out to be a one-off (which, let’s face it, is likely), why bother? If it’s so impractical that it can never make it to American roads, or any others, what was the point outside of a fun few seconds to watch?

Musk has his devotees, and I can see them tripping over themselves to applaud the unveiling (finally, after almost a decade) of this promised Tesla Roadster update, but for many others Musk is just another inscrutable tech bro who does what he does for his own reasons with very little, if any, of them having to do with the betterment of humankind.

So, thanks for the tech showcase, Elon. You have anything for what’s really ailing the world?

For years, organizations have focused on strengthening and fortifying endpoints, networks, and authentication at login. Meanwhile, one of the most critical control points in the identity lifecycle, the help desk, has remained surprisingly exposed, because most identity systems were not designed to extend strong verification into recovery and support workflows, leaving a critical gap that attackers are increasingly exploiting.

Without this shift, organizations continue to rely on human judgment in high-risk workflows, leaving a critical gap that attackers are increasingly exploiting.

With the rise of generative AI, deepfake videos, synthetic voice cloning, and AI agents, help desks have become a primary target for social engineering. Attackers can now synthesize a CFO’s voice from publicly available recordings, create deepfake video for live calls, or deploy AI agents to conduct persistent, multi-channel impersonation.

These bad actors can mine social media and breach data and corporate websites to collect personal details that make their story credible. They can also adjust their responses and adapt in real-time based on what a support agent says and asks.

Their goal is often straightforward, which is to convince the help desk to reset a password, enroll a new device, or override multi-factor authentication (MFA). If successful, the threat can quickly escalate to ransomware deployment, data theft, a full-scale account takeover, or more.

A modern identity architecture is necessary to address this significant verification gap, one that treats recovery and help desk interactions as first-class identity workflows, not exceptions outside the system.

The hidden weakness in manual verification

Most help desk workflows today still rely on knowledge-based questions, static employee data, or ad hoc manager approvals to validate user or customer identity. In a landscape where personal data is widely accessible and AI can convincingly impersonate individuals, these controls are brittle.

Knowledge-based verification assumes personal information is private, but it rarely is. Manager callbacks assume voice recognition is reliable, but it no longer is. Manual review assumes humans can detect AI deception, but that is not always the case. Many businesses are asking support staff to verify customer identity using tools built for a different era.

In many environments, help desk workflows sit outside core identity systems, operating as disconnected processes rather than being governed by the same policies and controls applied at login.

At the same time, expectations around identity protection are rising. As AI becomes more embedded in digital interactions, consumers expect organizations to have systems in place to effectively protect their identities. This is critical as 75% of consumers are more concerned about their personal data security than five years ago.

When protections break down, trust and brand reputation erode quickly and are difficult to rebuild. It is clear that trust can no longer depend on intuition or manual checks. It must be designed into identity systems and enforced through verifiable identity.

Modernizing help desk security with verifiable identity, at scale

Solving this requires rethinking how identity systems are designed, specifically how verification, recovery, and high-risk actions are handled as part of a unified architecture rather than fragmented processes. Modern identity architectures provide a path forward through decentralized identity and biometric-backed, phishing-resistant authentication.

Here’s what that looks like in practice:

1. Biometric-backed, phishing-resistant authentication

FIDO-based authentication tied to device-bound credentials and biometrics reduces reliance on passwords and shared data. Because credentials are cryptographically bound to a device and validated using advanced encryption, they are resistant to replay and phishing.

Enforcement moves from human decision-making to system-driven policy, ensuring consistency across both login and recovery workflows. Extending this model to account recovery and re-authentication workflows ensures that high-risk actions, like password resets or device enrollment, are protected by the same strong controls used when the user first signs in.

2. Decentralized, verifiable credentials

Decentralized identity frameworks enable organizations to issue tamper-resistant, verifiable credentials that users store in secure digital wallets. These credentials can represent employment status, role, or other attributes needed for verification, without centralizing sensitive data.

From an architectural standpoint, decentralized identity reduces reliance on centralized data stores and enables verification through cryptographic proof rather than static records.

This shifts identity verification from a knowledge-based to a proof-based model. When a user needs assistance from a help agent, they can present a verifiable credential that the organization can cryptographically validate without relying on knowledge-based questions. Therefore, the help desk is no longer in the position of “deciding” whether someone is who they claim to be. The system verifies it for them.

3. Self-service, high-assurance recovery

Self-service account recovery, supported by strong authentication and secure digital credentials, allows users to regain access without needing to call the help desk. This reduces support workload, while also removing the common target for social engineering attacks. Self-service recovery reduces reliance on manual intervention and allows identity systems to scale without increasing operational risk.

In this model, the help desk is no longer responsible for judging whether someone’s identity checks out, ultimately reducing risk.

Redefining the identity control plane

The security perimeter as we know it now reaches every workflow where identity is verified, delegated, or recovered. Help desks are directly in that flow. A more resilient model separates identity into a control layer that defines policy and a runtime layer that enforces decisions consistently across every interaction.

Modernizing identity verification systems with decentralized credentials, biometric-backed authentication, and consistent assurance policies closes one of the most exploited gaps in the security chain. It replaces the need for help desk agents to rely on personal judgment with verifiable proof of identity, and ensures that high-risk actions are governed by controls designed for today’s threat landscape.

AI-driven deception will continue to mature in speed, realism, and automation. Addressing it requires durable identity architecture that applies consistent verification across users, devices, and workflows.

When identity systems are designed to enforce trust automatically and uniformly, organizations gain resilience that scales with innovation, and a foundation for maintaining confidence in an AI-shaped digital environment for years to come. In this model, identity becomes a distributed system capability embedded across workflows, systems, and user interactions, rather than a point-in-time check.

This shift turns identity from a point-in-time control into a system-wide enforcement layer, closing one of the most consistently exploited gaps in enterprise security.

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At the start of every school year, schools, colleges and universities across the UK race to onboard thousands of new students, faculty and staff members, provisioning accounts, issuing credentials and connecting a wave of new devices to institutional networks.

That operational pressure creates opportunities for attackers, and this year, AI is amplifying the threat more than ever. Not only are phishing campaigns more convincing, but a hidden layer of unmanaged machine identities is expanding the cyber attack surface.

Attackers are persistent and the education sector remains a top target with many facing cyber attacks weekly. The UK Government’s cyber security breaches survey revealed that 27% of further and higher education institutions reported experiencing a breach or attack at least weekly, compared to 14% of primary schools and 20% of secondary schools.

Navigating the expanded threat window

The education sector is one of the highest targeted industries for ransomware, credential theft and data breaches. For hackers, schools and universities present an appealing combination of high-value data, including student records, financial information and research, alongside underfunded IT departments and an enormous, ever-rotating user base.

The back-to-school season intensifies this pressure. Bulk account creation, mass device enrolment and a surge of third-party application onboarding all happen simultaneously, creating a greater risk of misconfiguration and excessive access rights. The UK Government found that no tier of the education sector had a majority of institutions compliant with all of the government’s Cyber Security 10 Steps.

Just 14% of primary schools, 23% of secondary schools, 33% of further education colleges and 45% of higher education institutions had covered all 10. As thousands of new identities and devices enter this already insecure environment, gaps in security controls can become harder to identify and manage.

AI has made cybersecurity threats significantly more dangerous. The same government survey found phishing to be the main threat identified by institutions that had suffered a breach, and those emails can now precisely mimic communications from student funding offices, IT helpdesks or university leadership, with none of the traditional red flags that once made them easy to spot.

Keeper Security’s research into AI and cybersecurity in education found that 41% of schools have experienced AI-related cyber incidents, including phishing and misinformation, while 90% of education leaders expressed concern about AI-related cybersecurity threats. Yet only one in four respondents felt very confident recognizing AI-enabled threats such as deepfakes or AI-driven phishing.

As AI makes social engineering attacks more convincing, education institutions need to reinforce human awareness with stronger identity and access controls that do not rely on users spotting every malicious message.

The hidden attack surface: non-human identities in EdTech

As students and staff begin the new academic year, IT teams will rightly be focused on securing human accounts, but there is another, much larger population of digital identities that can easily escape attention: Non-Human Identities (NHIs).

NHIs are now embedded throughout modern education environments, from service accounts that synchronize student records with learning platforms to API keys connecting third-party applications, digital textbooks and payment systems.

Machine identities and certificates authenticate everything from campus Wi-Fi and smart boards to lab equipment and security cameras, while cloud workloads automate backups, research pipelines and administrative processes. Similarly, AI agents powering admissions chatbots, helpdesks and grading tools are adding yet another layer of identities and access privileges that institutions need to manage.

These identities are essential to keeping schools and universities running, but they can also create significant security gaps when credentials are rarely rotated, old integration tokens remain active or automated accounts are granted more access than they need.

Unlike employees and students, NHIs are not naturally caught by onboarding and offboarding processes. Without a complete inventory, organizations can struggle to know which identities exist, who owns them and what they can access.

The cybersecurity conversation in education has historically centered on protecting students, teachers and administrators, but the machine identities powering modern EdTech deserve the same scrutiny, because regardless of whether a breach stems from a human or machine, the impact is the same.

Back-to-school is a useful opportunity to review the full identity estate – both human and non-human – remove identities that are no longer required and tighten access for those that remain.

How education IT teams can reduce their risk

For most institutions, the fundamentals of managing humans are manageable. The critical gap is visibility and governance over the NHIs that power modern EdTech, like service accounts, API tokens, machine certificates and AI agents that multiply with every new integration. Security teams should take these steps to help secure NHIs and humans alike.

Enable multi-factor authentication

As the new semester begins, organizations should ensure MFA is enforced across all faculty, staff and student accounts, with new users defaulted into it from the moment they are provisioned. MFA remains a fundamental defense against credential-based attacks.

Audit privileged access

Many accounts give users privileged access beyond what’s necessary for their role. Privileges should be audited for both humans and non-humans. This includes removing access for departed employees and students, retiring expired service accounts and eliminating unnecessary administrative privileges.

Deploy enterprise password managers

Password security remains one of the hardest problems for security teams to solve. Awareness and training can provide students, faculty and staff with the knowledge on how to create strong passwords, but converting that knowledge into practice is difficult because password reuse, written passwords and browser storage remain common. Enterprise password managers remove that gap, enabling users to create and store strong, unique credentials.

Build a non-human identity inventory

You can’t secure what you can’t see. Schools should catalogue every service account, API key, machine certificate, cloud identity and AI agent. Establish ownership and record what each identity can access.

Establish credential rotation policies

It’s important to establish credential rotation policies for machine identities, paying particular attention to AI agents and third-party EdTech integrations added for the new school year. The Canvas learning management system hack, reported in April 2026, affected almost 9,000 educational institutions globally – a reminder that third-party credentials require the same rotation discipline as internal accounts.

Third-party access should be regularly examined and security teams should scrutinize the security of all current and potential partners and suppliers.

Updated training

Security awareness training needs to keep pace with the threat landscape, particularly around phishing, where AI-generated messages may be indistinguishable from legitimate institutional communications. Staff should independently verify unusual requests rather than relying on visual cues alone

Getting ahead of the rush

Back-to-school will always put education IT teams under pressure, but it does not have to create an open window for attackers. As institutions begin the new academic year, security must extend beyond onboarding students and staff to understanding every identity with access to critical systems and data.

By enforcing strong authentication, tightening privileged access, improving credential hygiene and gaining visibility over the growing population of non-human identities, IT teams can significantly reduce their exposure.

Security should be treated as a continuous discipline, not a back-to-school checklist. It is critical that every human and machine identity has the right access, for the right purpose and for only as long as it is needed.

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For years, quantum computing lived in a strange place in the cybersecurity conversation: important enough to worry about, but sufficiently distant to leave for another day. When we asked CISO what's keeping them up at night, quantum computing rarely made the top five. It still feels distant, theoretical, someone else's problem for 2030-something.

That calculation is starting to look wrong.

Gartner predicts that by 2030, advances in quantum computing will make conventional asymmetric cryptography unsafe to use. Four years away sounds like breathing room. It isn't. Replacing the cryptography embedded across a modern technology estate isn't a patch cycle. It's a multi-year transformation touching applications, infrastructure, identity systems, suppliers and developers all at once.

The clock is already running. Most organizations just haven't noticed it started or don’t appreciate how little time they really have.

This was never really a technology question

Quantum innovation is moving at pace with IBM, Amazon and Google publishing roadmaps or breakthroughs on error correction and qubit stability. Taken together, they tell a consistent story: the timeline to a cryptographically relevant quantum computer is shrinking, not stretching.

But for the average enterprise, that's not the most useful question to be asking right now. The more useful question is far more uncomfortable: how much cryptography do we actually have, do we know where it lives, and could we change it if we had to?

Cryptography isn't a product category you can point to. It's woven into everything, buried in vendor software, embedded in Transport Layer Security (TLS) handshakes, quietly doing the work inside identity and access management systems nobody thinks about. Gartner's research puts a number on the blind spot: 61% of organizations lack full visibility into their own cryptographic systems.

CIOs and IT leaders cannot migrate away from a risk they cannot see. That single statistic is arguably the real concern than the quantum breakthrough itself.

The attack has already started

Here's the unnerving part. You don't need a working quantum computer today for the threat to be live today. Adversaries, particularly well-resourced state actors are already engaged in what the industry calls "harvest now, decrypt later": hoovering up encrypted data now, sitting on it, and waiting for the day decryption becomes trivial.

For anything with a long shelf life such as government records, medical files, IP, legal contracts, identity documents, a five-year-away breakthrough is still a today problem, because the theft happens now.

Encrypted securely today doesn't mean secure for the data's whole useful life. That's the sentence every chief information security leader should be testing their current strategy against.

Crypto-agility is the actual prize

There's a temptation for CIOs and IT leaders to treat post-quantum cryptography as a swap: rip out an old algorithm, drop in a quantum-resistant one, done. It doesn't work that way. New algorithms aren't drop-in replacements.

Larger key sizes strain memory on older systems, signing operations run at different speeds, and performance assumptions baked into legacy applications quietly break. Moreover, additional standards are still being developed meaning today’s replacement might be tomorrow’s cryptographic debt.

Which is why the capability that actually matters isn't any specific algorithm. It's crypto-agility - the ability to change cryptographic algorithms, keys and protocols quickly, without re-engineering the business around them. And organizations are starting to focus on building it, alongside prototyping or evaluating post-quantum algorithms outright.

Build crypto-agility properly and you're not just preparing for quantum. You're fixing something older: cryptographic technical debt that would have caused pain at the next major algorithm weakness regardless of what triggered it. Quantum is the deadline. Technical debt is the disease.

Additionally, hybrid cryptography which blends classical and post-quantum mechanisms during the transition buys some breathing room. Treat that as scaffolding, not a destination. Classical methods have a shelf life measured in single-digit years.

Why now, when nobody can predict the exact date

There's an understandable instinct to wait for certainty, to see exactly when a cryptographically relevant quantum computer arrives before committing budget. That instinct is the wrong strategy for a transformation measured in years, not sprints.

Organizations don't need to know the date. They need to start building visibility and flexibility now, so they can act when required. The unglamorous first step: build an inventory. Scan the estate, catalogue every cryptographic component, feed the findings into whatever technical-debt dashboard has executive attention. It surfaces expired algorithms, outdated TLS versions and vendor dependencies — and gives the migration a factual basis instead of a guess.

Then prioritize. A marketing database that's stale in eighteen months carries a very different risk profile to a decade-long government contract or an identity credential meant to last a generation. Ask which information still needs protecting after 2030. That list moves to the top. Then ask which use cases could disable the business and tackle those next.

This cannot be handed to the security team

A mistake is treating post-quantum migration as a security project with a security owner. It isn't. Developers need to understand how algorithm changes hit application performance. Cloud and network teams inherit protocol changes. Identity teams own the authentication dependencies.

Procurement needs to interrogate vendors on their own timelines, because a huge share of enterprise cryptography arrives bundled inside third-party software nobody directly controls.

Equally, pushing this down to individual system owners without central coordination is a guaranteed way to fail, leading to inconsistent decisions, no organization-wide picture of progress, and gaps precisely where attackers look first.

A cross-functional Cryptographic Centre of Excellence, pulling IT, development and data security under one roof, isn't governance theatre. It's what keeps the response from fragmenting into a patchwork.

The real question isn't about quantum at all

Strip away the acronyms and this stops being a story about qubits. It becomes a test of how resilient an organization's architecture actually is, whether it knows where its cryptography sits, understands which data needs long-term protection, and can change cryptographic mechanisms without rebuilding from scratch.

The organizations most exposed won't be the ones with the weakest algorithms. They'll be the ones still treating quantum as a distant technology story rather than a resilience problem sitting on today's books. By the time the threat is obvious to everyone, the hard part—untangling years of cryptographic dependencies—will already be behind schedule.

The quantum era is still approaching. The migration effort isn't.

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Businesses are moving quickly to embed AI tools across all functions, from marketing and HR to sales and operations. In part because they don’t want to be left behind, but productivity gains, customer expectations, competitive pressure and revenue growth are all pushing organizations to adopt AI at speed.

The bigger story is that AI is moving faster than most businesses can adapt. The tools are already here, but the workforce models around them - the governance, workflows, training and specialist skills are still catching up.

As a result, businesses continue to face an AI skills gap: not just a shortage of technical expertise, but a shortage of the human capability needed to prove the return on large-scale AI investments is mounting.

Recent marketplace data echoes this. Searches for AI automation have increased 29% since November 2025, as businesses look for people who can turn AI tools into reliable, commercially useful outputs. The demand is not just for AI access, it’s for AI fluency.

So, the challenge with AI is that it has moved from ‘tooling issue’ to ’talent issue’, as businesses struggle to translate access into high-quality output. AI is aggregating the average, and average output requires constant human judgement to stand out.

For organizations trying to close that gap, the solution is two-fold: prioritize human judgement, and rethink workforce models around AI-native talent.

Stop prioritizing AI access and start prioritizing human judgement

The first stage of AI adoption was defined by access and experimentation. They wanted to know which AI tool was the best to buy, which platforms they should be testing and how their teams should start using them in their day-to-day work. With AI readily accessible, the innovation comes from implementation, so this is where AI adoption becomes a talent issue. Organizations that stop treating AI as a software rollout and start investing in human skills will be better placed to compete.

LLMs and generative tools are trained on existing patterns, often creating sameness with widespread adoption. When many organizations use similar tools, in similar ways, with similar prompts, the result is often generic. Although they can help teams move faster, focusing on speed does not create differentiation. Without human judgement, AI can make mediocre work easier to produce at scale. That is the challenge now facing businesses, the baseline has risen so the ability to create work that is distinctive still depends on people.

Businesses need talent that can understand quality, shape creative ideas, interpret audience needs and turn raw AI outputs into work that stands out. That requires more than prompting - it requires taste, context, critical thinking and, most importantly, individual thought, something that is now lacking from many AI tools. In creative industries, this is already visible in the rise of AI directors: specialists who combine creative judgement with technical fluency. They are not simply operating tools, they are shaping narrative, visual identity, production workflows and final execution. In many cases, they are helping smaller teams produce commercial-grade work that previously required far larger studios.

So, as generative technology becomes more accessible, the advantage no longer lies in having the tools, it lies in knowing how to direct them. Workers must, in tandem, focus on upskilling in AI for their own career development.

Rethink your workforce models around AI native talent

Businesses that are struggling to hire the right talent are likely focused on AI adoption then expecting teams to absorb the change without proper training, which creates a gap between ambition and readiness. To get more value from AI, leaders need to build workforce strategies around AI capability. That means identifying people who can turn AI outputs into high-quality work, while also spotting the errors, risks and limitations the technology can introduce.

Our data also shows that demand is growing for more specialist AI execution as searches for AI video and animation rose 42% since November 2025. As teams build AI fluency, businesses should not assume every capability needs to be built in-house from day one. In a market moving this quickly, the smarter model is often a hybrid one: upskill internal teams while bringing in flexible specialists to close urgent gaps, accelerate execution and transfer knowledge as the work evolves. AI skills are changing too fast for most businesses to build every function permanently, so this gives businesses the right expertise without the cost or delay of building an entire permanent team from scratch.

At the same time, AI deployment needs to start with the real business need - teams should identify the repetitive tasks, slow processes and low-value admin that are pulling people away from higher-impact work. Once those opportunities are clear, leaders can identify the real capability gap: is it something an existing employee can learn, something AI can support, or something that requires an external specialist?

AI adoption also raises legitimate concerns; leaders need to be clear that AI is not simply a way to cut people out of the process. A stronger workforce model keeps human talent and creativity at the center while giving employees new ways to apply and evolve their skills. This not only supports productivity but also retention and satisfaction. Teams are more likely to embrace AI when they understand how it can enhance their work, not just threaten it. Businesses that invest in upskilling, clear guardrails and access to specialist support will be better placed to build confidence as well as capability.

Talent is what defines AI success

With AI now the baseline, not the advantage, the businesses that will gain competitive advantage will pivot to focusing on judgement and skills development, with a new class of AI-native operators acting as directors to combine creative and technical decision-making.

That means teams who know how to question outputs and turn AI into work that actually matters. Access is easy but the execution is hard, so the next phase of AI adoption will be defined by workforce readiness.

AI can raise the baseline, but the right talent mix turns AI from a tool to a competitive edge.

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For technology teams, connecting telemetry has traditionally been seen as a route to greater visibility. The assumption is straightforward: the more information available about what is happening across an environment, the easier it should be to understand problems and respond to them.

But as modern technology environments have grown in scale and complexity, that equation no longer looks quite so simple and teams have found themselves dealing with fragmented telemetry and unclear signals. It now takes longer to understand what’s gone wrong, and even longer to fix it. And to add to the challenge further, the cost of storing and processing all this telemetry and data continues to be on the rise.

This is prompting organizations to reconsider where the problem should be addressed. Rather than collecting everything and attempting to make sense of it later, attention is shifting towards improving telemetry earlier in its lifecycle. Filtering, enriching and directing it from the outset can make the information reaching teams more relevant and ultimately easier to act on.

More data doesn’t always mean better insight

The volume of telemetry available isn’t the fundamental issue. Its quality is. As AI-driven capabilities become deeply embedded in software delivery and operations, the quality of observability telemetry becomes even more important. Collecting telemetry without filtering out what isn’t needed can leave employees spending more time managing information than extracting value from it.

Fragmentation makes this even more challenging. With data sitting across several tools and agents, each of which generates varied forms of signal and alert. When an outage occurs, teams may then need to piece together inconsistent information from across those tools before they can understand the root-cause of the problem. Consuming valuable time while the impact on the business continues to grow.

There is also a significant financial cost attached to this approach. Processing and storing telemetry and data at scale requires considerable resources, particularly when organizations are retaining information that contributes more noise than insight. In the traditional sense, this isn’t a technology problem. It’s a telemetry and data quality concern, and the longer it’s ignored, the worse it usually gets.

Rethinking where telemetry is processed

One way organizations are responding is by moving intelligence further upstream in the technology stack. In practice, that means making decisions about telemetry earlier – filtering out what isn’t useful, enriching important information and routing meaningful telemetry before it reaches downstream systems.

Teams are beginning to collect, process and route relevant observability telemetry in real-time before it is ingested into downstream systems. As a result, there is a less volume to handle, reduced expenses and cleaner telemetry to deal with since only the relevant signals are sent downstream.

Open standards such as OpenTelemtry are making it increasingly possible to take this approach across complex environments. They allow teams to gather telemetry consistently without being dependent on a particular vendor or set of tools. That flexibility is particularly valuable when working with intricate, distributed systems that span several clouds, security and storage platforms.

Furthermore, as environments continue to evolve, it makes adapting to them that much easier.

AI needs reliable information to make reliable decisions

The implications become particularly important as organizations introduce more AI-powered capabilities into their operations. AI systems depend on accurate, contextualized telemetry and data to produce insights and automate decisions.

Teams are not only slowed down by incomplete or cluttered telemetry, but it’s this simple: poor quality telemetry leads to poor quality insights. When those insights are applied to large-scale automated decision-making, the margin for error drastically lowers.

The same principle applies to trust. Teams responsible for automated systems need to understand what has happened and explain why decisions about performance or security have been made. Doing so becomes much more difficult when those decisions are based on inconsistent information.

And this requirement for confidence matters far beyond just the technical teams. As senior management and board members are being increasingly asked to support AI-driven decision making, they need to trust the information those decisions are built upon. Credible, consistent telemetry and data can make that conversation much simpler.

The operational advantages are also evident. Teams can work quicker if they aren’t continuously battling erratic telemetry and data. They spend less time on manual research, detect issues early, and automate with greater confidence.

A strong telemetry and data foundation and a unified, context-driven platform not only enhance daily operations but also increases the successes of the AI-powered capabilities the organization invests in over time.

Quality has to be built in from the beginning

Ultimately, faster innovation and better customer outcomes depend on organizations having useful telemetry and signals available when they are needed. The good news is that it doesn't require starting from scratch, it's about making choices early in the telemetry lifecycle.

By introducing intelligence at that stage, teams can improve the quality of what ultimately reaches their observability platforms and ensure it can scale as their environments grow. The advantage will not come from simply collecting more. It will come from ensuring the telemetry an organization does collect is relevant, trustworthy and ready to support action.

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European consumers continue to bank with traditional institutions, but many are clearly less satisfied with the experience. Earlier this year, a leading digital banking platform provider published the results of a survey of 6,300 online banking users across Europe.

Although 82 percent of respondents used a traditional bank as their main provider, 26 percent had switched banks in the past because of poor digital services, and would not hesitate to do so again. Also, 47 percent said that they would feel more satisfied if their bank improved the quality of digital services. Customers specifically called out personalized recommendations in this context.

What do customers want?

With the digitalization of banking, seamless transaction experiences have become the norm, standardized across banks with little to differentiate them. Digital channels are efficient, but impersonal. For the majority of customers, they are a way to complete quick, functional tasks such as checking account balance. This has gradually emotionally distanced consumers from their banks.

And therein lies the dilemma: The same consumers who are embracing digital banking are demanding more personal interactions with their bank. For instance, 59 percent of European consumers want financial recommendations tailored to their individual needs. Banks that fail to provide this will see customers walking away.

The good news is that artificial intelligence technologies are now enabling banking institutions to meet these seemingly irreconcilable expectations. Where previous digital technologies allowed some – mostly segment-level – personalization, AI is driving hyper-personalization of digital banking experiences, in real-time, at scale.

Personalize everything, from product recommendation to marketing communication

Advanced analytics models analyze customer and transaction data to identify insights into individual needs, preferences and behaviors, based on which intelligent recommendation engines suggest the most suitable products and services. Besides tailoring products using traditional financial metrics – think of loans and investment plans customized according to risk profile and financial goals – AI can personalize banking based on an individual’s data and activity outside the bank.

A great example is the hugely popular Buy Now Pay Later model where ecommerce businesses allow customers to pay for purchases in instalments based on their creditworthiness, assessed using previous transaction history and other variables.

AI-powered tools can also detect cash flow patterns or spending anomalies to offer proactive guidance to help every customer manage their finances better. Gen AI-powered chatbots and virtual tellers can support customers at any time of day or night, conversing in natural language just like a human relationship manager, to answer queries and even resolve complex issues.

Customers can ask specific questions about their portfolios or financial goals, and generative AI will respond independently, or along with a human advisor, with targeted recommendations and guidance.

The ability of generative AI to converse in a highly personal, human-like manner is transforming debt collection. Tapping natural language processing and machine learning, gen AI systems analyze borrower preferences and behaviors and then create personalized messages that improve both engagement and repayment. They also minimize confrontation by showing empathy, and suggesting solutions that are more acceptable to customers.

Revolutionizing communication

Coming to communication, bombarding people with generic messages is inefficient, ineffective and exposes a bank’s lack of in-depth customer understanding. It also annoys consumers who are already flooded with spam and unsolicited calls from various businesses. AI is revolutionizing communication by enabling banks to craft highly relevant messages that resonate with customers.

First, predictive tools identify which customers are likely to respond favorably to different types of messages; then generative AI swings into action, creating highly personalized content and campaigns, in the customers’ chosen language(s). Other AI solutions identify the right channel, format and time for sending the communication, making customers feel valued and truly understood by their bank.

The gaming industry also presents a compelling frontier for banks to engage younger, digitally native audiences with gen AI. With so many gamers interacting in immersive virtual environments, banks can leverage gen AI to create hyper-personalized financial experiences that align with gaming lifestyles and personas.

For example, AI-driven insights can enable banks to offer micro-investment products based on in-game spending behavior, offer dynamic credit options for digital purchases, and design savings plans that reward financial discipline with virtual incentives.

By integrating with gaming platforms and analyzing behavioral data in real time, banks can deliver contextual financial nudges—such as suggesting a budgeting tool or offering crypto-linked rewards for in-game achievements. The integration of gaming and finance, powered by gen AI, not only enhances customer engagement but also positions banks as lifestyle partners in the digital economy.

What’s more, AI models learn from the success and failure of past interactions to modify their own actions, reinforcing strategies and recommendations that invited interest, while dropping those that were met with disengagement. As it continuously learns and adapts, AI gets better and better at hyper-personalizing customer experiences at scale.

Moving forward in Europe

In Europe, next-generation providers are the clear leaders in AI adoption: Dutch neobank, Bunq, is Europe’s first AI-powered bank; Finn, its personal finance assistant, offers personalized, context-aware support through simple conversation, handling up to 40 percent of customer queries independently.

AI analyzes transaction data to offer personalized recommendations within Starling Bank’s mobile app, driving app engagement up by 25 percent. German digital bank, N26, personalizes customer experience through AI-powered multilingual support, conversational AI, and AI-generated next best action recommendations.

But overall, European financial service firms still have a long way to go. In a 2024 survey, although 90 percent of respondents said they had integrated some AI tools, just 9 percent believed they were ahead of the adoption curve. Investments are focused on back-office improvement, while AI adoption in customer-facing applications is clearly lagging.

Respondents named (lack of) understanding, experience and regulatory clarity as their biggest issues when integrating generative AI. Firms can address these concerns and accelerate implementations through training and development, by broadening AI use-cases, and by setting up strong governance and compliance mechanisms.

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Enterprise investment in artificial intelligence has, understandably, been concentrated on compute. Access to GPUs, model performance and the cost of inference have become central considerations as organizations move AI workloads from experimentation towards production.

However, an increasing proportion of the economic and operational challenge sits elsewhere in the infrastructure stack. AI depends on data at every stage of its lifecycle: datasets must be collected and prepared, training data must be made available to compute, checkpoints and model artefacts must be retained and information may need to move between infrastructure providers as workloads and commercial priorities change.

As these workloads scale, the storage layer is becoming a strategic component of AI infrastructure with significant implications for the economics of enterprise AI.

AI is changing the role of storage

Traditional storage strategies have generally been built around considerations such as capacity, availability, performance and retention. AI introduces another dimension: the relationship between storage and the changing requirements of the wider infrastructure environment.

Different stages of an AI workflow have different storage requirements. Data being actively processed may require high-performance infrastructure, while historical datasets, checkpoints and model artefacts may place greater emphasis on durability, security, accessibility and cost. Applying the same storage approach across the entire lifecycle can therefore result in inefficient infrastructure utilization.

The challenge is particularly significant because AI data volumes can be substantial and continue to expand as organizations generate additional training data, model versions and associated artefacts.

The objective should not be to minimize storage expenditure in isolation. It should be to ensure that data is held on infrastructure appropriate to its role, while maintaining the performance, security and accessibility required by the business.

This is the foundation of what can be described as AI storagemaxxing: the systematic optimization of storage across the AI lifecycle to ensure that infrastructure investment is aligned with business and workload requirements.

Data mobility is becoming an infrastructure requirement

Organizations are adopting a broader range of infrastructure providers, including hyperscalers, specialized GPU providers and emerging cloud platforms known as neoclouds. The economics and availability of these environments can change rapidly, creating incentives to move workloads between providers. Compute can be relatively straightforward to reallocate. The associated data is considerably less mobile.

Large training datasets and model artefacts can be expensive and operationally complex to transfer. Where significant charges are associated with moving or retrieving data, organizations may find themselves constrained by infrastructure decisions that were made at an earlier stage of their AI program, creating a form of unintended dependency.

The ability to move data should therefore be considered when designing AI infrastructure, rather than treated as a contingency for a future migration. Open standards and interoperable storage can provide an important degree of separation between data and compute, allowing organizations to adapt their infrastructure strategy as market conditions change.

For technology leaders, this creates a broader consideration than simply assessing the cost of storage capacity. The relevant question is the total economic and strategic cost of retaining data within a particular environment.

Storage optimization must include security

Training datasets, proprietary information and model artefacts can represent significant commercial value. At the same time, the number of applications and automated systems interacting with enterprise data is increasing. Storage resilience must therefore form part of the AI security strategy.

Immutable copies, appropriate access controls and robust recovery processes can help organizations protect critical data from ransomware, accidental deletion and unauthorized modification. These capabilities are particularly important for datasets that would be difficult or expensive to recreate.

Security should consequently be considered alongside performance and cost when determining where AI data resides. A storage environment that reduces expenditure but creates unacceptable recovery or security risks does not represent effective optimization.

Predictable economics will become increasingly important

Storage costs can include capacity, access, retrieval and data transfer. Where these costs are difficult to predict, they can complicate infrastructure planning and influence architectural decisions. This becomes particularly problematic when organizations need to move large datasets between storage and compute environments.

Predictable storage economics allow organizations to make infrastructure decisions based on workload requirements rather than uncertainty over the cost of accessing or moving their data. This distinction will become increasingly important as AI workloads mature. During experimentation, infrastructure decisions can be relatively temporary.

Production workloads, by contrast, may persist for years and generate substantial quantities of associated data, so the storage strategy needs to account for the full lifecycle of that data, rather than the requirements of a single workload or infrastructure deployment.

The strategic role of the data layer

The next phase of enterprise AI adoption will require organisations to look beyond compute optimisation. GPUs will remain critical. Model efficiency will remain important. However, neither can be optimized effectively without considering the data on which AI workloads depend.

AI storagemaxxing provides a useful framework for applying the principle: storage should be treated as part of the AI operating model. Assess storage according to several strategic criteria: whether it is appropriate for the workload, does it provide adequate protection and recovery, are the economics predictable, and could data be moved without friction as infrastructure requirements evolve.

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By now, most enterprise executives have heard plenty of generic claims that AI will transform the way work gets done. But in many cases, their initial AI investments haven’t delivered the level of transformation they were promised.

An LLM can summarize information, answer questions, and suggest next steps. But on its own, it can’t take action on that information. In fact, without connections to key business systems, an LLM is essentially a chatbot with a polished writing style.

Consider a finance team processing invoices. An isolated LLM might accurately summarize a supplier invoice. But it can’t match the invoice to a PO, determine whether it’s been paid, or see if it’s coded to the correct GL account. It needs more information to make those decisions, and that information exists outside of the AI tool.

For AI to take an active role in business processes, it needs access to the systems where context lives. The Model Context Protocol (MCP) can provide the infrastructure for secure connections. This shift is already taking shape in accounts payable.

What MCP actually changes

The impact of an LLM depends on what it has access to. If the information it needs to make a reliable decision lives in an ERP, vendor database, or other business system, the model needs a way to access it.

Connecting AI to business context hasn’t always been straightforward. Different applications may require different integrations for the same systems, which quickly gets complicated as businesses add more AI tools.

The Model Context Protocol provides a standardized way for AI applications to connect with external systems and data sources. When AI needs access to a new source, developers can use MCP, rather than creating a custom connection for every application and system.

MCP changes what AI can actually do. With the right connections, AI agents can pull relevant data, use that information to make decisions, and take action within the boundaries defined by the business. MCP can facilitate bidirectional connections, so AI can also send information back to core business systems. For example, an AI agent might update a record, route something for approval, or trigger another step in a process.

While MCP provides the infrastructure for connecting AI to business systems, it doesn’t mean agents should suddenly be given free rein. The level of autonomy AI agents can reasonably handle today still varies quite considerably by task and implementation. As Meng Liu, Principal Analyst at Forrester, recently said, “agentic branding is widespread, but real autonomy is not.”

That said, AI agents don’t need complete autonomy to take on a more active role in business processes. Accounts payable is a useful example of what connected AI looks like in practice and how it can drive impact for the business, even without total autonomy.

AP shows what connected AI can do

the business needs to confirm that item prices match what they agreed to in the contract, the products were delivered, the invoice hasn’t already been paid, and that each line item is coded to the right account. If those checks don’t happen, there are more opportunities for errors and avoidable losses to sneak their way through.

Traditionally, those checks depended on people. Even today, when automation is widespread, an exception often ends up in a queue, and an AP employee must dig through purchase orders, receiving records, contracts, and past invoices to sort it all out.

That manual work comes at a cost. According to Ardent Partners’ State of ePayables 2025, the average cost of processing an invoice is $9.84, compared to $2.65 for best-in-class organizations.

But consider how things could change if an AI agent had access to the same data sources the AP employee does. Let’s say a restaurant receives an invoice for 20 cases of chicken at $42 per case. The AI agent can compare the invoice to the purchase order and receiving records and determine that only 18 cases were delivered. It can also see that the invoice cost per case is $5 more than the agreed-upon contract price.

When the numbers don’t match, the invoice won’t automatically end up in an exception queue because the AI agent has the context to investigate further and take action.

Depending on the agent’s findings and the business’ policies and guardrails, it could request a credit from the vendor, route the discrepancy to the appropriate employee with recommended next steps and supporting evidence, or clear the invoice for payment if everything checks out.

This same concept can be applied to a whole host of other AP decisions. For example, an agent with access to vendor history data can identify a possible duplicate invoice before payment goes out. One that’s connected to the general ledger and historical coding can determine the appropriate GL codes for a new invoice. A third that has access to approval history and company policies can determine who needs to approve an invoice next.

In each scenario, the agent’s ability to decide and act depends on whether it can access the right information. In AP, there’s an easy way to tell whether those decisions are really working.

AP is good test of AI autonomy

Touchless processing is the goal of implementing AI in AP. This is when an invoice is captured, validated, coded, approved, and paid without a person stepping in. If an AI agent completed all of those steps accurately on its own, the invoice was processed touchlessly. If a person had to step in at some point, it wasn’t. There’s no in between.

That straightforwardness makes AP a great proving ground for connected AI. There’s something concrete to measure, and mistakes can have real consequences. If an invoice isn’t processed correctly by AI agents, it can lead to issues such as manual rework, overpayments, missed discounts, and inaccurate accounting records.

In other areas of the business, AI's success can be harder to measure. For instance, a chatbot can save an employee some time by summarizing information and drafting emails. But that impact is difficult to quantify, and there isn’t always a clear measure of whether the AI did its job successfully.

AP gives businesses that measure. As leaders consider where AI agents can take on more responsibility across the enterprise, AP is a useful place to see what autonomy really looks like.

Look beyond the “AI-powered” label

Today, plenty of enterprise technology vendors throw around the phrase “AI-powered.” Before making any technology investments, business leaders must ask the right questions to understand what this actually means in practice.

There’s a big difference between adding a chatbot to an existing platform and securely connecting AI to the context it needs to make decisions and take an active role in business processes. MCP can help close that gap and move enterprises closer to those promises of business transformation.

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Eric Steven Raymond, widely known as 'ESR' has been a large presence in the software development space for many decades, making key contributions to the school of development, programming languages, and the open source movement. Given the rise of AI, ESR has signalled the end of the closed-source model of software development.

Reverse engineering

ESR delivered this verdict on X recently, adding to his recent history of commenting on the rise of large language models (LLMs) and the impact on software.

In his post, the software pioneer remarked on how a fun experiment involving the use of AI to decompile an old game and port it into a new format alerted him to the potential for this practice to happen on a much grander scale.

"The thing is, I thought of this as a fun stunt but didn't have any confidence that the technique would scale up to large, real programs," he wrote, adding: "I have since learned that people are now doing this sort of thing with entire AAA games, which are among the most complex software artifacts ever to be shipped as a binary. If they can be decompiled, anything can be."

A paradigm shift

The implication of this practice, ESR said, is that there is no longer a secrecy moat around almost any software at all – and that the only way for the industry to maintain this "moat" is by hiding their executables behind SaaS shields. In that sense, cloud-enabled software will be much harder to crack open and reverse engineer.

Rather than closed source being gradually driven toward extinction by cost gradients, as he foresaw, he now believes that closed-source software may be "wiped out in a technoapocalypse" – albeit with the caveat that some laws will prevent the mass decompilation of binaries.

His thoughts mirror the views of Nvidia's CEO Jensen Huang, who put a twist on Marc Andreessen's famous comment that "software is eating the world" by suggesting in 2017 that "AI is going to eat software". This is something that we are arguably seeing play out as AI improves and becomes more prevalent across the enterprise.

It’s been a big week for Apple Home. The entirely new HomeView is the company's first smart display, and it seems purpose-built as a stationary, in-home hub for Apple’s ecosystem. Meanwhile, refreshes to the HomePod mini and Apple TV 4K promise to bring Siri AI to other devices around the home.

I’ve already written at length about HomeView, and why it’s the smart display and home hub I’ve been waiting for. Now, I’ve had the chance to spend more time with it. Not quite enough time for a full review, and I haven't tested it in my own home yet, since all my hands-on time has been in Apple’s demo space. But I’m still optimistic about what it could offer.

I already have a connected home of sorts. For me, though, it’s still very fragmented. I have HomePods, HomePod minis, and Apple TV 4Ks, but I mostly use them for quick questions, timers, taking calls, or media playback. I’ve also tried Echo Shows, Nest Hubs, and myriad other smart home gadgets like cameras, bulbs, and sensors, but none of those has really stuck, because they aren’t part of my core ecosystem.

Amazon’s ecosystem is heavily focused on commerce and shopping. That’s not necessarily a complaint, but the experience can also feel cluttered with targeted ads, and privacy concerns can be a sticking point.

Given Apple’s broader commitment to privacy and its carefully curated user experience, HomeView could help it carve out its own smart-home category. For people already invested in Apple’s ecosystem, it removes some of the friction that has kept other smart displays from becoming a central part of the home.

With an Echo Show or Nest Hub, someone who primarily uses Apple’s suite of services may have to move photos to another platform or service if they want to display them. The same goes for calendars, calls, and even music, depending on the services and features being used. And those devices don’t integrate with your iPhone the same way an Apple-made display can.

That’s not necessarily a bad thing, but when a smart display is something you might buy for a family member, a parent, or a friend, those differences can make for a less cohesive experience.

When you factor in the setup, the effort required to get everything working together, and the ongoing upkeep, some of the convenience promised by an Echo Show or Nest Hub starts to diminish. That’s what I’ve encountered in my own experience.

HomeView could help solve that disconnect. While the wall-mounted options seem like a less compelling value proposition to me, I can easily picture the speaker-base version sitting on a kitchen island or side table and becoming the household's hub device. And if you already know Apple’s ecosystem and its software, the learning curve should be much less daunting.

I’d seen HomeView before, but this time I got to interact with it, starting by swiping between its different faces; I almost called them watch faces, and for good reason.

HomeView feels like an Apple Watch on steroids, with a supersized, customizable watch face essentially plopped onto a display. That’s not a knock. The interface feels familiar, and is easy to get the hang of.

I especially like the Snoopy face, which offers a fun way to keep an eye on what’s happening throughout the day. You can check the weather as it rolls in, see an upcoming calendar event, or simply glance at the time with a little help from Snoopy and Woodstock. You can also go for something more functional, with modular faces that pack in widgets, or, as Apple calls them, complications, for things like your calendar, reminders, and the weather.

Swipe past those faces, and you arrive at the HomeView home screen. And for once, Apple’s answer to a shared household device isn’t to force you to buy one for everyone in your home. HomeView can recognize up to six people, effectively switching between their individual accounts and surfacing what matters most to each person while keeping their personal information private.

In my case, I could see myself having a HomeView setup that brings together quick controls for my thermostat, the weather, a rotating selection of photos, and, of course, a Bruce Springsteen playlist. The idea is that HomeView can surface a personalized experience when it recognizes you, with your iPhone playing a role in that process.

Privacy is central, too. If I open Messages, respond to someone, and walk away, HomeView locks the app when someone else approaches. It’s a small but important example of how Apple is thinking about a shared screen: convenient when it’s yours, but not an open book.

HomeView doesn’t feel exactly like iOS, iPadOS, or watchOS. Instead, it feels like a combination of all three, with familiar elements pulled together into something designed for a display you interact with throughout the day. App icons are larger, and I’d expect the interface’s emphasis on glanceable information to be particularly useful here. Widgets, in particular, seem designed to put the details you need front and center.

Swipe up, and you’ll find the App Drawer. There’s a selection of built-in apps, but notably, no App Store. I’m actually a fan of that approach. I don’t want to be glued to HomeView; it shouldn’t become another blue-light-filled screen demanding my attention. Instead, it should be actionable and glanceable, helping me get things done without becoming another device I have to manage.

Apple has a selection of core apps ready for HomeView, including Home, Intercom, Music, News, Photos, Safari, FaceTime, Messages, Calendar, Alarms, Timers, Find My, Podcasts, Reminders, Settings, and Weather. Developers can’t build dedicated apps for HomeView, but Apple can bring compatible experiences over from the iPhone. That includes apps such as Apple TV, Audiobooks, Apple Music Classical, and News.

There’s a distinction here: some of these experiences are effectively extensions of the iPhone, rather than apps running independently on HomeView. And while Apple supports third-party media apps that work with CarPlay, I wouldn’t count on specific apps such as Spotify arriving until Apple or the developers confirm compatibility. Still, the approach makes sense for a home hub. I like the idea of a curated selection of apps rather than hundreds of downloads competing for space and attention.

Music is one area where HomeView already makes a strong impression. Album artwork looks great on the display, and you can follow lyrics in real time. The built-in speakers also deliver clear, crisp, vibrant audio, although the volume wasn’t quite as powerful as I’d hoped during my time in Apple's demo space.

Even better, music doesn’t disappear into the background when you switch tasks. If you swipe over to another app, such as a cooking feature, a makeshift Dynamic Island appears at the top of the screen, giving you easy access to music controls. It’s a thoughtful touch that makes HomeView feel less like a collection of separate apps and more like a device designed around the way you actually use your home.

Smart home control is another central part of HomeView’s value proposition. Rather than opening the Home app on an iPhone, iPad, or Mac, this is a dedicated device designed to make controlling your home a more immediate, everyday experience. The Home app has been customized for the display, with larger touch targets and key information highlighted, and it looks nice.

You can move around elements within the app or bring them into other parts of the interface, adding controls as complications on your chosen clock face or widgets on the home screen. That flexibility means you can make HomeView work for the way you live, whether you want quick access to a few favorite accessories or a more comprehensive overview of your home.

The interface is the same whether you opt for HomeView on its speaker base or mount it on the wall. The latter does come with a trade-off, though: you lose the speakers in the base and have to pay separately for the wall mount. That makes the wall-mounted setup feel like a less compelling deal, at least to me.

Still, I can see the appeal of turning HomeView into a dedicated control panel near the front door, where it could become a natural first stop when you arrive home. It also feels like Apple is catering to people who have already invested in wall-mounted iPad setups for smart-home control.

You can interact with HomeView directly through its touchscreen, which reaches up to 500 nits of brightness and can dim as low as 0.3 nits — the lowest brightness Apple has engineered for a display. But touch is only part of the experience. Four built-in microphones are designed to pick up your voice while reducing background noise, whether you're making a FaceTime call or asking Siri for help.

Siri is central to the experience, and it’s clear that HomeView is designed with Apple's new Siri AI capabilities in mind. My time testing them was brief, and I sometimes had to repeat a request, but I was able to ask Siri to handle basic smart-home controls, start routines, and play music, as well as tackle more involved requests.

On my Mac, I enjoy typing to Siri for several tasks, including creating calendar events based on what's on my screen, but on the iPhone or iPad, I use it for multistep or topic-specific tasks. It is not always the fastest, but it is night and day over the former.

During my time with HomeView, I saw a similar approach in action for more complex questions. I saw a demo of Siri being asked what time someone was landing at an airport, then asked when that person would arrive home if they took a train. After a few seconds, Siri answered by drawing on information from Messages, its broader knowledge, and estimated transit times.

It was a brief demonstration, but I could immediately see how useful this could be for quick questions and more involved requests. For instance, my friend Benjamin and I love sharing music. If I missed a song he recommended, I could imagine asking Siri to identify that Chuck Berry track while cooking dinner or getting on with something else around the house.

That’s where I think HomeView could really distinguish itself from the ever-popular Echo Show. Amazon's displays can connect to a range of services and smart home devices, but for someone already invested in Apple, they remain part of a separate ecosystem. HomeView, by contrast, is designed to work with the devices, services, and information you already use. If Siri can reliably bring all of that together, HomeView could be considerably more useful than a smart display that simply answers questions or controls a few connected devices.

That integration extends to moving between devices, too. When I get home, I could transfer whatever I'm listening to on my iPhone simply by tapping it against the front of HomeView. That's iPhone Handoff, and it's a brilliant implementation of a feature that makes the most of Apple's hardware working together. The same idea extends to calls, episodes of content, and even Safari webpages, making it easier to pick up where you left off without having to start over on another device.

An ambient smart display, rather than another screen demanding attention

A few other details from my brief hands-on stood out. For one, HomeView doesn't have notifications, and I actually like that decision. It means there's less reason to linger in front of the display or constantly check it for updates. Instead, HomeView feels like an ambient device, designed to be useful when you need it and easy to ignore when you don't. It's arguably Apple's strongest implementation of that idea yet, and it already feels more purposeful than StandBy on the iPhone.

It still has familiar Apple touches, of course. You can pull down from the top-right corner to access Control Center and quickly reach key settings. The built-in 12-megapixel camera, which recognizes users and serves as the main lens for FaceTime, can also make HomeView double as a mirror. Who knows, perhaps the Photo Booth could return and find a home here, too.

I still need to spend more time with HomeView, and in my own home, before I can say whether it delivers on all that promise. But after getting hands-on with it, I'm even more convinced that Apple is approaching the smart display from the right angle. Rather than giving me another screen to manage, HomeView looks set to bring together the devices, services, and everyday tasks I already rely on. And for someone who's been waiting for Apple's smart home to feel more connected, that's an exciting prospect.

Of course, Siri AI still needs to deliver on that promise, but after the long wait, it's encouraging to see Apple finally moving in the right direction.

Most people spend more time in their browser than in almost any other piece of software they use. It's where they read the news, check their bank accounts, work, shop and more. Yet, for something so central to daily life, most people have a surprisingly limited understanding of what their browser is actually doing behind the scenes.

That gap was easier to overlook when browsers had a relatively simple job. They opened websites and displayed them on a screen. But today, browsers hold login credentials, manage permissions, block trackers and increasingly give AI agents the ability to act on a user's behalf. The browser has gone from being a window on the internet to one of the most important layers between people and everything they do online.

That shift is understandably enough to make users more cautious than they used to be when online. But, misunderstandings about browsers means that caution may be misdirected. Many users make decisions about tools based on outdated assumptions, shaping everything. Users make very different from what they install to what they let an AI feature access.

As the browser takes on more responsibility, understanding what it actually does is becoming a basic part of staying safe online. Here are three common misconceptions worth clearing up.

1. The browser is tracking everything users do

The browser is the interface through which almost everything online happens. So, when people see ads following them around the internet or notice that a website seems to know something about them, it's natural to assume the browser itself is responsible.

But seeing someone's activity isn't the same as the browser collecting, storing or monetizing it.

In most cases, the data collection comes from websites, advertising technology, embedded trackers and third-party scripts, not the browser itself. A single website can have dozens of companies collecting information about a visitor's behavior, even though the user may have no idea those companies are present.

With that in mind, the browser can actually be one of the most transparent parts of the ecosystem. It's the layer that can show users when a website wants access to their location, expose cookie controls and privacy settings, block trackers, and flag potentially dangerous connections.

Those protections don't mean browsers are inherently private, but the browser is often the place where users have the clearest opportunity to see and control what's happening.

That doesn’t mean every browser handles this the same way. Different browsers make different decisions about which protections are enabled by default, what information they expose to users, and how much control they give people over their data. So the browser isn’t necessarily the source of the privacy problem, but it is often one of the most important places to address it. That distinction becomes even more important as AI moves into the browser.

2. AI assistants inside browsers automatically see everything

People sometimes assume that giving an AI assistant access to one part of their browsing experience means giving it access to everything else. But browser access doesn't have to be all or nothing. An AI can be given access to a specific page, tab or task without being given unrestricted visibility into everything else the user has open.

A useful comparison here is employee privilege. No serious organization would give a new hire the keys to every system on day one, or leave that access in place permanently after a project ends. AI assistants inside a browser should be treated the same way. An AI drafting an email should only need access to the email tab, not the banking session running in another window. And when the task ends, access should end with it.

The practical implication for security teams is that AI browser features deserve the same scrutiny as any other tool touching sensitive data. That means asking vendors what the default permissions are, whether access is scoped to a task or granted broadly, and whether users are meaningfully informed about what the AI is doing on their behalf.

The overarching takeaway is that "AI is inside the browser" and "AI has access to everything in the browser" are not the same. Confusing the two is how users end up giving away more than they intended.

3. Privacy and security are the same thing

People often use "privacy" and "security" interchangeably, but they describe two different problems.

Security is primarily about preventing unauthorized access or stopping malicious activity. Privacy is about controlling what information is collected, shared and retained.

Anonymity is a good example of this. More anonymity can mean better privacy because less of a user’s activity can be traced back to them. But take anonymity too far and it can work against security, making malicious activity harder to identify and stop. The two aren’t necessarily opposites, but they aren’t interchangeable either, and maximizing one can sometimes come at the expense of the other.

The same idea also applies to AI tools. An AI feature can be highly secure, with encryption, sandboxing and carefully designed permissions, and still raise privacy concerns if a user’s prompts or page content are sent to a third-party AI provider. At the same time, an AI system can be highly private by running entirely on a user’s device and still create a security risk if a hostile webpage can manipulate it into taking an action the user never intended.

The better option is to ask two separate questions of any browser or feature. First, is my browser safe enough to protect me from malicious activity? And second, does my browser enable me to control the data I’m giving to other companies? A good browser answers yes to both.

The common theme running through all three misconceptions is that browsers have outgrown the way we talk about them. And they’re not done evolving. So, the better we understand where the real privacy and security risks live, and just as importantly where they don't, the better equipped users and organizations will be to make informed decisions about the tools they trust.

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Every week, another headline celebrates how AI made some task faster. A developer wrote code 55% faster. A marketer generated a first-draft blog post in minutes. A product manager summarized a 200-page requirements document in seconds. These are real numbers.

But they are the wrong numbers to celebrate.

If you step back and ask the harder question — what actually creates enterprise value? — task speed is rarely the bottleneck. Where the value traffic gets jammed is almost always upstream, in the question that precedes the task:

It’s not “What do we want IT to build?”

It’s “Why do we want IT to build it?”

It’s not “Why do we need that blog post?”

It’s “What content will drive business impact?”

It’s not “Which process should be optimized?”

It should be “How do we address the quality escape generating rework and scrap?” or “How do we prevent supply chain delays compounding into missed delivery commitments?”

These are questions that AI still cannot answer for you. And the gap between task automation and outcome generation is the defining challenge of this agentic era.

The sneaky way AI isn’t helping

Harvard Business Review put a name to this particular AI gap earlier in 2026: the micro-productivity trap. Companies optimize individual tasks, generate the code faster, summarize the report faster, route the support ticket faster, without ever rethinking the workflows or value-creation models those tasks belong to.

The result is isolated productivity gains that never compound into business impact. Almost like a value mirage. PwC’s 2026 AI Performance study found that 74% of AI's economic value is being captured by just 20% of organizations. That’s the micro-productivity trap in effect. It’s what’s separating AI leaders from the rest.

That AI leader wheat is only going to further separate itself from the chaff of other organizations continue to “do AI” for isolated productivity gains. Gartner’s warned that over 40% of agentic AI projects will be cancelled by the end of 2027, because organizations fail to define the outcome they’re building toward in the first place.

The companies that succeed, PWC found, are focused on growth and value capture. That encompasses a lot more than just productivity. I’ve noticed that a lot of organizations have a problem with prioritization. We are asking AI to go faster in the wrong direction. It’s like thinking that it’s ok to take the wrong exit off the highway just because you’re driving a Bugatti.

What's the real unlock to AI value?

The key to true AI value is in your knowledge. It’s knowing which blog topic will move an audience before a single word is written; whether the document will deliver business value before it’s created, which process to target, which data points to connect.

These are upstream decisions. They require business-domain intelligence and the understanding of what matters, what costs the most, and what a breaking change on the production floor actually means for the P&L. Pure task automation cannot supply that. Only people who sit at the intersection of business and technology can.

The solution to wringing every bit of value from AI is not a new one. Just an evolution of one. I’m talking about fusion teams, a cross-functional unit combining the domain knowledge of operational and business experts with the architectural judgment of professional developers, moving at the speed of the problem rather than the speed of the IT backlog.

What’s core to the fusion team is that they represent problem identification and faster business-technology alignment, not just going faster. That concept needs to be carried over into AI agents—how they’re built and how they collaborate across workflows.

For agents to truly work and help you avoid those micro-productivity traps, you need to connect contextualized data to working machine learning models so that they cannot just “go faster” but also make the highest quality of decisions.

It's all in the question

Success with AI—not that value mirage that micro-productivity can create—requires domain experts (e.g., plant engineers, process managers, and supply chain leaders, business analysts, procurement specialists, claims adjusters, etc.) at the table alongside IT and data teams.

It requires people who understand which product lines carry the thinnest margins, which supplier delays have the most downstream production impact, which quality control steps are creating the longest cycle time.

In other words, the fusion mindset: the discipline of defining the outcome before designing the solution, and measuring against business value rather than task throughput.

The organizations capturing the majority of AI's economic value in 2026 are not the ones with faster, AI-driven processes. They are the ones who identified the right operational problems first, governed their data well enough to trust it, and built the organizational frameworks to coordinate agents and people against outcomes that actually move the business.

So for your next AI initiative, re-frame the question: Are you building toward a fundamentally different outcome, or are you just automating a task?

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AI Agents are unlike any software development witnessed before. Traditionally, trustworthy software systems have followed a structured approach to being built and developed. One that plans, builds, tests, and maintains software to ensure its reliability and scalability. This created the Software Development LifeCycle (SDLC) (1) framework.

But AI agents are different: they are a team of autonomous, probabilistic actors that work most effectively in a real-time, event-driven environment, enabling the agents to coordinate with real-time data to develop, learn, and work.

To scale an enterprise workforce of AI agents, organizations need to combine the principles of employee management with software development rigor. These agents are not static software that can be coded and developed, due to probabilistic language models. They are built, evaluated, and governed differently.

This requires organizations to move from traditional SDLC to an Agent Development LifeCycle (ADLC) (2), which utilizes the structure and repeatable process of the SDLC framework and combines it with the development and management of AI agents.

A helping hand from HR

Since AI agents perform roles within an enterprise, the ADLC should draw on lessons from the human employee lifecycle.

When an organization brings on a new team member, there is a deliberate process: their role is defined; they’re given access to the systems they need; they are trained; and they are integrated into a team. Once they are empowered and put to work, a manager oversees their activity, monitoring and developing their performance over time.

As autonomous actors operating within a system of governance, AI agents deserve and require that same treatment.

AI agents need a similar structured approach to onboard and integrate them into a business and deliver the efficiency benefits they’re designed for – that means they need real-time contextual data to do their jobs!

Treat AI agents like humans at the hiring stage

Picture an organization where every employee is a generalist, there’s no hierarchy or structure, and all communication happens via synchronous one–to–one phone calls. No email. No Slack. No shared systems.

The organization can’t scale. Information doesn’t flow. People drown in data from every direction, with no real understanding of what’s going on or what to do. There are no accountability structures, no access controls, no coordination mechanisms beyond individual conversations.

You wouldn’t drop a new human hire into this chaos. Effective organizations look different, counting on people who are specialized and organized hierarchically, so coordination happens in parallel and communication is premised on asynchronous channels that let information flow without requiring everyone to be online at the same time. The same goes for onboarding AI agents.

AI agents and employees are no different in the eye of an agent mesh

Most early agentic AI deployments work as complex monolithic agents tasked with doing everything, accessing all data, communicating synchronously, and trying to maintain very large context or working synchronously with sub-agents. The result? A system that’s brittle, expensive, inconsistent, and completely unable to handle real enterprise complexity.

This is where an agent mesh can act as a development and runtime platform, helping build AI agents that actually work for a real-time enterprise, specialized for particular functions, organized hierarchically, and orchestrated by a development and runtime platform that specializes in delegating tasks to appropriate agents.

Communications between agents are asynchronous and event–driven, and access to data and systems is governed by role-based access controls, giving agents exactly the permissions they need and nothing more.

So, let’s look at how agents can be “onboarded” through an ADLC much like a new employee joining the organization:

1. Treat agent setup like drafting a job description

Before a human employee starts, you write the job description. You define what role they’ll fill, what they’re responsible for, what behaviors you expect, and what boundaries they operate within.

An agent mesh’s agent builder can use an internal AI agent to provide a guided interface for defining an agent’s purpose, scope, and configuration. You set up the instructions and system prompts that shape the agent’s persona and behavioral parameters. And you configure guardrails – hard constraints on what the agent can and cannot do – that prevent it from going off–script or taking unsafe actions.

2. Get the agent up to speed with tool and system access

A new employee’s first weeks are spent getting access to tools, systems, and data. Agent onboarding is the same.

An agent mesh should include a number of data connectors – pre-built integrations to enterprise databases, data warehouses, data lakes, enterprise application APIs, and MCP servers. This enables agents to access real-time data from day one.

Access is provisioned through role-based access controls (RBAC), enforcing least-privilege principles so agents only see what they need to see. It can also define skills that go with internal tools so agents know best how to use them.

3. Passing the probation period – stress-test agents before deployment

After getting access to systems, human employees undergo training. This is where general ability becomes company and job-specific skills. You wouldn’t put a new hire in front of customers without first verifying they know what they are doing. The same logic applies here.

An agent mesh can provide an Eval function that uses AI to suggest tests for agents, allows you to add more and then run them against your agents. This empowers the organization to validate agent competence against defined success criteria during development. This supports both initial testing and regression tests as you make changes.

4. Keep humans in control – validate AI actions in real-time

Even the most capable employee gets close oversight when they are new to a role. Supervision isn’t micromanagement; it’s the safety net that ensures quality, builds trust, and catches errors before they compound.

An agent mesh with human-in-the-loop architecture would support routing of specific agent actions or decisions to human reviewers for approval before execution. This is important in agentic systems because LLMs are non-deterministic, they make mistakes so when the impact of agents being wrong is too risky, humans can validate their actions, responses or conclusions.

5. Teamwork makes the AI agent dream work

The most transformative phase of the employee lifecycle is when individuals become part of high-performing teams, where collective capability exceeds the sum of its parts. For AI agents, this is where things get really interesting.

An agent mesh would support workflows that define repeatable processes, such as the steps involved in approving a loan, providing an insurance quote or opening an account. Dynamic orchestration is where the mesh comes alive: orchestrator agents route work to the right specialists, in sequence or in parallel, adapting in real time when the input is fluid and the path forward must be reasoned through.

The result is a multi–agent mesh topology – hierarchical agent organizations coordinating specialist agents across various functions that mirror the structure of an effective human organization.

6. Continuous AI improvement – catch performance drift early

Effective organizations don’t deploy employees and forget about them. They monitor performance, provide feedback, and create mechanisms for continuous improvement.

An agent mesh’s visualizer would give a real-time graphical interface for tracing agent interactions, tool and LLM calls, and decision pathways, so you can see exactly what your agents are doing and why. Ongoing evaluations detect performance drift or emerging failure modes via online evals that run in the background, monitoring production execution.

Further, Open Telemetry instrumentation can surface performance trends over time, giving you the data to make informed decisions about when and how to tune your agents.

Turning AI agents into mission-critical assets requires a new deployment approach

To turn AI agents from simple experiments into high-performance operational tools requires a whole new mindset for building and developing them. Traditional SDLCs work for predictable code, but AI agents act on their own! As a result, they require clearly defined roles, secure tool access, strict testing, human oversight, and constant monitoring.

To successfully deploy, manage, and scale a workforce of AI agents with confidence, organizations must deploy an ADLC on a real-time and event-driven platform.

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The hardest part of retail AI may soon be the part nobody puts in the demo.

A model can recommend a markdown, predict a stockout, adjust a picking route or identify a pattern in store traffic in milliseconds. But a retailer only gets value when that decision becomes an accurate action in the physical store. Whether it’s a price change or a sensor reading, it has to arrive promptly for it to be useful.

There is already a striking gap between AI ambition and execution. Deloitte's 2026 survey of 200 retail and consumer products executives found that 75% consider AI a top strategic priority, yet only 16.5% can quantify the return they are getting from it. Clearly, retailers believe in AI. The harder problem is turning isolated capabilities into something that works consistently across the business.

For physical retailers, the challenge does not end with models, data or software. AI-driven decisions have to pass through stores containing thousands of devices, existing networks and years of installed technology. In complex environments, the building itself can affect connectivity. As the number of connected endpoints grows, so does the importance of the wireless foundation carrying information across the store.

For CIOs and CTOs, that means the next constraint on AI may be something that has been underappreciated: whether the store estate underneath it can execute reliably at scale.

A store is not a cloud workload

Physical retail introduces conditions that software teams cannot abstract away. Stores have walls, metal shelving, refrigeration, back rooms, loading areas and legacy systems installed. They also have trading hours. Infrastructure changes that look simple on an architecture diagram can become expensive or disruptive when repeated across hundreds of sites.

Take inventory, for example. A forecasting model can improve a replenishment decision, but store-level execution may depend on shelf sensors, handheld devices, picking tools and accurate product information all exchanging data.

Computer vision, digital displays and environmental monitoring add further endpoints and a wider mix of data, from routine device updates and sensor readings to visual content and real-time operational data. These workloads place very different demands on the network.

Some require high bandwidth or low latency. Others involve large populations of battery-powered devices sending regular updates across difficult environments. Individually, none is necessarily a network problem. Collectively, they can become one.

If a pricing decision cannot reach every store quickly, a replenishment signal arrives too late or each new application requires another costly infrastructure layer, the retailer loses part of the value the technology was supposed to create.

One response is to add another gateway, controller or application layer for the next project. That can work for a pilot. Repeated often enough, though, every new device gains a separate integration path, security owner, management console and failure mode.

In difficult store environments, adding project-specific infrastructure to overcome coverage gaps can also change deployment economics. The technology works, but operating it becomes progressively harder.

The scaling problem appears after the pilot works

Retail technology teams are used to asking whether a new application meets its business case. AI adds another question: what happens to the infrastructure if the application succeeds?

A pilot may involve one store and a limited number of endpoints. Scale changes the economics. Coverage has to work throughout the building, updates must remain reliable during normal trading and security teams need a consistent way to onboard and manage devices. Operations teams also need clear ownership when something fails.

Connectivity should therefore be treated as an architectural choice, not a specification buried inside each procurement. The objective is not to replace every existing network or create one for every application, but to match workloads to the wireless layer best suited to their requirements while keeping the estate manageable.

Current designs may be adequate in some stores. In large or difficult formats, or where suitable retail IoT endpoints are growing quickly, an additional purpose-built wireless layer may be justified. The decision should be deliberate rather than inherited one project at a time.

Different workloads need different wireless foundations

The future-ready store is unlikely to depend on one network doing everything. It will more likely use a combination of connectivity layers, each supporting the workloads it suits best. Now, retailers should consider whether enterprise Wi-Fi should remain the de facto transport layer for every new category of connected device.

As electronic shelf label estates expand and retailers introduce more sensors and retail IoT applications, suitable workloads may be better served by a purpose-built wireless layer alongside the enterprise network.

This should not mean an isolated network for each use case, but a scalable, reusable connectivity layer able to support large device populations over time. Security belongs in the same conversation. Every endpoint needs consistent management, from trusted onboarding and credentials through to decommissioning.

Open standards remain important because they provide a more consistent foundation for future devices, applications and platforms. But openness alone is not the architecture. The decision is which workloads belong on which connectivity layer; standards help those layers remain manageable and adaptable.

The most successful retail AI programs will be judged by more than the quality of their predictions. As connected estates grow the test will be whether organizations can turn those predictions into dependable action across real stores.

Before approving the next AI pilot, retailers should ask what will their estate need if it succeeds, which workloads will grow, and should they all continue to be carried in the same way?

If success means more devices, richer data, more frequent updates or deployment across a much larger estate, the wireless architecture supporting those workloads has to be a central part of the AI strategy.

Retailers are not investing in AI tools or connectivity for their own sake. They want to protect margin, improve availability and make stores easier to operate. AI capability and AI scalability are different problems.

The objective should be an architecture in which new workloads can use the wireless layer best suited to them without forcing the estate to be rebuilt each time. The retailers that design for both will be in a much stronger position to turn ambition into reliable, repeatable execution.

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I love my Apple TV 4K, which I think is the best streaming device you can buy if, like me, you're already all-in on Apple. But while my Apple TV 4K (2022) is great, it's also four years old. That's an eternity in TV tech, so I was excited to see what new features and capabilities the new Apple TV 4K announced today would bring.

Unfortunately, there are hardly any new features or capabilities. But the new Apple TV is much more expensive, so… uh, thanks?

What I saw today was a bit like if Apple took four years to make a new season of Pluribus, restricted it to a new Apple TV tier costing twice as much, and it turned out to be identical to Season 1 but 3% brighter and 5% faster.

I'm not the only Apple TV fan to be disappointed. Reddit is unhappy too.

The 2026/2027 Apple TV 4K: the good, the bad, and the ugly reaction

I'm not an Apple TV newcomer: while I've used lots of different streamers I've had an Apple TV for everyday viewing for over a decade.

I bought my first Apple TV in 2012 for £85, and I'm currently on the 2022 Apple TV 4K. That came in two versions, one with Ethernet and one without, and the cheapest one was $129 / £149 / AU$219. Now there's only one version, and it's $299 / £299 / AU$449.

Apple was already more expensive than most rival streaming models, but not terrifyingly so — and fairly, given that the price wasn't subsidized by ads.

Now, though, it's way more pricey than its best competitors. Yes, there's a component shortage that's putting prices up. But that's affecting rivals too, and they're yet to up their prices so dramatically.

That raised price makes it a much tougher sell to newcomers, as the cost of entry has more than doubled. And it's a tough sell to existing owners because while the price has more than doubled, the spec and feature list… hasn't.

There are two key improvements here: four-speaker surround via HomePods (but not the first-gen models) and integrated Siri AI. There are also some other changes including hardware AV1 video decoding, and the ability for the remote to make a noise so you can find it if it's misplaced (and precision location tracking using UWB tech), which is welcome.

But if you're not doing the HomePods thing (my HomePods are elsewhere; I have wired speakers and an AV receiver for my TV) then this is pretty disappointing for three hundred bucks.

I'm not sold on the need for AI assistants in TVs, and Siri isn't impressing me much on any platform. I've been using the latest Siri since the iOS/tvOS 27 developer betas, and for me at least it's hopeless: simple tasks take forever to complete if they complete at all, and it's far and away my most sworn-at tech product.

The new built-in microphones might make Siri less cloth-eared than in the Apple Remote, but my problems are with what Siri does, or doesn't do, after it's finally listened to me. Most of the time using or trying to use Siri makes my TV experience worse, not better.

The reaction on the various Apple subreddits chimes with mine. On r/apple Santathe1 suggested that "somebody’s gotta pay for all the useless Siri AI no one likes", while Kaiathebluenose said that "Improving Siri for my Apple TV is at the bottom of my list for improvements." Heyboychica says "they've lost their minds with that price," and PuyoDead agrees: "The only upgrade is a microphone I don’t want, and AI I’m never ever going to use? No thank you, I just picked up a refurbished third gen from their store instead."

Other Redditors were disappointed that the Apple TV is still limited to 60Hz, not 120Hz, with comments including "60Hz for 300 bucks lol. No thanks", "being capped at 60Hz seems like a DOA", and similar sentiments, though I think that last one is a bit extreme for a device mainly used for 24fps and 30fps video.

To an extent, Apple is a victim of its own success here: the Apple TV 4K (2022) is a brilliant streaming device. Unfortunately it's so good at what it does that it would need some pretty major upgrades to persuade existing owners to buy a newer one — and this model doesn't seem to have anything to get seriously excited about. As redditor 17chan put it: "The good news is this saves me money. The bad news is it saves me money because I'm super disappointed and totally not buying this."

We will of course be reviewing the new Apple TV 4K and we'll give you our verdict very soon.

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