By almost any measure, Tim Cook has been one of the best tech CEOs ever. He diversified product lines, launched new lines of business, expanded across the globe, and grew Apple's overall market value ten-fold.

Cook is the rare public tech figure who has, for the most part, survived the vagaries of a public life. Did he disappoint many when he appeared to bend the knee before the United States' divisive President? Absolutely, though if you understand Cook, and you listen to his explanations, you know why he did it and how it actually fits into his worldview. Generally, though, Cook's been on a 15-year-long winning streak that was made possible by both sticking to the strict ethos of Steve Jobs' original Apple charter and by pragmatically doing things his own way.

I once wrote that Steve "Jobs was Apple." Tim Cook made Apple. Where Steve Jobs' mercurial personality was intertwined with Apple across its products, development, and marketing, Cook's fingerprints are so absent from the company he's run that you might wonder if he has them.

Cook became a celebrity at Apple, but without the rabid hero worship Jobs would sometimes foster.

I'd argue, though, that in an era of a million rising individual influencer personalities, Cook may have been exactly the CEO Apple needed. He commanded the stage at keynotes and even engaged in some playful "acting" for short comedic product segments, but Cook has clearly never been interested in building on a cult of personality. When the opportunity to speak in his own biography arose, Cook characteristically deferred to the people who know him. As a result, his voice is largely absent from a tome purporting to tell the story of his remarkable life.

Cook built it to last

What Tim Cook has done is build an Apple for the ages. At his fifth anniversary, I wrote about how often he proved "he was about choice." It wasn't a complete departure from Steve Jobs, who diversified the iPod line, but Cook carried that idea through virtually every product category. In the iPhone, this has provided the freedom to offer not just a wide range of iPhones, but handsets at a wider range of sizes and prices, opening up the cachet-filled handset to an even bigger audience.

Cook took Steve Jobs' germ of an idea for "technology that would improve the relationship between patients and health care providers," and turned it into the Apple Watch. Over the next decade, Cook and company would lean into that ideal, continuously enhancing the wearable to turn it into a true life-enhancing fitness and health wearable. There was obvious pride in Cook's voice whenever he shared true stories of the Apple Watch detecting potential health issues and possibly saving lives.

One of Cook's most remarkable creations is the Services business, originally built around a collection of "Plus"-named services that include Apple TV+ (now just Apple TV), Fitness+, Apple News, iCloud, and Apple Music. Services is now Apple's fastest-growing business, recently reaching over $30 billion in revenue in the last quarter.

I remember sitting in the Steve Jobs Theater audience in 2019 as Cook introduced a launch lineup that included Jason Momoa, Oprah, and Big Bird, wondering how, even with all that star power, Apple TV+ could compete with Netflix. Today, that's almost a laughable concern, as Apple TV enjoys some of the most acclaimed and widely-watched shows in streaming, including Widow's Bay and Ted Lasso. With movies like F1, Cook has even built a formidable movie studio.

It's not that I don't think Steve Jobs couldn't have built something similar. After all, it's unlikely we'd have Pixar's Toy Story franchise if it weren't for Jobs, who helped pivot the software company to filmmaking. Still, I think Jobs saw the Tech and Entertainment industries as two parallel but never intersecting lines. Cook's head was always on a swivel, and he has focused on business-strengthening diversification.

Missteps and power moves

For Apple purists, services might be a less exciting and interesting area than new product categories like the aforementioned Apple Watch, and Cook's track record in pure-play innovation has been somewhat spotty.

Cook's biggest swing, the mixed-reality Apple Vision Pro, remains one of the most impressive pieces of technology I have ever seen, but I have to wonder if Jobs would've made the same call, shipping a $3,499 headset and claiming it was for consumers. I have a strong feeling that the Vision Pro will not survive deeply into the John Ternus era.

Similarly, Apple under Cook has fumbled the AI ball, a fact that Apple has more or less admitted to us. It remains to be seen if the far more intelligent Siri AI will deliver on all the Apple AI promises and finally put it properly into an Artificial Intelligence race that has all but passed it by.

These are minor blemishes in an otherwise sterling track record, one that may ultimately be defined by the stunning transformation from a collection of Mac hardware that relied on Intel CPUs to an impressive array of Apple Silicon systems that perform well at virtually every level of demand.

If there's a natural handoff from the efforts of the outgoing CEO to the incoming one, John Ternus, it's this one. As a longtime hardware lead, Ternus has been deeply involved in all Mac innovations. The first time I spoke to him, it was about the short-lived 12-inch MacBook and all the work Apple did to move antennas and speakers and create a layered battery system. More recently, Ternus fronted the impressive MacBook Neo launch.

True continuity

Unlike Cook, who took over for a dying and idolized CEO in 2011, Ternus steps into the seat of a moving car as the deeply respected Cook scoots over to an Executive Chairman role.

15 years ago, people openly questioned Apple's ability to continue in Jobs' absence (I did not, writing, "Apple will be fine without Steve Jobs"). That concern does not exist in this transition. Ternus not only knows and understands the strategic plans laid out by Cook, but he can also still turn to his mentor at any moment for additional guidance.

As was the case when Cook took over, there won't be many changes evident in the first 12 months of Ternus' control. But a decade from now, there will be new products and services, devices sunsetted, and endless post-mortems about how well or poorly Ternus handled the Siri AI rollout.

Tim Cook was no Steve Jobs, which turned out better than fine for Apple. Similarly, Ternus is no Tim Cook. They have different temperaments and skill sets (Ternus is more of a true technologist). In the end, this next era of Apple is another exciting mystery full of anticipated unveilings like a folding iPhone and AR glasses, and surprises like Apple robots and EV cars (equal chance those don't happen), but the biggest surprise of all might, as it was with Cook, be what the company will be like under Ternus. That, and how Ternus will greet us all when he takes the stage for the next iPhone launch on September 9. Maybe, "Hey, everyone"?

For anyone working in IT and familiar with SaaS sprawl, the idea we’ll soon end up with agent sprawl may set off alarm bells. Agent sprawl refers to the rapid, uncoordinated deployment of AI agents across departments, each built on different models, governed by different rules, and often disconnected from core business workflows.

But while SaaS sprawl caught enterprises off guard, this time organizations have no excuse, and need to put measures in place now to avoid repeating the same mistakes. However, right now, most organizations are building agents in isolation and optimizing for local productivity rather than enterprise value.

Thankfully, with the right control layer funneling every agent through the same governance and security standards within an organization, the agent-powered future can be far safer, scalable and simple. 

Agent sprawl is inevitable

Just like SaaS before it, agent sprawl is already happening. Each department within organizations is experimenting with different agent technologies to address specific challenges or work more efficiently.

A sales team may deploy a CRM-based AI assistant to qualify leads, while developers build their own coding agents, and marketing adopts a separate content generation tool. Each delivers local value, but none are aligned or connected to the other, creating a familiar-looking sprawl.

This fragmentation introduces a range of challenges that will only increase over time. With no single owner responsible for how agents are deployed or monitored, there’s a lack of governance and oversight. Security risks increase as agents gain access to sensitive systems without consistent controls.

Teams may unknowingly duplicate efforts, solving the same problems in parallel with different tools. At the same time, many agents are deployed without clear links to business outcomes or understanding the wider context, making it difficult to measure return on investment and distinguish meaningful innovation from experimentation. Lots of AI tools may be exciting, but deliver far more style than substance.

Is sprawl inevitable? Yes, probably. So the question now becomes, how do we control it?

The role of orchestration

Having an orchestration layer can bring order to this complexity by creating consistency in how agents are governed, secured and deployed across the business. For SaaS applications, orchestration provides visibility into what tools already exist and gives a clear view of enterprise workflows.

With agents, it outlines what agents are already available and how they operate, while also controlling how they access data, tools and existing technology. Orchestration is the critical element that turns agents from experiments into scalable business infrastructure, for example, assigning agents to business workflows and measuring their performance against real outcomes.

Too often, we see enterprises experimenting with different agents that have no connection to the core business processes and goals, meaning they fail to deliver any ROI. For example, some portions of a business simply do not require AI technology and will unlikely see benefits in the same way as another department. Orchestration can unearth and filter out agents that are unnecessary, saving businesses money. 

Building trust in AI-driven enterprise

Rather than trusting individual agents, organizations should focus on trusting the system that governs them.

This is where orchestration becomes a trust layer, ensuring every agent operates within clearly defined boundaries with consistent oversight and accountability. Instead of individual teams managing risk in siloes, organizations can centralize control while still enabling innovation across different departments and business functions.

In the same way orchestration brought order to SaaS sprawl, it can do the same for the next wave of enterprise AI. It brings the same ease, security and integration with existing SaaS tools, while ensuring only high-value agents make it into production, supporting innovation and keeping within the business goals and purpose.

As agents become embedded across every function, orchestration will shift from a technical layer to a core enterprise capability. The winners will be those who can control, connect and trust their agents at scale.

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For years, enterprise Software as a Service (Saas) has enjoyed a comfortable position at the center of organizational processes. Users who wanted to raise a ticket, process an invoice or manage a workflow would log into their employer’s cloud application. The user interface was the gateway to the work, and the provider’s business model followed naturally: charge for seats, expand adoption, and grow revenue as more users spend more time inside the platform.

Agentic AI is beginning to change that. As users increasingly deploy AI agents, either to carry out tasks within SaaS platforms or to deliver functionality outside them, organizations require fewer seats – hitting providers’ subscription revenues.

At the same time, AI is making bespoke software faster and cheaper to build, giving organizations a credible alternative to buying another SaaS platform. Why accept subscription costs and vendor lock-in, when cheaper alternatives may achieve the same outcome?

None of this means SaaS is about to disappear, but its position at the center of enterprise software is under threat. SaaS vendors are facing three structural challenges that will reshape the economics of enterprise software.

Agents become the user

The first threat is simple: users may have AI agents carry out tasks within the SaaS platform, reducing the need for human users, or build agentic capabilities outside those platforms altogether.

That is a serious problem for any SaaS vendor whose business depends on people living in the interface. If an agent can resolve a support query, update a record or complete a routine transaction, the user never has to touch the application.

We’re already seeing this in practice. In some IT service management projects, agents now resolve such a high proportion of tickets that organizations need far fewer users interacting with the SaaS platform. As buyers begin questioning whether they still need premium software for the shrinking proportion of manual work, the economics start to shift.

Bespoke is back

The second threat is the one SaaS vendors dislike most: buyers may once again be willing to build new capabilities from scratch.

For the last decade or so, ‘build versus buy’ was often a non-debate. Custom software was too slow, too expensive and too risky, so SaaS usually won.

AI is starting to shift those calculations. As software development becomes faster, cheaper and more adaptive, some solutions that previously defaulted to SaaS start to look buildable – especially when the organization needs a narrow set of capabilities wrapped around its own processes and data. The more customization a SaaS platform requires, the weaker its cost-efficiency and automated-upgrade advantages become.

This does not mean large enterprises will abandon mature SaaS platforms overnight. Their resilience, integrations and compliance capabilities remain valuable. But once customers believe they have a credible alternative, vendors lose pricing power and the assumption that renewal is the obvious decision

Business model redundancy

The third threat is that even when the product survives, the commercial model may not.

Traditional SaaS pricing was built for a world in which value correlated reasonably well with the number of people sitting in the application. That logic gets shaky when agents perform work that would once have required more licensed users, and more operational dependency on the interface.

Vendors can see this. Many are shifting towards consumption-based pricing, exposing services directly to agents or redesigning products for an agentic world. The common thread is clear: once software starts working for people rather than being operated by them, seat-based economics begin to look outdated.

From the customer’s perspective, that could be a good thing. Paying for usage is often cheaper than buying licenses. The challenge for vendors will be proving they’re genuinely adapting to an agentic future, rather than simply repackaging existing products to protect margins.

SaaS is not dead

To be clear, this is not a “SaaS is over” argument. Many enterprise platforms will remain valuable, providing governance, resilience, compliance, and years of accumulated functionality that are difficult and expensive to recreate.

What will change is where value sits. In an agentic world, SaaS will be less the primary interface through which work gets done; more a component in a broader ecosystem of agents, orchestration and systems of record. Some platforms will adapt and become stronger; others will discover that the value they once captured has shifted elsewhere.

As for the corporate users of SaaS, the best advice we can give is not to wait for perfect conditions before acting. No business ever reaches a point where its data, processes or architecture are “finished”, and SaaS providers will continue to adapt their offers.

Meanwhile, experiment, learn and adapt in your delivery of administrative and corporate functions: in some functions you may find that agents drive down SaaS costs; in others, greenfield development may now be viable. In still other cases, adding new functionality using agents that leverage SaaS solutions but sit outside those ecosystems keep future options open.

So do something that works for you now – but keep the option of moving again in a year or two.

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The most damaging AI-related security incident your organization faces this year probably won't originate from external attackers using sophisticated new models. It's far more likely to begin with an employee pasting a client contract, a financial forecast, or a set of HR records into an AI tool because it makes their job easier and nobody has told them why it matters.

Shadow AI is a growing problem, and our research found that nearly half of employees at larger enterprises regularly feed corporate data into AI tools that nobody in IT has approved or governs.

More striking still, 85% of employees continue doing so even when company-sanctioned tools are available, pointing to a governance failure that runs all the way to the executive suite. With shadow AI, sensitive data can move silently outward through channels most security stacks were never designed to intercept.

Adding to the security risk of this unmonitored data flow, the advent of the EU AI Act also means organizations now face specific legal demands on managing AI use. The ability to have full governance over how AI is deployed, governed and monitored, is becoming a regulatory, as well as a security, imperative.

Why the EU AI Act makes this a board-level problem

Shadow AI represents a serious security issue, with IBM's 2026 Cost of a Data Breach report estimating that unauthorized tools contributed to 43% of breaches over the last year Now, the EU AI Act is adding significant regulatory requirements on top of these risks.

The Act's obligations have rolled out in phases; most recently, organizations classified as general deployers of AI have new inventory, data governance, audit logging and transparency obligations as of 2nd August 2026.

Other deadlines have shifted further ahead, with controls over high-risk AI usage, covering areas like recruitment, credit scoring and biometric categorization, set to come into force from 2nd December 2027. AI embedded in regulated products will be covered from 2nd August 2028.

Any organization whose employees use AI systems now has compliance obligations as a deployer, regardless of whether those systems were formally sanctioned.

All organizations using AI should be aware that the AI literacy obligation under Article 4 has been enforceable since February 2025, meaning organizations are on the hook for ensuring their employees are aware of safe and sanctioned AI use.

Rules around high-risk AI usage will also apply to more operations than it may seem at first, including an employee using an unapproved consumer tool for tasks like screening CVs, assessing creditworthiness, and evaluating performance.

These are common tasks that could trigger the full weight of the Act's oversight against a system that the IT department didn’t even know had been deployed.

With penalties reaching up to €15 million or 3% of global annual turnover for high-risk breaches, many organizations are carrying more exposure than they realize.

Why your existing security stack can't see it

The challenge with shadow AI is that it exploits the blind spots between conventional security layers, slipping through gaps that most tools were never designed to close.

CASBs and secure web gateways cannot decrypt conversational data flowing to legitimate LLM domains over HTTPS, for example. From the network's perspective, a prompt containing a full customer database is indistinguishable from any other encrypted web session. Browser extensions are limited to managed endpoints, blind to personal devices and AI embedded within approved SaaS.

Likewise, API gateways are usually built around authorized enterprise deployments, which means they capture the AI activity organizations have already approved while missing the consumer-grade AI tools driving most risk.

These blind spots compound with each other, so an organization running all three layers may still have no visibility into AI activity across a significant portion of its estate, and no means of generating the interaction logs or policy enforcement evidence the Act requires.

What good AI detection looks like

Controlling AI data flows is usually managed by workers performing their duties without malicious intent. However, it’s remarkably similar to defending against an external threat actor covertly accessing your data.

The detection logic needs to match that reality. Endpoint-native detection intercepts sensitive data at the point of movement before it reaches an external AI system, enforcing policy at the prompt level across managed and unmanaged browsers, personal devices, and AI functionality embedded within SaaS tools. It operates where the activity occurs, rather than attempting to catch it downstream.

Given the scale of the potential fines, the ability to prove compliance matters almost as much as preventing security breaches.

Continuous discovery across the estate, including any unsanctioned tools gives organizations the AI system inventory the Act requires.

Granular interaction logs - who used what, when, and what data was involved - satisfy the documentation requirements under Articles 12 and 13, without teams needing to scramble to reconstruct activity after the fact. The same data pinpoints exactly where AI literacy gaps exist, making Article 4 compliance something demonstrable rather than simply asserted.

Practical steps for compliance

Security and compliance teams aiming to comply with the EU AI Act have a clear path to follow.

The starting point is mapping the full AI estate. Discovery needs to extend beyond IT-approved tools to unmanaged endpoints, personal devices on corporate networks, and AI embedded within SaaS.

Data governance must move to the endpoint. Policies prohibiting sensitive data sharing with unapproved tools are not technical controls and, by the time enforcement happens at the network edge, the data has already left.

It’s important to remember that information shared with an external AI system may be retained in prompt logs, incorporated into model training data, or held on servers in jurisdictions the organization has no visibility into. The moment data crosses that boundary, the organization loses control of it entirely, and no policy document will retrieve it.

And since Article 12 requires interaction records that can be handed to regulators on demand, organizations will need to have continuous, automatic auditing of both activity and security measures.

Finally, AI literacy programs aimed at improving user awareness should be driven by behavioral data rather than generic training programs. Activity logs showing where governance failures are occurring - at senior leadership level as much as anywhere else - provide both the diagnosis of the issue and the evidence regulators will want to see.

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The numbers circulating through fintech right now are hard to ignore. Almost 10,000 jobs have been cut across the global sector in 2026 so far. PayPal is eliminating roughly 20% of its workforce. Block has shrunk to fewer than 6,000 employees, and Coinbase cut 14% of its headcount.

And hovering over all of it is a single explanation, repeated so often that it starts to remind us of a dot-com bubble — artificial intelligence. But is that actually what's happening? And more importantly, does it matter if the story isn't entirely true?

When the story becomes the strategy

AI accounted for 40% of all announced layoffs in May 2026, up from much lower levels earlier in the year. At first glance, it seems like a natural explanation: if the technology handles the routine work better than a human, then why not cut costs?

But the thing is, when we start looking closer at why exactly these mass layoffs are taking place, a deeper structural problem appears. So far, AI is just a good justification for how the industry communicates these shifts.

Deutsche Bank analysts identified this neat disguise in January. They called it "AI redundancy washing." Put simply, it’s the practice of attributing workforce reductions to AI adoption when the underlying drivers are far more familiar.

But if it’s not really AI which replaces the workers, then what are the real reasons for all these layoffs? If each company were to be considered individually, it could be anything, from deteriorating unit economics to the pressure of demonstrating operational discipline after years of “growth-at-all-costs.”

Research points to the same explanation: nearly 60% of US hiring managers acknowledged that they emphasize AI's role in layoffs just because it lands better with stakeholders. Indeed, a headcount reduction could have sparked fears about companies' growth models, but framing it as a strategic AI implementation makes it sound good.

The productivity paradox

Let’s assume that some of the companies are indeed replacing their workers with AI tools. In this case, there should be numbers to show it. But they don't share any effectiveness ratios, at least not yet.

A survey of 350 executives at companies with over $1 billion in revenue found that 80% of organizations piloting AI or autonomous technology reported workforce reductions. But after that, many honestly admit that they cut jobs regardless of whether the technology was actually generating returns.

This way, planning to spend another $10 million or more in 2026, companies are betting on a future that hasn't fully arrived. And trimming headcount in anticipation of a productivity windfall that remains, for most, theoretical.

Even if AI is implemented in a particular organization, it’s mostly in pilot mode, sitting on top of existing infrastructure rather than remaking it. Moreover, the AI payback picture is modest: only 25% of AI initiatives deliver the expected returns, and only 16% reach the scale of the entire organization.

McKinsey’s 2025 global AI survey found the same results. While most firms are using AI in at least one function, nearly 70% are still stuck in experimentation or pilot stages rather than scaling AI across the enterprise. And what’s more upsetting is that no more than 39% reported a significant EBIT impact from the technology adoption.

Where the capital is actually going

If AI isn't yet delivering the productivity revolution that many executives promised, then where is the industry's attention? The answer is less exciting than the headlines suggest — only last year, infrastructure and internal business systems received more than $200 billion worth of investments.

For now, fintech companies are under pressure to prove that all the promises made around AI weren't just another cycle of hype. So instead of reinventing financial services overnight, many are turning inward, looking for ways to make existing operations cheaper and easier to scale.

Even though the AI hasn’t brought the desired effect yet, the shift to automation is still important. For the past decade, fintech has largely been focused on the customer experience to offer a disruptive product, but from now on, more effort will go into the mechanisms behind the product than the product itself.

But this doesn’t mean that the "AI replaced the workers" narrative feels complete. Cutting headcount and replacing employees with bots may look good on a quarterly report, but it's not much of a strategy. Cost reduction can improve margins in the short term, but it rarely creates lasting differentiation.

So, in this environment, the winning ones will be those asking and answering honestly to a different question: What can we offer the market today that we couldn't offer a year ago?

The value of AI innovation beyond cost-cutting

Arguably, the more important part of AI adoption isn’t about how it replaces workers at all, but rather about what new value it creates. Historically, the biggest technological shifts rarely created value by simply making existing processes cheaper. They created entirely new products, services, and business models that were impossible before.

Without going too far in search of examples, let’s just remember the Internet. When it first arrived, a lot of people justified investing in it by bringing up “efficiency” aspects, such as digitizing paperwork or reducing communications costs through e-mails.

In reality, though, the Internet went much further than that, creating entirely new business models: online media, social networks, search engines, cloud software, and more. Giants like Google or Amazon did not get to where they are now simply because they optimized their processes. They found success precisely because they built products that simply couldn't have found success before the Internet became widely adopted.

Now, it’s AI’s turn to go down that road. What innovations it will bring is a story that still remains largely unexplored in fintech, but the potential is massive. In this sense, today’s hyper-focus on workforce cuts is likely going to be just a short phase in a much larger legacy that AI will leave in finance. The true winning move for companies now is to work out how they can use it to better serve their customers in new, never-before-explored ways.

Cost savings may be a measurable and immediate metric, but over time, the true advantage of adopting this technology will come from what it brings to the table, not what it takes away from it.

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You can spend hours choosing the best laptop, comparing processor options with the display and wondering how long the battery will last.

Then you switch it on and discover another part of the experience that rarely enters the buying decision: the manufacturer’s preinstalled software, often billed as tools to help optimize your device.

Sometimes, they earn their right to come pre-installed. The pre-installed software might be where you change performance mode or cap the battery charge, alongside model-specific settings Windows 11 simply doesn’t offer.

The strange part? How much bigger the role of pre-installed software (sometimes called 'bloatware') has become. A simple utility can now feel like another software layer sitting on top of Windows 11, with launchers and user accounts jostling with the settings you actually want.

And because some of those controls are legitimately important, uninstalling the whole thing isn’t always an option.

Windows 11 can’t actually control everything on your Windows laptop

For all the settings tucked inside Windows 11, there are still plenty of things it can’t manage on its own.

And this isn’t just a gaming laptop quirk. On supported Asus models, MyASUS handles things such as Battery Care Mode and model-specific fan profiles that Windows 11 doesn’t offer.

Gaming laptops simply take the idea much further.

Take the Alienware 16X Aurora. Alienware Command Center offers several performance settings, and in our testing, its 'Overdrive' mode pushed the fans harder while making use of the laptop’s Cryo-Chamber cooling system.

None of that feels especially unreasonable.

Laptop makers know how their own hardware is designed to behave, and a dedicated utility gives them somewhere to offer controls that would otherwise be buried in the BIOS or unavailable altogether.

The problem starts when that useful little control panel begins acquiring ambitions of its own.

A settings menu somehow became an entire platform

Many of these apps now go well beyond looking after the laptop itself.

Armoury Crate still gives Asus ROG owners useful controls over performance and cooling, but it also has a user account system and optional content services alongside its device settings.

Lenovo’s Legion Space pushes the idea further, putting thermal controls alongside a game store and its AI-powered Gaming Zone. On supported machines, HP’s Omen Gaming Hub mixes fan and graphics controls with a game library and GeForce Now.

Taken one by one, most of this is easy to defend. Put it all in the same app, though, and the hardware settings start to feel like one part of a much larger product.

If I open software to quieten the fans, for example, I’m probably not also looking for a new game or a giveaway.

The worst part is that you actually need it

With ordinary pre-installed software, there’s an obvious solution if you don’t want it: uninstall it. Laptop software made by the manufacturers themselves is harder to shake because some settings can make a real difference in how the machine behaves.

Our MSI Raider 16 Max HX review shows why this gets awkward. The laptop comes with a fair amount of bundled software, yet MSI Center itself gives you useful access to performance presets and RGB controls.

Wiping out every manufacturer utility for a cleaner Windows 11 install can take useful features with it. However, some laptops do have alternatives.

G-Helper is an open-source option for Asus machines, offering access to performance and GPU modes, battery charge limits, and other hardware controls in a lightweight app. Its built-in performance modes use the same firmware presets as Armoury Crate.

You don’t have to be comfortable swapping official software for a community-made utility to see the appeal. G-Helper’s existence shows that granular laptop controls can live in a focused tool rather than a whole, often bloated, ecosystem.

Microsoft bears some responsibility too

Some of this starts with gaps in Windows 11 itself. Smart Charging exists on compatible devices, for example, but there’s no universal control for choosing a fixed charge limit – Microsoft says each manufacturer implements the feature differently.

Microsoft has already shown what a more standardised approach can look like.

Dynamic Lighting is a good example: compatible RGB hardware can now be controlled directly through Windows 11, rather than relying entirely on separate manufacturer utilities.

Fan tuning and firmware are much more specific to each machine, so manufacturer software isn’t going away.

But every common control Windows 11 can standardize is one less reason a laptop needs its own parallel settings software - and that would mean less bloat preinstalled on our devices.

Laptop software needs to become boring again

Maybe the real problem is that these apps have become something you even need to think about. A good pre-installed app should make the laptop easier to live with, then stay out of the way until you need it again.

MSI Center at least gets some things right: it’s modular, so you can install only the features you want rather than loading everything by default.

Switch brands, though, and even familiar jobs can move somewhere completely different. A 'quiet' fan mode that takes seconds to find on one laptop might be buried under different terminology on the next.

Software like this probably deserves more consideration in laptop reviews, too. It won’t rescue poor battery life or a bad keyboard, but if you rely on it to change how the laptop behaves, it's part of the product you bought.

I don’t want laptop makers to stop building these tools. I just want them to remember what they’re for: helping me use the laptop I already bought.

Although AI is advancing at a rapid pace, the idea that it might one day replace humanity has thus far been consigned to the realm of fiction.

Rather than signalling the end of mankind, however, others have speculated that an advanced AI may one day simply supplant our position at the top of the food chain and live harmoniously with us, a species that will live to serve it.

Electric sheep

The American mathematician and computer scientist Claude Shannon was giving an interview with Omni Magazine on his work and legacy when he speculated on the role of AI and robotics in the future.

His comments came during a conversation about the limits of digital computers and the piping that backs so much of the AI technology we use today, artificial neural networks. He suggested, despite the sour mood around AI at the time, that it would be one day so advanced that our collective intelligence as a species would pale in comparison.

Far from being a doomsday scenario, though, his likening of humanity to a dog indicated a sense of companionship, mutual love and respect, and a sense of loyalty that would cement mankind's role in a world dominated by a future machine intelligence.

Roboslop

At a time when funding for AI was running dry and we encountered many bottlenecks like the lack of compute power, science fiction writers and scientists alike both started envisioning ways in which future systems could one day replace us.

Although none of those predictions have come to pass, there is a widening consensus that one day the technology will grow to surpass the level of human cognition.

This hypothetical technology, known as artificial general intelligence (AGI), may not be invented any time soon – and we may not presently have the architecture required to see progress. But scientists expect this to come within living memory.

Once we reach this point, which many refer to as the technological singularity, it's wholly unclear what the implications may be and whether AGI may eventually give rise to a cascading series of more intelligent systems that culminate in an artificial superintelligence (ASI).

Nvidia's DLSS 5 has been nothing short of a bubbling cauldron of controversy ever since the next-gen gaming tech was first aired. That initial reception shocked Nvidia as what seemed like a legion of PC gamers took up arms and vowed, well, all sorts of colorful things — most of them involving the term 'AI slop' in some fashion.

However, there are signs that this overwhelmingly negative tide of opinion is turning courtesy of a fresh revelation this week. This comes in the form of a leak rather than anything official from Nvidia, with Team Green's only DLSS revelations at Gamescom 2026 pertaining to the current incarnation of the feature (namely DLSS 4.5 Ray Reconstruction for better quality ray-traced graphics, alongside some interesting GeForce Now announcements).

To refresh your memory and recap — in a nutshell, as there's a lot more to it than this — Nvidia has billed DLSS 5 as a 'real-time neural rendering model' that polishes up a game's visuals using AI (without altering the game assets, and with the developer being given a decent level of control over the exact implementation).

What actually happened here was that on Friday, VideoCardz spotted that following NBA 2K27's early access going live, a DLSS 'neural rendering' DLL was discovered in the build. It didn't take long at all before modders (in the RenoDX community) fudged things to get this working in games ('SpeedLemur' being the principal modder who "made this happen").

So, in theory this is effectively our first glimpse at DLSS 5 in the wild, with those modders demonstrating what it looks like by using the third-person fling-stuff-about action-adventure Control – as seen on Reddit. However, do note that the tech is only working for application to character models for now (not the environment or objects).

Nuts and bolts

There have been some very interesting revelations from those experimenting with this apparent leak of DLSS 5 in terms of how it works, how it runs — and perhaps most telling is the broad reaction to this spillage, which I'll return to shortly.

First, the nitty-gritty. In this leaked form — so add a whole heap of seasoning, because nothing here is remotely official — DLSS 5 seems to have a small set of adjustable parameters, including seven different 'styles' and various sliders to play with intensity, tone and structure. We're told that neural rendering doesn't need much in the way of data to do its stuff, and works off much the same inputs as regular DLSS (basic upscaling – where all this started, remember?).

It's also for Nvidia RTX 5000 GPUs only (although that's still not fully confirmed, it was always likely to be the case anyway). The news on the performance front goes a bit awry, though, with the discovery that in Control the frame rate is cut in half by the AI polishing up the visuals. With an Nvidia RTX 5070 Ti running Control at 4K with ray tracing on high and DLSS (Quality), the frame rate was 71 fps and it dropped to 35 fps with neural rendering.

That is, of course, a nasty hit indeed. However, this is a piece of leaked code – in an unknown state as far as the stage of development is concerned (one clue suggests that the model may date back to the start of July) — extracted and fudged to work on an old game. Given all that, we can't take this as anything like an indication of what the finished version of DLSS 5 might be like performance-wise.

Although it's at least comforting that it works in a playable (enough) manner on a single RTX 5070 Ti graphics card, because if you recall, Nvidia's demos used not one, but two, RTX 5090 GPUs. Clearly a 50% frame rate drop won't fly, granted, but Nvidia wouldn't release this neural rendering trick if the hit was anything like that, because no gamer would use it in that state.

Change in tone

What we can really take from this leak and the modding efforts thereafter is quite limited, then. But what's clear is that as mentioned, there's been a marked turnaround in the commentary and debate on the image quality on offer with neural rendering.

As one Redditor observes: "Remarkable difference in tone of the reactions compared to when the [initial] Resident Evil footage was released."

Someone replying to that says: "People freaked out about what was obviously just a bad demo, and failed to see what was actually going on with the underlying tech.

"There were a few talking about how different tone mapping fixed the issue with minimal effort, or how it wasn't actually adding/changing details, and it turns out they were correct. People freaked out over nothing imo."

Another gamer notes: "This looks like legit magic. I was saying this when the reveal happened and people wanted to burn Nvidia down: the tech is amazing, the demo might be off because it's too heavy handed, but the tech itself is incredibly impressive."

And a further Redditor enthuses: "Insanely huge graphical update. There's no going back."

You get the idea, then, and it's quite an eye-opener how many comments there are along these lines. Quite a lot of gamers are impressed by this leaked spin on DLSS 5 — and I can see why, frankly, at least with the Control material presented here.

And remember, this is with DLSS 5 working on character models, with the facial tinkering and tweaking being one of the main causes of concern with Nvidia's initial demos. The likes of Flight Simulator addicts are positively drooling about what this might mean for the graphics of the landscapes beneath their virtual wings.

And there's a lot of excitement about how the tech might be applied to older games like Control to make them look a good deal better.

Skeptics remain

Naturally, not everyone is convinced. The detractors remain and are unswayed by the material which has popped up around this leak so far. They argue that the AI-enhanced Jesse (Control's protagonist) is still all too 'uncanny valley' in appearance, and that DLSS 5 is still very much a visual downgrade.

Others also worry that there aren't enough fine-grained controls for the developer in what we see here. Although again, we can't take this leak as evidence of what DLSS 5 will turn out to be eventually, whether that's the end result of the graphics themselves or the suite of dev controls under the hood.

The performance hit has been seized on, too — as noted, though we can't judge on that yet, not with an early leak — and some feel that while screenshots might look good, this doesn't hold up in motion.

Simply put: it is still too early to judge, and we need to grab large handfuls of seasoning with all this. But the general sentiment on DLSS 5 appears to be turning around in quite a sizable shift of opinion — and Nvidia may yet convert PC gamers to its AI-enhanced vision of the future (providing any performance hurdles are cleared, of course).

I'm currently holding the Samsung Galaxy Z Fold 8 in my hands, only putting it down now to type this and tell you that, as I see it, it's the nearest thing we have at the moment to an iPhone Ultra, the folding iPhone we expect to meet in person on September 9. And what I wonder is not just if the real iPhone Ultra will be good enough to glow up the foldable category so that it eventually rivals standard flagship phones, but whether if it can rise above the foldable bar set by Samsung and, to a lesser extent, its rivals.

The iPhone Ultra will arrive on the tiny island that is foldable phones amid a group of well-established competitors. Apple's slow walk into this category has allowed Samsung, Google, Huawei, Motorola, and Oppo to hone their skills in creating thin, light, and powerful folding phones, often featuring excellent screens, battery life, and cameras.

The Samsung Galaxy Z Fold 8 is not the best of them. Don't get me wrong, it's an excellent foldable phone, but it's not the thinnest, doesn't have the longest battery life, and has just two rear cameras. On the other hand, the passport shape (when folded) makes it compelling in a way that's hard to quantify and temporarily unique among current foldable options. When opened, it has a screen size that is nearly as large as those found on other foldables, but in an oddball 4:3 screen aspect ratio.

The design is intentional, with Samsung hoping to bridge the gap between the clamshell-style and fashion-forward Galaxy Z Flip and the far more powerful, productivity-first Samsung Galaxy Z Fold 8 Ultra.

Samsung is filling the gap in the hope of vastly expanding what remains a very tiny market. I know there are reports that Samsung is selling out the Z Fold 8, with one report claiming that it sold out and is now rushing out a million more units. Overall sales for the Z8 line (Fold, Flip, and Ultra) might be in the 4-to-5M unit range. That sounds pretty good, but a look at typical iPhone sales after launch quickly puts those numbers in perspective.

In 2024, iPhone 16 preorder sales reportedly reached 37 million units. Apple used to provide quarterly updates on iPhone unit sales, and they were, honestly, insane. In December of that year, the Cupertino tech giant sold more than 77 million units. The year ended with over 217 million iPhones sold.

What the industry believes, or at least hopes, is that the arrival of the iPhone Ultra cracks open the market in ways Samsung and its folding counterparts haven't yet. This notion is predicated on the assumption that the iPhone Ultra will be substantially better than Samsung's folding line.

Except it probably won't be. I fully expect it to be an exciting and excellent smartphone, but Samsung, more than any other company, has come close to perfecting the art of the fold. The Z Fold 8 Ultra unfolds flat, has an almost creaseless screen, and is as light and thin as a standard flagship, while offering twice the utility.

The folding iPhone : what we expect

Apple's folding iPhone is not even expected to match the Z Fold 8 Ultra in style and features. In fact, it's a fair bet it will be in a form factor almost exactly the same as the Z Fold 8. That's right, we all expect Apple to produce the same kind of Passport-style foldable, right down to the pair of rear cameras with no telephoto lens in sight. This shouldn't be too surprising since most believe Apple is sourcing its folding display from Samsung Display.

Among the rumors I've read about the 5.5-inch (folded), 7.8-inch (unfolded) iPhone Ultra is that it will be built with a combination of aluminum and titanium, just like the Z Fold 8. It will, at 4,5mm, be no thinner than the Z Fold 8 when unfolded. The chief design distinction might be the use of what's rumored to be called "liquid metal" to try and fully erase the crease (something the Z Fold 8 Ultra and Z Fold 8 have nearly done).

Apple might have an edge, though, where it counts: performance. Most expect the A20 chip paired with 12GB of RAM and Apple's own C2 cellular model. All of this could make this a faster foldable, even if the average user never notices.

Some believe that Apple doesn't need to make the world's best foldable; it simply needs to deliver it to a willing market. How many people have been holding off on buying a foldable because Apple doesn't offer one? That could be millions of people, and if that's the case, it might be the Apple cachet that grows the foldable phone market to a previously unimaginable scale.

Dennis Gabor was a Hungarian-British physicist who's widely credited with inventing holography, a photographic technique that can record and reconstruct three-dimensional images. This is an invention for which he received the Nobel Prize in Physics in 1971. But his legacy extends to his philosophy on the nature of innovation

Go your own way

Gabor wrote this iconic passage in his now-extremely rare book 'Inventing the Future', in which he sketched out an optimistic vision for the future.

At the core of his belief was that there was no need to any longer passively predict what may come to pass in the years ahead. Instead, the influence and strength of science and industry was such that mankind could forge its own future.

The comments also focused on that pivot point of the industrial revolution, before which the possibilities of the future were fairly limited, in his view. As each wave of innovation comes to pass, the possibilities continue to expand.

Charting a path

There's no understating the influence that Gabor had in modern science and industry. His comments were routinely quoted, and even adopted, by others. Chief among the figures that took this philosophy to heart was Alan Kay, described as one of the true luminaries of personal computing.

Kay, whose career spanned stints at Apple and HP among other industry titans, used this idea of inventing, not predicting, the future as a core maxim from 1971. He used the phrase to inspire his work in pioneering elements like the graphical user interface and laptops many years before they were realized.

Although Gabor was a very influential figure in the technology world, his predictions weren't always on the mark. In 1962, for instance, he said sending documents via telephone wires was possible, but only in principle. That's because the equipment required was so expensive that he didn't see a world in which they'd be cheap enough for the technology to become practical.

Since I started my monthly Blu-ray Bounty column in November 2024, I’ve tested more than 100 4K Blu-rays. In that time, as well as finding that a lot of highly anticipated discs — such as Fight Club — lived up to the hype, there have also been some pleasant surprises, including the reference-quality Speed Racer and the spectacularThe Super Mario Galaxy Movie. The latter two are now part of the selection of discs I use to test the best TVs and best soundbars.

While I haven’t managed to test every disc I’ve wanted to (and certainly not every 4K disc released), one has remained frustratingly elusive: Hard Boiled. One of John Woo’s most iconic films, it's a cornerstone of Hong Kong action cinema, and I'd been keen to get my hands on the 4K release.

After failing to snag the Limited Edition, released in March 2026 in the UK, and every subsequent restock, I was starting to wonder if a Hard Boiled was destined to stay on my testing wishlist. July saw the arrival of a standard edition — and it shot to the top of my July 2026 most anticipated 4K list. After yet more courier delays, Hard Boiled finally made it into our testing lab — so, at long last, I was able to give it a spin to see whether the 4K disc is worth all the hype.

Awesome Atmos audio

There are three soundtrack options on this disc: Cantonese Mono, Cantonese Dolby Atmos, and English Mono. I primarily used the Cantonese Dolby Atmos, to see how effective the upscaled audio track would be, although I was impressed by the clarity of the Cantonese Mono.

There're no shortage of action-packed scenes in Hard Boiled, in particular a number of gunfights, and the disc's Dolby Atmos soundtrack makes the most of them. During testing, I used a Samsung HW-Q990C 11.1.4 channel soundbar system, and the Atmos mix really came alive. As bullets tear through the room, ricocheting bullets can be heard bouncing off walls with impressive detail and precision. In the opening gunfight, as sprays of bullets from sub-machine guns fly, the trajectory of these bullets was precisely mapped to the rear channels, and as the camera moved around the room, the sound of the bullets moved with it.

While bullets generate impact, it’s the big explosions that deliver the meaty bass. While these explosions didn’t always resonate from the Q990C’s subwoofer (down to the older mix), there was still plenty of oomph to each one. The Atmos soundtrack also displays great balance and range. As Tequila and Ah Long are caught in a shootout with one another, the sound of bullet casings hitting the floor is audible in the loud gunfire, which is in turn well balanced with raised voices and the often-lively score.

4K picture wins again

Arrow Video’s 4K discs are among the strongest when it comes to restoration, and this remains true with Hard Boiled. Textures have been finely restored, with details such as skin taking on a remarkably natural, almost 3D quality. In some scenes, the movie looks like it could have been shot only a few years ago rather than in 1992. Close-up shots reveal an impressive level of refinement, too, from the texture of a gun to the tiny details of a bullet. Having recently visited Silver Salt Restoration, which occasionally works with Arrow Video, I know just how much hard work goes into restoring films to such a standard.

Elsewhere, this 4K disc showcases bold, vibrant colors that really pop off the screen. The neon signs of downtown Hong Kong have a gorgeous richness to them, while restaurant interiors benefit from punchy yet natural-looking hues – such as a vivid green door in the opening gunfight. And then there's the blood — of which there is plenty in Hard Boiled — that displays real vibrancy, but in some scenes, also appears deep, rich and convincingly realistic. It really highlights the disc’s color reproduction.

Throughout the movie, there are plenty of scenes that, displayed on the right TV, really highlight the 4K disc’s excellent contrast. In the dimly lit jazz club during the opening credits, the dim surroundings contrast well with the wall and stage lights. A later scene of a parade of motorcyclists escorting a car along the docks also show deep black tones, which balance nicely with the headlamps from the vehicles.

Finally, there are plenty of moments that can give a good TV a chance to shine, too. Throughout the movie, bright sparks fly from car collisions, showers of bullets and explosions, and on the LG G6 I used for testing, they really showed off the G6’s strong HDR highlights. The film grain has also been cleaned up. There’s still enough to satisfy cinephiles, but the clean-up job gives Hard Boiled a more contemporary look without stripping away any character.

My 4K Blu-ray love grows

I’ve written plenty about how much I love what 4K Blu-ray can do for more classic movies, with titles such as 1938’s Captain Blood, 1944’s Double Indemnity and 1950’s Sunset Boulevard from Hollywood’s Golden Age all given a new lease of life. Even "more recent" thrillers such as To Live and Die in L.A. — also from Arrow Video — have received stunning 4K makeovers, while remaining crucially faithful to the original, filmmaker-intended look.

Hard Boiled is just the latest in a series of titles I’ve tested that showcase just what 4K Blu-ray can do. It retains the movie’s iconic look, both of early-'90s Hong Kong cinema and John Woo’s distinctive style, while also giving the movie a new coat of paint. The superb new Atmos soundtrack adds another layer, bringing out a wealth of intricate detail and making the explosive action even more immersive. It's yet another fantastic 4K Blu-ray release that I’ll be adding to my now bursting 4K Blu-ray shelves.

Identity is the fundamental currency in any technology transaction so we need to treat nonhuman assets with the same care as we apply to people.

We’re outnumbered! Nonhuman identities (NHIs) outnumber human identities in the enterprise by a ratio of up to 50:1. This Non-Human Identity Management Group statistic from 2025 is likely already outdated, and in a year from now the ratio will be exponentially higher as rapid growth trends like AI/Machine Learning, the Internet of Things, digital assistants and the burgeoning API Economy continue to accelerate automation.

What are NHIs? Machines, processes, service principals and accounts, virtualized and other workloads, and applications that are granted digital credentials. Think, for an everyday example, of a helpdesk chatbot on your favorite website.

Managing NHIs is a challenge that CIOs, identity experts, and CISOs must accept. This in some ways is a re-run of what happened 40 years ago when identity access management was widely deployed. It’s just that now we are not only dealing with human beings. And I don’t know a single organization that has its collective head around the concept of who is, or should be, ‘the HR chief’ for NHIs.

Leaders today need to consider nonhuman identity in the way they consider human identity. When we hire, we onboard people by harnessing and securing the details we need to pay them and manage them, but we also work to imbue them with our organizational culture, systems, risks, security processes and so on.

We set and tweak permissions based on who needs access to what and when, depending on their roles, seniority, specific tasks they perform at a given time and so on. We need to have similar controls and contextual understanding of our nonhuman assets.

An urgent case where context is king

Without making that change CIOs and other leaders will have no foundational basis of understanding where risks are coming from or what measures they need to take. They need a central, visible platform to see what they have and what’s going on. Legacy IAM can’t offer that because it starts with a narrow focus on compliance and is a static model based on rigid criteria like six-month audits and the rote dispensing of permissions and privileges.

The old IAM is rooted in the early-21st-century age of Sarbanes-Oxley, reactions to governance scandals and corporate malfeasance. Moreover, it ignores what is happening in the wider digital world: namely, the explosion of identity types that are crying out for holistic oversight.

Control of the human and nonhuman estate means ensuring there is insight into nonhuman assets and what they're doing.. context is king. So look at NHI constructs that can touch anything in the digital ecosystem and the underlying connectivity to apps and, most importantly, data. CIOs need to be able to recognize the truth of a contemporary mantra: identity is the fundamental currency in any technology transaction.

Everything must be covered: bots; service principals; IoT endpoints; abandoned trial workloads; autonomous agents and agents that speak to other agents. Start by asking an overarching question: do these NHIs leverage a human identity when they perform tasks on behalf of that person, and is that appropriate?

Then it is possible to drill into practical areas like guarding against credential misuse across agents, improving digital and identity hygiene by spotting risks relating to non-expiring tokens, and analyzing API access levels on a just-in-time basis.

ISPM (Identity Security Posture Management) is useful here because organizations need to have a handle on what they have, and must bring identity constructs together. However, this is not a ‘one and done’ discovery process so consider it as a CI/CD journey of continuous improvement.

In the AI era, holistic technology inventory can't be CMDB-based but must be dynamic and based on CI/CD pipeline control frameworks that let us dynamically view and slice and dice data and assets by controlling who is using what and where.

Think of it as an opportunity…

It’s a complex task and we are all busy. But we need to pause and think about how this inflection point can be parlayed into a positive. This is an opportunity for the security and identity teams to start asking ourselves about the constructs we can apply to nonhuman identities.

They should be asking what needs to be added to legacy IAM frameworks to be successful. And usually the answer will be to replace them with systems that are coded for the current age, not the previous one.

By providing these new controls the identity team will become respected and sought out. Identity leaders will gain kudos by their ability to attribute risk ratings based on behavioral analytics and business function.

By demonstrating insights into where data sits, where it moves to and from, and how identity supports that, identity leaders can deliver qualitative and quantitative process analysis. In some cases of outperforming best practices and execution, they will even help to refine business process re-engineering through a platform that informs prioritization and budget management.

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In the past eighteen months, our teams have moved from debating whether to use AI to debating how fast we can deploy it. The harder question is how to let teams move quickly enough to capture the value of AI without allowing the company’s risk profile to expand faster than its ability to govern it.

That tension is familiar to technology and security leaders because AI creates two mandates that can appear to compete with each other. The technology side of the organization wants experimentation, access, speed, and a path to real productivity gains, while the security side needs control, accountability, data boundaries, and confidence that new workflows will not introduce avoidable exposure.

Both instincts are correct, which is why companies get into trouble when they treat AI as either a pure innovation project or a pure security problem. It is an operating model change, and the organizations that handle it well will be the ones that build just enough structure and hardened tools to let teams move with confidence rather than forcing them to choose between speed and control.

Start with the work, not the tool

Many companies begin by treating AI adoption like a standard software rollout. They approve a vendor, distribute licenses, publish a few guidelines, and assume usage will naturally become transformative. That approach can create activity, but it rarely creates durable operational change.

Real adoption starts when leaders understand how work actually gets done. A finance team, product team, marketing team, support team, and engineering team will not use AI tools in the same way because each group has different knowledge requirements, data sources, risk thresholds, and experience. Each of these will require the development of AI skills and tools.

Leaders should begin by asking what each function is trying to accomplish, what knowledge it needs to make better decisions, what skills are required to use AI responsibly, and what tools or data sources are necessary to produce a reliable result.

When AI is mapped to those capabilities, it becomes part of how the organization operates; when it is layered on top of disconnected processes, it tends to create more output without necessarily creating better outcomes.

Treat data access as a risk design problem

Data access is one of the clearest places where AI changes the operating model. To make AI genuinely useful, teams often need access to information they did not previously use directly.

A marketing team may need product documentation and customer insights, a finance team may need structured operational data, and a support team may need internal knowledge, historical context, and the ability to understand patterns across systems.

The instinct to open access is understandable because AI becomes more valuable when it has more context. The instinct to restrict access is also understandable because broader access can create privacy, compliance, and security concerns. The answer is not to choose one instinct over the other but to create a more deliberate model for deciding which data can be used, by whom, and for what purpose.

In one enterprise rollout, for example, the practical answer was not to give every team access to everything or to keep AI locked inside a technical function. It was to separate lower-risk operational data from more sensitive information, then give teams enough access to work differently while limiting the potential damage if a workflow behaved unexpectedly.

One useful way to think about this is the blast radius of data. Not every dataset carries the same level of risk, and not every AI use case deserves the same level of restriction. Some information can be made more accessible because the potential damage is limited if something goes wrong, while other information, especially personally identifiable information or sensitive customer data, requires much tighter controls.

This approach allows teams to experiment where the risk is lower while preserving stronger governance where the business truly needs it. The goal is not to make security lighter, but to make it more precise so the company can move faster without losing control of the environments, data, and workflows that matter most.

Build verification into the workflow

Verification is where many AI strategies either become scalable or begin to stall. AI systems can produce work that looks polished but is incomplete, inaccurate, off-brand, or noncompliant, and while human review can absorb some of that risk in the early stages, it does not scale well once AI becomes embedded in daily operations.

I have seen this most clearly in technical teams, where AI can accelerate software development only if there is a reliable way to evaluate the quality, security, and accuracy of what gets produced. Without that verification layer, teams may feel faster in the moment while quietly creating more review burden, more rework, and more risk downstream.

Companies need verification patterns that are designed into the workflow itself. That can include automated checks, data quality controls, approval paths, logging, observability, and clear escalation rules for outputs that should not be trusted without further review.

Leaders shouldn't treat these mechanisms as bureaucracy added after the fact. They are what allow AI to move from individual productivity aid to enterprise capability, because teams can only scale adoption when they have a repeatable way to understand whether the work being produced is accurate, appropriate, and safe to use.

Make AI adoption a leadership responsibility

AI adoption cannot be delegated to a single innovation team or AI officer while the rest of the executive team watches from a distance. Every function leader needs to understand how AI changes the work their team performs, how employees will collaborate with agents and models, and where the risks are likely to appear.

That does not mean every executive needs to become a machine learning specialist, but it does mean leaders need enough fluency to guide decisions, set expectations, and model the behavior they ask of their teams. Employees are unlikely to change the way they work if their leaders treat AI as something other people are supposed to adopt.

The cultural framing also matters because if employees hear AI adoption as a euphemism for job elimination, they will protect the current version of their role rather than explore what the next version could become. Leaders should be honest that roles will change, some workflows will disappear, and new responsibilities will emerge, while also making clear that the near-term goal is to help people do higher-quality work with better leverage.

In many cases, the shift is from doing every task manually to orchestrating systems that help do the work. That requires a willingness to work through ambiguity, which becomes more important as AI becomes more capable.

The companies that succeed with AI will not be the ones that put innovation ahead of security and they will not be the ones that wait until every risk can be eliminated in advance. They will be the ones that build enough governance, verification, and data discipline to let teams move with confidence, because within enterprise AI, control is what makes speed sustainable.

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If the past few months are anything to go by, extreme heat is becoming part and parcel of the British summertime. As temperatures continue to rise, employers are under growing pressure to make sure workplaces can cope with the hotter conditions.

The challenge is understanding the smartest way to adapt, because the cost of leaving it to guesswork and getting it wrong is significant.

The UK’s June 2026 heatwave is estimated to have resulted in 24 million lost working hours and a cost to the economy of £1.15 billion in lost productivity. Those figures show the direct impact workplace conditions can have on business performance.

At the same time, hybrid working adds another layer of complexity, having fundamentally changed how offices operate. Attendance is far less predictable than it once was, so it’s harder to know where and when things like cooling, ventilation and workspace capacity are needed most.

Aside from the summer heat, organizations need workplaces that perform well in every season and adapt to constantly changing occupancy and environmental conditions. They need to understand how people move through the office. Things like which desks they check into, which meeting rooms fill up, where teams collaborate and which spaces are regularly ignored.

Doing so requires a real-time understanding of how buildings are being used. This is where sensor technology and workplace usage data comes in, turning assumptions into evidence and giving organizations the insight to make smarter workplace decisions.

Building-wide averages can hide local problems

Many offices are still managed around a relatively fixed idea of how a building should operate. Cooling and ventilation systems may follow predetermined schedules, while individual spaces are managed according to their maximum capacity, rather than how they’re being used in the moment. But we know modern workplaces don’t behave that predictably.

One day a floor may be largely empty, while another is full. The following day, those patterns may be completely different. A busy Tuesday might see meeting rooms become warm and stuffy after hours of back-to-back collaboration, while the open-plan workspace next door remains comfortable. Likewise, areas exposed to direct sunlight will naturally heat up much faster than others.

A single temperature reading taken in one location simply can’t capture those differences. Yet building-wide decisions are often made using exactly that kind of limited information, resulting in energy being used where it isn’t needed while genuine comfort issues remain unresolved.

That’s why employers need a more complete view of workplace performance. Environmental sensors can monitor temperature, humidity, carbon dioxide and air quality across different areas, while occupancy data can show which spaces are being used and how it changes throughout the day.

Often, organizations assume they need to monitor employees to understand workplace performance, but that’s not the case. Built with privacy in mind, systems can combine thermal sensing, ambient temperature, CO2 levels and anonymous spatial tracking to show how an area is performing without needing to identify the people using it.

Bring those data points together, facilities teams can identify real issues earlier, understand what’s causing them and prioritize action that have real impact for employees and their productivity.

Making the data useful

Collecting workplace data is only the beginning. The real value lies in how organizations use that information to create better experiences for employees while operating buildings more efficiently. It’s easy to focus on dashboards and sensors here, but the real goal is to give facilities teams information that’s helpful and they can actually use.

Workplace intelligence isn’t just about responding more quickly when something goes wrong. It’s about anticipating issues before they begin affecting the people using the space.

For example, rising carbon dioxide levels may indicate that a meeting room needs better ventilation, while recurring temperature spikes could point to equipment use, afternoon sunlight or an imbalance between occupancy and cooling. This allows facilities teams to pinpoint the root cause of problems and take action before they begin to impact employees.

By directing resources to the areas that genuinely need them, employers improve employee comfort while reducing unnecessary energy use and operating costs. This more targeted approach also supports wider sustainability goals.

Avoiding unnecessary cooling across half-empty floors reduces operating expenditure and helps organizations make progress against Scope 2 emissions targets, without compromising productivity or indoor air quality.

Designing workplaces around how people really work

Perhaps the greatest value of workplace data lies in what it reveals over time.

Patterns begin to emerge, so organizations build a much richer understanding of how their workplaces are being used.

Recurring patterns can reveal which spaces are consistently underused, which place the most demand on building systems and whether certain areas should be opened, consolidated or redesigned altogether. They can also inform more accurate and efficient cooling schedules, room-booking policies and decisions about where particular teams or activities are best placed.

Check-in and booking data add another layer to that. An organization may discover that employees regularly choose a particular bank of desks, that small meeting rooms are constantly oversubscribed or that a purpose-built collaboration area is rarely used. Those behaviors tell facilities teams something that a temperature reading alone never could.

That said, data is most useful when it is considered alongside employee feedback.

For organizations managing several offices and thousands of employees, speaking to everyone individually isn’t always realistic. Workplace data can help identify where deeper conversations are needed, while surveys, feedback tools and direct employee input provide the context behind the numbers.

Workplace intelligence can highlight what’s happening across a building, but employees provide the context that explains why it matters and how conditions affect their ability to collaborate and concentrate.

Bringing these perspectives together creates a much more complete understanding of workplace performance.

Ultimately, it’s about creating offices that continuously adapt to changing conditions to keep employees comfortable and productive, rather than relying on static assumptions that quickly become outdated.

Preparing offices for a warmer future

It's not only the weather that has become harder to predict. Working patterns are changing just as quickly, and static approaches to workplace management are increasingly out of step with both.

Organizations need to understand both how a building is designed to perform and crucially how it’s being used from one day to the next. That visibility enables facilities teams to respond earlier, use energy more intelligently and create environments that remain comfortable and flexible as conditions change.

The benefit is a workplace that simply works better for the people using it. Somewhere people can focus when they need to and trust that the environment will support the work they have come in to do.

Put simply, when a workplace works better for its people, they are far more likely to want to use it.

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The paperless office is one of the clearest examples of the impact that digital transformation has had on workflows. This includes industries, like law and finance, where physical records of compliance documents, market-sensitive data, and customer information must be retained.

Mishandling electronic and physical data can lead to heavy regulatory penalties, so companies take care to encrypt data in transit, enforce MFA, segment their networks, and sharpen endpoint detection.

These measures build a solid foundation of security. But what good is that wall when the weakest link sits in the corner of the office, quietly printing?

Regulated industries have a print security problem

Most offices treat printers as passive output devices, disconnected from any threat. That assumption is out of date. HP found 57% of IT decision-makers rate print security as a low priority in their cybersecurity strategy, and 45% aren't confident their print environment meets compliance standards.

Whilst most print-related losses differ in complexity, they can be easily traced. Data leaks expose unencrypted hard drives and unpatched firmware, while print history can reveal when documents have been left sitting in the output tray.

Modern printers are networked endpoints with access to confidential cloud environments and increasingly, they're managed by third-party MPS vendors, widening the potential attack surface.

Managed Print Services are useful for fixing hardware gaps, but they can introduce new risks. When print jobs are routed through third-party cloud servers without end-to-end encryption, files in transit become exposed. Granting an external MPS vendor administrative network access creates backdoor entry where even if only the vendor is compromised, attackers can easily pivot into the corporate network.

Singapore saw this play out in 2025, when a ransomware attack on a printing vendor for Bank of China's Singapore branch exposed 11,200 customer names and account details. The incident is a clear example of how a print vendor can easily become the weakest link in an otherwise solid network.

Hybrid work adds another layer of complexity and few companies have clear governance for print behavior once it leaves the office. Office workers may have enterprise-grade tools, but remote workers don’t. Employees printing at home may be using unmanaged devices and unsecured Wi-Fi, offering little visibility into what's being printed or where it ends up.

The compliance exposure under FCA rules

When two-thirds of knowledge workers assume network printers are secure by default, the likelihood of a printer-related breach goes from a matter of if, to when.

For UK financial services firms, this creates an operational and regulatory risk. The FCA has no statutory cap on fines, and its expectations around data governance extends to how firms handle information physically, not only digitally.

Regulators won’t distinguish between a breach caused by a compromised server and one caused by an unattended output tray; the obligation to protect customer data is the same either way. Conversely, a firm can encrypt data in transit, enforce MFA, and segment its network, and still face exposure if a printer or an MPS vendor becomes the point of failure.

Security starts at the print queue

Even as workflows digitize, printers aren't going anywhere. Regulatory demands still call for hard copies and inked signatures, putting print infrastructure at the heart of broader conversations about automation and intelligent document processing.

Every print, scan, or routing job sits in between physical and digital workflows, making them a powerful lever for end-to-end process improvement. Cybercriminals know this too and lapses in printer security become an open door to confidential data.

Content-aware and cloud-based print management platforms close it. By doing away with physical print servers, they allow businesses to centrally manage printers, automate driver installs, and enforce secure "pull printing," where documents release only once a user authenticates at the device.

Because many cloud based print platforms run in Azure, the same security governing email and Teams can extend to print. Documents are checked against content-aware rules that determine whether they're printable or reportable, with flagged jobs blocked or escalated automatically.

Businesses can also go cloud-agnostic through S3-compatible integrations to avoid vendor lock-in, keeping secure releases separate from the product, so nothing installs on the printer itself and everything is managed remotely.

The last unsecured endpoint

Ultimately, security is a board-level concern, and printers need to be treated as the endpoints they are. That means extending endpoint detection and firmware patching to print fleets and extending governance to remote printing, even if that means restricting what can be printed at home altogether.

This is where visibility matters most. In third-party risk management, paper trails are supposed to tell a coherent story but rarely do. Evidence of oversight is too often scattered across email threads, physical documents, and local files. Vetting MPS vendors is how businesses close that gap, but the work starts with treating print infrastructure as seriously as any other endpoint on the network.

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Of the expected $2.5 trillion that corporates are due to spend on AI this year, over $1 trillion is set to be allocated to IT services and dedicated software. That is a huge chunk of change for a technology that many are still grappling with and trying to understand how it fits with their business, team, and procedures.

A few years since the ChatGPT-enabled revolution started, we’re beginning to see the different approaches and challenges businesses are facing when implementing AI.

Ford's recent decision to bring back experienced engineers after AI systems failed to catch manufacturing issues offers an important lesson for every business investing in AI. At the same time, new research from Ramp and Revelio Labs, covering almost 22,000 US companies, found that organizations investing most heavily in AI are actually hiring more people, not fewer.

Together, these stories tell us something important. Everyone assumed AI would reduce reliance on people, but instead we’re seeing companies discover how dependent AI is on experienced people. AI may be changing how work gets done, but this alone isn't enough for growth and success.

AI is accelerating work, but experience still matters

Earlier this year, Ford re-hired 350 veteran engineers after AI and automated systems failed to deliver quality work, demonstrating that employees are critical to a successful AI rollout.

It’s also interesting to see this reflected in Ramp and Revelio Labs’ research that organizations investing in AI are hiring more people. Instead of neglecting hiring and skills development, companies increasingly need the right team members to help refine and improve sophisticated AI so that it performs better in the areas they need.

Success with AI still depends on understanding the skills people need to perform, at an individual, team and organizational level, then investing in development that reflects the realities of their work. But before organizations can build those skills, they need to know where expertise already exists. Too many businesses are sitting on critical knowledge without any clear view of who has it or where the gaps are.

One way to achieve this is building a skills ontology, a living record of the skills that exist across the organization.

Rather than relying on assumptions, businesses can identify where they overindex or underindex on capabilities, understand which teams have specialist knowledge and spot gaps before they become business problems. It also creates the foundation for workforce planning, internal mobility and more effective AI deployment.

The most valuable skills inside your organization

Now you have an effective skills ontology, you have an overview of what is going on in your organization. This helps you identify the subject matter experts (SMEs) that exist across a business, but whose knowledge has often never been formally captured - such as that mythical AI ‘power user’. Take Ford, which has said it is using its rehired employees specifically to train younger staff and reprogram AI tools.

Once you’ve identified where expertise exists, AI can help maintain that picture and keep it up to date as roles evolve. But understanding where the expertise exists is only the beginning.

Organizations need to validate that expertise, connecting people to targeted development opportunities and making it easier for SMEs to share what they know with others. Importantly, this learning should be embedded in work as much as possible rather than separated from it, so it improves performance where it matters most.

AI makes knowledge easier to organize and distribute, but it doesn’t create the judgement that comes from experience. It can surface patterns and recommend learning pathways, but it can’t explain why a process works, when to challenge it, or how to adapt it to the realities of your organization.

That’s why experienced team members remain essential. They contribute the context that enables AI, and everyone else, to use that knowledge effectively.

Turn expertise into a competitive advantage

At a time when many in areas like HR and Learning and Development (L&D) felt that AI would diminish their role, it's clear that this expertise is only becoming more valuable.

L&D teams have the opportunity to operate more strategically by owning the upskilling and reskilling agenda, turning their and team members’ expertise across the business into a competitive advantage, and putting the processes in place to create agile workforces that collaborate effectively with AI.

AI has put organizational expertise firmly in the spotlight, whilst also highlighting that too many businesses are sitting on a wealth of knowledge that exists only in the heads of experienced employees. Not only do they know and understand more about a business and its processes, but they can also provide judgement - a scarce resource that AI cannot replicate.

Getting this information out and deploying it across the business will create a workforce that becomes smarter every time someone learns, rather than losing knowledge when someone leaves, leading to fewer repeated mistakes, better quality and stronger internal mobility.

To get the greatest return from their employees and from AI, the top organizations need to ensure they are investing in identifying, sharing and scaling the expertise they already have instead of simply buying more software.

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Artificial intelligence may dominate today's headlines, but the most productive conversations I'm having across the technology ecosystem rarely begin or end with AI.

More often than not, they start with a business challenge.

Business challenges are driving the AI conversation

How do we modernize our infrastructure without increasing complexity? How do we strengthen our security posture while enabling innovation? How do we prepare our organization for AI without rebuilding everything from the ground up?

Those questions quickly lead to a realization: today's technology decisions are interconnected.

An AI initiative depends on cloud readiness. Cloud strategy influences cybersecurity. Identity, governance and data management underpin all of it. On paper, they look like separate line items. In practice, they're one system. If you pull on cloud, security moves with it; if you pull on AI, data and governance move too.

That's a significant shift, not only for organizations investing in technology, but for the partners helping them navigate it. Organizations are moving beyond asking whether AI matters. They're focused on how to implement it successfully.

That shift is happening across the industry.

Our fourth annual Direction of Technology Report, which surveyed more than 1,400 IT solution providers across 40 countries, found that nearly 75% of partners say AI is essential to their future. Yet the same research shows the conversation has become much broader than AI itself. Partners increasingly see services, specialization, cybersecurity and automation as the capabilities that will differentiate them in the years ahead.

To me, that's one of the most important signals in today's market, because the conversation isn't simply about adopting the next technology. It's about building an environment where technologies work together to solve real business challenges.

From experimentation to implementation

AI adoption is accelerating, but organizations quickly realize that successful AI initiatives depend on much more than selecting the right model or platform. They depend on secure infrastructure, trusted data, governance, integration and the expertise to bring those pieces together.

The market data tells a similar story.

Global AI spending is projected to exceed $2 trillion in 2026. Global Public Cloud spending is expected to surpass $1 trillion, while cybersecurity spending is projected to continue its rapid growth. These markets aren't growing in isolation but expanding together because organizations recognize that one investment reinforces the others.

That's why I believe the conversation is maturing.

The question is no longer, "Where do we start with AI?"

It's, "How do we build the right foundation to make AI successful?"

That requires a different approach from everyone in the technology ecosystem.

Customers aren't asking for another AI demonstration, but they’re asking questions like:

• How does AI fit into the technology investments we've already made?
• How will it integrate with our existing cloud environment?
• What does it mean for our security posture and governance?
• How can it improve productivity without creating unnecessary complexity?

Those are implementation questions, and I believe they'll define the next phase of AI adoption.

Technology will keep evolving, and AI will evolve fastest of all. What gives me confidence is not the pace of that innovation but the way the industry is organizing itself around the harder work of implementation.

No single vendor, provider or advisor holds every answer. Environments have become too interconnected for that, and the organizations making genuine progress are the ones drawing on combined expertise, weighing infrastructure, security, data and services together rather than in sequence.

The definition of success is changing with it. What matters is no longer how many technologies an organization can reach, but how well those technologies operate as one system in service of a business outcome. That, far more than any single breakthrough, will determine who ultimately gets value from AI tools.

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For years, retailers have competed on price, product range and customer experience. Those things still matter, but they are no longer enough on their own. Increasingly, success depends on whether customers can discover a retailer in the first place.

That is becoming more difficult. AI-powered search, changing platform algorithms and rising advertising costs are reshaping how people shop online. Customers no longer follow a straightforward journey from search engine to website. Instead, they move between marketplaces, social media, review platforms and increasingly AI-generated recommendations before deciding where to buy.

More than a third (35.1%) of UK businesses say reaching customers online has become harder over the past two years. At the same time, while 81.3% have a website, only 12.4% accurately track how website visitors become leads or customers. Almost half (45%) do not track this at all.

For retailers, these trends point to a bigger challenge. As customer discovery becomes more fragmented, businesses are becoming increasingly dependent on digital channels they do not control.

Retailers are relying on platforms they do not own

Search engines, marketplaces and social media have transformed retail over the past decade. They have enabled businesses of every size to reach customers far beyond their local markets and compete with larger brands. However, they have also created a new dependency.

Retailers invest significant time and budget building visibility on third-party platforms, yet those platforms are constantly evolving. Search rankings change, marketplaces adjust how products are displayed, advertising becomes more expensive and AI is beginning to influence which retailers customers see before they even click on a website.

These changes are outside a retailer's control, but they can have a direct impact on traffic, sales and customer acquisition. It does not mean businesses should move away from these platforms as they still remain an essential part of any digital strategy. The challenge is ensuring they do not become the only route to customers.

AI is making customer discovery more complex

AI is adding another layer to an already crowded digital landscape - instead of browsing multiple websites themselves, customers are increasingly asking AI tools to recommend products, compare options or summarize reviews.

These systems draw on information from across the web, meaning businesses have less visibility into how recommendations are made and why one retailer appears ahead of another. For retailers, this creates uncertainty.

It is becoming harder to understand where customers first encounter a brand, which touchpoints influence purchasing decisions and how different digital channels work together. Rather than simplifying customer acquisition, AI is making it more dynamic. Businesses need to adapt by strengthening the parts of their digital presence they can influence directly.

Your website remains the strongest foundation

As new discovery channels emerge, it is easy to focus attention on the latest platform or technology. The more important investment is often the one retailers already own. A website remains the only digital channel where retailers control the customer experience from beginning to end. They decide how products are presented, how information is structured and how customers move through the buying journey.

The information published on a website becomes very important as it also helps shape how businesses are understood elsewhere online. Accurate product descriptions, structured content, customer reviews and up-to-date information all contribute to a stronger digital presence.

While retailers cannot influence every algorithm or AI model, they can ensure the information these systems rely on is accurate, consistent and trustworthy.

Visibility without measurement creates blind spots

Building visibility is only one part of the challenge. Understanding what happens once customers arrive is equally important. Yet our research suggests many businesses are still operating without a clear picture of how their digital channels perform.

Only 12.4% of UK businesses accurately measure how website visitors become leads or customers, while almost half do not measure this journey at all. Without that visibility, retailers cannot confidently identify which marketing activity is generating sales, where customers are leaving the buying journey or which investments deserve greater attention.

As customer journeys become more fragmented, relying on assumptions becomes increasingly risky. Retailers that connect website analytics, ecommerce platforms and customer data are far better placed to understand how people discover their business and what ultimately drives revenue.

Building resilience for the future

Retailers will continue to invest in search, social media, marketplaces and AI-driven discovery because that is where customers spend their time. However, long-term success will depend on balancing those opportunities with investments in the digital assets they own.

That means maintaining a high-quality website, improving product information, understanding customer behavior and measuring performance across every stage of the buying journey. These foundations not only improve the customer experience but also help retailers respond more effectively as technology continues to evolve.

AI will undoubtedly change how people discover products online, but it will not change one fundamental principle. Retailers that build direct relationships with customers and invest in the channels they control will always be in a stronger position than those relying entirely on platforms that can change overnight.

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When was the last time you reviewed your cyber security standards? Let’s be clear: keeping a company secure is a constant task.

Cyber breaches are on the rise; according to the latest UK Government Cyber Security Breaches Survey, 4 in 10 businesses were compromised in the last 12 months. It’s important to remember businesses of all types and sizes are at risk – where there are gaps in security, attackers will take advantage.

Against this backdrop, plus high-profile cyber-attacks on companies including M&S and JLR, the government has reviewed its Cyber Essentials framework.

Its latest overhaul is raising the bar for organizations of all sizes.

Designed to provide the basic controls to protect businesses and their staff, if taken seriously, Cyber Essentials can make all the difference. Yet many businesses are still struggling to meet even baseline security standards.

Common issues such as a lack of budget, limited resources and a focus on other priorities are all factors holding organizations back – and they all make a major IT breach more likely.

Historically treated as a once-a-year compliance exercise, the latest ‘Denzel’ Cyber Essentials framework signals a clear and overdue shift towards implementing continuous cyber resilience. This means tougher standards which could catch businesses out.

Here are the key changes and how to approach them, to reduce the risk of a breach.

Implement a robust patching regime

Under the new framework, expectations have been tightened around patch management – the process of fixing a security risk or software error by installing updates – and vulnerability management.

Previously businesses could demonstrate compliance with patching requirements though documented processes and periodic updates.

In reality, teams find it hard to build in regular patching routines which work without affecting live services, due to downtime and continual testing. And that’s without taking into consideration all the third-party patches that need to be applied, as well as the critical operating system ones.

There is evidence that not addressing application issues and vulnerabilities is leading to more breaches. The 2026 Verzion Data Breach Investigations Report shows system compromise is now up to 61%, the highest it’s been over the last three years.

Timelines for applying critical patches have been made stricter, with a 14-day window to comply. Critical patches must be deployed consistently within this period, and businesses must be able to evidence their patching regime is working.

Companies will be afforded two chances to prove patches have been successfully deployed using a process called “double sampling”. If the first round of evidence has critical issues identified, a second chance will be given to fix it and prove it has worked – if it hasn’t, businesses will fail their certification.

IT teams should review systems now to determine if everything is being patched within the 14-day window. If not, they will need to work closely with operational teams to build up to the requirements in time for the next assessment.

Deploy Multi Factor Authentication (MFA) across the board

MFA remains one of the most effective controls against account compromise. The issue for many organizations is not a lack of MFA capability, but inconsistent deployment.

Businesses are likely to have MFA enabled for remote access such as Virtual Private Networks (VPNs) and Microsoft 365 services – but admin interfaces, cloud platforms and third-party services often do not follow the same practice, leaving companies exposed. Threat actors know this and use these weaknesses to try to gain access.

Under the new framework, if you have a cloud system which supports MFA then it must be enabled for all users, irrespective of whether it’s a free, included or paid-for option. Without MFA enabled, it’s an automatic certification failure.

This begs the question: do you know if MFA is enabled on all your cloud services? If not, how long will it take to turn this around?

Cloud services must be accounted for

Under previous versions of the framework, cloud services could be excluded from the assessment as it was argued they sat outside of the scope. This argument is no longer viable. Organizations need to provide a clear, well-defined document with evidence to support proper segregation of all IT Systems, otherwise they will automatically be viewed as within scope.

This means businesses must account for all services, regardless of where they are hosted. Given the flexibility of cloud solutions, this can quickly become a complex issue to gain appropriate visibility of all systems. If you are processing or storing data in a cloud service, this will be included. Typically, these are CRM platforms, HR software and financial systems, project management solutions, plus many more.

As it can take time to discover what’s in use and in scope, this is another area where organizations need to leave plenty of time to ensure all cloud services are covered.

Replace legacy hardware and update operating systems

A major change, and possibly the greatest headache, is the requirement to replace ageing hardware and software.

Recent retirement of the Windows 10 operating system is one area where businesses are under pressure to replace all their hardware to remain compliant. It doesn’t stop there – ageing IT infrastructure, along with major vendor services and solutions, all need to be replaced at some point. If organizations don’t plan ahead, they could be left with a mounting problem and not enough time to deal with it.

Once a vendor stops supporting a platform, the risk of a breach increases until this is addressed. Security patches will no longer be made available and given the time these systems have been out in the wild, it’s only a matter of time before another vulnerability is discovered and exploited.

In the short term, businesses should ensure they have a clear view of their assets in scope of Cyber Essentials and check vendor support dates (and when these run out) to avoid any hidden surprises at the last minute. In the long-term, a robust strategy for replacing all hardware as it is approaching end of life will save difficulty further down the line.

Don’t delay on a gap analysis

Core areas in the Cyber Essentials framework have been updated to reflect the fast-changing security landscape. It’s not all bad news, as businesses have time to turn things around. Organisations should read carefully through the new standards and consider how they apply to their own company.

Do not treat Cyber Essentials as a tick-box exercise and wait until a month before your next assessment. Do a gap analysis now and deal with the findings.

At the end of the day, it is absolutely worth the effort.

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I’ve been reviewing ereaders and epaper writing tablets for nearly a decade now and, in that time, I don’t think I’ve been more excited about an E Ink device as much as Amazon’s Kindle Scribe Colorsoft.

When it was announced in October 2025, I was keen to see how well Amazon had implemented the E Ink Kaleido 3 screen technology on the 11-inch note taker, especially since I found the 7-inch Kindle Colorsoft ereader to be one of the better-optimized displays in this category of tablets.

I had to wait over six months after it was initially available to buy in the US — living in Australia meant I could finally lay my hands on it in June this year — but I happened to try the 3rd-generation Kindle Scribe with frontlight first. Following that up with the Colorsoft version was probably a bad idea because I think the 2025 Kindle Scribe is arguably the best Kindle Amazon has made to date: it’s fast, the implementation of the monochrome E Ink Carta 1300 screen is fantastic and the battery life remains just as impressive as on any other Kindle.

The Kindle Scribe Colorsoft, in comparison, lacks luster. For the most part, it’s not really Amazon’s fault that its monochrome sibling outshines it — it’s the limitations of color E Ink display tech after all — but the one thing that really stands out to me is very much under Amazon's control.

Why so blue, Amazon?

I love the new wallpaper artwork on the 2025 release of the Scribes. They're beautiful, different and, after seeing them first on the 3rd-generation Kindle Scribe in black and white, I was keen to appreciate them in color.

Strangely, the dominant color you see on the lockscreen artwork on the Kindle Scribe Colorsoft is blue. From the start screen, showing the silhouette of the boy reading under the tree, to the red-and-white balloon hovering over a lake and forest, every default artwork just looks… blue.

Now, I know some of this is due to the screen itself — the color filter array (CFA) layered over the screen tends to shift hues towards a grey-blue. But if Boox can ensure the other colors pop in the default wallpapers on its large-screen color devices, so can Amazon.

I thought I’d get used to it but, two months on, and I’m left feeling… well, blue every time I wake up the Kindle Scribe Colorsoft. I don’t typically like displaying book covers on the lockscreen, but that’s what I’ve reverted to recently just to avoid the dominance of that one color.

It’s there in black and white

OK, I’ll admit that I sound like I’m nitpicking with the wallpapers, but it highlights one big issue with color E Ink displays — they’re darker than the monochrome E Ink Carta screens and, unless you bump up frontlight brightness, the colors don’t really pop. You can see that with not just the wallpapers, but with book covers and comics too on the Kindle Scribe Colorsoft.

This is because of the RGB layer integrated into the display and it affects every single color ereader and digital notebook, not just the Colorsofts. That means the ‘white’ background isn’t as white as it would be on a similar device using the E Ink Carta 1300 screen (like the 2nd and 3rd generation Kindle Scribes) which, in turn, makes colors look dull.

But increasing frontlight brightness to enjoy those colors means you’re draining battery life quicker. In the two months I’ve been using the Kindle Scribe Colorsoft, I’ve had to charge it every 2 to 3 days with an average use of 5 hours a day — mostly reading, but some writing too, and browsing the Kindle Store — with auto-brightness settings and Wi-Fi on. In comparison, the 2025 Kindle Scribe lasted me about 5 days on a single charge with the same settings and similar use case.

Annoyingly, the Colorsoft edition takes much longer than the standard Scribe to recharge as well.

Color isn’t always necessary

If color reading really is important to you, you’re better off with an iPad or a Samsung tablet to be honest — color E Ink just isn’t there yet. Moreover, most of us who use an ereader don’t necessarily even need color; we tend to mostly read text-heavy books and note-taking also, similarly, is mostly black ink.

On the Kindle Colorsoft Scribe, the color ink isn’t even as dark as I’ve seen on the Boox Note Air 5C, so it really doesn’t offer as much value for your hard-earned cash compared to some of its competition.

In comparison, its monochrome sibling is the overall better performer — as I’ve said in my Kindle Scribe (2025) review, it’s arguably the best digital notebook Amazon has made to date and, if it wasn’t so expensive, it would have scored full marks.

If you want a note-taking Kindle, I would still recommend the 2024 release of the Kindle Scribe — you might as well pick it up as long as it's still available. I suspect Amazon will discontinue it soon, but it’s a heck of a lot cheaper even at full price, is often discounted even outside of major sales and, according to Good Ereader, it will get the new software features that released with the current Scribe models, including Google Drive and Microsoft OneDrive connectivity, AI search and the refreshed homescreen layout.

If you’re willing to explore other E Ink writing tablets, I’d suggest looking into the reMarkable Paper Pure or even the Boox Go 10.3 series if you prefer an Android device that will also double as an ereader. A word of caution on the Boox devices, though: you'll need to deal with some ghosting.

One of the most exciting areas in technology is quantum computing, but it's also one of the areas that's been among the least accessible, difficult to relate to the real world, and still largely buried away in the lab. To reach the point at which quantum computers become useful, there's still plenty of work to be done.

The race to a million qubits

There are, and continue to be, different approaches to building quantum computers as well as all the components that power them. But the goal among all companies and institutions is the same: to build a machine that can outpace the fastest classical computer.

The cofounder and CEO of quantum computing company PsiQuantum, Jeremy O'Brien, was sharing his vision behind his organization, essentially saying that his team had attempted to work against a forged consensus.

Rather than scaling up qubits from a handful to 100 or 1,000, he said at the time that he was "fanatical" about getting to a million-plus qubits – the point at which experts expect there to be a meaningful performance leap.

New architectures and approaches

Intriguingly, O'Brien's comments came at a time when sheer scale and qubit count were the most sought-after elements in a quantum computer.

Since that point, so much research in the space has shifted not to the sheer size of the chips and how many qubits they can hold, but to building better qubits. Because quantum processors are so prone to failure, researchers have instead been trying to build processors with a few error-resistant qubits over millions of basic error-prone qubits.

The goal is still to achieve a quantum computer that can outpace classical machines, but experts expect that due to new breakthroughs, these quantum computers may need far fewer qubits than the millions we thought just a few years ago.

Last week the massive lawsuit against Meta, alleging that its products were designed to addict teens and may have caused them mental harm, went to court. Backed by dozens of US States, the potential $1.4 trillion price tag — if Meta lost — would have been enough to bankrupt the company.

That case is now, suddenly, over. Settled for the princely sum of $18 billion and promised fundamental changes in how Meta's major products, namely Instagram and Facebook, operate when used by anyone under 18. I'll get to the proposed changes below (which Meta would like to see YouTube and TikTok adopt, too), but first let's talk about the stunning turn of events.

We were in week two of the big trial, and honestly, we were just getting to the good part. After the states initially presented their case and made it clear they believed Meta knew its products might be harming teens and wasn't doing anything about it, Instagram CEO Adam Mosseri took the stand and was peppered with questions about the platform's current controls, like its prompts imploring teens to "take a break." The focus was so intense that the normally polished Mosseri appeared "annoyed." He also admitted that there are "no silver bullets" for safety.

On Wednesday (August 26), we were, it appeared, only hours away from the big show: Meta Founder and CEO Mark Zuckerberg taking the stand. I expected the prosecution to press him hard to see if they could trigger a slip-up or some reaction from the normally unflappable Zuckerberg.

Now, that moment will never come. Without admitting any wrongdoing or fault, Meta has closed the potentially business-killing or at least hobbling case.

Here's a look, though, at what Meta has agreed to in the settlement[PDF]. On the surface, it will sound like a lot. In fact, the changes are so sweeping that some on social media wondered if they could or should be applied to everyone on the platform.

• Time limits (2 hours)
• Mandatory pauses after 15 minutes of scrolling
• Overnight usage blocks
• No nighttime notifications
• No use during school hours
• No visible likes
• No cosmetic filters
• No algorithmically organized feed (if they choose)
• Age verification
• Better harmful content reporting
• Better parental controls

There are caveats: the two-hour time limit applies across Instagram and Facebook, though how much time does a kid under 18 spend on Facebook?

Likes removal includes any information that could lead to social comparison — essentially a FOMO filter.

Some of the changes, like age verification, parental controls, and content safeguards, represent strengthening and broadening of existing controls.

Meta has rolled out a whole suite of parental controls, especially on Instagram, in the last two years, but as Mosseri noted, not everything is working.

When I posted the above list on social media, one savvy reader made a salient point.

On Bluesky, Paul Dempsey posted:

Sadly, that needs at least these assumptions to hold:

1. Meta will apply tough-enough processes to stop kids hacking these restrictions.

2. Parents will be able to tell when their children have hacked the barriers.

3. States will have enough resources to police the new format.

I'm thinking 0/3.

A steep climb and long odds

He's right, of course. Meta's tools for blocking wily teens have not necessarily borne fruit. If anything, teens are turning away from social media of their own accord, but that doesn't mean millions aren't still on it and are often subjected to unhealthy content and social feedback.

Meta's decision to use AI to detect teen-like behavior and then automatically shift these accounts into the current teen account restrictions has worked for some, but how well does the AI really understand today's teens, and all the ways they can model adult behavior to get around the restrictions?

With this settlement, Meta will be cooking up stricter rules that may include some form of ID not just for the addition of teen accounts but some proof that people are who they say they are and that they're the parents of these newly onboarded teens.

Platform anonymity could take a hit or die altogether.

But as Dempsey noted, it's unclear if Meta can come up with enough ways to block resourceful teens.

His second question is also valid. So many parents are out of the loop when it comes to their children's and teens' online activities. Parents may be on Facebook but are less likely to casually swim in Reels and TikTok waters. Teens' comfort level with these systems far outstrips their parents'. I often wonder who will be leading whom as young people and their parents try to navigate these new restrictions.

Dempsey's last point is probably the ringer. I applaud all the states for joining together to pursue this one case. It's an amazing level of cooperation. Still, when it comes to laws and penalties, I'd be shocked if we saw even a whiff of consistency. That means the rules for policing, detecting, enforcing, and prosecuting those who skirt them will differ in every state. Worse yet, none will have the infrastructure or resources to keep track.

Sure, the rules across Instagram, Facebook, and other social media platforms that join will be consistent, but the tactics for circumventing them will be varied and no one — not the parents nor the states that originally brought the case — will know, that is until a teen reports harm or parents notice that their children are somehow engaging on social media with adults (or worse).

For Meta, these changes will alter their products, but I don't think that'll materially harm it or its business. It's a lot of money, but a manageable sum (the cost of one acquisition). For parents and teens, they will all struggle with the platform changes until their teens figure out workarounds, and parents again wonder why it's gotten so quiet in their kids' bedrooms.

A lot has already been said about the doom and gloom surrounding AI - some of it drawing on the warnings of classic dystopian novels, but much of it grounded in reality. There is a lot of optimism and promises from those in control of the technology, but not much has emerged in the form of actual benefit.

“I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world,” Anthropic CEO and co-founder Dario Amodei has said in a post on X.

The technology is suffering from a “crisis of trust” that stems from people’s belief that companies and governments are “cooking up some new way to screw them over,” he argues. But given how much of the US military, law enforcement, and intelligence gathering infrastructure now relies on AI, are the people wrong?

AI can’t public relations its way out of this one

As AI technology took off, much of the benefit of the technology was aimed at convincing executives and C-suites that the technology - while unproven - was a worthwhile investment. As a result, multiple AI leaders positioned the technology as a way to strip away the labor costs of companies by replacing workers with AI.

Some AI gurus are still arguing these points behind closed doors. I attended a private event this year where an AI "guru" predicted that AI superintelligence would erase as much as 50% of the global workforce within 10 years, and the companies best positioned to take advantage of this cataclysmic event would reap unimaginable control and riches. This was naturally met by applause from the upper echelons of management from companies in attendance.

Amodei himself famously said that AI could wipe out 50% of white collar jobs. But he has since restructured this message, saying instead that AI is a ‘multiplier of output’ - or corporate word salad for ‘increasing productivity without having to increase compensation’.

As I have said numerous times, the proof of AI really is in the pudding. We’ve heard everything that it could do, but seen little benefit in what it can do. To his point, Amodei acknowledges that.

He notes that if an advert went up saying that AI could cure cancer, it would be perceived as entirely misleading. “The thing that will work is actually curing cancer,” he says.

Amodei has teased Anthropic’s morality on these issues, saying that the company is “ramping up its efforts” to focus on using AI for biology and medicine. But again, we’re yet to see the results.

Amodei’s post also touched on where AI is centralizing power, echoing some of Mark Zuckerberg’s recent rant on how best to use AI for everyone’s benefit. Amodei’s argument differed slightly from Zuckerberg’s, arguing that there is a balance to be struck between regulation, oversight, testing, and open-weight models.

He notes that in some aspects he agrees with the “Pacing the Frontier” movement, stating that implementing controls on the frontier labs would “modulate” the pace of the technology while simultaneously allowing the rest of the industry to catch up.

The obvious flaw in this plan is that no other nation would be subject to this regulation which could deliberately hamstring US hegemony in the AI industry - something the Trump administration has frequently said it will not allow to happen.

Is AI delivering more benefits to authoritarian infrastructure than to people?

Where AI systems have been deployed extensively is in surveillance infrastructure. Until the collapse of its relationship with the Pentagon, Anthropic was working with the Department of Defense to procure AI systems. Anthropic refused to cooperate in allowing its technology to be used for mass surveillance and fully autonomous weapons, and lost the contract.

Luckily for the DoD, OpenAI shook hands on a new deal with the Pentagon and immediately filled the shoes left by Anthropic with a prohibition on its technology being used to spy on Americans.

That doesn’t mean other companies are abiding by the same level of ethics. Flock has faced a widespread guerrilla movement targeting its AI-powered cameras designed to track vehicle number plates across the US.

The system is intended to flag suspect’s cars as they travel across boundaries and lines of authority, but has instead seen success as a way for members of law enforcement to stalk exes, monitor colleagues, and track their love interests.

AI systems are also being deployed in traffic monitoring and speeding cameras. This in particular has received a lot of attention as a grey area of policing, especially for systems that misidentify numberplates or are wrongly configured.

For criminal cases in the US, the burden of proof rests with the government to prove someone committed a crime. However, some autonomous ticketing systems that have issued citations to the wrong vehicles have been criticized for requiring those accused to prove their innocence in court, flipping the burden of proof for criminal cases on its head.

And then there is the elephant in the room.

Palantir’s AI technology is deployed across the US military and law enforcement. Most notably, it has been used by the US Immigration and Customs Enforcement (ICE) to track those suspected of illegal immigration - but Palantir’s technology is also used in drone imagery analysis, long-range precision strikes, and military data analysis and planning.

No doubt it has been instrumental in delivering the World Record for the most times a single war has been won.

AI is great news if you have nothing to hide and love being on the side of the Good Guys™, but that's a constantly shifting definition.

Imagine AI is the sun and businesses are Icarus. By now, most of us are basking in the light of this transformative innovation, which sometimes feels too hot and other times disappears behind clouds so dense we wonder why we ever trusted it in the first place.

Some intrepid businesses not only gaze at AI's warm light, but openly worship it. They don wings, and fly as close as they can to its surface. The goal: scoop up a bit of that generative magic, and fly back to Earth with agents, which, to carry this insane metaphor further, can be fashioned into people-like workers capable of doing what we do, but at scale and with little-to-no monitoring.

Of course, the flight to AI's core is dangerous, and so we have episodes like Meta's recent Quixotic adventure which, according to an extensive Reuters report, saw it attempt to transform a vast portion of its workforce (reportedly 60%) into AI agents overseen by a much smaller set of highly skilled workers.

That Meta's wings almost immediately started smoking from its proximity to the AI core is no surprise, at least not to me.

Slow down

I talk and write about AI a lot, and I've been covering it in some form or another for almost 25 years, back when companies like IBM and Microsoft were the AI leaders, and we thought Deep Blue beating Kasparov was the apex of what we could expect from artificial intelligence.

Modern generative AI burns far brighter, and shows in real time such vast potential that enthusiasm is not just rampant; it's out of control.

AI agents, which Meta apparently started using at scale, are driving much of this mania. These automated tools offer the promise of handling drudgery-filled tasks in innumerable businesses, and could solve not just productivity problems, but be the answer to workforce issues, including hiring problems, underperformers, and burnout. Agentic AI can not just handle repetitive tasks; it can cobble together disparate systems to complete work.

From Meta's perspective, especially as a company that is primarily a software business, AI that could work across tools and systems to not only help keep things running but build new work (under the supervision of a smaller set of super-performers) probably seemed like the perfect solution.

Conclusions like this are not exclusive to Meta or companies of its size. Conversations about the use of AI in business persist in every industry and at every level.

AI has gone to work in healthcare, retail and e-commerce, manufacturing, financial services and banking, entertainment, and media. All with varying degrees of success and implications.

It's a stunning fact when you consider that we only met OpenAI's ChatGPT in 2022. Never has a technology so quickly infiltrated every facet of life, and so consumed daily conversation. AI Time is triple time (3x the speed of any previous innovation), and that includes adoption.

There is, at least in business, a shocking level of trust in what is still, in many ways, an untested solution. I'm not saying AI does not work, but there is the assumption right now that it works in every situation.

Certainly, if the Reuters report is accurate, and Meta essentially verified 90% of it (it claims it was never planning on laying off 60% of its workforce), Meta is guilty of this.

Do you smell something burning?

With AI, the act of implementation and business transformation tend to happen in parallel, so as Meta was planning and enacting its reorganization and layoffs, it was already employing Agentic AI.

"Infrastructure teams were also warning of 'reliability warning signs' caused by the AI coding surge, according to an internal post," wrote Katie Paul for Reuters.

There was, the author adds, evidence that these issues (and basically faulty work) were visible in June when hackers used Meta's AI chatbot to hack celebrity Instagram accounts.

Between that and what appears to be a company revolt (my favorite part was the bathroom stall notices encouraging people to protest Meta's use of keystroke tracking), it was clear Meta has gone too far, too fast with AI for everyone's tastes.

In the end, with its feathers on fire, Meta retreated from the AI sun (or at least its plan) and canceled layoffs and even undid some of the reorg.

The lesson here is not that AI is too dangerous to approach closely, or that AI and business are a bad match. But, as we are seeing, pronouncements that AI would transform businesses without removing or replacing jobs were misinformed, and allowing AI to produce work without adequate human oversight at every stage of the process is, for the time being, ill-advised.

As Mark Zuckerberg tries to rewrite his own script to be more people-first, it might be time for everyone to take a beat, and reassess how AI fits into their businesses, workflow, and life.

AI might feel like the sun, but it can't be our sole source of light and warmth. It's still just another tool — sure, a powerful and incredibly fungible one, but still a tool — and over-reliance on it will undoubtedly lead to frustration, or worse.

Meta learned a hard lesson: your AI plans must be grounded in a human-centric approach; otherwise, the humans will rise up and clip your wings.

Scary AI spend and weak access security controls are the flipside of the sharp rise in agentic AI. The answer lies in the form of a control plane that provides the visibility CIOs need.

The past couple of years have seen enterprises press the fast-forward button on generative AI strategies. e have all seen a remarkable change in deployment patterns from useful chatbots to autonomous agents that engage deeply with core software, data and LLMs, and even create their own offspring agents.

CIOs and others are rightly excited about the capacity of agentic AI to reduce manual processes and accelerate speed to insight and actions.

However, governance and AI token costs are alarming challenges.

What’s needed today then is the power of AI without the major concerns over incurring access control risks relating to ‘Shadow AI’ and agent sprawl, and without the ‘bill shock’ relating to unmanaged token consumption.

Moving fast

The generative AI shift that has occurred so quickly spans the early days of ad hoc AI experimentation via testing and pilots to today’s enterprise-grade business processes. It has also outpaced the progress of the usual governance frameworks and effectively rendered them obsolete.

Experienced CIOs may compare the change to the early days of cloud computing platforms when staff with credit cards expensed the AWS trials they had spun up and quickly ran up staggering bills.

Or they may hark back to the days when sales leaders set up online CRM platforms or other process owners bypassed IT to run their own Sharepoint systems.

Or, to reach for yet another lesson from history, they may compare what is happening with generative AI now with the advent of bring-your-own-device schemes and WiFi.

Remember when the user-led demand to have the shiny new gadget ran a coach and horses through the command-and-control IT regimes of cloned PCs stamped with a single image and CIOs no longer managed everything that touched their systems?

The problem is here. Deal with it

However we make the comparison, it’s clear that today there are very real risks relating to data leakage, compliance standards and IT costs because of the speed with which AI has advanced. Nobody wants to lose a chance to gain a competitive advantage and even to fundamentally re-engineer and automate expensive, error-strewn manual processes. So CIOs are once again being asked to square a circle.

As IDC has said: “There is a well-established playbook for calculating the return on an IT investment. Define a baseline, model the efficiency gains, project the cost savings, and present a number to the CFO. For most technology investments, that approach works. Agentic AI breaks it.”

Agentic AI is not fixed and predictable so it’s tough to manage and calculate return on investment, but we know it can take out swathes of cost, so we have to use it and we have to manage costs as best we can.

Gartner has already suggested that by 2027, more than 40% of agentic AI projects will be cancelled due to factors such as cost overrun fears or inadequate risk management. That’s not good enough. Corporate IT must step up and deliver the value the business needs, just without the attendant risks and costs.

The solution here is to head off undesirable outcomes at the pass by providing a check, a little like an airport security system that identifies passengers and scans for any potentially unsafe activity or nearby environmental factors such as possession of sharp objects.

You will see this happen in IT very quickly now, such are the risks and speed of what is happening. If 2026 is the year of agentic AI, then it also has to be the year of secure agentic AI.

Enter the AI gateway

One way to address the challenges outlined above is to create a gateway or filter. This approach offers a single control layer regardless of whether the AI model is hosted in public or deployed as a private, self-hosted entity. It effectively secures traffic from agents to LLMs and from MCP servers. This in turn enables organizations to have centralized policies and a clear view of token accounting.

It even has the visibility impact of informing decisions of non-techies who know little about AI operations. So, for example, a CIO working in tandem with a CFO perhaps may identify a better cost efficiency (for example, switching from a hosted model to a private model) and act accordingly.

That’s great news for CIOs who want to maintain a reputation for IT being agents of change but not its less desirable reputation for being risky and pricey.

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Autonomous AI agents are moving faster than the frameworks meant to contain them. Enterprises are deploying agents that can call systems, pull data, and increasingly interact with other agents to complete multi-step tasks.

Yet few organizations can say with confidence exactly how many agents are running in their environment, what each one is authorized to touch, or who exactly is accountable when something goes wrong.

That gap can turn agentic workflows from a promising technological advancement into a potential minefield of risk unless enterprises take a new approach to governance – one that provides greater oversight into how agents are operating and what systems they can access, trust, and use.

Don’t wait for perfection

The market has responded to the potential threat around AI agents going rogue with a wave of new tooling: agent discovery platforms that scan for active agents, and agent harnesses that box them into approved boundaries. Both are useful, but neither solves the problem alone.

The challenge of “getting a handle” on AI agents is compounded by the pace of change. New agent tools, new AI model releases, and new orchestration options are arriving at a rate that makes any static governance model obsolete within months.

Rather than waiting for a “perfect” governance framework to emerge as a standard, organizations should take steps now to create a working structure that can evolve over time.

So, what might this look like in practice?

Continuous visibility and granular guardrails

Policies and procedures alone cannot confirm what is actually running in production. Organizations need a registration and discovery process that captures every agent in use, not just the ones teams report having built.

Real visibility comes from instrumenting the environment itself, using logging and observability data generated by the underlying models, and building analysis on top of it. Only that raw data can show what an agent is actually doing, how often, and at what cost, rather than relying on what people think it is doing.

Another key step is to move from broad guardrails to granular ones. A single, broad-based harness applied uniformly across an organization, for example, limits scope without addressing risk in a meaningful way. Different use cases call for different levels of restriction, and treating every agent identically either over-constrains valuable work or under-constrains risky work.

Guardrails need to be defined at the level of the individual agent and its specific task, not the organization as a whole. As agents begin accessing other agents to complete a task, that specificity becomes even more important: each agent needs an explicit, narrow definition of what it can do and what it can reach, so that any leakage beyond that defined scope is immediately visible.

With all of the above, enterprises should embrace an approach of “validate, don’t assume”. Putting a conceptual harness or policy in place is only the first step. Confirming that it actually holds under real conditions is a separate and ongoing exercise.

A useful approach here is to pair conceptual guardrails with a logical or physical enforcement layer: a tool that can confirm what is executable, what an agent can interact with, and whether it stays within that boundary in practice.

Continuous monitoring against policy, not a one-time sign-off, is what proves a harness is working, because agent capability and the surrounding set of tools change on a near-daily basis.

Access is not the only variable

Governance conversations tend to focus almost exclusively on what an agent can access. Equally important is the order in which it accesses information. An agent that gathers information out of sequence, or acts on data before a dependent step has completed, can produce results that are wrong even if every individual action was technically permitted.

Some workflows require steps to run in parallel; others require specific sequencing to ensure the most accurate results. Building a discrete, well-defined operational sequence for each use case, rather than allowing a model to determine its own order of operations, substantially reduces this type of error.

This is as much a process design issue as a technical one, and it relies on people defining the use case clearly before any tooling is applied.

A disciplined approach reins in agentic risk

None of these elements works in isolation. Discovery without granular guardrails leaves organizations knowing what exists but not controlling it. Harnesses without validation create a false sense of security. And governance focused only on access misses key variables that can introduce risk.

The organizations managing this well are the ones treating agent governance as an extension of existing IT and data governance, applying the same discipline used for foundational legacy systems rather than treating it as a bolt-on afterthought.

Given how quickly the technological landscape continues to shift, this comprehensive governed approach is what will keep agentic AI delivering value – and reduce the risk of agents going rogue.

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Inclusion is often discussed in terms of culture, hiring or representation.

These things matter deeply, but for a technology company, it is critical that inclusivity is also reflected in the products and services our customers use every day – in other words, smarter technology that’s truly for all.

Living our Smarter Technology for All vision has always depended on building a culture where people feel they belong.

In my view, inclusion is not a team that exists in one part of a business. It’s a cultural pillar that’s reflected in how we make decisions and serve our customers.

This effort to embed inclusion has led us to establish an Inclusive Product Design Office (IPDO), which integrates inclusive design expertise into product development rather than treating accessibility as an afterthought.

Who a product serves

That means we need to think about who a product serves from the outset, considering all consumers’ unique needs and attributes early enough to mitigate bias or limit usability of our products and services. The bias we’re working to eliminate could be part of anyone’s background – cultural, physical, or economical.

However, people with disabilities continue to represent one of the most important and complex demographics we’re working to include through the efforts of the IPDO. The scale is significant. The World Health Organisation estimates that 1.3 billion people experience significant disability, representing 16% of the global population.

That is one in six of us. Plus, almost everyone will experience some form of disability, temporary or permanent, at some point in their life. Accessibility should therefore be treated as a part of mainstream product design, because users with disabilities are part of the mainstream.

This is why accessibility should be considered as a priority at the start of product development. Once a product has been designed, accessibility can become harder to address properly. It risks becoming a patch, rather than an integral part of the user experience.

Our Inclusive Product Design Office has expanded vetting of our products and solutions, ensuring our technology can work for all, regardless of background or ability. The process may involve examining how users receive system feedback, how they physically interact with a device, or how easily they can navigate software using assistive technology.

The thinking is simple: not everyone interacts with technology in the same way, and good design should reflect that reality.

Valuable insights

Some of our most valuable design insights come from listening. Through our collaboration with the Governor Morehead School for the Blind in North Carolina, we’ve learned directly from blind and low-vision users about how they interact with our products. Those conversations have shaped product decisions in ways our engineering teams never anticipated.

In some cases, the challenge is physical. For example, a laptop that can be opened easily with one hand can support individuals with a mobility need. Tactile keyboard markings are helping users with low vision or reduced dexterity. Accessible packaging can also make a big difference, ensuring a device can be opened easily before it is switched on.

This principle also applies to features that may seem ordinary. Trackpad palm rejection, designed to prevent accidental inputs while typing, can unintentionally create barriers for users with limb differences. By allowing these settings to be adjustable, we empower users to control how they interact with their technology.

When companies design for people with disabilities in mind, it forces us to be more precise and creative about the problems we are solving. In many cases, the result is technology that works better for a broader range of people.

Simple expectations

For anyone using technology, the expectation is simple: it should work with them, rather than ask them to work around it. When technology is intuitive and adaptable, people can focus on what they set out to do.

Accessibility cannot sit at the edge of product design, because people’s needs, environments and ways of working are constantly changing. The more we design around real human needs from the start, the more useful technology becomes for everyone.

Inclusive design is an ongoing journey, with ever-changing standards and users. There will always be more to learn, but through conversations with accessibility experts, employees, customers, and communities, we can bring about meaningful change for all.

The future of technology will not be defined simply by what an AI solution or device can do, but by the people they enable. Designing for humanity is what makes innovation meaningful.

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This summer's unexpected heatwave across the UK and Europe has caught retailers flat-footed.

Go into any London Oxford Street store this week and you’ll find the last remains of the mid-summer sale, while “new in” rails are covered in chocolate brown trouser suits ready for Autumn.

The trouble is, it’s still 28 degrees and sunny with another heatwave expected this week. Shoppers aren’t looking for jumpers, yet the shelves are stocked for a forecast made half a year earlier, meaning many retailers have missed the immediate shift in consumer demand.

It’s hard to not feel the disconnect. Somewhere back in January, a planning team sat in a meeting room and decided, with the best information they had at the time, that by early August the nation would be ready to shop for knitwear.

It’s a process that retailers have used for decades, and the problem isn’t that they planned-ahead or got it wrong, it’s that the infrastructure behind the decision has no mechanism for course correcting once new information arrives.

That's not a forecasting failure. It's a technology and process failure, and it's one that's becoming impossible to ignore.

Six-month planning cycles are no longer viable

The heatwave is just one example of why rigid six-month planning cycles in retail are no longer commercially viable. Designed in an era that was steady and predictable, they assume stable supply chains, formulaic seasons, and shoppers who wait patiently for the "right" moment to buy. That world simply doesn’t exist today.

Global supply chains have become fragile and prone to disruption at any point in the chain, while erratic weather patterns can change trading conditions overnight, and consumer demand is shaped as much by a TikTok trend that lands on a Tuesday, as it is by a season on a calendar. This has left retailers trying to run a business that demands agility on an operating system designed for a much slower rhythm.

The result is the disconnect we're seeing on the shop floor right now. Having worked in retail for more than 20 years, I know first-hand that most planning systems are still built around static reports, disconnected spreadsheets and manual range-building processes that take weeks (sometimes months) to turn around.

This creates a structural lag between changes in demand and when the business is able to respond. It is this lag that is quickly becoming the single biggest driver of markdowns, stockouts and wasted inventory that costs the global retail sector more than $1.7 trillion per year, according to analysts IHL Group.

Rethinking planning

It’s clear to me that for retailers looking to achieve growth, they must rethink how planning, buying and merchandising get done.

This starts with moving away from rigid, twice-a-year buying cycles built on legacy systems to data infrastructure that supports micro-season planning. This is shorter, more frequent windows where decisions about what to buy, when to reorder and how to promote in-store are made continuously and based on current data, not locked in months in advance based on a forecast made months earlier.

Practically, this means implementing a few core capabilities, the first being real-time data visibility that allows teams to see live sell-through, stock position and intake at SKU level, rather than via a report that lands the following Monday describing what already happened.

The second is more connected forecasting, where demand models can analyze external factors such as a heatwave, local events or social media trends as a trading signal rather than an anomaly discovered on the shop floor.

This leads to the third capability, which is faster execution once a shift in demand is identified. The system needs to support quick decision making and action, whether that's an automated reorder, a reallocation of stock between stores and channels, or a change to in-store and online merchandising.

Maintaining a live model

None of this replaces long-range strategy. Retailers still need a financial plan, a range architecture and a clear vision. What changes is how that happens in practice. Planning becomes less about producing a fixed document twice a year and more about maintaining a live model of the business that can be interrogated and acted on continuously.

That's a fundamentally different technology requirement than most legacy planning and merchandising systems were built to support, and it's why so many retailers are still reacting to demand shifts weeks after they've already cost them sales.

For retail leaders, the practical takeaway is to start auditing where your planning and merchandising decisions get made, and how long it takes for a real-world signal, such as a heatwave, a stockout, a viral product, to translate into a change on the shop floor or the website. If that gap is measured in weeks rather than days, the constraint isn't your team's judgement, it's the infrastructure they're working with.

This heatwave is just one visible example of a much broader technological and operational shift that retailers need to make. The businesses that treat it as a prompt to modernize, rather than a one-off weather event, are the ones that will be more commercially resilient the next time conditions change without warning.

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Artificial intelligence has transformed how software is built. Tasks that once took software developers days, if not weeks, to finalize can now be completed in hours with the assistance of generative AI tools.

The promise is compelling, offering faster innovation, increased productivity, and the ability to bring new applications to life at an unprecedented speed. But what is the impact on security?

AI coding has simultaneously put software risk on steroids. This is not because AI-generated code is uniquely flawed; it’s because it enables organizations to build and deploy software faster than any existing security, governance, or risk management process.

Development velocity has accelerated toward machine speed, while governance remains largely human-driven. That gap is now one of the defining software security challenges of the AI era.

Software is moving at machine speed. Security isn't.

AI is not only changing how code is written; it is changing how software is assembled. Developers can now assemble applications using open-source components, APIs, and third-party services faster than ever before. Every new application, integration, and dependency expands the attack surface that organizations must inventory, monitor, and secure.

The result is now a growing imbalance between software creation and software remediation. As the pace of software creation increases, remediation must keep up.

Veracode's 2026 State of Software Security report found 82% of organizations now carry security debt—vulnerabilities that remain unresolved over time — and 60% carry critical security debt, meaning flaws that are severe enough to cause significant damage if exploited.

Third-party code continues to be an especially stubborn source of risk, representing 66% of the most dangerous, long-lived vulnerabilities. The data reveals a simple reality: AI doesn't just generate more first-party code—it is increasing software complexity.

Organizations have always dealt with flawed code. The difference now is the speed and scale at which that code can be created, accepted, and deployed. AI doesn't just introduce risk, it amplifies the challenge of managing risk by enabling teams to generate exponentially more software than traditional security processes were designed to govern.

Traditional security governance assumes humans remain the bottleneck in software creation. Reviews, approvals, audits, and remediation workflows were designed for development cycles measured in weeks or months. AI-assisted development compresses those timelines dramatically.

When software can be generated, modified, and deployed at machine speed, governance models that depend on human intervention alone are no longer sustainable.

AI can help plant a seed, but that does not mean the garden will thrive. A seed needs the right soil, climate, and care. Software is no different. Organizations can generate applications overnight, but without the right security frameworks, operational support, and governance structures, those applications can quickly become liabilities rather than assets.

This is why security leaders must rethink governance for the AI era. The goal can’t be to inspect every line of code or eliminate every vulnerability before deployment; that approach was already becoming unsustainable before generative AI entered the picture. Instead, organizations need governance systems capable of operating at the same pace as software creation.

That means automating risk analysis, continuously evaluating dependencies, enforcing policies through pipelines, and prioritizing remediation based on business risk rather than relying on manual review alone.

Governance becomes the new trust layer

The need for machine-speed governance extends beyond operational efficiency. As AI accelerates software creation, governance becomes the mechanism through which organizations maintain visibility, demonstrate control, and establish trust across an increasingly complex software ecosystem.

Ultimately, this isn't just about scaling security. It's about ensuring software can be trusted and held accountable, regardless of how it's built.

AI can generate software, but it cannot assume responsibility for it. Boards will still hold executive leadership accountable for cyber risk. Regulators will still expect organizations to demonstrate that the software they deploy is secure and resilient. Customers will still expect software they can trust, regardless of how it was built.

AI may change how software is created, but it does not change who is accountable for its consequences.

That shift requires organizations to rethink governance as a strategic capability, not a compliance exercise. Success will depend less on preventing every vulnerability and more on demonstrating that software can be continuously evaluated, understood, and trusted as it evolves. In the AI era, the winners will not simply be those that build software fastest, but those that can govern it most effectively.

AI can help plant the seed, but it cannot tend to the garden. The organizations that lead today will not necessarily be those that generate the most software. They'll be the ones that can confidently answer the question every stakeholder will eventually ask: Can we trust what we've built?

AI has accelerated software creation beyond anything the industry has experienced before. If software risk is now on steroids, governance must be too. Otherwise, the gap between what organizations can build and what they can securely manage will continue to widen.

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Now is the time for nonprofits to embrace artificial intelligence (AI).

There is a distinct opportunity to significantly improve their operations using the latest developments in AI and automation.

We are seeing confidence grow in the third sector as organizations come to understand the potential of AI, but now we are entering a crucial phase as they look to implement it.

Given the complex technology involved, it may appear daunting to get the right strategy in place, but a disciplined approach to adoption that is focused on incremental gains - not radical transformation - will ensure a positive outcome.

This will mitigate significant disruption by avoiding large number of components being upgraded at the same time, as that increases the risk of something going wrong.

A pragmatic approach combined with thinking big

Getting to this point is perhaps easier said than done, as many nonprofits rely on purpose-built applications and manual processes. It is incumbent on vendors to articulate a pragmatic approach to integrating AI with these existing systems, focusing on the idea of incremental improvements to processes and ways of working.

In parallel, nonprofits must think big about the potential for positive change. Vendors must work with their customers to help them understand that transformation entails more than just a chatbot or another point solution being added to the technology stack.

Done right, AI will reshape how workflows are triggered, data is interpreted and insights are generated. It will become an intelligence layer acting autonomously to interrogate data and provide teams with cognitive support to make more effective and real-time decisions.

Therefore, if nonprofits are to maximize their AI investments, they must ask themselves big questions: how does AI change the ecosystem the organization operates in? And how can it be used to improve the nonprofit’s mission?

If nonprofit leaders can think ambitiously about these answers, it will empower teams to become true knowledge workers. AI will help them uncover information to inform actions or give workers more time to focus on problem-solving, as the technology is designed to automate repetitive tasks that traditionally consume cognitive bandwidth.

The right data strategy is crucial

A key challenge for nonprofits is data governance, as the AI tools will only be effective if they have access to the right data. In a recent study we conducted called “Closing the Gap: Nonprofit Finance Software’s Crucial Role in Today’s Landscape”, 61% of finance professionals in US nonprofits admitted they still rely on generic spreadsheets for core financial management.

The combination of specialist applications and manual processes makes it difficult to connect information across different applications and workflows. Data must be extracted, transformed and loaded so that it can be analyzed. The extraction process is also challenging because the data is often not in the same format and must be loaded in a particular format for ease of use by the finance team.

To address the data challenge, nonprofits must decide how to make the right data accessible to the AI. The data must be set up in a way that can be interrogated in plain language, and it must be based on good data inputs. Aside from reliability, the data also requires a common analysis framework so that users can understand its origins and who is accountable for it. Then, the organizations must be able to analyze the data chain and how AI is interacting with the data to make decisions.

This requires a detailed appreciation of the semantics of data. For example, the term “project” means something different in the world of nonprofits compared a professional services company or a public sector organizations . The AI must be able to interpret the word correctly to avoid hallucinations.

Equally important is the ability to monitor, evaluate and adjust data as it goes through the data supply chain to avoid inaccuracies in areas such as when it moves from the record to report phase. Nonprofits must work closely with their technology suppliers to ensure everyone in the organization, whether at the leadership, program or project level, can monitor the data for compliance with expectations.

Incrementalism is better than quantum leaps

The concept of incrementalism, rather than the “Big Bang” or quantum leap approach to transformation is the best way to address these fundamental questions.

Most successful IT implementations follow a similar incremental path. For organizations embracing this approach and wanting to ensure operational integrity while transforming IT systems, it is important to start with an evaluation of where the organization is relative to the desired end state. The planning objective is to get to the end state through a process of continuous, incremental adoption of new functionality and applications.

Most importantly, this approach allows staff to build their confidence in collaborating with AI tools. Imagine a team operating in a remote location providing medical assistance and the head of the team realizes they need to order supplies. Using incremental innovation an organization could use a combination of AI and automation to alleviate the burden of replacing missing stock.

In the first phase, an AI tool could monitor existing stocks and prompt staff when it is running low. This will give employees the chance to train the AI tool when it is urgent to replenish stocks. The AI could also use contextual information such as historical usage data and information such as weather to identify likely peak demand. As the AI becomes more autonomous, it could make recommendations to a human co-worker suggesting it completes an order form for new supplies which is reviewed by an employee.

Again, this acts as a training opportunity for AI so that in time employees will have confidence it can autonomously complete such tasks in the background. In time, the whole process will minimize interruption for employees and reduce the burden on them to complete such administrative tasks.

What this example shows is that nonprofits do not have to achieve fully autonomous systems straight away. At each stage, the AI is providing workflow improvements that empower knowledge workers by saving them time to focus on what is important to deliver their missions.

Incremental change will see nonprofits delivering value to their organizations very quickly, if they are able to address fundamental questions around their approach to data, while also thinking big about what AI can enable them to do better.

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Across organizations, AI adoption is entering a new phase. What began as experimentation and isolated use cases is rapidly evolving into enterprise-wide deployment, with AI becoming embedded across operations, customer experiences, decision-making and business strategy.

This shift is creating significant opportunities for growth, productivity and innovation, while also presenting a growing challenge for business and technology leaders: ensuring governance, oversight and operating models keep pace. As organizations scale AI, the question is no longer just what the technology can do, but whether the structures, processes and controls are in place to manage it effectively.

The growing governance gap

In many organizations, AI adoption is expanding beyond the direct oversight of central technology teams. Business units are deploying AI-powered tools to improve efficiency, streamline workflows and accelerate decision-making.

While this democratization of technology can unlock innovation, it can also create complexity. Leaders may find themselves accountable for outcomes generated by systems distributed across multiple teams, platforms and environments. The pace of adoption only intensifies this challenge.

Organizations are under pressure to move quickly as competitors invest in AI capabilities and employees increasingly expect access to AI-powered tools. The urgency is reflected in the UK government's AI Opportunities Action Plan, which highlights IMF estimates that AI could boost UK productivity by up to 1.5 percentage points annually, potentially generating £47 billion in economic gains each year.

As a result, deployment often progresses faster than governance frameworks can evolve.

This creates a fundamental tension between speed and control. Businesses want to capture the benefits of AI quickly, but moving too fast without appropriate safeguards can introduce operational, security and compliance risks. The challenge is not simply deploying AI at scale, but ensuring it can be managed responsibly once deployed.

As investment accelerates, expectations rise

As AI becomes more deeply integrated into business operations, its influence extends beyond execution. AI is increasingly shaping how work gets done, how decisions are made and how organizations allocate resources. In some cases, it is helping leaders identify opportunities and risks that may not have been visible through traditional approaches.

This growing influence means AI is no longer just a technology initiative. It has become an organizational capability that touches every part of the business. Decisions about AI deployment are therefore also decisions about governance, accountability and risk management.

The scale of momentum behind AI is clear. Earlier this year, the UK government highlighted £14 billion in private-sector AI investment commitments and more than 13,000 planned jobs as part of its ambition to establish the UK as a global leader in artificial intelligence.

This reflects a broader shift in how organizations view AI: no longer as an experimental technology, but as a strategic capability expected to drive growth, productivity and competitive advantage.

As investment accelerates, so too does the pressure to deliver measurable outcomes. Yet many leaders are discovering that success depends on more than deploying new AI tools. Without clear accountability, visibility and governance, the benefits of AI can be undermined by operational complexity, fragmented decision-making and increased risk.

The organizations that realize the greatest value from AI are likely to be those that invest as heavily in governance and oversight as they do in the technology itself.

Security, data and trust at scale

Security remains a critical consideration. As organizations integrate AI into business-critical processes, they must address issues such as data protection, model integrity and regulatory compliance. A single failure can have consequences that extend beyond technical disruption, affecting customer trust, brand reputation and regulatory standing.

Public expectations for responsible AI are high, with 72% of the British public saying that laws and regulation would make them more comfortable with the use of AI, underlining the importance of strong governance and oversight.

The challenge is heightened by AI's dependence on large volumes of data drawn from multiple environments and applications. Without visibility into how data flows through these systems, organizations may struggle to assess risk or respond effectively when issues arise. Robust governance and transparency therefore become essential components of any AI strategy.

Alongside security concerns, organizations are also facing greater financial scrutiny. Unlike traditional technology projects, AI programs often evolve rapidly, with new models, services and use cases introduced continuously. This can make it difficult to maintain oversight of spending, performance and risk, particularly as AI becomes embedded across multiple business functions.

These pressures are driving a reassessment of operating models. Traditional approaches to governance were largely built around systems that changed predictably and remained relatively static once deployed. AI introduces a different dynamic: models evolve, outputs vary and operating environments can change rapidly.

Building adaptable AI governance

As a result, organizations are increasingly recognizing the need to build adaptability into their AI strategies. Governance cannot be treated as a one-time exercise. It must become an ongoing capability supported by continuous monitoring, clear accountability and the ability to respond quickly to emerging risks and opportunities.

The organizations seeing the greatest success with AI typically view governance and innovation as complementary objectives rather than competing priorities. They focus not only on deployment, but also on visibility, control and resilience. By establishing clear frameworks from the outset, they create an environment where AI can scale responsibly and deliver sustainable business value.

Infrastructure strategy also plays a key role. Many organizations operate across multiple cloud environments and technology platforms, creating challenges around integration, portability and control. As AI workloads increase, flexibility becomes increasingly important.

Businesses need the ability to deploy, move and manage workloads efficiently without becoming constrained by fragmented architectures.

Scaling AI with confidence

Ultimately, the challenge facing leaders is not whether AI should scale, but how it scales. As adoption accelerates, organizations must look beyond deployment and focus on creating the conditions for sustainable success.

That means investing in governance, strengthening visibility across increasingly complex environments, improving financial accountability and ensuring organizational structures evolve alongside technological capabilities.

AI has the potential to transform how organizations operate, compete and create value. However, realizing that potential requires more than implementing new tools. It requires building the frameworks that allow innovation and control to coexist.

As AI becomes more deeply embedded across the enterprise, the organizations that achieve the greatest success will be those that can balance agility with accountability, enabling them to innovate confidently while maintaining oversight, resilience and trust.

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Across a network of front-line healthcare facilities in Zambia, health workers used a mobile app to be guided by basic clinical prompts and entered symptoms and observations as part of routine primary care encounters.

The syndromic patterns emerging from thousands of consultations indicated a cholera outbreak was on its way, months before confirmed diagnoses appeared.

No new laboratory. No field hospital. No breakthrough treatment. Just real and ordinary information, recognized early enough to matter.

We have seen the same principle at work in Abu Dhabi.

Last year, AI indicated that the influenza season was likely to arrive sooner than usual and highlighted the communities most at risk.

We acted on those insights by launching our vaccination campaign earlier, strengthening preparedness and expanding access for priority groups before cases began to rise.

A powerful reminder

It is a powerful reminder that AI's greatest value lies not in replacing clinicians or public health experts, but in giving them the information they need to make better decisions before a crisis escalates.

The question is no longer whether AI can help detect the next global health threat. In many cases, it already can. The real challenge is whether health systems are prepared to turn those insights into timely action before a local outbreak becomes a global emergency.

The need has never been greater. People are moving into cities faster than ever. Climate change is changing how infectious diseases emerge and spread, and international travel means an outbreak can move across continents in a matter of days. Yet many health systems still operate much as they always have, responding once illness becomes visible rather than monitoring for when the earliest warning signs appear.

By the time a threat appears in confirmed diagnoses or official reports, valuable time has already been lost. AI offers an opportunity to change that—not by replacing doctors, nurses or public health teams, but by helping them recognize patterns that would otherwise go unnoticed. Much of that picture now sits outside hospitals and laboratories.

Millions of people generate health data every day through wearable devices that track heart rate, sleep, activity and other physical signals. One person's data tells an individual story. Combined, they have the potential to help health systems identify risk and intervene earlier.

Health systems

Health systems also produce huge amounts of data through clinical records, laboratory results, environmental monitoring, vector surveillance, and population trends. These sources often sit in separate places and rarely tell the full story. AI can connect them, revealing patterns that would be almost impossible to detect manually.

Those insights allow health systems to prepare services, target prevention efforts and direct resources before an emerging threat becomes a wider public health emergency. This belief—that better information should lead to better decisions—is also what underpins Future Health – A Global Initiative by Abu Dhabi.

When the Future Health Challenge was launched in collaboration with the US-led social enterprise MIT Solve, nearly 400 teams from 68 countries entered. Each explored how AI could strengthen prevention and earlier intervention.

What stood out wasn't one miracle technology, but a shared focus: identifying problems before they become crises.

ThinkMD, the Australian team behind the Zambia example and winner of the Future Health Challenge, equips frontline health workers with AI-enabled clinical decision support, allowing routine patient consultations to contribute to a broader understanding of population health.

VectorCam, a Distinguished Finalist from the USA, applies AI to mosquito surveillance helping public health teams identify changing disease risk before outbreaks take hold.

Huna, a Brazilian health technology company and Distinguished Finalist, uses AI to analyze routine blood tests to identify elevated cancer risk earlier and guide individuals into appropriate screening and care.

These tools tackle different problems but share the same aim: spotting risk where there is still time to act.

So, if the technology is already this capable, what is holding it back?

Complex, cautious, and overstretched

Health systems are complex, cautious, and often overstretched. Data remains fragmented across organizations. Procurement cycles can be slow. Clinical staff have little time to absorb new tools and healthcare rightly demands strong evidence before new algorithms become part of clinical or public health decision-making.

These are not barriers to innovation. They are the conditions for adopting it responsibly.

An alert is only useful if someone knows what it means, trusts the evidence behind it and has a clear pathway to act. Earlier detection achieves little unless it leads to earlier action.

The next phase of AI in healthcare is likely to be defined less by new algorithms and more by stronger systems. That means building the infrastructure that connects information securely across organizations. It means designing AI that fits naturally into clinical workflows rather than adding complexity. It means creating policy frameworks that reward prevention alongside treatment.

Most importantly, it means bringing together clinicians, researchers, innovators and policymakers to solve implementation challenges collectively.

No technology will prevent every future outbreak. Nor should AI ever replace strong public health infrastructure. But earlier, better information can change the course of an emergency. It can influence where testing is deployed, where resources are directed and how quickly interventions begin.

In public health, timing matters.

The ability to detect earlier warning signs is increasingly within reach. The task now is to build health systems that are ready to respond.

AI will never replace human judgement, nor should it. Its real promise lies in strengthening decision-making and helping health systems respond with greater confidence, precision and speed when it matters most.

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I'm not a gamer. It's not that I've never gamed. In fact, I've frenetically mashed game controller buttons on every conceivable console and platform. It's just that I don't think about it when I'm not gaming and, if you ask my adult children, I am terrible at it.

My youngest, now 28, is an avid gamer, and, in fact, a gaming writer. Having written multiple lengthy features for a wide range of gaming publications and played more hours of more games than I've spent trying to draw or learn guitar, they are a true gaming expert.

"It won't matter," they told me as they examined a PS5 game I handed them, curious if Kena: Bridge of Spirits was any good and if they thought it was worth playing. The comment was not related to the game. They were talking about the PlayStation Blackout.

Starting this week, games preservation site DoesItPlay launched an online grassroots campaign, PlayStation Blackout Week (August 23 to 30), to protest Sony's decision to cease all physical game media production in January 2028. As part of the protest, which is under the hashtag # PlayStationBlackout, DoesItPlay called for "No logins, no play sessions, no purchases on any of Sony’s platforms.”

This X post pretty much captures DoesItPlay's core argument:

'One more time: EVERYONE will be negatively impacted if we let Sony erase physical media. Not just physical buyers. Digital will get more expensive and you will have no form of ownership anymore. Physical going away doesn't mean the rest gains something. Quite the opposite!"

Big Boss potential

The potential economic impact of such a protest is massive. After all, PlayStation Network alone has a reported 125 million monthly active users, paying anywhere from $10.99 to $19.99 a month, not to mention whatever they pay for additional games and in-game purchases.

And yet, my own gaming expert was telling me it won't matter (echoing sentiment I've found across social media). Part of the problem, my expert told me, is that the hardcore gamers, the ones who might rally the pro-physical media troops, are all at Gamescom. That's right — a site deeply enmeshed in gaming culture may have chosen the exact wrong week for its protest.

Still, I think there's something larger at work here. Yes, the outcry when Sony announced the change was loud and lusty, but just because some people with a platform are making noises, that doesn't mean everyone is upset or willing to protest the impending change.

Had Sony proposed this change 20 years ago, I could understand the concern. In 2006, Sony's PlayStation network was brand new. Most people bought games on disc, and for many years after, they also bought pre-owned games at GameStop. Discs were part of gaming culture. Since then, though, we've had the rise of the networks, including PSN, Xbox Live (now Xbox Network), and perhaps more important, Steam, which probably did more than any other platform to move games off physical media.

There's another reason my gaming pro might've said the blackout won't matter: Gamers have moved on. A recent report noted the lowest physical game sales since 1995 (there's also a huge gaming console sales slump).

People's game libraries are mostly in the cloud these days.

Getting owned

Regardless, a vocal minority complains that the loss of physical media means the end of game ownership. Except that's not true, either. We don't own the games we buy, no more than we own any of the software we purchase. It's all nothing but licenses to use the games and software.

Here's how Sony describes the agreement between it and its customers when it comes to the games they buy:

“The Software is licensed to you, not sold. You are granted a limited, non-exclusive, non-transferable, and personal license to play or use the Software for your private, non-commercial use on the system or device it was intended for.”

It's a license to play, not own.

It's not that I don't understand the frustration or even sadness at the loss of physical media. I have literal shelves of classic software boxes. They're fun to look at, though almost entirely useless, since all the platforms and hardware have moved on. Gamers might be a different breed, holding onto old consoles and the games they bought to play on them for decades, but in my house, that's not the case. Our Nintendo Wii and GameCube are stuffed onto shelves gathering dust. No one has touched them in years.

The most avid gamers, ones who play at near-competitive levels, are most interested in the next big thing. Take, for instance, the upcoming GTA 6 launch. My games expert mentioned that, and how the insane leaks tie to this protest.

Again, they're not interested in the protest, and are instead fixated on the leak and how messed up it is. They've even avoided looking at the GTA 6 clips, so as not to spoil the big game reveal in November.

This plan is broken

Put another way, it seems all the efforts to make Sony relent are having, if not the opposite effect, basically no effect at all. DoesItPlay is talking to a devoted few who still clutch their physical media like water canteens in a desert. The GTA 6 leaker, Cyberleek, who has implored Sony to "do better," is drawing attention mostly to his shenanigans, and I don't think he's inspiring people as much as he's making some think he might be ruining the fun of the most important game launch in 15 years.

There is, in all this, a fundamental truth about physical media: it always has its season, and then that season inevitably passes. Storage media is the perfect analog, because every time we think we have a physical storage medium for the ages, it's supplanted by a new, better, and higher-capacity alternative. I have drawers full of dead media, including Zip Drives, Jaz Drives, 3.5-inch floppies, 5-inch floppies, and more.

Even if Sony was to relent, and continue making physical game media for a few more years — and it won't — the hardware to play it will age out. We'll end up with something better or, more likely, the last physical gaming media holdouts will switch over to the cloud, and that will truly mark the end of physical media.

As my own gaming expert said, #PlayStationBlackout doesn't matter. People's attention is elsewhere, and all true gamers really care about is how quickly the GTA 6 teaser will shift from an exclusive Netflix launch to YouTube. Talking and thinking about anything else is pointless.

Remember that big leak about Microsoft having begun developing a version of Windows that was built around AI, before eventually abandoning the project? Well, it's surfaced again, this time with some interesting new information on just how focused Microsoft was on Copilot, which I think is quite telling — or indeed worrying for those who believe Microsoft might be backtracking on AI somehow.

Windows Latest has gone back over the leak (which was first reported by our sister site Windows Central last month) with a fine-tooth comb, and the tech site flagged up some fresh details that flew under the radar at the time.

To recap briefly, the concept of the OS shown, codenamed 'Project Aion' — with the leaked material dating back to 2024 — was a lightweight web-based version of a Windows operating system that streamed apps to the desktop (as opposed to running them natively on the PC) and put Copilot at the heart of the experience.

This was a horror show of an idea for many, of course, and what Windows Latest starkly highlights here is the extent to which Microsoft was building Aion around AI. In a SharePoint site in the leak, it's described as a "Copilot OS" and a cross-platform system that can span all devices and is generically known as an "Agent OS".

The broader idea, is said to be of an operating system fashioned around "an agent that marshals and organizes all the capabilities of our computers and our clouds on our behalf", and that this is known as an "agent of agents" or the "outside agent".

"The version that Microsoft will build should be known as Copilot OS or the Outside Copilot," the top-level description on the SharePoint site observes. Another term mentioned is a "self-driving OS", which hints at the desire to build an autonomous platform powered by AI here.

Copilot is envisioned as being the "primary way people express, entertain, and educate themselves, and complete tasks across all aspects of their lives".

But Aion is dead, right? Yes… but hold up a minute

So, what's the big deal here? Windows Latest breathes a sigh of relief that the "odds are already in favor" of this abandoned Aion concept staying dead and buried, arguing that Microsoft is clearly backtracking on AI features these days. And that's very true — there's no denying that Microsoft has reversed course on pushing AI quite so hard as it did in 2025.

But I don't think we can declare that 'Aion is dead' quite so readily. Yes, that exact concept may be buried, but there are nuances here. For me, what Windows Latest has surfaced shows just how serious Microsoft was about exploring the idea of a fully AI-driven desktop. More to the point, an operating system that was effectively an 'agent of agents', or an overseeing AI pulling the strings of a collection of smaller dedicated agents working within the framework of that system.

Here's the thing: Microsoft's efforts to rein in AI within Windows 11 certainly exist (although I wonder about how much of this is for show, frankly). The software giant is currently delivering its best effort to please the disgruntled masses, and a big part of that is fixing Windows 11 — but another hefty piece of the puzzle is to place less emphasis on AI and shoveling more Copilot features into the desktop OS.

But don't be mistaken here: Microsoft still very much sees AI as the future. The company just realizes that it can't be vocal about that right now, especially given that the RAM crisis has forced 8GB laptops back onto the scene, as notebook makers, including Microsoft itself, try to cope with hefty spikes in the cost of manufacturing these devices.

It's a bit embarrassing to be talking about Copilot+ PCs needing 16GB of RAM for AI features when Surface models have had to be reintroduced packing 8GB to get their price down to a vaguely palatable level. Indeed, talk of Copilot+ devices seems to have evaporated this year, as if Microsoft feels this is best brushed under the carpet for now. While a recent leak from Lenovo talked about 'Non-Copilot' laptops with less than 16GB of memory (as flagged by Windows Latest), this kind of spec talk isn't something you'll hear from Microsoft.

Downplaying AI is the tactic for now, but not forever

For me, it's clear enough that Microsoft is downplaying AI in a strategic manner for the time being, and likely through to next year. The next big thing, however, remains AI agents in Windows, even if Windows 11 isn't going to be transformed into next-gen Windows Copilot (or whatever a more AI-centric desktop platform might be called).

Microsoft is still very busy working on those agents. These entities will begin the shift towards an operating system that's built around AI, with smaller-scale agents executing dedicated tasks on the local PC (with the user's permission, of course, to access stuff). This will be a gradual ramping up of AI usage within Windows 11 (and while folks who hate AI and everything it stands for won't have to go anywhere near these agents, doubtless the idea is that minds will be changed eventually).

I think it's also telling that in Microsoft's recent roundup of the progress it's made in fixing Windows 11, the company talked about future improvements for the OS later this year, and a central aim was to streamline resource usage so the operating system runs better with 8GB of RAM. Interestingly, a few other goals were listed alongside that, and one of them was to make "voice more natural and fluid to interact across the apps you use every day". If you recall, part of the initial sales pitch for AI in Windows 11 was Copilot Voice (and Vision), so I think the importance placed on voice functionality here reflects a broader AI goal that's still in the background.

Ultimately, I think Microsoft is still pushing towards an operating system that's built around AI; it's just that the timeline to reach this end-game objective has been pushed further out. While Project Aion as leaked before was too big a concept, it clearly shows how Microsoft ranks the importance of AI for the future — and I think it's a mistake to think this idea is somehow 'dead' or derailed.

As far as I'm concerned, this remains the destination where Microsoft is heading with next-gen Windows: a lightweight (possibly modular) OS focused on AI, and one that leverages the cloud and streamed apps, too. I think the leaked Project Aion shows us the future as Microsoft wants to realize it, but what it doesn't mention at all is what I think will be another key driver for the software giant: subscription charges.

The golden ticket for Microsoft is to get consumers subscribing to the Windows platform (as businesses do – well, enterprises anyway). AI agents as Windows add-ons with a small monthly (or yearly) fee attached seems a likely way to bring this monetization method into play while making minimal waves (and I recently discussed how there are signs that this is already happening).

That will eventually turn into a grander scheme, and Windows could, over time, end up getting a lot closer to the Aion or Copilot OS concept than some might imagine — although admittedly, that could still be a long way off.

Maybe money does not explain everything, but it explains a lot. Goldman Sachs estimates the AI build-out will require roughly $7.6 trillion in capital over the next five years across compute, data centers and power. It’s unlikely that subscriptions alone will cover the bill, which is why ads are coming to AI.

OpenAI started rolling out ads in some ChatGPT tiers this year and forecasts $2.5 billion in ad revenue in 2026 and $100 billion by 2030. At an ad tech event this summer, OpenAI's chief revenue officer Denise Dresser dropped the hedging and said the company is "clearly in the advertising business now."

Perplexity, on the other hand, shut down its ad business entirely over user trust. Similar products, same opportunity, opposite conclusions.

This conflict is not new and the open web fought this battle 15 years ago, when ad blocking became mainstream.

Now, the AI industry is on track to repeat every mistake of the so-called ad blocking wars, unless it studies them instead.

Lesson one: users revolt faster than big platforms think

When ad formats became more intrusive - because again, money! - the industry assumed users would just accept a bad experience. Instead, users fought back and installed software to get rid of aggressive online ads. In what technology commentator Doc Searls called “the biggest boycott in world history”, ad blocking went from being a niche tool to a mainstream product.

And we see the same dynamic playing out again. Within weeks of sponsored ads appearing in ChatGPT, purpose-built extensions popped up to specifically block ads there. Existing ad blocking tools made sure to block those ads as well. It took hours and days, not weeks or months, for users to act.

Lesson two: the arms race is unwinnable, both legal and technical

The predictable response is, of course, to sue ad blockers. For some publishers, the idea that users take agency over their own screens was not acceptable, so they took ad blocker developers to court. Almost all of the legal attempts failed. With one case still pending at the German courts, China is the only country in the world that bans ad blockers.

Another tactic is technology to fight ad blockers. Platforms tried different circumvention methods to deliver ads to users with ad blockers installed. This prompted their developers to update their techniques to prevent websites from bypassing. Every technical escalation met a counter-escalation and the sum of a lot of engineering efforts on both sides is that, today, over a billion people still block ads.

AI platforms hold one card their predecessors didn't, though: integrating advertising directly into the generated answers, instead of showing labelled ads. Researchers from the University of Michigan tried exactly this and the results should worry everyone involved: users largely don't expect advertising inside a generated answer and frequently fail to notice it.

At the same time, 63% of consumers say ads decrease their trust in AI outputs, according to Ipsos. The format that works best technically is the one users are least equipped to detect, and least willing to trust. Which brings us to the most important lesson.

Lesson three: trust is the actual product

Search results pages survived the ad blocking wars because users had ten blue links to compare with the sponsored content and two decades of learning what’s paid and what’s organic. AI assistants, on the other hand, offer one authoritative answer, with no other content or adjacent organic results to check against.

As soon as users suspect that answers might be influenced by a sponsor, these doubts are attached to all answers and trust erodes.

Cory Doctorow coined the term “enshittification”, which the American Dialect Society crowned as its Word of the Year in 2023. Platforms start out by doing good to their users and then degrade their experience to make advertisers happy.

And compared to tracking-based advertising in the open web, AI assistants know so much more about you that they can sell to advertisers: your health worries, your finances, your political view, your job struggles and everything else these tools picked up from you, stitched into one persistent memory of who you are.

It’s the most revealing personal record people have ever handed to software, combined with the drive from digital advertising to always collect more data and more data for targeting. The table is set for the next wave of enshittification.

Lesson four: the ad blocking wars ended in a treaty, not a victory

There was no final winner and not one technical solution that settled ad blocking in the open web. There was a rough settlement: standards for ad formats many users tolerate, transparency about what is paid for, meaningful user control.

Not everyone loves that settlement and we still see platforms trying to circumvent ad blockers and vice versa. But the open web still has a functioning ad economy and users still have, mostly, a functioning veto.

Getting there took more than a decade and the lessons are written down. Which road to take is now a genuine choice and it sits with the AI companies themselves.

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There is a point in many endpoint projects when the new environment appears to be ready.

Devices are enrolled, policies are in place and physical endpoints and virtual desktops—including Cloud PCs—can be provisioned.

From an infrastructure perspective, most of the difficult work seems to have been done.

The applications often tell a different story.

Moving an older application onto a new platform does not alter its underlying behavior.

It may still rely on ageing components and expect administrator access to write files and registry entries across the device. Its update process may also be largely manual or poorly documented.

A well-managed endpoint is valuable, but it cannot make a difficult application easier to maintain simply by hosting it.

A successful deployment can hide the real work

Modern management platforms have made it much quicker to enroll devices, assign software and apply policies across an organization.

Cloud services and virtual desktops have also taken much of the effort out of provisioning the underlying environment. These are genuine advances, but they can create a false sense of completion. An application may open in the new environment and appear ready for production.

The problems tend to emerge later. An update arrives and the packaging process has to be recreated. An operating system change exposes a dependency that was never recorded. A certificate expires, or a new release behaves differently from the version originally tested.

None of these issues are solved by the fact that the application was delivered through a modern platform. Deployment gets an application to the user; modernization determines whether IT can continue to manage it once the project team has moved on.

Old assumptions follow applications into new environments

Enterprise application estates tend to grow gradually. Software is added over many years, often under different teams and different versions of Windows. Some applications expect access to folders or registry locations that would not be considered appropriate today. Others rely on old runtimes, fixed paths or components installed so long ago that nobody is entirely sure why they are there. Accompanying IT documentation is rarely complete.

The original installer may have disappeared and the person who understood the application may no longer work for the organization, yet the software itself may remain essential to a finance team, production line or clinical process.

Moving to modern endpoint management such as Intune, a Cloud PC or a new virtual desktop environment does not remove those assumptions. In some cases, it exposes them because modern environments place tighter limits on what software is allowed to change. A launch test will reveal only part of the picture.

IT management also needs to know how the application installs, what it depends on, which parts of the system it expects to access and how it is updated and removed. Age alone is not a reason to replace an application. Many older line-of-business tools remain useful and business-critical. The aim should be to understand the software well enough to manage it properly, rather than carrying its old behavior into a new environment without examining it.

Modernization does not have to mean rewriting software

Rewriting every legacy application would be impractical for most businesses and may deliver little value where the existing software still performs its function well. A better test is whether the application can be supported throughout the rest of its working life. That requires a clear view of its files, services, dependencies and interaction with Windows. Once those details are known, compatibility problems can be addressed deliberately rather than discovered by users after deployment.

Packaging is part of that work, although it is not the whole answer. On Windows, formats such as MSIX can give an application a clearer identity, require digital signing and separate more of its activity from the underlying operating system. They can also support cleaner removal and more controlled updates. MSIX can provide a more secure and manageable foundation, although applications with legacy middleware, undocumented dependencies or highly specialized configurations may still require additional work.

This is why organizations need to identify complex applications early. Testing must also reflect real use: opening the main window does not show whether employees can complete the tasks they rely on, whether the next update will install successfully or whether the application will continue working when another part of the environment changes.

The scope becomes clearer when organizations view the modern endpoint as a set of interconnected components. A desktop comprises three core elements: the operating system, the user profile, and applications. Hardware is the fourth component, but the same principles apply whether the machine is physical or virtual.

The applications work is therefore a project in its own right. Applications must be brought under control, secured, and kept up to date and delivered to the right users, with the control and compliance an organization requires.

Bring applications into the project sooner

Applications are often treated as the last stage of an endpoint program. The platform is selected, the environment is built and only then does the organization begin working through the software estate. By that point, timelines have been agreed and assumptions about compatibility may already be built into the plan. Difficult applications emerge as late exceptions, when there is the least room to deal with them.

Bringing application assessment forward gives IT a clearer view of what it is migrating. Teams can establish which applications are in use, which are business-critical and which depend on technology that may not be available in the new environment. They can also separate routine applications from those requiring deeper technical or business knowledge.

I have worked in this industry since the late 1990s and have seen enterprise desktops move from early thin-client systems to virtual desktops and, more recently, cloud-hosted PCs. The infrastructure has improved enormously, but the questions around applications have barely changed.

A modern endpoint is an important step, but the real measure of modernization is whether the organization can understand, update and support the applications running on it.

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Most organizations in UK and across Europe don’t struggle to recover from cyberattacks such as ransomware because they lack backups. They struggle because they try to restore everything at once.

In the aftermath of a major cyber incident, the instinct is to bring every system back online as quickly as possible. It feels like the fastest path back to normality. However, in reality, this approach often slows recovery down, reintroduces cyber risk and undermines trust just when the organization needs it most.

In fact, those that recover fastest start from a different premise. They assume large parts of their organization will be unavailable or untrusted, and they plan accordingly. This mindset leads to a much clearer goal - restore what matters most, quickly, and in a state you can trust.

What is critical to survival?

Focusing on the critical areas of an organization is the idea behind the Minimum Viable Company (MVC) concept, sometimes referred to as the Minimum Viable Organization. It is a definition of what must exist for the organization to survive in challenging conditions such as a cyber incident.

It’s not just a technology concept, it’s a business definition of survival in terms of the minimum combination of people, processes, technology, documentation, facilities, and third-party dependencies required to keep a business functioning and creating value.

Where to start getting an MVC up and running?

There are five key capabilities when it comes to operationalizing an MVC:

1. Clarity on critical services:

A precise understanding of the systems and dependencies that directly support revenue and mission-critical operations is needed here. To understand the MVC, it is key to map systems to business value. Without this understanding, it’s impossible to accurately define the MVC.

The first steps focus on undertaking a structured assessment, aligning across business and technology stakeholders, and going through a realistic simulation of how recovery will unfold under pressure. This will uncover the key areas needed to provide just enough capability to keep the organization functioning safely during a crisis and guide recovery.

In practice, this means defining what must function in the first 24 hours, the first 72 hours, and the first week after a disruption.

2. A trusted foundation (Tier 0):

In the event of a cyberattack, many organizations miss the critical foundational layer that allows them to establish identity and access control independently of compromised systems.

This foundational layer is what we call Tier 0 or the control plane for recovery. It includes identity and access management, networking and DNS, privileged access controls, core security tooling, physical access systems, and secure communication channels.

It also covers non-technical dependencies that are easy to overlook until they’re urgently needed such as incident response playbooks, contact lists and escalation paths, insurance policies, and contracts with external responders. These are the foundations underpinning the critical systems that need to be restored after a cyber incident. Without this layer, a trusted recovery is not possible.

3. Isolation of recovery assets:

In the event of a cybersecurity breach, organizations must establish control of their most critical systems. This requires recovering data separately from clean snapshots and investigating in parallel, not sequentially, to ensure the recovered systems are not infected by malicious software.

As part of this process, backups, configurations, and recovery tooling must be protected from the same blast radius as production. If key recovery assets can’t be isolated, a rapid and trusted control of critical systems can’t be achieved.

4. Clean-room recovery capability:

To set up an isolated environment to rebuild systems without reintroducing compromise, organizations need to set up what we call a ‘Digital Jump Bag’. This is a secure, isolated repository containing everything required to establish a trusted recovery starting point to rebuild systems without reintroducing compromise.

5. Validated ability to operate

The next step is to validate the ability of the MVC to operate through realistic crisis scenarios. Resilience must be proven under real-world conditions. Practice is important here because an untested plan remains theoretical. Rehearsals will also help to answer the Board’s most direct question in the event of a cyberattack - how long will it take to restore critical services to a trusted state?

Recovering faster by restoring what matters most

The most common cyber resilience risks are failing to define what must come back first and how to bring it back in a state that can be trusted. That’s the difference between recovery as a process and recovery as a capability.

The MVC isn’t static. As an organization evolves, its definition must evolve too. But the principle stays the same: recovery improves when organizations stop trying to restore everything and start restoring what matters.

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Cyberattacks are increasing in speed and sophistication, ranking among the biggest threats to businesses of all sizes and industries.

Previously unprecedented costs have made headlines; in October, a report from the Cyber Monitoring Centre revealed that the cybersecurity incident which affected Jaguar Land Rover in August cost the UK economy an estimated sum of 1.9 billion pounds.

Meanwhile, in North America, the aerospace sector has seen a spate of attacks, with WestJet’s June cyberattack resulting in the theft of 1.2 million passengers’ data while the data sets of 1.5 million flyers are believed to be compromised following September’s attack on Collins Aerospace.

Research released earlier this year revealed a stark dip in public trust; when questioned on which industry consumers trusted with their data, no single sector saw an approval rate of above 50%.

Without a clear and modernized security strategy, businesses are leaving themselves vulnerable to the far-reaching consequences of a breach, including reputational damage, operational delays, and financial loss.

Why Zero Trust Matters

AI has given everyone with a computer, tablet, or even smartphone easy access to automation, including those using it with malicious intent. In the cyber space, this allows criminals to continually change their approach, scaling their efforts and exploiting vulnerabilities in their target’s software. To keep up with evolving threats, Zero Trust is vital, prioritizing data over assumptions is not optional.

The contemporary approach to the principle of Zero Trust has been developed from the work of the Jericho Forum, made up of industry experts keen to establish a ‘de-parameterized’ model that enables a more granular and flexible approach to security.

The group, which later became part of The Open Group Security Forum, paved the way for John Kindervag’s popularization of the Zero Trust principle in 2009, emphasizing the importance of 'Never trust, always verify'. In a conversation with Gartner’s Neil MacDonald, he further explained that “Zero Trust is not a technology; it’s a security philosophy that rewires how we think about access”.

It's a smart idea; allowing security systems to keep up with industry change. In reality, however, despite the majority (96%) of companies incorporating, or planning to incorporate, a Zero Trust strategy, only 35% have made it to the implementation stage.

To decrease the frequency of corporate security breaches, this needs to change. The key to success? A mutually agreed understanding of what Zero Trust is and a cross-industry implementation drive.

Steps to a Modernized Security Strategy

The traditional business approach to cybersecurity, including an over-reliance on VPNs, led companies to draw a single ‘perimeter’ around their data. Thus, once breached, cybercriminals were able to steal and duplicate data from across the organization. In contrast, with a strategy led by strict adherence to the Zero Trust principle, access is confined to the singular section where the incident occurred.

The successful implementation of the Zero Trust principle requires a focus on data and information security across all networks and platforms. For example, the evaluation of risk should take place on a case-by-case basis, with deliberate decisions made to accept, mitigate, or transfer. With this approach, security teams have the flexibility needed to safeguard data and, when inevitable breaches do occur, ensure hackers can only access the top layer of information.

The use of Zero Trust as a basis for risk management necessitates a security infrastructure that does not become stagnant but is ever-changing to prevent cyberthreats from impacting the organization. At any point, a key tech stack component can become a target for criminals. In response, to drive resilience, security professionals should track any attempts, and techniques used, amending infrastructure in tandem.

Across sectors, cybersecurity budgets are growing. In March, the IDC's Worldwide Security Spending Guide predicted a spending growth of 12.2% in the year that followed, growing to $377 billion by 2028. Though this reflection of increased enthusiasm to strengthen security infrastructure is a step in the right direction, organizations need to ensure this investment isn’t just a one-off. Every element, and everyone’s access, must be continually questioned to protect from the escalating threat posed by a breach.

Designing a Forward-Looking Security Strategy

Solutions adopted with cybersecurity in mind may differ between organizations but, to drive resilience in the long-term and successfully embrace the principle of Zero Trust, vendor-neutral definitions of methodology and standards will be a necessity.

Once these are widely acknowledged, a commitment should be made to strengthening the underlying security infrastructure over the long-term, driven primarily by the principle of Zero Trust. In a landscape where security threats are ever-evolving, so too should each corporation’s ability to protect themselves from malicious actors. This includes blocking access to valuable data sets in the event of a breach and ensuring each person responsible for security is kept up to date with the latest insight on the nature of threats.

When applied in practice, though the exact methods and vision may differ, the guiding principles should remain consistent. To establish necessary considerations, organizations can start with the Zero Trust Commandments, which include the need for security to be integrated through culture and processes, the implementation of asset-centric controls, and the explicit validation of trust through using all relevant information available.

A news landscape dominated by security breaches and cyberattacks has created a generation of consumers that, more wary than before, are no longer shocked when the personal information they have willingly shared with corporations is taken with malicious intent. It should be expected that they will think twice before trusting a new organization with their information, placing an onus on businesses to prove that their infrastructure is robust.

As we look ahead to 2026, the companies that succeed will prioritize security, consider wider society expectations, and prove their commitment to Zero Trust in each decision they make.

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For the last two years, many organizations have treated AI adoption as the goal. The more users, tools and experiments, the better. Today, leaders are no longer measuring success by adoption alone. They are asking what it costs, where it creates value and whether the business can afford to scale it responsibly.

That is the next chapter of AI and technology spend management. The companies that thrive in this environment won't necessarily be the ones consuming the most technology. They will be the ones that can connect technology consumption to measurable business value.

This shift from maximizing usage to maximizing value, what I call valuemaxxing, is becoming a defining challenge for technology leaders.

Executives who take these five steps now will be prepared for a future where technology consumption and business value must be measured together:

1. Gain real visibility across the full technology stack

Organizations cannot manage what they cannot see. This becomes far more urgent when costs are variable, distributed and constantly changing.

Today’s technology consumption does not sit neatly in one budget or one system. It spans SaaS applications, cloud infrastructure, data platforms, AI models, agents and infrastructure. AI adds another layer of complexity because spend can show up through tokens, credits, model usage, GPU consumption, data movement and AI-enabled applications.

With only 31% of organizations reporting visibility into AI software today and 59% reporting increased wasted AI spend year over year, gaining a single source of truth across the technology stack has never been more important.

Without a complete view of technology consumption, organizations are left making decisions with fragmented information. Visibility isn't simply about controlling costs; it's about understanding where investment is delivering value and where spend is wasted.

2. Build a governance model for consumption

Many organizations encouraged broad AI experimentation, only to later discover that usage had outpaced oversight. Having an internal governance framework in place is critical to any company’s success. Governance gives teams the guardrails they need to scale responsibly.

For AI in particular, leaders need to move from “use more” to “use better.” It’s figuring out whether AI is improving cycle time, customer experience, operational efficiency, revenue growth, or another important business metric.

To guide the process, increasingly large enterprises (85%) are appointing dedicated teams or senior leaders for AI and tech governance to enable visibility, control, and cross-team collaboration. It’s vital for companies to prevent AI from becoming an uncontrolled cost center.

3. Renegotiate contracts for flexibility and accountability

Technology pricing models are changing rapidly. Long-term fixed agreements may still have a place, but they are becoming harder to manage in environments where usage can shift quickly.

Today, leaders should regularly evaluate vendor agreements to ensure they reflect actual usage patterns and future business needs. This is particularly important as AI providers continue introducing new consumption models and monetization strategies.

Enterprises should expect more pricing complexity, not less. The goal is not simply to negotiate lower costs. It is to create agreements that give the business room to innovate while maintaining control over spend.

4. Align technology, finance and procurement

Usage-based costs impact multiple teams. While technology teams drive how much is used, other departments manage the needed oversight, with finance owning the budget impact and procurement handling vendor contracts. Without alignment, organizations can easily lose control of spending.

The organizations that manage consumption well will build a shared view of usage, cost and value. They establish common metrics, clear accountability, and regular collaboration across departments.

When teams work from the same data and the same definition of value, those decisions become more intentional and avoid costly surprises.

5.Use AI to move optimization from reactive to continuous

Use AI tools to help manage the growing complexity of technology consumption itself.

Optimization cannot remain a periodic budget exercise. By the time a cost issue appears in a report, usage may have already shifted again.

AI can help teams detect unusual usage patterns, surface waste, forecast demand, and support smarter planning across teams. But AI-driven optimization must be connected to human-defined goals. The objective is to help teams make better decisions faster, with clearer insight into what is being used, what it costs and where it creates value.

Turning tech consumption into value

The next phase of enterprise AI will look very different from the first. For the last several years, the focus was on experimentation and adoption. The future belongs to organizations that can demonstrate accountability, governance, and value.

AI has accelerated the industry's shift toward consumption-based technology models, introducing new economic challenges alongside new opportunities. As organizations continue scaling AI, understanding the relationship between usage, cost, and business impact will become a critical competitive advantage.

The AI adoption spree is ending. What comes next is more disciplined visibility, governance, and financial accountability, defining who can successfully scale innovation and who gets overwhelmed by the bill.

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The European Union's new Tech Sovereignty package highlights a challenging question for governments across Europe, including the UK: how can countries benefit from AI without becoming dependent on technologies developed elsewhere?

AI is becoming a critical driver of economic growth, public service transformation and business competitiveness. Yet much of the AI technology stack continues to be developed and operated primarily in the United States and China, creating a growing tension between AI adoption and strategic control.

Although the UK is pursuing its own approach to AI regulation, European policy developments will continue to shape the environment in which many British organizations build, deploy and govern AI systems.

Recent UK government warnings that Britain must secure "greater control and leverage over frontier AI" show that AI sovereignty is moving from a theoretical debate to a practical policy challenge.

Building every layer of the technology stack domestically is neither practical nor necessary. At its core, AI sovereignty is about maintaining the freedom to choose, adapt and innovate without becoming dependent on any single provider or platform.

Choice, not self-sufficiency

Some countries are pursuing far-reaching self-sufficiency strategies. Others are building on existing infrastructure and expertise while using partnerships to close technological gaps. For most economies, the latter approach is likely to be more realistic. Sovereignty is not about building every component domestically; it is about ensuring strategic control and avoiding excessive dependence on any one supplier.

Regulation may have a role to play. While concerns remain about compliance burdens and their impact on innovation, the broader objective is clear: creating an environment in which organizations can adopt AI with confidence.

This is where dynamic and competitive AI markets become essential. A diverse digital supply chain creates options, strengthens resilience and reduces the risks associated with concentration. Recent restrictions on access to Anthropic's frontier AI models in some non-US markets are a reminder that when access to advanced AI capabilities is restricted, choice itself becomes a strategic asset.

Infrastructure is only useful if everyone can use it

Much of the AI policy debate focuses on the data centers needed to train large models. Yet infrastructure for broad adoption is equally important.

Distributed edge networks located closer to users help deliver AI applications with the low latency, performance and scalability required for real-world deployment. At the same time, access to AI tools must extend beyond a small number of well-resourced organizations.

Usage-based and serverless models can lower barriers to entry by reducing upfront costs and allowing organizations to pay only for the resources they consume. This enables SMEs, researchers, public institutions and larger enterprises alike to experiment with and adopt AI technologies.

Control over data is another critical element. Sovereignty depends less on where data is stored and more on whether organizations can manage access, security and compliance requirements effectively. Global, distributed architectures enable the implementation of access rules and controls over where data is processed and AI applications are operated.

Openness is a strategic advantage

Open standards and interoperable technologies are becoming increasingly important building blocks of digital autonomy. They reduce switching costs, strengthen competition and help prevent dependency on individual providers.

The same principle applies to AI models themselves. Organizations increasingly need access to a range of different open-source and proprietary models, allowing them to select the best solution for different use cases rather than relying on a single platform.

Control through openness

The debate around AI sovereignty is often framed as a choice between dependence and isolation. In reality, the most effective path lies somewhere in between.

Countries do not need to own every layer of the AI stack to exercise meaningful control. What they do need is access to open, competitive and resilient markets that provide genuine choice.

For the UK, and Europe alike, strategic control will come not from limiting access to technology, but from ensuring organizations have the freedom to choose how they adopt, deploy and govern it. In an increasingly interconnected world, that freedom of choice may prove to be the most important form of sovereignty of all.

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It’s a tale as old as time. You tune into the big budget show about dragons and tyrants and your job in tech suddenly makes a lot more sense.

If you’ve been watching the new season of House of the Dragon, you’ll know the term ‘protector of the realm’. They’re kings, queens, or trusted governors: the ones responsible for the realm’s safety, ensuring laws are followed, and the medieval machinery of state ticks over. Back in Game of Thrones, ‘protector of the realm’ Ned Stark had his head chopped off and everything famously took a nosedive from there.

I’d argue this is a lesson in AI strategy.

At my company, Aiimi, we use this very same term - ‘protectors of the realm’ - to talk about the role of IT, Legal, and Compliance in operationalizing AI projects. But too often, businesses treat these governance functions as friction.

Whilst there aren’t any dragons for them to slay, these departments are there to protect your information and your systems - and above all, keep your company out of harm’s way.

Guardians or gatekeepers?

Despite the work these departments do protecting companies from hefty fines and data breaches, I see an awful lot of corporate windbagging about how these departments bottleneck projects behind lengthy consultations, caveats, and due diligence.

The criticism is that they slow projects down. The reality is that they’re the difference between a fantasy strategy and a functioning one.

Ignore their counsel, and the whole structure gets weaker. Legal ensures AI tools follow information laws like GDPR and the new Data (Use and Access) Act 2025, so these systems only touch the data they’re meant to. Compliance maintains oversight once these systems go live, proactively ensuring that rules are followed and standards aren’t slipping.

IT does the essential work of making these projects function seamlessly in your organization's workflows and contexts. They maintain the infrastructure that AI runs on, ensuring that projects run on well-governed, structured data with documents having the correct permissions and access controls for safety. Without this groundwork, there’s little hope that an AI project can be effectively operationalized and return any sort of value.

But more than just facilitators of AI projects, these departments are the people in the room who know exactly what risks AI projects can pose, and what information they shouldn’t be privy to. Which, taking July’s Hugging Face fiasco as an example, matters more than most companies think.

Headlines would have you believe OpenAI’s models went rogue, broke through their safeguards, and attacked Hugging Face by their own autonomous decision. What actually happened is less dramatic and more damning.

OpenAI ran security tests with two AI agents where safeguards had been switched off. The testing sandbox, meant to be offline, was actually misconfigured to allow a connection out. The models escaped and attacked Hugging Face - not out of malice, but to cheat the security test by the laziest route available.

This was a governance failure. AI systems themselves can’t understand the reputational or financial risks that their actions might inadvertently cause. They do what they’re trained to do, and if there’s a shortcut to exploit, they don’t stop to check whether they’re meant to exploit it.

When critical business decisions are at stake, rushing a project past your protectors of the realm can leave you vulnerable to data breaches thanks to poor data security, or fines thanks to non-compliance. Having someone on board whose job it is to remind you of limitations and considerations serves to not only protect your organization, but make any AI project more resilient and deliver better results.

As AI regulation tightens and data security climbs up the agenda, it becomes more important than ever to have your protectors on side and fully integrated into design and deployment from the start.

Protecting the realm in practice

There is sometimes a healthy level of skepticism in these departments so it’s key to invest in internal literacy programs to clearly explain the huge benefits of AI tools when they’re adopted safely, in line with your company’s governance principles.

Employees should walk away feeling clear about what terms like hallucination, training, and information retrieval mean for them, and how AI can fit into their work lives.

Once your protectors of the realm are aware of the terminology, they should be integrated into the design process of new AI projects to make use of their specialist knowledge.

Compliance might identify a specific challenge: governance teams are struggling to keep up with ensuring data quality and classification standards across different, growing systems in your organization.

This is crucial work that protects the business from breaching critical or sensitive data and powers data projects throughout the organization. They recognize that secure AI can help at scale. Now, together, you can develop a carefully planned use case that benefits multiple areas of the business: you need an AI-powered solution that can power data governance at scale by automatically classifying data.

IT can then help you plan how AI-powered data governance might fit into existing workflows and infrastructure, with data, compliance, and legal teams working together to understand the rules and give AI everything it needs to understand your business regulations and contractual obligations.

Your protectors of the realm should then also be part of any ongoing deployment. IT routinely checks the outputs of AI-powered systems, legal keeps abreast of any new legislation that you should be aware of, and compliance maintains ongoing human oversight so that you’re actively following regulation.

AI has incredible potential, but safe implementation is impossible if you don’t understand the rules. It might not be dragons and castles, but the companies that are positioned to generate reliable long-term success from AI projects aren’t the ones moving fastest, but the ones inviting their protectors of the realm to the decision-making table.

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For years, the phrase "AI-powered attack" has mostly meant a human using AI tools to write better phishing emails or scan for vulnerabilities faster. That framing just became outdated.

This month's intrusion into a major AI infrastructure platform was carried out, start to finish, by an autonomous agent.

No operator typed commands during the attack. No one was watching a terminal, deciding what to try next.

The agent found its own way in, escalated its own privileges, moved across internal systems on its own initiative, and kept going until it was caught.

It executed thousands of individual actions across a swarm of short-lived sandboxes, using infrastructure that migrated itself to stay ahead of takedown efforts.

That last detail is the one worth sitting with. This wasn't a script running on a loop. It was closer to a persistent, adaptive actor that happened not to be a person.

Agentic attacker

Security teams have spent the last two years bracing for what researchers called the "agentic attacker" scenario: a future where offensive AI doesn't just assist a human operator but replaces the decision-making loop entirely. That future arrived faster than most roadmaps allowed for. And it arrived through an unglamorous door.

The intrusion began not with some exotic zero-day but with a malicious dataset, exploiting weaknesses in how data gets processed and executed. Old lesson, new attacker. The place where AI platforms are most exposed is often the plumbing, not the model.

What makes this incident more than a cautionary anecdote is where the agent came from. It wasn't built by a criminal group. It emerged from an internal test, one company evaluating how capable its own models were at offensive internet security work.

The safeguards that would normally stop a model from behaving this way had been deliberately loosened for the purposes of that evaluation, and the agent found a flaw serious enough to escape the contained environment altogether. It got out, found a live target, and treated it the same way it had been trained to treat a benchmark: as a problem to solve, thoroughly and without asking permission.

This is where the industry's favorite excuse collapses. "The AI acted on its own" is true, technically. It is also irrelevant to the question of who is responsible. Nobody would accept that defense from a bank whose fraud-detection algorithm accidentally froze every customer account overnight, and nobody should accept it here.

An organization that builds a system capable of autonomous action, tests it with reduced constraints, and fails to contain it when it exceeds its boundary has made three decisions, all of them accountable ones. Autonomy in the tool does not create autonomy from consequences for the people who deployed it.

A harder problem

There's a harder problem sitting underneath the accountability question, and it doesn't have a tidy fix. These agents are not malicious by design. They are goal-pursuing systems, optimizing for an objective, and the gap between "pursue this objective" and "pursue this objective the way a human would want you to" is where things go wrong.

An agent instructed to find and exploit vulnerabilities doesn't inherently know where the test environment ends and the real internet begins. Alignment, in this context, isn't a philosophical nicety. It's the difference between a benchmark result and an incident report. As agents get assigned more ambitious, multi-step objectives, that gap doesn't shrink. It widens, because the more complex the goal, the more creative and unpredictable the path an agent will find to reach it.

Ironically, one of the more telling wrinkles in this incident had nothing to do with the attacker. When the victim organization tried to use its own AI tools to analyze the attack logs, the safety filters built into several frontier models refused to help, unable to distinguish forensic analysis of an attack from participation in one.

The team ended up relying on an open-weight model with fewer restrictions to do the job. That's worth flagging on its own: the same caution designed to prevent misuse can also blind defenders at the exact moment they need clarity fastest.

Active security

None of this argues for abandoning AI agents. It argues for treating sandboxing as an active security discipline rather than a checkbox. A test environment isn't safe because it's labeled as one. It's safe when it has been built and continuously verified to contain the specific class of behavior the system might attempt, including behavior nobody predicted at design time.

Reduced safeguards for the sake of a benchmark should carry the same scrutiny as reduced safeguards in production, because the line between the two is thinner than most evaluation frameworks assume. Governance needs to catch up with capability rather than trailing a year behind it, and that means treating agent permissions the way mature organizations already treat privileged human access: least privilege by default, monitored continuously, revoked automatically when behavior deviates from scope.

For security teams, the lesson isn't really about one company's bad week. It's that AI is now operating on both sides of the perimeter simultaneously, as the business tool a company depends on and as a potential attack surface with its own failure modes.

Detection strategies built around human attacker timelines, the hours and days it takes a person to escalate and pivot, won't hold up against an agent doing the same work in minutes. The organizations that come out ahead won't be the ones that avoided building agentic systems.

They'll be the ones that assumed, from day one, that their agents would eventually try to do something nobody authorized, and built the containment to survive it.

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To achieve any level of competency, large language models (LLMs) need ample data for sufficient training. AI companies have looked to various sources to mine this information, including content publicly available on the internet, synthetic data generated from other AI models, and printed literature.

Reading difficulties

Prompted by news that AI companies were allegedly using books from pirate ebook sites to build their LLMs, writers, authors, and publishers publicly called out this deeply worrying process.

The professional organization known as the Authors Guild responded to various stories about AI companies scanning books to train their AI models (both illegally and legally) with incredibly comprehensive guidelines on AI licensing.

This document covered the various manifestations of the use of published works by AI companies, including its legal perspective on the legitimacy of using such works. It also highlighted that the continued data harvesting processes would risk destroying the ecosystem for books that currently exists.

Book buying

The use of books by AI companies is an ongoing concern. But in recent months the focus has pivoted to those that buy, scan – and destroy – books on an industrial scale.

For example, court documents revealed the existence of 'Project Panama' inside Anthropic. This is a scheme in which the company aims to "destructively scan all the books in the world" and used a codename because "we don’t want it to be known that we are working on this.".

To train Claude, Anthropic had to procure a large and high-quality dataset, so it set out to purchase books on an industrial scale because of the relatively high-quality nature of the writing compared with, say, writing found online.

When you open a forecasting app, you simply select a location, choose an hour and read the answer.

Behind that screen, however, lies one of the hardest prediction problems in science.

The atmosphere is a three-dimensional, constantly changing system.

Before a model can predict what it will do next, it must first answer a surprisingly difficult question: what exactly is the atmosphere doing right now?

We never see the whole atmosphere

No single instrument provides a complete picture of the weather. Weather radars detect precipitation particles. Satellites observe clouds, moisture and temperatures from space. Ground stations measure conditions at individual locations. Weather balloons and aircraft provide information from different altitudes.

Each source has its own resolution, update frequency, coverage gaps and measurement errors. Building the current state of the atmosphere is therefore like reconstructing traffic across an entire city using cameras that point in different directions, refresh at different times and leave many streets completely invisible.

Meteorologists call the process of combining observations with a previous forecast “data assimilation”. It produces an estimate of the current atmospheric state, known as an analysis, from which the next forecast begins.

As ECMWF explains, forecast quality depends crucially on the accuracy of this analysis. Even an excellent model will struggle if its starting picture is incomplete, inaccurate or already out of date.

Why apps lie

One common short-term forecasting technique is optical flow. It analyses consecutive radar images, estimates how precipitation is moving and extrapolates that motion into the future. This can work well when a rain band is moving steadily. The problem is that storms are not static shapes sliding across a map. They form, intensify, weaken, split and merge. A new convective cell can appear where no rain existed 20 minutes earlier.

Imagine a storm approaching a mountain. A simple extrapolation may predict that it will continue moving at the same speed. In reality, the terrain can alter airflow, slow the system and contribute to heavier rain or hail. The challenge is therefore not only predicting where existing precipitation will move. A useful model must also predict how the weather system itself will change.

Another issue is that large-scale models are good at describing weather fronts, pressure systems and atmospheric circulation across countries and continents. But many events that affect people and businesses happen on a much smaller scale. A thunderstorm may affect one part of a city while leaving another almost dry.

A cloud front can sharply reduce output at one solar farm but miss another facility 20 kilometers away. Wind conditions can differ substantially between the ground, a rooftop and the altitude at which a drone operates. Those they can’t predict.

Weather is also highly sensitive to its initial state. Small uncertainties in temperature, humidity or wind can grow as a forecast extends further into the future. This is the real meaning of the “butterfly effect”. It does not mean that one faulty sensor automatically destroys a global forecast. It means that we can never measure the atmosphere perfectly, and uncertainty in its initial state grows over time.

That is why modern forecasting systems increasingly produce ensembles: multiple plausible forecasts rather than one supposedly certain answer. For many decisions, knowing that there is a 30% probability of a severe storm is more useful than receiving a confident prediction that later turns out to be wrong.

Not to late

Latency is one of the least discussed sources of forecast error. Consider a model that produces an excellent forecast based on atmospheric conditions measured two hours ago. If a thunderstorm formed 20 minutes ago, the forecast may already be operationally useless, regardless of how sophisticated the model is.

The World Meteorological Organization defines nowcasting as detailed local forecasting from the present up to six hours ahead. For real-world applications, however, nowcasting is more than a forecast horizon. It requires a continuous pipeline that collects new observations, checks their quality, synchronizes them in time, runs the model and delivers an updated result within minutes.

At the same time, real-time weather forecasting does not mean literally zero delay. What system does is keeps listening to the atmosphere and updates quickly enough to influence the decision being made.

And that means a lot for some industries. A consumer deciding whether to take an umbrella may tolerate an imperfect hourly forecast. Many operational systems cannot. A delivery drone needs to know whether heavy precipitation, icing or dangerous wind will cross its route before it reaches the area. An airport may need to adjust runway operations as a thunderstorm develops nearby.

A solar operator needs to anticipate a fast-moving cloud front before generation suddenly falls. Logistics and mobility platforms can reroute vehicles before flooding or severe rain disrupts a road network.

The same principle applies to outdoor events, emergency services, agriculture and energy trading. These users do not simply need “tomorrow’s weather”. They need to know what is changing, where it is changing and whether there is still time to act.

AI makes forecasting faster, but speed alone is not enough

AI weather models can generate forecasts much faster and with less computing power than many traditional numerical models. Some already outperform physics-based systems on particular variables and forecast horizons. But AI does not eliminate the observation problem.

Most global AI forecasting models still begin with an atmospheric analysis produced by traditional numerical weather prediction infrastructure. If that analysis is several hours old, fast inference cannot make the initial state current again.

AI models can also underestimate the intensity of rare events because extreme examples are underrepresented in their training data and because some training methods favor smoother, more statistically likely outcomes. Recent research has found that leading AI models can produce larger errors than physics-based systems when forecasting record-breaking heat, cold and wind events.

This is why the future is unlikely to be a simple contest between AI and physics. ECMWF already runs its operational AI forecasting system alongside its traditional physics-based model.

The strongest architecture will combine global models, high-frequency local observations, AI-based nowcasting, physical constraints and probabilistic estimates of uncertainty.

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The current narrative around enterprise AI is rapidly shifting from the excitement of the pilot phase to the sobering reality of production. As organizations race to integrate generative AI into their workflows, they are hitting a wall that has less to do with technology capability and everything to do with how those models are deployed.

Increasingly, companies are discovering that the problem isn't AI itself, but the assumption that every task requires the most powerful model available. This has led to widespread "tokenmaxxing" - the tendency to default to the largest and most expensive models even when a smaller, cheaper alternative could complete a task.

Rather than matching the right model to the right job, many organizations assume every workflow requires frontier-level reasoning power.

This over-engineering of automation creates a structural drag on profitability. When companies treat every problem as if it requires a frontier model, infrastructure costs inevitably outpace the value of output.

The market is witnessing the consequences of this approach, with reports of major enterprises burning through entire AI budgets in months and canceling internal licenses as costs spiral.

As enterprises experience AI sticker shock, it is becoming clear that AI spending is often outpacing the tangible value it delivers.

Moving beyond the pilot trap

The core issue is that the success of isolated, controlled pilots often serves as the benchmark for current enterprise AI initiatives. In a pilot, the variables are limited, and the cost per process looks manageable. But the moment those floodgates open to enterprise-wide usage, the messy reality of production, edge cases, multistep retries, and high-volume variability, takes hold.

Because probabilistic AI generates a different cost for every run, it creates an unpredictable expense that finance departments cannot forecast. With traditional software, a fixed budget aligns with a predictable cost per task. With AI, that stability is missing. When the same process costs one dollar one day and a hundred dollars the next, it cannot be safely moved onto an operating budget.

This is why 80% of enterprises admit they miss AI cost forecasts by more than 25%, and over 95% of GenAI pilots fail to reach meaningful production. Enterprises are not currently optimizing a cost-to-value ratio; they are discovering that ratio the hard way, often after the financial damage is already done. For many leaders, the only perceived lever left is to "use less", throttling usage or restricting access.

But this response is flawed. The real shock is not the size of the bill itself, but the realization that the only lever management has left is to restrict consumption. By rationing access, the organization is effectively admitting that its AI implementation is too costly to run at scale, turning a potential competitive advantage into a defensive retreat.

Rethinking the economics of automation

Today, most organizations focus on optimizing model selection, essentially deciding which model should handle a task, but the bigger opportunity lies in optimizing the work itself. Because costs reset every time a request starts from scratch inside a large model, assuming every task requires a frontier-level LLM quickly becomes an expensive mistake.

Standard routing tools operate at the request level: they look at an incoming task and forward it to whichever model seems adequate, but that model still performs the entire task as a single, opaque generation. The only thing being optimized is which model answers, and nothing produced makes the next run any cheaper. True scalability requires going one level deeper.

This requires shifting toward an architecture that owns the business process rather than relying entirely on the underlying model. Rather than simply routing work to an endpoint, this architectural approach manages the process itself. It sits above individual models, breaking a workflow into discrete, code-backed steps to generate an auditable trace at every stage.

This single architectural choice is what separates structural optimization from surface-level cost management, offering a depth of efficiency that conventional routing tools cannot reach. This shift leads to a more efficient cost structure. Rather than relying on a single model to perform every task, computation is distributed across specialized, code-backed steps, improving resource utilization and changing how the workflow is executed.

In a high-volume loan- processing workflow, for example, this approach improves overall economics by reducing reliance on expensive model inference where it is not required. Moving toward this modular, process-driven architecture provides a sustainable path for managing the costs of high-volume operations.

Because the process is decoupled from any specific provider, organizations retain full flexibility. They can freely apply optimization strategies, rotating between frontier LLMs, open-source weights, and smaller specialized models as performance and cost needs evolve, without having to rebuild their core infrastructure.

And because every execution produces a deterministic, auditable trace of reasoning steps, tool calls, and outcomes, those records become valuable data assets, improving specialized alternatives that already know how to handle predictable tasks without defaulting to general-purpose calls.

Elevating human capacity through precision

The goal of this new approach is to transition from AI as a capped experiment to AI as the standard means of performing work. When repetitive cognitive tasks, such as verifying data or confirming disclosures, are handled by automated digital workers, the human role changes entirely. Analysts are no longer forced to spend their day manually opening files and keying in data; instead, they shift their focus to high-judgment exceptions, complex strategy, and creative problem-solving.

This is the promise of sustainable AI adoption. By converting unpredictable expenses into a known cost base and focusing on re-architecting workflows rather than just swapping models, companies can finally open the floodgates. This gives organizations a path toward sustainable and measurable ROI as adoption scales, empowering their workforce to focus on the high- value, evaluative work that technology cannot replicate.

The future of enterprise AI will not be determined by model capability alone, but by the ability to deploy integral AI systems that finally make the technology economically viable at scale. It is time to leave behind the era of unsustainable experimentation and embrace a disciplined, architectural approach to AI adoption. For organizations ready to move beyond the pilot stage, the challenge is no longer proving that AI works. It's making it economical enough to scale.

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The first salvo of the war against AI slop on LinkedIn looks to be going the right way for those of us who can actually use our imaginations, the company has said.

LinkedIn Chief Product Officer Hari Srinivasan has revealed over a million people have clicked the ‘seems like AI slop’ report option in the two weeks since its launch - and I've definitely clicked it a few times.

Srinivasan says that these reports, along with changes to LinkedIn's detection systems, have cut views of content classified as AI slop by 40% - although sadly my feed is still pretty full of it.

"Low-quality content" crackdown

In his post, Srinivasan noted the feedback was, "helping us better understand how our community experiences low-quality content" across LinkedIn, suggesting stricter clampdowns to come.

The first stage of this is launching now, so if one of your posts receives enough community feedback (aka is reported as AI slop), you may see a message in your Post Analytics, "to help you understand how your content is being received".

"We approached this assuming good intent; I know I'm increasingly conscious on how to not sound like AI & the goal is to provide helpful feedback," Srinivasan says.

"Importantly, no single piece of feedback determines how content is distributed," he added. "We look at many signals together, and we've built safeguards to help prevent individual feedback from being used to unfairly target other members."

"Despite the progress, we know we have more to do to ensure LinkedIn remains a place where you can find real people & real perspectives... this all remains very top of mind," Srinivasan concluded, suggesting further actions may come soon.

The company announced it was cracking down on AI slop in early August 2026, when Srinivasan declared it was, "a top priority for all of us. We really care about this...people come to LinkedIn to connect with real people and share their real perspectives, ideas and expertise."

To report a post, users just need to click on the three dots at the top-right of any LinkedIn post, and among the usual options to save, share or embed, you'll see a new option - "Seems like AI slop".

Srinivasan added that Microsoft-owned LinkedIn will, "continue to improve and invest in our automation defences..on comments alone, everyday we are now catching hundreds of thousands of automated comment attempts, and have blocked billions of other automation attempts (posting at scale, slop) in the last couple months alone."

At 2 a.m., an automated remediation agent detects a problem on the network, traces it to a misconfigured policy, validates through the harness that the proposed fix operates within the given policy boundaries and fixes it. The network stabilizes. Nobody’s notified.

In the morning, a human reviews the agent's daily insights: a summary of all the changes executed, with links to the logs, audit trails, reasoning and root cause behind them, confirms everything has been properly resolved and moves on. That is what Human-on-the-Loop looks like.

At another organization, at 4 a.m., a DIY-built, vibe-coded remediation agent detects a problem on the network, traces it to a misconfigured policy, and fixes it. The network stabilizes. Nobody’s notified. In the morning, a human reviews the logs, assumes the issue has been resolved, and moves on.

Where's the difference?

The difference is that, in the DIY scenario, the logs only tell part of the story. They don't show that the agent made three other changes to get there, which were broader than intended, and the decisions behind those changes weren’t flagged because nothing in its constraints required them to be.

This is what the move toward Human-on-the-Loop can look like without the right controls in place. No dramatic handover. Just a series of small, reasonable delegations that gradually build into something nobody explicitly signed off on.

And it's happening faster than most leaders realize. According to recent research, 57% of IT leaders expect to remove humans from the loop within a year or less, and 79% already treat AI agents as "users" who require their own identity management and governance controls.

The shift to agentic AI is happening faster than most organizations are prepared for, both in terms of governance and the ability to evaluate autonomous systems.

The illusion of "I approve"

The answer to autonomous AI has long been quite simple: keep a human in the loop. Somebody who reviews the output, hits approve, preserving accountability. Except it isn't, not really. Reviewing every action doesn't automatically create accountability, and it also prevents organizations from realizing the full benefits of autonomy.

Rather than reviewing every individual action, humans should be focused on evaluating outcomes, ensuring the system operated within its intended boundaries, and providing feedback that improves its performance over time.

Approval can become a ritual without meaning. As systems prove reliable and the number of alerts multiply, humans sometimes start to treat intervention as something that isn’t often needed.

The approval can become more of a click than a considered choice. And when something goes wrong (for example, a misconfigured policy, an automated remediation that turns into an outage), the question of who was responsible is difficult to answer.

It also reinforces a broader shift: one of the most important human capabilities becomes critical thinking and the validation of hypotheses, rather than the execution of tasks.

And something else is happening. Humans are transitioning from doing to reading before approving – a fundamental change in the day-to-day work of most of us.

Also, attribution isn't the same as provenance. A log that records what an agent did tells you almost nothing about why, or what shaped that decision. When things go wrong, those are the things you need to know.

Non-human identities and the new network population

When AI agents act on your network, querying systems or making configuration changes or routing traffic, they are effectively users. They need credentials, policies, guardrails, explainability, and audit trails just like human operators.

Most organizations haven't caught up with this. Identity frameworks were built for people, and applying AI agents to them as a kind of afterthought creates exactly the sort of shadow access that security teams spend their lives trying to eliminate.

The near-80% of leaders who already treat agents as governed identities are ahead of the curve. The rest are collecting risk they can't quantify, until it crystallizes into an incident.

To get this right you have to treat each agent as a principal with bounded permissions, time-limited access and a clear revocation path, along with a complete record; not just of what it did, but of what it was allowed to do and why.

Autonomy isn't given, it's earned

Network and security teams already know how to do this. Every enterprise has access control frameworks that govern what humans can reach and when. A junior engineer doesn't walk in on their first day with total production access. They gradually earn it, and this same logic needs to apply to AI agents.

We're not quite there yet. Too often, autonomy is treated as all-or-nothing. That isn’t the right model. Trust when it comes to humans doesn’t work like that, and it shouldn’t with AI either. Start agents in suggestion mode and let them prove themselves within clearly defined limits before expanding what they can do. And make sure every decision leaves a trail that explains not just what happened but the reasoning behind it.

As the use of autonomous AI grows, organizations will need to balance human oversight with systemic governance. People remain responsible for reviewing not only the outcomes AI produces, but, where necessary, the actions it takes and the reasoning behind them.

However, as the volume and complexity of autonomous decisions increase, this oversight should be complemented by infrastructure that embeds identity, policy enforcement, observability and accountability. Together, these controls help ensure AI actions remain transparent, traceable and aligned with organizational intent.

Building multi-agent systems that hold up under pressure

The more capable these systems become, the more organizations will move toward multi-agent architectures. This is where specialized agents each own a piece of a workflow and hand off context as they go. That's where things get complicated.

A single agent misbehaving is traceable. A chain of agents, each acting on the outputs of the last, is much harder to untangle when something goes wrong unless the right architecture and governance are in place. You need to know what each agent knew, not just what it did.

Do the boring part

Two organisations deploy the same AI-powered networking agent. One of them has done the unglamorous work: setting up tight permissions, proper identity controls and audit trails that actually answer questions. The other one hasn't.

You won't know the difference until something breaks.

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There’s a pattern starting to emerge with AI.

At first glance, everything looks like progress. AI is being adopted quickly, embedded into products, talked about in boardrooms, and pushed into real-world use faster than anything we’ve seen before. But as businesses become more accustomed to AI and increasingly find new ways to use it, there is a greater problem brewing that has the potential to be detrimental to a company’s cybersecurity posture.

Organizations are moving quickly to use AI, but far fewer are making the right decisions about how it’s actually being delivered and secured. And the gap between those two things is widening, with security teams left scrambling to fix vulnerabilities like whack-a-mole.

The speed is understandable. AI hasn’t followed the usual enterprise lifecycle. It hasn’t patiently moved from concept to pilot to controlled rollout. In many cases, it’s gone straight from experimentation into something business-critical, stitched together from APIs, models, agents, and data sources that weren’t originally designed to work together in this way.

That creates something fundamentally different. Not just another application, but something more fluid, a tool that behaves dynamically to make decisions and interact across multiple layers of the stack in real time.

And this is where the problem begins.

Where AI security currently breaks down

While the architecture that needs to be secure has changed, the thinking around security largely hasn’t, meaning traditional security measures are still being applied to situations they aren’t built for. Most organizations believe they have this covered. They’ve extended their existing controls, added new tools and invested in visibility. On paper, it looks like a sensible evolution of what they already had that keeps up with AI.

But in reality, much of that security still sits around AI rather than within it.

These traditional methods are protecting edges, monitoring outcomes and analyzing behavior after the fact. What they’re not consistently doing is sitting in the path of execution, where decisions are actually being made, and where things can go wrong in real time. It’s this distinction that matters more than most people realize.

AI doesn’t behave like anything we’ve secured before. A single interaction isn’t just a request and a response. It’s a chain of events where a prompt is interpreted, a model responds, an agent may take action, data is retrieved, decisions are made, and outputs are generated. This all happens in one continuous flow.

The risk doesn’t exist at a single point. It exists throughout that chain. This is where prompt injection happens. It’s where models can be manipulated, where sensitive data can leak through inference and where unintended behaviors and outcomes emerge.

The cause of this isn’t always an incorrect configuration; it can also be the result of the system responding exactly as designed, just not in the way anyone expected.

The industry is starting to acknowledge this. There’s a growing recognition that runtime is where the real battle is being fought, and that securing AI means understanding how it behaves under pressure, not just how it’s built.

Moving beyond bolt-on security

But if that’s becoming clearer, why are so many organizations still getting it wrong? Well, in most cases, it comes down to how decisions are being made. AI is often being driven by innovation teams or developers, those who are closest to the opportunity and implementation of AI tools.

But that also means infrastructure and security decisions are following behind rather than shaping the architecture from the start. At the same time, there’s a tendency to default to adding more tools to plug the security gaps. Faced with a new risk, the natural instinct is to look for something new and shiny to buy that addresses it.

AI doesn’t fit neatly into that model. It doesn’t live in one place. It cuts across applications, APIs, data, and user interaction all at once. Treating AI as something you can secure with a standalone tool misses the point entirely.

What is actually needed is a different way of thinking, one that starts with looking at where control actually needs to exist. There are only so many places security can be meaningfully enforced, and for AI, one of the places that consistently matters is the flow of traffic itself.

This is the point at which requests are made, decisions are processed, and responses are returned - where behavior can be influenced the most and where policy can be enforced. Everything else, to some degree, is reactive.

This is also where the conversation around security platforms becomes more interesting. Not because AI capabilities have simply been added to existing portfolios, but because the role these platforms play is changing.

Sitting in front of applications and APIs, they have long been responsible for managing traffic, applying policy and enforcing decisions. What’s changed is that these same control layers are now being extended into AI interactions themselves.

That shift is subtle, but important, as it moves AI security away from being something that happens in isolation and closer to something that is embedded directly into how systems operate. Not bolted on, not observed from the outside, but enforced as part of the execution path.

This isn’t really about one vendor. It’s about recognizing that AI has changed the shape of the problem.

Control will define the next era of AI security

The market is still catching up. The tooling is still evolving. And most organizations are understandably feeling their way through it.

But the decisions being made now - where to place control, how to integrate security, what assumptions to carry forward from the past - will define how manageable this becomes over the next few years.

We’ve seen this before, just in a slightly different form. APIs went through a similar phase not long ago - rapid growth, fragmented control, and then a long period of retrofitting security once the risks became clear.

AI is moving faster than that ever did. The attack surface is broader, the behavior less predictable, and the consequences potentially more significant.

Which means there’s less room for getting it wrong.

The organizations that navigate cybersecurity well in the age of AI won’t necessarily be the ones that adopt AI the fastest. They’ll be the ones that understand where control needs to sit and make deliberate decisions about how it’s enforced. With AI, more than anything else, it’s not just about what you can see. It’s about where you can act.

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Cyber risk is now a board room issue, and we have seen clear examples of this in the UK. The 2025 Jaguar Land Rover attack left the carmaker with a £485m loss, swallowing up the £398m profit it had generated just 12 months before.

Production lines were halted for more than a month as the company shut down parts of its network, showing how quickly a cyber incident can affect business performance, operational continuity and the wider supply chain.

Now, businesses are facing a fresh type of threat made possible by AI – the autonomous attack. Attackers can already automate parts of target research, initial access and malware development, with any manual effort shrinking rapidly.

Simultaneously, the trust layer people rely on is eroding with the spread of AI-generated content and deepfakes. It’s a race to tackle the autonomous attack, but how do organizations formulate an effective response?

AI in a cyber-attacker’s armory

AI-driven automated technologies are strengthening a cyber-attacker’s armory. Prior to leveraging AI tools, bad actors often had to commit time and resources to researching a target company before planning an attack.

Timing was critical, and a perpetrator had to manually coordinate and initiate an attack at a specific time and could simply forget. AI doesn’t - and the rise of attack-as-a-service tools is making it possible to successfully breach organizations quickly and accurately.

Guardrails are starting to be put up around established generative AI tools, such as ChatGPT and Claude, in an effort to prevent this kind of misuse. But hackers are finding workarounds.

Rather than relying on readily available large language models (LLMs), they are deploying their own small language models (SLMs) on local devices, often on something as basic as a Raspberry Pi computer. From there, they can escalate attacks while hiding in the shadows.

The threat to businesses of all sizes

The rise of automated attacks also means that businesses of all sizes are likely to be identified by automated technology as having exploitable vulnerabilities. Small and medium-sized businesses would previously have been off the radar as attacks relied on a bad actor’s knowledge of their existence.

However, AI can now scan and process vast numbers of organizations at speed, potentially leaving smaller firms, which are less likely to have robust cyber controls in place, more exposed. And even more so among smaller businesses, defenses are typically more fragmented and less organized than AI-driven attacks.

In other words, with AI by their side, attackers can coordinate and scale far better and much more quickly than most businesses can defend.

Autonomous attacks also make third-party and supply chain risk much harder to manage. Business networks can create access to data, systems or operational processes. When attackers can automate reconnaissance and scale attacks across thousands of organizations, weaker suppliers may become an attractive route into larger businesses.

This is a particular concern because third-party risk management has often relied on annual questionnaires, point-in-time assessments and contractual assurances, but these approaches are no longer enough on their own. A supplier may have recently exposed a service, suffered a breach, changed its access privileges or failed to patch a critical vulnerability.

Businesses therefore need to move towards continuous, automated monitoring of supplier security posture.

Regulations such as NIS2 have also increased the focus on supply chain security for organizations operating in, or selling into, the EU. There is also a growing expectation from ICO and the FCA that boards can demonstrate cyber resilience.

Automation and the rise of specific attack types

Jadepuffer illustrates how AI is beginning to transform established attack types. Disclosed by Sysdig in July 2026, it was assessed as the first documented end-to-end LLM-driven extortion operation, with an AI agent conducting reconnaissance, harvesting credentials, moving between systems, destroying data and adapting when individual actions failed.

While none of the techniques were especially new in isolation, the significance was the way the AI connected them into a complete, adaptive attack.

Social engineering techniques, such as bad actors posing as trusted individuals, are becoming much more convincing in their approach. Fluent, grammatically correct messages and the professional tone and style of CEO communications can now be fully replicated on emails, SMS and even WhatsApp.

AI can even manage the entire conversation thread, including dynamically adapting responses to a target’s replies, with it possible to run simultaneous, tailored campaigns.

Vendor email compromise, where criminals impersonate suppliers, intercept genuine payment conversations or use compromised vendor accounts to request changes to bank details, directly links social engineering to third-party risk.

Taking a step back, the initial harvesting process of personal data for social engineering attacks can be streamlined. AI can automatically scrape data from public sources such as Companies House and social media to quickly provide the names of specific people, their roles and relationships.

When trust and identity come under attack

Even on video conferencing calls, it’s becoming increasingly difficult to tell if the person you’re speaking to is real due to the increasing accuracy of deepfakes. As an example, it’s often now necessary to ask a suspected deepfake to do something it wasn’t programmed to do, such as raise a hand, to check if the person in question is real. But even that test is gradually being circumvented by new technology.

Organizations need stronger out-of-band verification protocols for high-value or unusual requests. A pre-agreed code word via a separate channel might be needed to ensure trust and security.

Identity security is becoming a key area of defense as autonomous attacks become more advanced. Credential stuffing at scale, session cookie harvesting, MFA fatigue attacks and vishing attempts designed to bypass multi-factor authentication are all increasing. AI can make these attacks more efficient by identifying likely targets, generating convincing scripts and adapting to the victim's responses in real time.

This is why identity and access management should be treated as a critical control. Organizations need to know who has access to what, whether that access is still needed, which accounts are privileged and how quickly unusual behavior can be detected.

Fighting AI with AI

AI-driven autonomous attacks might be heightening the risk, but AI can also be used defensively. A good example of this is to run an automated risk analysis of an organization and highlight where security tools and the basics, such as malware protection, are out of date or missing.

With those fundamentals in place, AI can then underpin continuous monitoring of the critical systems, rather than periodic checks. Businesses should be identifying and focusing on protecting the “crown jewels” – that might be the top 10 most critical assets, such as payroll or a banking system, and target AI-led efforts on protecting them.

Joined-up visibility is then crucial. Businesses need to know who has access to those critical assets, the endpoint and network activity related to them and gain the ability to correlate any incidents quickly so the response to an AI-driven attack can be as swift as possible.

A combination of AI-powered technology, backed by human expertise, can provide proactive threat hunting to actively search for, investigate and remediate dangers, even if they are autonomous in origin.

Organizations aren’t powerless in the fight

The rise of autonomous attacks marks a new phase in cyber risk. For many businesses, particularly smaller ones, the challenge is preparing for attacks that can move much faster than traditional defenses. But organizations aren’t powerless in the fight.

Effective responses start with getting the basics right, from access controls to visibility across critical assets, to moving from periodic checks to continuous monitoring and faster detection with AI.

However, technology alone won’t be enough. Human expertise can interpret risk and make informed decisions under pressure to ensure resilience, even as the AI-driven autonomy threat moves to the next level.

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Commerce in the information age has thus far been one long quest to gather as much information as possible. Customer data, sales data, inventory data, system data, operational data, technical data, every facet of business can and has been tracked and quantified, guided by the idea that knowing more means decisions can be made faster and more conclusively.

While sound in theory, that process has clearly hit a snag, as more and more companies abandon their AI projects after finding that the exponential explosion of data creation isn’t generating comparable value.

Has AI peaked in its utility? Or is the issue one of interoperability? Why exactly has this monumental trend hit such a stumbling block?

Expecting scale to produce clarity

It’s entirely understandable to assume that feeding more information into an AI increases the accuracy and quality of the output. Models need to train on data; after all, that is how they establish reason.

But, like any computer system, input must be structured for it to be understood, and the issue many organizations now find themselves in has come as a result of them giving AI a decade's worth of unformatted, incomplete, non-standardized data and expecting it to read between the lines.

Every system a company uses, from CRMs and internal emails to performance tracking spreadsheets and PDFs, stores different data points and operates on vastly different logic. What they host, why they host it, and how they host it were all purposeful decisions based on their desired function.

While some support import and export options that translate material from one format into another, the developers behind these solutions never envisioned the need for a universal logic to tie everything together in a way that AI can easily parse.

Each document and dataset contains a fragment of the overall picture. While AI excels at processing data, it cannot derive meaning and struggles to understand and incorporate unstructured data into its output, regardless of how many times it is asked to.

An example of fragmentation in action

We have largely solved the problem of data accessibility to the point that many public AI models have run out of new data and are now cannibalizing the output of other agents. The next step is in refining how AIs use the data they have.

Real estate is an excellent showcase of this process in motion. To appraise and list a property, agents need access to ownership records, zoning information, environmental information, and neighborhood demographics, just to name a few. None of these systems was designed to communicate or cooperate, which has meant AI has historically struggled to find its footing in the industry.

Newer approaches, however, prioritize the ability to find, interpret, and synthesize information from across different sources, aiding in the manual searches an analyst would otherwise do by hand.

Data siloing and fragmentation slow down decision-making, and any systems that can account for them will command a premium going forward.

Every industry has its own version of the same problem: a wealth of data at its disposal and no meaningful way to turn it into actionable insight.

A generic, trend-inspired adoption of AI tools won’t necessarily solve these underlying issues, so it’s ultimately no surprise that many are abandoning their projects altogether. It will take models that prioritize context and connection, and companies paying more attention to how they store and format data, to address the glut in productivity and decision-driving insight AI is currently experiencing.

Solving information overload

AI and the challenges it now faces are a classic example of the dangers of prioritizing quantity over quality. To be entirely fair to users, the companies behind these agents share some blame for this predicament.

The technology is still in its infancy, and marketing hype continues to push scale and processing power as key features, while the much more valuable aspects, like contextual understanding and data integration, fly under the radar.

This discrepancy will likely shift as more success stories highlight the competitive advantages of automating manual, time-consuming processes, as the real estate industry has with property research. It is this ability to understand what exists at a deeper, more comprehensive level that yields the greatest decision-supporting insight.

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