Over the last year, one topic has dominated conversations across the cybersecurity industry: artificial intelligence.

Every week seems to bring another announcement, another capability, or another prediction about how AI will transform security. In offensive security, the discussion has become particularly intense. We are seeing AI-assisted vulnerability discovery, AI-generated attack simulations, AI-powered analysis tools, and increasingly bold claims about autonomous security testing.

The question I am asked most often is surprisingly simple:

“Will AI replace penetration testers?”

My answer is equally simple: No.

What AI will do is change how penetration testers work.

And in many ways, it will make experienced penetration testers even more valuable.

AI is already changing security testing:

Let’s start with the obvious.

AI is genuinely impressive.

Modern AI models can process vast amounts of information, identify patterns, summarize findings, correlate data sources, and surface potential issues far faster than any individual analyst could achieve manually.

Within offensive security, AI is already helping teams:

• Identify vulnerabilities more quickly
• Analyze large datasets
• Correlate findings across environments
• Surface potential attack paths
• Generate documentation and reporting
• Reduce repetitive manual tasks

These are meaningful improvements.

Many of the activities that traditionally consumed valuable consultant time can now be accelerated significantly.

As a result, organizations are gaining greater visibility into their environments than ever before.

But visibility alone has never been the ultimate goal.

Finding vulnerabilities has never been the hard part:

One of the biggest misconceptions in cybersecurity is that finding vulnerabilities is the primary challenge.

It isn’t.

Understanding risk is.

Most organizations already have access to large amounts of security data. They run vulnerability scanners. They receive penetration testing reports. They consume threat intelligence. They deploy attack surface management tools. They monitor logs and alerts.

The problem is rarely a complete lack of information. The problem is understanding what matters.

• Which vulnerabilities are genuinely exploitable?
• Which attack paths represent realistic threats?
• Which issues require immediate remediation?
• Which findings can safely wait?

These questions are considerably harder to answer than simply identifying a vulnerability. And they are questions that require context.

Context is where human expertise matters

A vulnerability rarely exists in isolation.

The real-world risk associated with any finding depends on a range of factors, including asset criticality, business impact, compensating controls, user privileges, environmental configuration, attacker motivation, and the relationships between multiple weaknesses.

This is where experienced penetration testers provide value that AI alone cannot replicate.

When performing an assessment, we are not simply identifying vulnerabilities. We are thinking like attackers.

We are asking questions such as:

• How would I gain initial access?
• What would I target next?
• How could I chain these weaknesses together?
• What data could be accessed?
• How difficult would exploitation actually be?
• What is the likely business impact?

These decisions are rarely straightforward. They require judgement, creativity, and experience.

Two organizations may have the same vulnerability present within their environments, yet the associated risk could be dramatically different depending on the surrounding context.

Understanding that difference is where human expertise becomes critical.

The future isn’t autonomous testing:

There is currently a great deal of excitement around autonomous security testing. The idea is appealing. Feed an environment into an AI model and receive a complete understanding of risk in return.

The reality is significantly more complex.

Attackers do not operate according to predefined workflows.

• They adapt.
• They improvise.
• They exploit unexpected opportunities.
• They combine seemingly insignificant weaknesses into meaningful attack chains.

Successful offensive security assessments require the same flexibility.

While AI can assist with analysis and discovery, security testing remains fundamentally an exercise in understanding human behavior, business context, and attacker decision-making. These are areas where human expertise continues to outperform automation.

For the foreseeable future, I believe the most effective approach will be human-led, AI-assisted testing. Not human versus AI. Human plus AI.

AI should make penetration testers better:

The conversation should not be about replacing penetration testers. It should be about enabling them. When repetitive activities are automated, consultants can spend more time focusing on the areas where they create the greatest value.

Instead of manually processing information, they can spend more time:

• Investigating attack paths
• Validating exploitability
• Understanding business impact
• Identifying complex attack chains
• Advising clients on remediation priorities
• Delivering meaningful security outcomes

In many respects, AI allows skilled security professionals to operate at a higher level. It augments expertise rather than replacing it. The result is not fewer penetration testers.

It is more effective penetration testers.

The real challenge is prioritization:

As AI continues to improve vulnerability discovery and analysis, organizations will inevitably uncover more security findings.

That sounds positive, but it introduces a new challenge. More findings do not automatically reduce risk. In fact, without effective prioritization, they can create additional noise.

The organizations that succeed over the next decade will not necessarily be the ones finding the most vulnerabilities. They will be the ones that can most effectively distinguish genuine risk from background noise, understand how attackers are likely to exploit weaknesses in practice, and make informed decisions about where to focus finite resources.

As AI continues to improve vulnerability discovery and analysis, security teams will inevitably gain access to more data, more findings, and greater visibility than ever before. While that represents a significant advancement for the industry, visibility alone does not reduce risk. The real value lies in understanding what matters, what is exploitable, and what action should be taken next.

That is why I believe the future of security testing is not autonomous. It is human-led and AI-assisted. AI tools will continue to accelerate analysis, improve visibility, and help uncover opportunities that may previously have been missed. However, understanding business context, assessing real-world risk, and making sound security decisions will remain fundamentally human responsibilities.

The cybersecurity industry has spent years trying to solve the visibility problem. AI is helping us make enormous progress. The next challenge is prioritization, and that is where experienced security professionals will continue to play their most important role.

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In May, WordPress shipped the most consequential release in its history. Version 7.0 brought AI into the core of the CMS platform for the first time, and the people who built it made a choice that's easy to miss in all the noise about the feature itself. They left it switched off.

The infrastructure is in the codebase, but nothing reaches an AI service until the site owner connects a provider and turns it on. Upgrade a site and walk away, and it behaves exactly as it did the day before.

The on-switch was handed to the person who owns the site, not flipped on their behalf.

It's worth sitting with how deliberate that was. The team had just shipped the most powerful capability the platform has ever carried, and the posture they chose for it was opt-in, plugin-based, with nothing injected into anyone's site automatically.

In an industry that loves a sensible default, that restraint was itself a statement: this decision is yours to make.

Then the more interesting thing happened.

A reasonable instinct, taken one step too far

Within days of the release, SiteGround, one of the most established managed WordPress hosting companies in the business, did close to the opposite. It pre-installed and activated its own AI product across its customer base, configured it as the default connector, and bundled in a generous allowance of free usage to get people going.

The active-install count crossed a million almost immediately. Plenty of site owners logged in to find capable new software already running on sites they had never touched to put it there.

I want to be fair to SiteGround here, because fairness is where the useful lesson lives. This is a serious operator with a long, well-earned reputation, and the product it built is genuinely good, a real piece of engineering rather than a thin upsell. The reasoning behind the rollout isn't hard to reconstruct either.

The "correct" path to native AI is fiddly, and most people would stall somewhere in the middle and never finish it. Pre-installing the whole thing, free usage attached, removes that friction in a single stroke. From an operator's chair, that's a tempting piece of customer service, and I've sat in that chair for the better part of two decades. I understand the pull of it completely.

So this isn't a story about a company behaving badly. It's a story about a reasonable instinct (reduce friction, help the customer get to the good part faster) carried one step past the line. And the reaction told us exactly where that line is.

The objection wasn't AI. It was consent.

The pushback

The pushback was quick and pointed, and the striking thing about it was its subject. Almost none of it was about whether AI belongs in WordPress, or whether the tool was any good. Many of the people objecting use AI every day. What they objected to was finding it already switched on.

That distinction matters more than it first appears, because it separates two things the industry tends to blur: the quality of a change, and the consent to it. A genuinely good feature, installed without asking, still lands as something done to you rather than for you.

The standard defense (it's optional, you can remove it whenever you like) is all true, and none of it is the same as agreement. "We switched it on and you can switch it off" quietly moves the work of noticing, understanding, and undoing onto the customer, for a change they never approved. "Here's one-click setup if you'd like it" delivers the identical convenience and leaves the decision where it belongs.

This isn't a new tension. Webhosting companies have always made changes customers never see, and most of the time they're glad we do. But AI is going to surface this question over and over, because it's the most consequential thing most of us will ever be tempted to switch on by default. Getting the principle right now, while the stakes are still mostly reputational, is a lot cheaper than getting it wrong later.

The line worth holding

The honest objection to all of this is that hosts intervene on customer sites all the time, and nobody asks permission for that. True, and the distinction is the whole point.

When a host patches a vulnerability, blocks a malicious request, or disables a plugin that's being actively exploited, it's protecting the customer's site and the wider platform from harm. Customers extend us that trust precisely because it's defensive, narrow, and in their interest. Installing a new product is a different category of act.

It isn't protecting anything; it's changing what the website is. The trouble starts when the second borrows the permission we were granted for the first, when goodwill extended for security work quietly gets spent on shipping features. That's the line. Maintain the platform freely; change the product only with a yes.

Holding it doesn't mean making customers do more work. New capabilities can arrive off by default and one click away for anyone who wants them. Multi-site managers can get a single place to see and control what's running, rather than a hunt site by site.

Anything a host pushes can be pulled back as easily as it went out. And changes can be announced in plain language before they happen, including how to say no, because the absence of a clear, opt-out-inclusive heads-up is usually what turns an ordinary product decision into a breach of trust.

Parts of the ecosystem are already moving this way. None of it is anti-AI. If anything, it's what lets hosts lean into AI confidently, because customers can trust that nothing shows up uninvited.

Whose site is it, anyway?

As AI moves from novelty to default across the web, every host will face its own version of this question. Here's a genuinely useful new capability. Do we switch it on for everyone, or do we let people choose? The convenient answer and the right answer won't always be the same one, and the gap between them is where reputations are quietly made or lost.

It helps to remember who actually lives with the answer. When a host changes something on a site, the host moves on to the next ticket. The owner is the one who stays: the one whose visitor hits a page that behaves differently than it did the day before, whose inbox fills up when something looks off, whose name is on the business the site exists to represent. We get to make the change. They have to live with it. That asymmetry, more than anything written into the terms of service, is the real reason asking first isn't a nicety. It's an acknowledgement that the consequences were never ours to carry in the first place.

The site owners who pushed back this spring weren't standing against progress. Most of them, I'd wager, will happily adopt the very tooling they objected to, the moment they get to be the ones who switch it on. They were defending something simple that's easy to lose sight of when the technology is moving this fast: it's their site. Not the site we host for them. Theirs. A host's authority runs right up to the edge of the customer's ownership and stops there, and the best operators I've worked alongside never needed reminding of it. They saw their role as stewardship rather than possession.

That trust is the real product. Not the servers, not the dashboard, not even the support, though every bit of it matters. What a customer is buying is the confidence that nothing happens to their site that they didn't choose, and that when we do step in uninvited, it's to protect what's theirs and never to quietly redraw it. Trust like that takes years to earn and an afternoon to spend. Asking first is simply how you keep from spending it.

Get the boundary right, and AI in hosting becomes exactly what it should be: useful, and genuinely welcome. Get it wrong, and even the best feature in the world arrives as something taken rather than offered. The difference was never the technology. It was only ever whether anyone thought to ask.

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The UK's payment landscape is undergoing a rapid transformation. While regulatory initiatives, such as the mandatory authorized push payment (APP) reimbursement scheme, provide safeguards, sophisticated cyber-attacks and elaborate scams relentlessly evolve.

To reduce fraud, strong data-sharing frameworks and collaboration across the financial services industry are essential. Collaboration between big tech, telecoms, banks and the public sector can help combat fraud through a joined-up approach to data-sharing aimed at driving out scammers and identifying potentially fraudulent transactions.

Current landscape

The scale of UK fraud is stark, with losses reaching £1.28 billion in 2025, a 4% increase year-on-year. Fraud is now operating on an industrial scale, with criminals using increasingly advanced tools and techniques to target victims. Fraud increasingly funds serious and organized crime in the UK and globally, reinforcing its status as a national security threat.

Authorized Push Payment (APP) fraud is a growing area of concern – this type of fraud continues to rise, with 248,070 cases recorded in 2025 (up 7%), showing that fraudsters are consistently adapting, pivoting to exploit new vulnerabilities, even as defenses strengthen. New scams have focused on investment, purchase-related, advance fee, invoice, and mandate scams as well as romance and impersonation scams.

Total losses from APP fraud rose sharply to £576.4 million (up 19%). This reflects a clear shift in criminal behavior, from exploiting systems to manipulating people through increasingly sophisticated social engineering.

Purchase scams made up 71% of all APP cases, demonstrating the scale and diversity of modern fraud tactics. This impact extends beyond financial loss, affecting individual livelihoods, disrupting businesses, and undermining national economic confidence.

Crucially, most APP fraud now originates outside the banking system. 66% of cases begin online (accounting for 32% of losses) and 17% via telecommunications networks, highlighting the growing role of digital platforms and telecoms in enabling fraud. Criminals are no longer primarily hacking systems; they are manipulating people, using sophisticated social engineering to bypass even the strongest technical controls.

Key considerations for the payments industry

Tackling these challenges requires a multi-faceted approach, combining robust technology, seamless collaboration to enable effective and compliant data-sharing, effective regulation, and public awareness. Key considerations are:

What does government strategy mean in practice?

Initiatives, such as the Government Fraud Strategy, provide an important framework for government, law enforcement, and the private sector. Infrastructure and data-sharing initiatives need to be effective, compliant, and aligned with national priorities to disrupt and prevent fraud. This ensures the fight against fraud remains a national priority that continuously adapts.

New data-sharing initiatives with Faster Payment System participants can play a key role here. Pay.UK’s work with participants on Enhanced Data Exchange (EDEx) will facilitate secure, timely data-sharing to empower financial institutions to detect and prevent fraudulent payments before they happen.

While still in development, its principles will complement the FCA’s APP fraud guidance and the Home Office’s Data Strategy ambitions: to enable secure, proportionate information exchange that helps prevent fraud before funds leave the system.

How can the industry continue to build cross-sector collaboration?

Cross-sector intelligence sharing and advanced data analytics are increasingly vital. Real-time, secure data exchange illuminates patterns, identifies emerging threats, and enables proactive intervention before attacks occur. When combined with strong governance and clear accountability, this kind of collaboration shifts fraud defense from isolated warning signs to a coordinated, system‑wide response.

Confirmation of Payee significantly reduces misdirected payments and various APP fraud types such as impersonation and invoice scams. It's a vital, preventative layer of security before funds are transferred. It has implications beyond its intended purpose and has strongly influenced the development of Verification of Payee in Europe.

There is a growing call for greater enforceable responsibilities for technology platforms and telecommunications providers, not only to prevent fraud at source, but also to contribute financially and operationally to combating it.

How can the industry empower and educate end users?

Beyond technology and industry collaboration, fraud prevention has a vital human dimension. Educating and empowering end users remain central – recognizing the warning signs of a scam is still one of the strongest protections available. But education alone is not enough. Consumers also need better information at the moment a decision is made.

It’s encouraging to see that, as a payments community, we are already building richer data-sharing across the ecosystem to provide clearer, more relevant context when prompting customers to pause before making a payment. Banks are moving beyond generic warnings, providing genuinely useful guidance and strengthening the point of payment as a powerful, collective line of defense.

The APP reimbursement scheme is a significant consumer protection funded by UK banks. Data from the PSR shows that £215 million was reimbursed to victims of APP fraud in 2025 alone. Across the first 15 months of the scheme (October 2024 to December 2025), 89% of the money lost to APP scams has been successfully claimed back from a payment firm and returned to victims.

While not a direct comparison, this is a significant uptick from the 65% reimbursement rate reported by UK Finance for personal accounts in 2024. Providing a safety net of up to £85,000 for victims, this scheme offers a clear recovery mechanism and brings more consistency for customers than the previous voluntary Contingent Reimbursement Model (CRM) Code.

Further, the scheme continues to evolve in line with the shifting payment landscape. The PSR has appointed Frontier Economics to carry out an independent evaluation and review of the APP fraud policies, the results of which are due to be published in the second half of 2026.

These findings, which look at the current effectiveness of the policies, fraud performance reporting and the reimbursement requirement, will influence the future of APP fraud prevention strategies and regulatory requirements, ensuring the creation of safe and trusted payment infrastructure

The battle against payment fraud is ongoing, demanding constant vigilance and strategic adaptation. While the digital age has transformed how we transact, it has also presented fraudsters with new avenues for exploitation. Yet, as outlined, it is a battle we are actively and collectively winning.

By embracing a multi-faceted approach, combining robust technological defenses, seamless industry collaboration, effective regulatory frameworks, and comprehensive public awareness, we are building a formidable shield against these threats.

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The enterprise technology industry has a peculiar relationship with accountability. When it comes to cloud uptime, latency, and data security, we expect contractual guarantees, SLAs, and clearly defined remedies. But when it comes to AI-generated outputs, the actual content these systems produce, we've quietly accepted a different standard: best effort.

I've spent years in commercial and operational roles at companies like Gap, Amazon, and Door Dash / Wolt. In every one of those environments, product visuals weren't a marketing nice-to-have. They were infrastructure. A wrong color on a listing didn't just look bad; it drove returns. A missing ingredient on a food image wasn't an aesthetic issue; it was a trust issue that compounded at scale and was dangerous to our customers.

So when AI-generated images started entering enterprise workflows in earnest, I watched with real interest. The efficiency gains were compelling: the ability to generate, retouch, and adapt product visuals at a speed and scale that traditional studio workflows simply cannot match.

But something fundamental was missing from the enterprise conversation: accountability for outputs.

The gap between impressive and dependable

There's a difference between AI that produces impressive results in demos and AI tools you can stake commercial operations on. For enterprise buyers, that gap matters enormously.

Consider what happens when am AI-generated product image fails at volume. A wrong product color in a hero image doesn't trigger one return; it triggers thousands. A distorted shape on a fashion listing doesn't affect one conversion; it affects an entire category. The commercial exposure from visual inaccuracy compounds at scale in a way that individual errors simply don't.

Yet for most of the AI visual tools currently available to enterprise buyers, the contractual position on this exposure is essentially zero. You buy credits, you run images, and what comes out is what you get. If the output doesn't match the brief, you absorb the cost: in regeneration time, in quality control overhead, and ultimately in the downstream commercial impact of content that doesn't perform.

This isn't an indictment of the technology. AI-generated images have genuinely transformed what's operationally possible for enterprise visual production. But the commercial model hasn't kept up with the commercial reality.

Why ownership changes everything

The reason most AI visual vendors can't offer meaningful output guarantees isn't reluctance; it's architecture. If you're building on third-party foundation models, you have no ability to evaluate, course-correct, or stand behind the quality of what those models produce at the output level. The accountability stops at the API.

The vendors who can make guarantees are the ones who own the full stack: the generation models, the evaluation models, and the remediation process. This is the structural distinction that makes contractual guarantees viable, not as a commercial gesture, but as something that can actually be operationalized.

When a proprietary fidelity evaluation model is running on every output before delivery, you have a mechanism for identifying failures before the client does. When you own the rater, the fixer, and the generation pipeline, you have the ability to correct those failures.

When you've run a feasibility check on a customer's actual catalogue before any commercial commitment, you know what the pass rate will look like in production.

That's the architecture that makes a guarantee meaningful: not a promise, but an auditable process with contractual teeth.

What contractual accountability looks like in practice

The mechanics matter here, because "guarantee" can mean many things. In practice, an enterprise visual guarantee should do three things: define pass/fail criteria upfront based on the customer's actual brief; evaluate every output against those criteria before delivery; and trigger a clear remedy, regeneration or credit refund, when failures occur.

Critically, the criteria need to be specific. Product fidelity failures, an altered color, a missing ingredient, a distorted product shape, are measurable and contractually defensible. Subjective aesthetic preferences, a lighting angle, a background tone, are not. The boundary between these two things is where a real guarantee lives, and where vague commitments fall apart.

For enterprise buyers, this specificity is valuable in itself. It forces the conversation about what "quality" actually means for a given catalogue before procurement, rather than after. That clarity typically improves outcomes on both sides.

The accountability moment for enterprise AI

We're at a point in the enterprise AI cycle where the conversation needs to shift from what these systems can do to what vendors are willing to stand behind. Capability is no longer the differentiator; the market is full of capable tools. Dependability is.

For enterprise procurement teams, this means starting to ask harder questions. Not just "what's your accuracy rate?" but "what happens when it's wrong, and what are the contractual terms?" Not just "can you handle our volume?" but "what remedies apply when you don't meet the standard we've agreed?"

For the vendor community, it means recognising that the era of best-effort AI in enterprise contexts is ending. Buyers who are running tens of thousands of product images through AI pipelines need the same accountability from those systems that they expect from any other mission-critical infrastructure.

The goal in commerce was never the most beautiful image. It was always an image that sells, reliably, accurately, at scale. Enterprise AI that can guarantee that outcome is the next competitive frontier. The vendors willing to back their outputs contractually are the ones that will earn a place in enterprise infrastructure for the long term.

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A decade ago, technology used data to make commercial real estate processes faster. Brokers leveraged tech platforms to gather data, run it through a process, and serve up solutions that flowed from predefined formulas.

Today, data is used to make processes smarter. With the power of AI, platforms are giving brokers more than fast access to data and computations. They are also serving up decisions. Data is no longer simply an input for a system, but rather the intelligence of the system.

The change introduces a host of new capabilities. AI doesn’t just accelerate processes; it automates them.

But the change also introduces new dangers. Pre-AI, data issues resulted in platform failures. Now, data issues result in faulty thinking.

For commercial real estate brokers, as for any business professional who has integrated AI into their processes, faulty thinking leads to misleading outputs.

And decisions based on those outputs have the potential to lead to big losses. Consequently, brokers need clean data to execute in the Age of AI.

AI-driven systems need data that delivers context

When it comes to data, context is the key to AI’s effectiveness. Accessing data is not enough. AI systems need to understand data.

The traditional platforms brokers would use to gain real estate insights might show parcel boundaries, zoning codes, permits, or points of interest as separate layers. They streamlined the access and filtering process, but users had to determine the context.

For AI to function as intended, it needs to understand how the layers of data relate to each other. It has to know whether a zoning district allows a use, whether the parcel size supports the intended development, whether permit activity signals market momentum, and whether surrounding demand drivers support the investment thesis.

Clean data allows AI to reason across categories. It does away with fragmentation, inconsistencies, and exaggerations. The platforms that empower brokers have refined, normalized, and blended data into one usable intelligence layer.

In the world of AI, reliable data is often described as having representativeness. It gives AI an accurate representation of the environment it is being asked to assess. Clean data ensures representativeness.

AI-driven systems don’t warn users when data is bad

Brokers use AI-driven systems to uncover insights they need to make confident decisions. But when those systems run on bad data, brokers end up with dangerously misplaced confidence.

The threat of being misled by AI systems is often overlooked because AI doesn’t warn users when it is running on bad data. It will confidently provide an answer that sounds precise, even when the response is questionable because it is built on data that is incomplete, outdated, misclassified, or overstated.

For real estate brokers, moving on any outputs built on bad data can lead to real financial consequences. A developer may overestimate the buildable area. A retailer may misread a trade area. An analyst may recommend a site that fails zoning review. An investor may compare markets using datasets that are not actually comparable.

AI’s intelligence is based on the data used to train it. Good or bad, that is the well it has to draw from.

General AI models can’t deliver the context brokers need

General AI models like ChatGPT or Claude can help real estate brokers if they are looking for general information. They can explain zoning, offer alternative financing options, or help explore the possible outcomes of a real estate scenario. But their intelligence is constrained by their data, which typically won’t include the localized, up-to-date, contextual content that drives real estate developers’ decision-making.

To qualify as “clean,” the data driving platforms used by brokers needs to be complete and contextually connected. Foundation models like those developed by OpenAI are extremely powerful, but they are not a substitute for clean, domain-specific data.

They cannot reliably know whether a specific parcel in a specific county has current zoning coverage, whether the local assessor data is missing a building attribute, whether a permit record has been matched to the correct parcel, or whether two providers are using conflicting land-use definitions — unless that data has been cleaned, governed, and connected.

AI can provide brokers with the reason they need, but only when it is given trusted context. In commercial real estate, that context is highly local, highly fragmented, and constantly changing.

Counties publish data differently, municipal zoning codes vary, and permit structures are inconsistent, to name just a few of the contextual challenges. AI systems become useful for real estate decision-making by giving them a reliable data layer underneath.

Demanding clean data is especially important for commercial real estate brokers because the cost of error is high. A site evaluation can influence acquisition strategy, entitlement risk, development feasibility, lending assumptions, and a host of other critical components. A small data issue upstream can become a large financial mistake downstream.

The only platforms brokers should trust are those that treat data quality like infrastructure. You wouldn’t build a high-rise on a weak foundation. The same applies to AI. The model, interface, and automation layer are only as strong as the data foundation beneath them.

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The movement to preserve online privacy runs counter to the business model of many big tech companies, which have devised profitable business models that hinge on capitalizing on personal data users surrender by using their platforms. This reality cannot be divorced from the facts that prominent figures, like former Google CEO Eric Schmidt, have often made remarks like the following.

Don't be evil

Schmidt, who was Google chief at the time, was being interviewed on CNBC when he was asked whether users should feel comfortable sharing their data with Google.

His statement in response came as a surprise given how much Google had claimed (during this much simpler era of Silicon Valley) to care about user privacy.

These arguments, after all, are the same used by those who are adamant that we must break encryption in the name of safety, as well as those who defended the now-clear government surveillance programs disclosed by the NSA whistleblower Edward Snowden.

Worry versus harm

Scrutiny intensified in the months after these comments, with Google attempting to assuage concerns by reiterating policies around data anonymization despite continued criticism.

Co-founder Larry Page then tried to help the situation in mid-2010 by saying users should distinguish between "worry versus harm".

He tried to imply that bulk collection of data, including harvesting data from private Wi-Fi networks via Street View cars, may be harmless if that data has no utility. He challenged detractors to specifically name individuals harmed by such practices.

Then, a couple of years later, Google faced several federal fines, including a then-record $22.5 million penalty after the firm was found to have bypassed default privacy settings on Apple's Safari browsers. The EU also argued that controversial privacy changes breached European law.

Many organizations are still trying to get to grips with cloud sprawl. Years of cloud adoption have delivered undeniable benefits, giving businesses greater agility, scalability and access to innovation.

But they have also created a significant management challenge. As environments have expanded across multiple cloud providers, accounts and services, maintaining visibility has become increasingly difficult.

Security and IT teams have spent years trying to establish consistent visibility across increasingly complex cloud environments, identifying what assets exist, where they are located, who is responsible for them and whether they are adequately secured. For many organizations, those questions still remain difficult to answer.

Now, as enterprises accelerate their adoption of AI, a new layer of complexity is being added on top of existing cloud environments. AI models, agents, APIs, vector databases and automated workflows are appearing across organizations at remarkable speed, creating a fresh challenge that many security leaders are only beginning to confront.

In many respects, AI sprawl is becoming the new cloud sprawl.

The pace of AI adoption is creating a visibility problem

While previous technology shifts unfolded over years, AI capabilities are evolving in months. New models, tools and services are constantly emerging, while software providers are rapidly embedding AI functionality into existing products. This pace of change presents a unique challenge for governance.

Traditionally, organizations have introduced new technologies through relatively structured processes involving the usual things like procurement, security reviews and compliance assessments. AI adoption often looks very different.

Teams can experiment with models in development environments, departments can adopt AI powered applications independently, and new capabilities can appear within existing software platforms almost overnight.

As a result, many organizations lack a complete inventory of where AI is being used across their business.

This is both a security concern and visibility problem for effective governance and compliance. If organizations cannot identify where AI is running, they will struggle to understand what data it can access, what decisions it is influencing and what risks it may introduce.

AI is adding another layer to cloud complexity

As organizations embraced multi cloud strategies, adopted SaaS applications and empowered development teams to move faster, cloud estates became increasingly distributed. Security teams found themselves managing thousands of assets spread across multiple environments.

AI is now introducing another set of services and technologies that need to be inventoried, understood and secured. A modern AI deployment rarely consists of a single model operating in isolation. Instead, organizations are building interconnected ecosystems involving cloud infrastructure, data pipelines, APIs, machine learning platforms, third-party services and increasingly autonomous agents.

Each additional connection creates another dependency to monitor and another potential point of failure. The challenge is not necessarily that AI introduces entirely new security risks. In many cases, it amplifies existing visibility and governance issues that organizations were already struggling to address.

Many security leaders are still working to achieve comprehensive visibility across their cloud environments. Adding AI systems into the mix means managing another layer of complexity without an established playbook for doing so effectively. That lack of maturity is one of the defining characteristics of AI governance today.

Why AI agents are changing the conversation

Unlike traditional software applications, AI agents are increasingly capable of taking actions on behalf of users. They can retrieve information, access systems, trigger workflows and interact with other applications with varying degrees of autonomy.

Historically, security strategies have focused primarily on managing human access to systems and data. Concepts such as identity governance, multi factor authentication and zero trust were designed around human users. But AI is beginning to change those assumptions.

Organizations are creating growing numbers of non human identities, each requiring the right permissions and access rights. These systems may interact with sensitive information, business applications and critical infrastructure in ways that are difficult to monitor using traditional approaches.

As AI adoption accelerates, identity is likely to become one of the most important control points for managing risk. The principle of least privilege remains just as relevant as it has always been, but organizations must now apply it to both human and machine actors.

That requires a much clearer understanding of how AI systems operate, what resources they can access and how those permissions are governed over time.

Securing AI at the speed of AI

AI capabilities are evolving at extraordinary speed, while security and governance processes are often constrained by regulatory requirements, internal approvals and operational realities.

Attackers do not face the same constraints. They can experiment, adapt and exploit emerging opportunities far more quickly than most organizations can implement new controls. This creates an ongoing race between innovation and governance.

The objective should not be to slow AI adoption. Few organizations can afford to ignore the opportunities AI presents, whether through productivity gains, operational efficiencies or competitive advantage. Instead, the focus should be on ensuring that governance evolves alongside adoption.

This means recognizing that AI security is both about protecting models and understanding how AI interacts with cloud environments, business processes, identities and data. Organizations need to tackle this by establishing visibility early rather than attempting to retrofit governance once complexity has already taken hold.

Applying the lessons learned from cloud

The good news is that organizations do not need to start from scratch. The cloud era provided valuable lessons about the relationship between innovation, visibility and governance. Many of those lessons are directly applicable to AI.

Organizations must begin by understanding their AI footprint. This way they can identify where AI is being used, what systems it connects to and what data it can access. They can establish clear ownership, extend existing risk management frameworks and ensure that AI deployments are subject to the same level of scrutiny as other critical technologies.

Most importantly, they can recognize that visibility is not a one off exercise. As AI capabilities continue to evolve, maintaining an accurate understanding of the environment will become an ongoing requirement.

Cloud sprawl demonstrated how quickly complexity can accumulate when technology adoption outpaces governance. AI presents a similar challenge, but at an even greater pace.

As AI becomes increasingly embedded across the enterprise, that lesson may prove more important than ever.

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In the summer of 1995, the future of computing briefly seemed to belong to Netscape.

Netscape went public that August, barely sixteen months after it had been founded. Its stock doubled on the first day. The company had no empire of hardware, no installed operating system, no grip on the office desktop. What it had was a window into a new world.

Open Navigator, type an address, and the Internet appeared with a little throb of electricity. The browser did not feel like an application. It felt like a passage out of Microsoft's world.

Microsoft noticed.

The battle that followed was called the browser wars, a phrase that makes it sound tidier than it was. Really, it was a fight over who had the right to stand between the user and the next era of computing.

Netscape believed the browser would make the operating system less important. Microsoft believed that anything capable of making Windows less important needed to become part of Windows, preferably yesterday.

By the end of the decade, the company that had introduced so many people to the Web was no longer the Web's gatekeeper. It had been out-distributed, out-bundled, and finally absorbed into a stranger corporate afterlife.

A familiar temptation

There is a familiar temptation now to ask which artificial intelligence company is "the Netscape of AI." The answer usually offered is OpenAI, and the comparison is not wrong. ChatGPT did for artificial intelligence what Navigator did for the Web. It turned a technical architecture into a public experience. It gave the future a text box.

But the more important question is this: Who is today's Microsoft? Who understands that the winner is often the company that owns the default?

Every platform shift begins with a miracle and ends with a map of choke points. The miracle is what users remember. The choke points are where the money goes.

The early Web was sold as an escape from gatekeepers. It created new ones. Search. Browsers. Marketplaces. Mobile operating systems. Cloud platforms. Social media. The AI era is being sold with the same democratic glow.

And yet the deeper stack is already hardening. It is made of chips, power contracts, data centers, model weights, enterprise identity, workflow data, cloud credits, procurement channels, and the small tyrannies of default settings. The romance is in the chatbot. The control is somewhere colder, louder, and much more expensive.

That was true in the nineties, too. The Web looked like a page. The winners understood it was a stack.

The danger to OpenAI

OpenAI is the obvious Netscape figure because it supplied the first mass-market revelation. Before ChatGPT, artificial intelligence was a research field, a back-office tool, or a phrase executives used when they meant analytics with a larger budget. After ChatGPT, it was something anyone could talk to.

People underrate the power of the first interface that makes a new technology feel inevitable. Netscape did not invent the Internet. It made the Internet feel reachable. OpenAI did not invent the transformer. It made the transformer feel conversational.

But Netscape's story is not a founder myth. It is a warning label.

Netscape had the user's excitement but not enough control over distribution. Microsoft had the operating system. It could place Internet Explorer where users already lived. It could make the browser free. It could turn a product category into a feature.

The lesson was simple: if your rival owns the layer beneath you, your brilliance may become their menu option.

OpenAI is better protected than Netscape was, but not safely protected. It has a huge brand, astonishing usage, and deep ties to Microsoft. It also has the curse of being expensive in a way software companies used to avoid. Each improvement requires compute, chips, talent, energy, and capital. The old software dream was scaling with almost no marginal cost. AI can't do that.

That is why OpenAI's partnership with Microsoft is both strength and vulnerability. Microsoft gives it cloud computing infrastructure, enterprise access, and capital. Microsoft also sits close enough to learn from it, package it, hedge around it, and sell AI into the places where people already do work.

If Netscape's problem was that Microsoft stood underneath it, OpenAI's problem is subtler: Microsoft stands underneath it, beside it, and increasingly in front of the customer.

Netscape had the dazzling demo. Yahoo had the traffic. AOL had the subscribers. None of that was enough. The durable winners were the companies that turned their software layers into a control point and a tollbooth.

Nvidia may be doing exactly that.

The rise of Nvidia

In the nineties, Intel was the metronome inside the personal-computer boom. Microsoft owned the software platform. Intel owned the pace of the machine.

Nvidia occupies a similar place in AI, but the analogy understates the ambition. Nvidia is not merely selling chips into a boom. It is selling the industrial base of the boom: GPUs, networking, software libraries, developer habits, and a vision of the data center as an AI factory.

Every major AI player is both Nvidia customer and Nvidia escape artist. Google has TPUs. Amazon has Trainium. Microsoft is developing its own silicon. Everyone wants an alternative. The problem is that wanting one does not create an ecosystem.

Nvidia's position today may be the purest example of moving up the stack from below. A chip company becomes a systems company. A systems company becomes a software company. A software company becomes a developer environment. A developer environment becomes a tax on ambition.

For now, Nvidia is the toll collector.

The AI market will not resolve into one winner. Platform shifts rarely do. The Web did not produce one winner. It produced layers of power. Microsoft kept the desktop. Google won search. Amazon won commerce and cloud. Apple won mobile hardware and the app economy. Meta won social attention.

AI will do the same.

Microsoft may become the default enterprise AI company, not because every Copilot is brilliant, but because Microsoft sits where work already happens. Nvidia may remain the dominant compute toll collector. Amazon will likely win much of the infrastructure layer. Google must reinvent search while defending it. Meta will use AI to extend attention. Apple may yet turn personal AI into a device-native experience.

The likely losers are the companies attached to the wrong layer.

The changing landscape of AI

In the nineties, AOL looked invincible because it owned access. Broadband made that access less special. Yahoo looked inevitable because it owned attention. Search made that attention less decisive. Netscape looked revolutionary because it owned the browser. Microsoft made the browser a dependency of the operating system.

In AI, the same demotions will happen. Some model companies will become features. Some application companies will become demos. Some incumbents will decorate old products with AI and call it transformation, which is the corporate version of putting a spoiler on a minivan.

The mistake, in every technological boom, is to confuse the moment of wonder with the arrangement of power that follows it.

The wonder is sincere. The arrangement is not.

The early Web made people feel as if they had slipped the old gatekeepers. Then came the search box, the app store, the marketplace, the cloud account, the login, the subscription, the default. Each solved a real inconvenience. Each left behind a narrower path.

AI will likely travel the same road, only faster and with a larger electricity bill. It will begin as a conversation and mature into an administrative system for human intention: what we ask, what we buy, what we write, whom we trust, which choices are shown, and which never quite appear.

The future does not usually arrive wearing chains. It arrives offering to save time.

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Artificial intelligence is moving faster than most enterprise governance models were designed to support.

Organizations are rapidly embedding AI into customer operations, internal workflows, decision-making systems, software development, supply chains, analytics, and automation initiatives.

But while adoption accelerates quickly, this creates a situation where accountability is fragmented.

That gap is creating a new category of enterprise risk.

For years, cybersecurity leaders focused on protecting systems, managing threats, and securing data. Today, that already broad mandate is expanding: as AI becomes increasingly embedded across operational environments, CISOs are pulled into broader questions of trust, assurance, resilience, and executive accountability.

A recent best practices report from Forrester noted that “CISOs will be the trust and assurance authority for the business.”

This shift reflects a growing reality: enterprises are increasingly struggling to determine who owns AI risk as decisions become distributed across systems, business functions, and autonomous processes.

Governance Models Are Struggling to Keep Pace

Most enterprise governance structures were designed around the concept of centralized oversight models. Security teams managed cybersecurity risk. Compliance managed regulatory obligations. Operations managed execution. Business leaders managed strategic outcomes. AI disrupts those boundaries.

Today, AI increasingly influences operational decisions across functions. Different tools and models may be used simultaneously for customer interactions, fraud detection, procurement, workforce management, software development, and supply chain operations internally, as well as throughout a vendor or supply chain ecosystem. As a result, visibility is limited and accountability becomes difficult to define.

When disruptions affect legal, privacy, operational, and technology functions simultaneously, many organizations lack a clear view of how those risks intersect.

Most governance frameworks were designed for software that supported decisions. Now, AI increasingly participates in making them.

The Visibility Gap Behind AI Risk

Many organizations still rely on fragmented governance processes, static documentation, spreadsheets, and disconnected reporting workflows to manage environments made even more complex by AI.

AI systems do not operate in isolation. They rely on interconnected data pipelines, third-party models, cloud infrastructure, APIs, operational systems, and business-process dependencies that continuously evolve. When visibility across those dependencies is limited, organizations struggle to understand where AI-driven decisions originate, how they propagate, and what downstream impacts they create.

That visibility gap quickly becomes a resilience problem. If organizations cannot trace how AI-driven actions connect to operational systems and business outcomes, they cannot effectively assess exposure during disruption, validate continuity plans, or demonstrate accountability under pressure.

This is where many organizations discover that AI governance is no longer just a policy challenge. It is an operational resilience challenge that can have customer and financial impact.

Because cybersecurity teams already operate at the intersection of technology risk, resilience, governance, and incident response, many organizations are increasingly looking to CISOs for enterprise-wide trust and assurance.

AI Resilience Requires Operational Context

The conversation around AI governance has centered on ethics frameworks, policies, and regulatory controls, which remain important. But resilience increasingly depends on something more operational: understanding how AI-driven actions affect real business environments during disruption.

That requires organizations to move beyond static governance models toward continuous operational visibility.

Leading organizations are increasingly focusing on questions such as:

• Which business services depend on AI-driven systems?
• What operational processes become vulnerable if AI outputs fail?
• Where do third-party dependencies create downstream exposure?
• How quickly can teams trace AI-driven decisions during an incident?
• Can leaders demonstrate operational accountability in real time?
• Can we revert back to more traditional operating models if an AI agent or capability fails?

Those questions span cybersecurity, resilience, operations, and executive governance. They also represent a broader shift occurring across enterprise risk management itself.

Trust, accountability, and resilience

Organizations are no longer being measured solely by whether governance frameworks exist. Increasingly, they are being judged by whether they can operationally demonstrate trust, accountability, and resilience when complex systems fail under pressure, and AI is accelerating this shift.

The organizations that adapt fastest will not necessarily be the ones deploying the most AI. They will be the ones that can most clearly understand, govern, and recover from the operational consequences AI can create when disruption occurs.

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Shadow AI is a security issue. When employees use unsanctioned AI tools without formal oversight, sensitive information can end up in platforms the business has not approved, monitored or secured.

The risks around data protection, compliance and governance are real, and organizations are right to take them seriously.

But if we view shadow AI only through a security lens, we risk treating the symptom rather than the cause.

Most employees are not deliberately trying to bypass policies or create risk.

In most cases, they are trying to solve a problem quickly and move their work forward. When approved tools are slow, difficult to access, limited in functionality or unclear to use, people naturally look for alternatives that help them get the job done.

That means shadow AI is not just a security challenge. It is also a signal that workplace technology is failing to meet employee needs.

For organizations, the lesson is clear: reducing shadow AI requires more than stronger controls. It requires providing employees with secure, accessible tools that are capable of supporting the way work actually happens.

When approved tools are not enough

Many organizations still frame unsanctioned AI use as a failure of employee behavior. From that perspective, the answer seems simple: issue stricter policies, block more tools and remind people of the risks.

This may reduce some exposure in the short term, but it rarely solves the underlying problem. Employees turn to AI tools because they offer speed, convenience and support with tasks that official systems may not handle well.

The challenge is not only that employees are using the wrong tools. It is that the approved route may not feel practical enough to use.

That distinction matters. If the official process is too slow, people may bypass it. If the guidance is too vague, teams may make their own decisions. If approved tools do not meet real business needs, unofficial ones will fill the gap.

Shadow AI is therefore not just a sign of poor compliance, but can also be a signal of underlying friction.

Digital friction creates hidden risk

Digital friction refers to the everyday technology barriers that make it harder for employees to do their jobs efficiently. It might be a login process that takes too long, a blocked platform that prevents a simple task, an approval workflow that slows a project or a sanctioned tool that lacks the functionality employees need.

Individually, these problems may seem minor. Together, they shape how employees behave.

When workplace technology makes work harder, employees become more likely to find their own solutions. Research has found that 80% of employees lose time to dysfunctional IT, costing them an average of 1.3 workdays per month. Almost half also say it has delayed critical operations or projects.

The risk is not only lost productivity. Digital friction can also weaken trust in approved systems, pushing work into less visible environments where security teams have less oversight.

This is why blocking tools without addressing employee needs can backfire. It may push behavior further out of sight rather than bringing it under control.

Security cannot succeed if it competes with productivity

For years, security has often been seen by employees as something that interrupts work.

Password resets, access requests, approval chains and tool restrictions all exist for valid reasons, but they can still feel like barriers when they are poorly designed.

The same is true for AI governance. A policy that simply says which tools cannot be used is unlikely to be enough. Employees need practical guidance on what they can use, what information can be entered and where to go when they are unsure.

The secure route has to be clear enough to follow and useful enough to choose.

This does not mean weakening security. It means designing security around how work actually happens. The strongest controls are often the ones that employees can follow without feeling they are being forced to choose between protection and productivity.

Authentication is a useful example. Passwords have long been a source of frustration for employees and a known weakness for organizations. Approaches such as zero trust and biometric authentication can strengthen protection while improving the user experience.

The principle is simple: good security should reduce risk without adding unnecessary friction.

AI governance needs an owner

One reason shadow AI can grow quickly is that responsibility is often unclear.

AI tools can enter an organisation through different teams for different reasons. A small experiment in one department can become part of a core workflow before anyone has assessed the risk, agreed ownership or defined the rules.

As adoption grows, that governance gap becomes harder to ignore. This is especially true when employees are already questioning whether official routes can keep pace. Research has found that 62% of employees lack confidence that their IT teams are providing the latest AI and digital tools, while 57% do not trust their IT team to resolve issues quickly or effectively and 47% fear their IT team will not adequately protect personal or work-related data.

Security teams have an important role to play, but they cannot solve this alone. AI governance needs input from IT, legal, compliance, HR and leaders across the whole organisation. It must become a core part of the organization's operating model rather than a standalone policy.

That means establishing clear ownership for how AI is introduced, used and governed across the organization. It also means recognizing that governance is not only about stopping unsafe behavior. It is about enabling safe behaviorat scale.

Human oversight remains essential. AI can process information quickly, but it does not understand every business context, regulatory requirement or reputational consequence. People still need to challenge outputs and take responsibility for decisions that carry real-world impact.

Employees need guidance they can actually use

AI policies often fail because they are too abstract. Employees may be told to avoid sharing sensitive data and use approved tools, but that doesn't always help in the moment.

A team under pressure needs practical answers. Can this document be uploaded? Can this customer query be summarized? Can this dataset be analyzed? Which tool is approved for this task? Who should be asked if the answer is unclear?

Guidance needs to be specific, accessible and easy to apply during the working day. If employees have to search through long policy documents or wait days for an answer, they may default to the fastest available option.

This is where trust becomes critical. Employees are more likely to follow security guidance when they believe approved systems will help them do their jobs effectively. If they see official processes as slow, restrictive or disconnected from reality, they are more likely to look elsewhere.

Trust also depends on transparency. People need to understand why certain tools are restricted, how data is protected and what the approved route is designed to achieve. A policy that simply says “do not use this tool” does not build confidence. It creates a rule. Rules matter, but they work best when employees understand the reason behind them.

Security-by-design must apply to the workplace

Security-by-design is often discussed in relation to products and software development, but the same principle should apply to the digital workplace.

Too often, security is bolted on after a tool or process has already been adopted. By that point, controls can feel like an extra layer rather than a natural part of the workflow. Bringing security into the conversation earlier helps organizations identify risk before behaviors become embedded.

For AI, this means involving security, IT and governance teams before tools are rolled out widely. It also means listening to employees about what they need from those tools.

If approved AI systems are too limited, employees will work around them. If the access process is too slow, adoption will fragment. If guidance is unclear, teams will interpret the rules differently.

Understanding those pressures is central to reducing risk.

The easiest path should be the secure one

Shadow AI shows that workplace systems are struggling to keep pace with how work is changing. When employees turn to unsanctioned tools, it often points to a gap between what people need to do their jobs and what approved systems allow them to do.

The organizations that respond well will not be those that only add stricter controls. They will be those that make secure behavior easier to adopt than unsafe workarounds.

That requires clear ownership, practical tools, accessible guidance and security processes designed around real workflows.

In the age of AI, reducing risk means giving employees secure routes that are practical enough to use. When the approved path is also the easiest path, businesses can protect data without slowing people down.

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Agentic commerce has become the latest obsession in ecommerce.

The technology is certainly compelling. Instead of consumers browsing websites, comparing products and making purchases themselves, AI agents increasingly do the work for them. They search, compare, recommend and transact on our behalf. According to the hype, agentic commerce has the potential to transform the way we shop online.

It is easy to see why merchants are excited. For an industry under constant pressure to drive growth, improve efficiency, capture shoppers' attention, and deliver better customer experiences, agentic commerce promises a great deal. Some believe it could become as significant as the shift to mobile commerce. Others see it as the next logical evolution of ecommerce itself.

Whether those predictions prove accurate remains to be seen. New technologies often arrive surrounded by hype. Not all of them live up to expectations.

But retailers are taking agentic commerce seriously. Many are moving rapidly to integrate it into their businesses. No one wants to be left behind when a new technology goldrush gathers momentum.

That urgency is understandable. But it should also give us pause for thought. Every new development in ecommerce has brought with it new opportunities for fraud. Online payments made shopping more convenient but opened the door to card-not-present fraud.

Mobile commerce transformed how consumers shop but introduced new security challenges. More recently, AI has begun reshaping how businesses work while simultaneously giving fraudsters powerful new tools to scale attacks. There is little reason to believe agentic will be any different

The pattern is familiar: innovation creates opportunity, fraudsters adapt, and businesses scramble to catch up. Agentic commerce has all the ingredients to follow a similar path.

Part of the reason adoption is moving so quickly is that ecommerce growth has become harder. Competition is intense and customer acquisition costs remain stubbornly high. No wonder many merchants are searching for new ways to improve efficiency.

Early adoption of AI agents appears to offer the golden ticket solution. They promise faster transactions, less friction and greater automation at a time when growth is harder to achieve. The challenge is ensuring that risk management keeps pace.

Are we trusting AI agents too much?

One of the more interesting aspects of the agentic commerce debate is the level of trust many businesses are already placing in AI systems. That may sound surprising until you consider the reality facing many merchants today.

Customer fraud continues to grow. Refund abuse, chargeback fraud, promotion abuse and policy exploitation have become major challenges for ecommerce businesses. Our own extensive polling of merchants shows many place greater trust in the promise of AI agents than in their own customers.

On the face of it, AI agents are attractive. They appear predictable. They follow instructions. They don't deliberately manipulate systems for personal gain. But it's crucial to understand replacing one source of risk with another is not the same as eliminating risk altogether.

AI agents may not behave like human fraudsters, but they introduce new vulnerabilities that businesses are only now beginning to understand. More on that shortly.

The accountability problem

One of the biggest unanswered questions surrounding agentic commerce centers on liability.

If an AI agent makes a poor purchasing decision, where does accountability sit? If a fraudulent transaction takes place, who is liable? If an agent is manipulated into making purchases it shouldn't make, who is responsible for the resulting losses? The consumer? The retailer? The technology provider? The operator of the agent itself?

The problem is existing frameworks were built around human decision-making. Agentic commerce introduces a new layer of autonomy that blurs traditional lines of responsibility. At the moment, many of these questions remain unresolved.

That doesn't mean businesses should avoid agentic commerce. But it does mean they should think carefully about governance, oversight and accountability before deploying it at scale. Trust cannot simply be assumed because a transaction is being conducted by an AI agent.

Merchants need visibility over who is operating that agent, how decisions are being made and what safeguards exist when things go wrong.

Fraudsters will move quickly

If history teaches us anything, it is that criminals are among the fastest adopters of new technology. While businesses focus on the opportunities created by innovation, fraudsters focus on the weaknesses. Agentic commerce is likely to create plenty of both.

We will see attempts to hijack legitimate agents and manipulate their behavior. Fraudsters could also create fake agents that impersonate trusted services or brands. Criminal networks may also deploy their own autonomous agents to identify vulnerabilities and exploit them at scale.

Promotions, loyalty schemes and refund processes will almost certainly also become targets for increasingly sophisticated forms of automated abuse.

What makes this new environment particularly challenging is the speed of evolution. Most forms of online fraud still require a degree of human input. Autonomous agents have the potential to operate around the clock, making decisions, testing vulnerabilities and exploiting weaknesses at machine speed, as well as adapting their tactics automatically when they fail.

The result may not be more fraud in absolute terms, but we will see more sophisticated and fast-moving threats. That will require merchants to rethink how they identify risk, monitor activity and respond.

Proceed, but with your eyes open

None of this should be interpreted as an argument against agentic commerce. The technology has genuine potential. It could make shopping easier, reduce friction and unlock new opportunities for both consumers and businesses. But innovation and risk have always evolved together.

The companies that succeed in the next phase of ecommerce will not necessarily be those that move first. They will be the organizations that understand the risks as well as the opportunities. That means understanding their own data.

Maintaining visibility over customer and transaction behavior and continuously monitoring for unusual activity means controls can be adapted as new threats emerge.

In a world where both shoppers and fraudsters may increasingly be represented by AI, capturing diverse data at scale and knowing how to interpret it to protect against online crime and serve good customers better will become more important than ever.

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Fortune 500 companies are expected to operate more than 150,000 agents by 2028, according to an analysis from Gartner.

If achieved, it would represent a 10,000-fold increase compared to 2025, when enterprises averaged just 15 agents. Indeed, this shift toward agentic systems is the most significant architectural evolution of our lifetime.

With adoption accelerating so rapidly, governance is rising to the top of the AI agenda. Businesses of all sizes are seeking safeguards and systems of control to ensure they can trust the agents they work alongside, yet current methods often fail.

The data speaks for itself: although almost every global enterprise is using AI agents, only 12% have introduced a centralized governance approach. For most, governance remains piecemeal, exacerbating the chaos it is intended to control.

This disconnect raises a critical question: as AI scales rapidly, how can organizations maintain governance to manage it responsibly? Nearly every leadership team is facing the same problem: they want to harness AI’s full potential but also maintain control.

AI enthusiasm leads to AI sprawl

While most businesses recognize the value of agentic AI, and are experimenting and deploying various agents across their organisation, the risk of AI sprawl is high. The growing mix of customer-built and pre-built agents creates disparate agentic tools with no centralized strategy.

As enthusiastic employees develop their own agents – an approach which is increasingly common as the technology becomes more accessible - AI usage becomes siloed and inconsistent across the business.

This type of AI sprawl drives diverging standards and duplicated efforts, with research showing 94% of businesses are experiencing increased complexity, technical debt and security risk.

The autonomous nature of agentic AI, where systems interact with data and other agents in real time, means a lack of governance in the early stages can see AI sprawl quickly spiral out of control.

Fragmented governance is ineffective

Forward thinking businesses are investing time and effort into new compliance processes and building better safeguards to ensure AI outputs can be trusted. One such method is the use of human-in-the-loop models for key decisions, an approach currently used by 52% of businesses.

However, as AI scales further and more rapidly, new challenges will emerge. There is a risk that human-in-the-loop methods lead to unintended inconsistency, where employees spanning different teams, regions or departments introduce uncoordinated rules for access, security and usage.

Instead of enforcing control, this has the opposite effect. Businesses are then left grappling with governance systems which reinforce the fragmentation they were trying to overcome.

Centralized rules, centralized standards, centralized knowledge

The only effective way to maintain control of scaling AI is to build governance into the AI itself. Governance must be designed as part of the foundations of an agentic system; it cannot be added as an afterthought.

To ensure meaningful control, enterprises require centralized visibility into which agents are running, how they are connected and where their dependencies lie. This represents a major opportunity, given that only 12% of businesses had introduced a centralized governance approach as of January 2026.

Historically, manual governance fails because enterprises cannot keep pace with the speed and scale at which AI agents interact. One of the core governance challenges is coordinating agents so they do not duplicate work or conflict with one another.

This requires orchestrating agents through a central neutral system layer that connects them to a real-time understanding of enterprise architecture and its operational parameters, ensuring outputs remain secure, compliant and coherent.

By ensuring there is a centralized governance layer built into the enterprise, all agents can follow the same rules, comply with the same standards and draw from shared knowledge. The governance system can then evolve and grow alongside the business itself.

Governance doesn’t need to be slow to be successful

The modern enterprise is already shaped by legacy systems, fragmented architectures and technical complexity, but it is not inevitable for AI to follow the same path.

The explosion of agentic tools offers enormous potential for business productivity, even if it does not automatically create coherent, high-quality architecture. The biggest challenge organizations face right now isn't the capability of the models themselves - it's connecting AI to where real work actually happens inside the enterprise.

It is now up to each organisation to pick and choose the agents best suited to their organisation and prioritize their integration in a unified way. This means moving away from old habits – often where governance is added incrementally every time a new agent or application is introduced – to remove the risk of fragmentation and AI sprawl.

The optimal approach is one which works with the unified and agnostic governance layer built into the foundations of the AI system to combat technical debt at scale. Only then will enterprises be able to harness AI’s full long-term potential in a sustainable manner.

Governance should not restrict the speed of AI adoption; it can scale alongside it. The successful enterprise of the future depends on its ability to approach AI with speed and efficiency without compromising trust.

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The software industry has always been shaped by new tools that promise speed and efficiency. From high-level languages to cloud infrastructure, each wave of innovation has reduced friction and raised expectations. The current wave is AI-generated code, but this new era of innovation is different in both scale and consequence.

For the first time, developers can produce large amounts of working software without fully understanding how it functions. This shift is creating a new challenge that engineering leaders are only beginning to articulate, a concept we’ll call comprehension debt.

Technical debt is a familiar concept. Teams knowingly trade long-term maintainability for short-term delivery and accept the cost later. Comprehension debt is a similar concept but where that debt resides is different.

Whereas with technical debt is located within a codebase, comprehension lives in the people building the systems. When developers ship software faster than they can truly understand it, the gap between output and understanding of the codebase widens. Over time, that gap can grow to be a serious organizational risk.

This tension is already visible across the industry. The most recent Developer Survey found that 84% of developers use or plan to use AI tools in their workflow, yet 75.3% say they do not fully trust AI-generated answers.

The findings highlight a growing contradiction at the center of modern software development: teams are increasingly dependent on AI, while remaining cautious about the quality and reliability of its outputs.

The disappearance of productive friction

For decades, the early years of a developer’s career were shaped by friction. Junior engineers learned by wrestling with compiler errors, reading documentation, and debugging unfamiliar systems. The process was often frustrating, but it helped developers build mental models of how software behaves and understand not just what worked, but why it worked.

Today, AI tools remove a good deal of friction. A junior engineer can generate a functional service, build an interface, and resolve common errors within minutes. The experience feels empowering and productive, but the friction was never purely an obstacle, it also acts as a teacher. When that struggle disappears, some of the learning disappears with it.

This does not mean the next generation of developers will know less. In many ways, they will be exposed to more complex systems earlier in their careers. However, developers may begin to feel productive before they have gained the deeper intuition that traditionally comes from years of problem solving and debugging. The sense of progress may outpace genuine understanding.

Faster progression, weaker foundations?

AI is also reshaping career development. Traditionally, engineers progressed from writing small pieces of code to reasoning about complex systems and eventually designing architectures.

AI compresses the earliest stages of that journey and engineers are able to contribute sooner and move more quickly towards higher levels of responsibility. In the short term, this looks like success with teams being able to deliver faster, and organizations expand their talent pipelines.

The long-term risk arises down the road, when organizations find themselves with engineers who can produce solutions quickly but struggle when those solutions fail in unexpected ways. Debugging, architectural thinking, and systems design all rely on deep mental models that are built slowly through experience. If those foundations are weaker, the consequences may only emerge months or even years later.

Enter vibe coding

Alongside this shift, the explosion of vibe coding as a practice, which entails a workflow built around prompts, rapid iteration, and intuition rather than deliberate engineering, has further exacerbated comprehension debt. There is genuine value in the vibe coding approach. Rapid experimentation has always driven innovation, and AI has the ability to unlock creativity in remarkable ways.

The danger appears when this becomes the default mode of production rather than a tool for exploration. When speed becomes the dominant priority, understanding can quietly become optional. Teams may accept generated code without fully reviewing it, and systems may be deployed that few people feel confident enough to explain in detail. The result is not immediate failure, but a gradual erosion of collective understanding.

Some skepticism about new tools is inevitable. Every generation has worried that higher levels of abstraction would weaken engineering skills. However, AI introduces an important difference. Previous tools abstracted complexity while still requiring developers to reason about behavior.

AI can now generate working solutions without the developer forming the underlying mental model at all. That changes not only how software is written, but also how expertise develops.

Building AI-native engineering cultures

The answer is not to resist AI adoption as the benefits are too significant to ignore, and organizations that fail to embrace these tools risk falling behind. The real challenge is adopting AI while protecting the growth of genuine understanding.

Some organizations are beginning to view comprehension as an explicit goal rather than an assumed outcome. Engineers are encouraged to explain generated code in their own words and document the reasoning behind AI-assisted decisions. This approach reinforces that understanding is part of the deliverable, not a nice to have.

Code review is evolving too. Historically, the focus was whether the code worked and met quality standards. Increasingly, teams are also asking whether the developer truly understands what they have produced and review conversations are shifting towards explanation, walkthroughs, and shared learning.

Hands-on learning also remains essential. Some teams are intentionally creating environments where AI plays a secondary role, whether through debugging exercises, architecture discussions, or smaller projects built from scratch. The goal is not to reduce productivity, but to preserve the experiences that build engineering intuition over time.

The most effective organizations are not choosing between AI and traditional development, but rather thoughtfully combining the two approaches. AI is increasingly used for repetitive tasks and scaffolding, while humans retain responsibility for critical thinking, system design, and architectural judgement.

This moment places a new responsibility on engineering leaders. Productivity is accelerating rapidly, yet learning must keep pace. If we succeed, AI will help create a generation of developers who build faster and think more deeply than ever before.

If we fail, we risk creating teams capable of shipping almost anything, yet uncertain about how the products they build truly work or how to fix them when something inevitably goes wrong.

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When organizations first started formalizing AI policies, the problem they were solving for was narrow: keep employees from sharing sensitive data with a public model. A clear, manageable risk with a clear, manageable response.

What those policies didn't account for was how quickly AI would evolve, or how far the organizational guardrails would fall behind it.

Most companies are still running governance frameworks written for that original moment, applied to a technology that looks nothing like it did then.

What happens when outdated governance meets AI agents

The governance frameworks most organizations are running were designed to manage exposure, not enable work—and that design shows.

Organizations are running agents that autonomously query databases, update records, and trigger downstream workflows, across connected systems, at a pace no approval cycle was designed to match.

Those agents can be governed, but vague policies won't cut it. The governance has to be specific enough to translate into actual system-level constraints, like which systems the agent can access and under what conditions.

Governance that can't keep up with the technology it covers isn't governing anything. It's just documentation.

4 questions to audit your AI governance framework

1. Can your employees find out right now what AI can access on their behalf?

When someone deploys an AI tool or agent at work, that tool is often connected to real systems (email, CRM, databases, and calendars) on behalf of the person using it. Without a governance framework that clearly outlines what those connections look like or what the tool is authorized to do within them, your organization doesn't have a reliable way to assess what's actually exposed.

To make this clear, build and maintain a permissions inventory: a living record of which AI tools are approved, which systems each tool can connect to, what actions it's authorized to take, and which team or individual owns each integration. If your organization uses an AI governance platform, much of this can be tracked and managed there rather than maintained manually.

Either way, it doesn't need to be overly sophisticated out the gate. But it does need to be current discoverable.

2. If an AI agent takes a wrong action, how quickly can you revoke it?

Agents take actions, sometimes sequences of them, across connected systems. When something goes wrong, the ability to stop it quickly depends entirely on how access was set up in the first place. If credentials are scattered across sessions, scripts, and individual tool configurations, revoking access to one system means tracking down every place that credential was used.

Consolidating agent credentials under a centralized auth system changes that. Each agent operates under a defined identity with explicit, scoped permissions, so removing access is a single action with a clear audit trail and no cleanup exercise required.

Standards like the Model Context Protocol (MCP) are designed specifically for this. MCP can give agents a structured, auditable channel to access external systems through OAuth rather than credentials embedded in prompts or scripts. For organizations evaluating how to centralize agent access, it's worth understanding what's possible when the connection layer itself is built with governance in mind.

3. Does your governance policy describe what's permitted, or only what's prohibited?

A policy built around prohibitions tells employees what they can't do, but it doesn't tell them what they can. For humans, that leaves a gray zone they have to interpret. For agents, the problem is more concrete: an agent given a prohibition list and no permitted-use definition has no reliable boundary for what falls inside or outside its scope.

The fix is to define use cases affirmatively: approved tools, permitted system connections, and authorized actions. That gives both employees and the agents they deploy a clear framework to operate within, and it makes the governed path the default one.

4. Does your governance framework specify what your AI agents can access and act on, at the system level?

A framework that addresses AI in general terms (for example, responsible use and acceptable outputs) gives humans enough to make informed decisions about how they use AI.

Agents require something more specific. When you deploy an agent, it operates based on two things: the instructions and context it's been given, and the systems it's been granted access to. If your governance framework isn't precise enough to be included in that context, then it isn't governing the agent. Governance that actually covers agents defines access at the system level: which systems, which actions, and under what conditions.

In practice, that looks like provisioning agent access through an identity and access management system, assigning each agent a defined role with scoped permissions, and logging every action it takes against those permissions.

Build for what's next, not just what's now

AI capabilities are moving fast enough that some of what's true today about how agents operate will look different in a year. Governance frameworks that are current now will need to be revisited.

Getting the fundamentals right today matters precisely because the landscape keeps shifting. That means treating governance as an ongoing operational practice: reviewing access definitions when new tools are deployed, auditing permissions when agent capabilities expand, and updating permitted-use frameworks when the technology changes scope.

The cadence matters as much as the content.

The principle that holds, regardless of what AI looks like next year, is this: governance that can't keep pace with the technology it's supposed to cover isn't governing anything. Building governance that's designed to evolve is the work.

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The AI buildout is well and truly underway with OpenAI CEO Sam Altman leading the charge, having made various deals with companies including Oracle and Nvidia to guarantee the infrastructure needed to train future AI models is installed. But as this ensues, the spotlight has been thrown on how much energy these models will need.

Eating machines

Altman was speaking during an AI summit in India earlier this year when making the remarks to The Indian Express.

The OpenAI chief was being questioned about the substantial amount of energy that AI has already been consuming – and will be projected to continue consuming – for both training and inference. Bringing these models online, after all, and keeping them running require a huge amount of resources, not only in terms of the energy to power the data centers, but the water for cooling, and the components and resources in building the hardware.

Altman's defence hinged on the idea that people, too, require plenty of energy in order to reach utility — while AI can be trained much quicker. This view, in essence, frames machine intelligence as a like-for-like competitor with human intelligence.

Energy efficiency

Altman argued that humans are deeply inefficient, and that you should compare the total energy spent to create a human expert versus a machine expert. There's also an argument that energy efficiency of AI could improve over time.

But detractors were scathing in their criticism of this entire point of view – saying this framing doesn't take into account the fact that the human brain operates on roughly 20 watts of power. This isn't to mention the ethically gray and dehumanizing nature of the remarks.

OpenAI is building an AI-powered smart speaker nobody wants. That is, if you believe the Bloomberg report from Mark Gurman and you've read his description of said rumored device.

According to the report:

"OpenAI believes the product’s defining feature will be its personality and ability to connect on a humanlike level with users. The speaker incorporates mechanical elements that can move on their own, creating a sense that it is alive and not just an object responding to commands. The machine also will draw on personal information such as emails to better understand its owner."

The news sources, it appears, come from an insider who decided to spill all the juicy details mere hours after Apple dropped a blockbuster trade-secrets lawsuit on OpenAI's head. OpenAI claims it's done nothing of the sort, and recent reports say that Apple's claim that the AI giant has not even responded to Apple's earliest concerns was based on it potentially incorrectly identifying the former Apple employees who left to join OpenAI (allegedly with Apple trade secrets in tow).

Apple's concerns here are twofold: First, these former employees had access to many of Apple's secretive product development details and may even have asked recruits to share fresh details when they approached them to interview for jobs at OpenAI. The other concern is that Apple is already far behind in the AI race, and if Apple's plans for Siri, AI, and a potential robotic desktop home assistant were also leaked, it could harm its ability to catch up in multiple market sectors.

This latest news, which may or may not be accurate, should put Apple's fears to rest.

OpenAI is apparently not building something that could ably compete with any of Apple's key hardware or future hardware initiatives.

First of all, there's the smart-speaker-ness of the whole rumored OpenAI concept. There are already too many smart speakers on the market, many of them with their own smart assistants. Amazon, for instance, is smack in the middle of trying to convince millions of customers that not only do they need Echo devices throughout the home, but they need the AI-powered Alexa+ to guide them through their smart home experiences and, to some extent, their lives.

Apple has its own HomePod, Siri-infused speakers, which may get considerably more powerful with the Gemini foundation model-backed version arriving this Fall.

Put another way, smart speakers are a known quantity in the home consumer electronics space, and I think what most tech companies are realizing is that people like and use them, but mostly in limited ways: they want music, occasional answers to simple questions, and voice control of their smart home devices. That's it.

Why does my speaker think it's alive?

OpenAI appears to be prepared to offer something different: a personality-filled speaker that can watch you, move to engage, seem alive, and generally make you feel uncomfortable.

Obviously, that would not be the objective, but it could be the result. Who needs a speaker that quietly watches you as you walk from your kitchen to the den, waiting and hoping for you to say, "hey ChatGPT, what's up with the Strait of Hormuz today?"

In my home, we have a Psync smart security webcam with one oddball feature: it has a motorized body that can turn almost 360 degrees on its base and lift its thin, rectangular face and camera to keep track of people and alert me to intruders. However, most of the time, it's just watching us move around the house, and I can tell you that my family hates it. Sometimes I come home and find its face forced down so it can't pop up and track anything.

Now, imagine a larger and far smarter OpenAI AI smart speaker in your home, watching, waiting, chiming in when you don't want it to, and generally making people feel uncomfortable.

This will not be the breakout hardware hit OpenAI is hoping for.

Look, I was under the impression that OpenAI (really Jony Ive and Sam Altman) were working on an AI wearable. I didn't love that idea either, but it was a lot less creepy than this.

So, Apple, chill out. OpenAI's plans are no threat to you, even if they do allegedly have a bunch of insidery Apple information.

As the common phrase goes – ‘patience is a virtue’. The ability to endure, wait and yield to time without complaint is widely seen as a positive personality trait. Yet patience is situational, and everyone has a limit.

In a customer service context, people will wait longer when the stakes are high, or when they trust that delays will lead to better security, accuracy or care. If they’re dealing with another human, they also tend to be far more forgiving.

Alongside that limited patience, customers are also increasingly distracted. The average Brit now sits on over 1,000 unread emails in their inbox, according to recent research, alongside juggling around 25 non-work notifications a day.

People are not short of communication, but saturated. Attention is at a premium, and anything irrelevant is quickly deprioritized or disappears entirely.

This is particularly acute for the 36-55-year-old cohort, which boast the highest unread email counts, and the greatest pressure to stay online for work. Two-fifths of 36-50-year-olds say they feel more disconnected than ever, despite increased digital interactions.

Balancing careers, caregiving, mortgages and performance metrics, the attention and mental load on this group is significant. If brands are adding irrelevant noise and difficulty into these environments, tolerance is low.

This simultaneous feeling of burnout and impatience is heightened by the fact that the instant nature of the attention economy, and the incentives of digital platforms, has rewired expectations.

If there is no sense of progress or urgency conveyed with a digital service, then reassurance falls away and the chances someone will give up on the process rapidly increases. Clear, proactive explanations, combined with customer experience design that maintains continuity across every channel and interaction are vital.

Setting clear expectations

Digital saturation and the attention economy have also meant that first impressions are decisive. If in those initial moments of engagement there is any sense of delay or stalling, people will reconsider if they need the product or service in the first place.

These early moments are therefore a critical opportunity to build seamless customer journeys, closing these ‘patience deficits’ by finding and fixing areas that cause frustration. When there are unavoidable moments where waiting is required, brands must turn these into opportunities to strengthen loyalty and trust.

Customers want assurance that every click, confirmation or verification step serves a purpose. Uncertainty is the true enemy of patience, so give customers better visibility and control to help build trust.

Other industry research found that 25% of consumers there think ‘transparency about what’s happening and why’ is one of the most important things to them in a digital customer experience, with 45% valuing clear instructions and easy-to-follow steps. If there’s an action they don’t need to take or unnecessary duplication, remove it.

Poor design can lead to digital impatience, and exacerbate the overload of digital admin. Ensure that digital and automated channels are fast, reliable and transparent when it comes to waiting times. Competitive advantage will come from being the most considered, not the loudest.

Turning impatience into opportunity

At the same time, when customers know brands are protecting their data, there is an opportunity to earn patience through careful application of friction. Digital speed bumps like two-factor authentication, framed in the right way, can become symbols of care, not inconvenience – so in designing the customer journey, brands should take advantage.

The reality of digital patience is made more complex by the fact that more people are interacting with machines for customer service than ever before. AI agents, designed and used effectively, have raised expectations – but when AI gets stuck, misunderstands intent or is otherwise poorly designed, it backfires and becomes counterproductive. In those moments, many of us prefer to just talk to a human.

The purpose and context of interactions matter, so organizations must take care to match automation to the complexity and stakes of each task. Examine which tasks are best for AI agents to take on, and be clear about when AI is in use, explaining its role in plain language.

For those tasks which require more reassurance, empathy and accountability, human alternatives may be preferable. In these situations, we tend to be slightly more patient – 84% will stay patient on the phone with a real person, perhaps because we know we’re more likely to be able to connect on a human level, and get some understanding and reassurance in response to our enquiry.

If a service is too slow, however, then more than half of consumers say that delays lead them to think less of a brand, leave negative reviews, or warn others to steer clear.

Brands should provide the choice of human support and digital self-service tools in a way that reflects these dynamics. When switching between the two, brands should also carry context over, so that customers never need to repeat themselves.

Digital patience is a precious resource. Poor design which frustrates and builds impatience can see it easily lost, but with care, it is equally easy to earn. When brands anticipate frustration and design for reassurance, they convert fleeting attention into enduring trust, and find opportunities to strengthen relationships and loyalty.

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Every day, millions of people type a web address into their browser, usually in a flurry of rapid keystrokes, and arrive exactly where they intended.

However, a small but significant number of people don’t.

They might miss a letter, type an extra letter in their haste, or get some letters mixed up. Instead of hitting linkedin[.]com, they hit linkdein[.]com. Those mistakes gave way to one of the internet’s least glamorous destinations – the parked domain.

Most internet users have encountered them at some point, even if they didn't know what they were looking at. Typically, a parked domain would just be a sparse, messy page filled with adverts and a search bar with very little else.

They existed because someone, somewhere, recognized that in the early days of the internet a percentage of users would inevitably mistype a popular website – so they registered the most similar-looking domain names for themselves in a move known as “typosquatting” and earned advertising revenue from the resulting traffic.

If just 0.1% of the millions of people accessing amazon[.]com accidentally went to the amazn[.]com domain they’d bought, that’s still a worthwhile payday. It was a mundane corner of the digital economy, built on convenience, coincidence, and the occasional typo.

History can be a harsh teacher, but it can also sow complacency. In 2026, a lot of security teams still regard parked domains as little more than lazy digital billboards – inconvenient and annoying, but not a meaningful security concern.

However, the infrastructure surrounding parked domains has evolved considerably from the amateurish, pop-up-ridden advertising pages of the early internet. What was once a simple case of opportunistic domain monetization now sits inside a far more complex ecosystem of advertisers, brokers, and traffic distribution networks.

In many cases, a user's accidental visit no longer ends on a parked page at all. Instead, it triggers a journey through a chain of intermediaries operating largely out of sight. Somewhere along that journey, legitimate advertising can give way to fraud, scams, and malware distribution.

In other words, one of the web's most familiar and overlooked mechanisms has become one of the most lucrative and insidious vehicles for cybercrime.

From Mistype to Malware

The transformation of parked domains from digital curiosities into cybersecurity risks has been subtle, and that’s one of the reasons it’s so dangerous. For decades, the model followed the same patterns – a user would land on a parked domain, see a collection of banners, click on something accidental or otherwise, and generate a small amount of revenue for the domain owner.

It was cynical, but at least it was transparent because users could at least see where they had ended up and decide for themselves what to do next – usually just close the tab and go where they meant to. The only real danger here came from the occasional misleading or malicious ad rather than the mechanics of the domain itself.

Today things are different. Changes within the online advertising industry, including tighter policies around traditional domain monetization, have encouraged cybercriminals and fraudsters to try new approaches to keep the train of monetization moving.

Increasingly, users who arrive at a parked domain don't encounter a parked page at all. Instead, they’re immediately redirected elsewhere through a process known as “zero-click advertising”, sometimes referred to as direct search.

What appears to be a simple typo can trigger a rapid auction in which a user's visit is bought, sold, and passed between multiple advertising partners before they ever see a destination website. Most of this activity unfolds in fractions of a second and entirely beyond the user's view, and while many of those transactions remain legitimate, the sheer complexity of the ecosystem creates opportunities for abuse.

Somewhere within that chain, traffic can be acquired by actors whose interests extend far beyond advertising revenue, opening the door to scams, malware, fraudulent software, and a host of other malicious outcomes.

The Malvertising Economy

One of the reasons parked domain abuse is still underestimated and difficult to pin down is that the attack path rarely follows a straight line. When most people imagine a cyberattack, they picture a malicious website waiting at the end of a link, ready to ensnare an unsuspecting user.

But in this case, by the time a user reaches the content they're ultimately shown, their traffic may have already passed through a maze of advertising exchanges, brokers, redirectors, and cloaking services.

Each participant sees only a fragment of the overall journey, making it remarkably difficult for the “good guys” to determine which “bad guys” are responsible for what. It’s a little like trying to investigate a crime scene where the evidence constantly rearranges itself.

The cowardly threat actors involved in this type of cybercrime exploit this ambiguity. They use sophisticated cloaking techniques which allow them to examine visitors before deciding which content to serve up – where are they based? What kind of browser are they using? What operating system is their device running?

A security researcher in California might see a harmless landing page, while a finance broker in London might be served up a credential harvesting scam. This selective delivery makes malicious activity harder to detect and even harder to reproduce.

What’s worse, parked domain abuse is rarely aimed at a specific industry or organization. The actors deploying parked domains are usually financially motivated, and their primary interest is in acquiring traffic, so they’re not going to discriminate.

Once they’ve ensnared a victim, they become a commodity moving through an invisible marketplace where every click has value and every redirection creates another opportunity for exploitation.

The blind spot in traditional security

So where does all of this leave defenders? Parked domain abuse doesn’t behave like a conventional cyber threat. While security teams are used to investigating suspicious websites, malicious files, or compromised accounts that leave a relatively obvious trail, parked domain campaigns are different because the underlying traffic distribution is constantly changing.

The same typo domain can send one user down an entirely different path than the next. By the time an analyst attempts to recreate what a victim experienced, the route may no longer exist and any “evidence” has effectively evaporated. How do they defend against something they can't see or recreate?

One thing is guaranteed – regardless of how many redirects, intermediaries, cloaking systems, or advertising platforms sit between the initial typo and the final destination, every step in the journey depends on the domain name system (DNS). Often described as the internet's address book, DNS is responsible for translating domain names into the destinations users ultimately reach.

Put simply, each lookup leaves behind a breadcrumb that helps reveal relationships that would otherwise remain hidden, and that visibility has allowed researchers investigating typosquatted versions of well-known domains to follow the trail beyond the initial deception. Patterns start to emerge between seemingly unrelated cases of malware, involving the same parking providers, cloaking services, and traffic distribution infrastructure.

By examining historical DNS records and mapping the relationships between domains over time, it has become possible to connect incidents that appear to be unrelated and expose the networks operating behind them. Instead of playing “whack a mole” and chasing surface level domains, DNS mapping has allowed defenders to target the entire machine.

The greatest danger posed by parked domains isn't the typo itself, but the assumption that the infrastructure behind that typo is benign. For years, parked domains occupied a strange corner of the internet, largely ignored by security teams and rarely considered worthy of serious scrutiny.

But today, they offer cybercriminals something far more valuable than advertising revenue – access to legitimate systems, trusted business models, and vast streams of user traffic that can be manipulated and monetized at scale.

As threat actors continue to refine their use of cloaking, traffic distribution, and advertising networks, the distinction between legitimate online activity and malicious activity will become increasingly difficult to spot from the outside.

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When a trusted friend recommends a product or service to you, it can alter the way you think about the potential purchase. You tend to lower your guard, with the knowledge that some of the filtering work has already been done on your behalf.

This is particularly true when that friend understands your tastes and priorities, and how much you’re looking to spend; the recommendation carries weight before you even visit the website or look at the product page. By the time you click, a large part of the decision has already been made.

Now there is evidence that AI recommendations can have a similar effect on consumers.

We recently analyzed web traffic and conversion data from more than 35,000 ecommerce brands using Shopify and it revealed something significant: referrals from AI tools such as ChatGPT are converting at an average rate of 3.6%, compared with 1.23% for traditional Google search traffic. AI referrals are also generating around 30% higher revenue per session.

For ecommerce businesses, these findings point to an important change in consumer behavior that presents both an opportunity and a challenge.

The buying journey is changing

For years, digital marketing strategies have been built around search engines. A user enters keywords, compares links, visits multiple sites and eventually makes a decision. Brands compete to win the click, then persuade the customer after they arrive.

AI is changing the sequence.

Consumers are no longer simply typing broad phrases like “best running shoes” or “cheap Bluetooth speaker”. Instead, they are asking the likes of ChatGPT highly detailed questions tailored to their exact needs.

Where previously the user might have received a list of product websites to explore, now they are being given a specific tailor-made recommendation.

In effect, AI platforms are compressing the consideration stage of the buying journey. Much of the evaluation happens before the consumer reaches the brand’s website. By the time they click through, they already have a degree of confidence in the recommendation they have been given – much like the case of the recommendation from a friend.

This creates a different type of visitor altogether.

Traditional search traffic can often be broad and exploratory. AI-referred visitors, however, increasingly resemble ‘pre-qualified’ leads. They arrive with clearer expectations and stronger buying intent, which helps explain the higher conversion rates.

Why visibility now means something different

For brands, the opportunity is clear: higher intent traffic generally means stronger revenue per visitor and more efficient acquisition.

The challenge, however, is that many ecommerce businesses are still measuring success using frameworks built for the ‘traditional’ approach.

A large proportion of marketing strategies remain heavily focused on traffic volumes, click-through rates and keyword rankings. Yet AI recommendation systems rely on different signals. Visibility inside AI-generated answers depends less on traditional advertising tactics and more on credibility, authority and contextual relevance across the wider web.

Brands are no longer only competing for search rankings. They are competing to become trusted sources within the information ecosystem AI tools rely upon.

That has major implications for how companies think about content and discoverability.

Reviews become more influential because AI systems frequently incorporate them into recommendations. Third-party editorial coverage matters more because it contributes to authority and trustworthiness. Community discussions on forums and social media also gain importance because they help establish credibility and context.

The businesses that perform well in AI-driven discovery are likely to be those with strong reputations spread consistently across multiple trusted sources, rather than those relying purely on aggressive performance marketing.

Rethinking marketing measurement

There is another important lesson in this data. Many businesses may currently have an incomplete understanding of which channels are genuinely driving growth.

One of the perennial challenges with digital advertising is ‘attribution distortion’. In simple terms, platforms optimize for conversions, often retargeting existing customers or users who were already close to purchasing. This can create inflated perceptions of acquisition performance.

At the same time, AI-referred traffic may still be under-measured inside many organizations because it remains a comparatively new source of inbound visitors.

The danger is that businesses continue over-investing in channels that appear successful according to legacy metrics while underestimating emerging forms of high-intent traffic.

This matters because AI-driven discovery is likely to become more important over time.

Consumer expectations are evolving rapidly. People increasingly want answers tailored to their exact circumstances rather than broad lists of generic options. AI interfaces are naturally suited to that type of interaction because they can process nuance and context in ways traditional search engines struggle to replicate.

What brands should do next

Brands therefore need to rethink not only where they advertise, but how they present their offer online altogether.

That starts with understanding how the business appears across the wider digital landscape. Are reviews consistent and trustworthy? Is the brand being referenced by credible publications and communities? Is product information clear, accurate and useful?

It also means creating content that answers real consumer questions in detailed and genuinely helpful ways, rather than simply targeting high-volume keywords.

None of this means traditional search is disappearing overnight. Search engines remain hugely important, and publishers continue to play a central role in shaping the information AI systems consume and reference.

What is changing is the path consumers take before making decisions.

The era of winning attention purely through visibility is gradually giving way to an era of winning trust before the click ever happens.

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AI took enterprises by storm, with many opting for integration as fast as possible in fear of falling behind the more ambitious tech adopters. But speed alone isn’t always an advantage and as a result, 95% of enterprise pilot programs still failed to deliver measurable financial returns just last year.

Now that we’re a few years past the initial AI explosion, the pressure is on to prove true ROI from these projects.

Businesses have most frequently poured resources into AI tools aimed at boosting productivity and automating workflow in hopes to target the most universal, longest standing business goal: doing more with less. But what leaders should be doing is identifying where the technology can solve the biggest issues specific to today’s business climate. One of those issues is cash flow forecasting.

This year, 52% of American CFOs named cost management as their most worrisome internal concern. While a well-oiled cost management strategy remains critical for creating a strong financial cushion and remaining resilient, balancing fixed operations with constantly shifting real world variables is never easy.

As companies increasingly look for ways to remain nimble and improve decision-making, those that can leverage AI to forecast trends in cash flow demand, churn risk, and spending pattern shifts will find themselves on a quicker path to ROI. Doing so requires harnessing the right data, and these financial signals are hidden in the transaction layer.

While businesses have long mined transaction data for traditional analytics and reporting, it’s far under-utilized in AI strategies. There needs to be a shift from viewing these insights as archival records of past performance to real time indicators of what’s to come.

Making revenue forecasting more adaptive

There are several revenue indicators that lie within bottom funnel operations that AI has the ability of turning into actionable insights. From frequently adjusted terms within contract renewals to the average time it’s taking customers to finalize transactions, purchase signals like these can help AI systems make smarter predictions about demand or accounts receivable.

To provide a more granular view into the value of this data layer, let’s look at upgrade or renewal activity for example. Customer retention is a key element to maintaining predictable cash flow and is among the first to go during an economic shakeup.

Tracking accounts that consistently upgrade a product or service on time to see that they suddenly miss a milestone could immediately flag eventual churn risk. These deviations should also be compared across similar accounts to segment risk based on geography, product lines, or size and industry.

From there, leaders can act proactively with strategies like targeted discounts or incentive measures to encourage retention. Alternatively, accounts that are expanding faster than expected could provide predictions into other customers who might be ready for higher value offerings.

Identifying cues like those that often precede cancellations, along with delayed payments, reduced usage, or smaller order size for instance, can equip finance or leadership teams with rolling forecasts. Whereas on the other hand, monthly or quarterly forecasts typically only rely on historical averages and don’t provide the real time guidance needed for a quickly shifting marketplace.

This can manifest into a powerful decision-making engine. One that is dynamic enough to support flexible cost management strategies. Seeing where cash-flow is moving allows leaders to make more informed decisions.

For example, if it’s consistently being found that these customers are upgrading at slower rates then it may be a good indicator to preemptively reduce inventory or relax timelines for product development teams. In turn, leaders can avoid allocating too many resources to demand that may not end up materializing.

This also gives teams more flexibility to adjust spending and production before any cash flow pressure sets in.

An important element to keep in mind is that these purchasing behavior insights often sit across separate systems. While sales teams may have insights into average order values, only legal or finance may know how payment terms are changing across clients.

First mapping where all of these metrics currently live is critical to then unify them into one place for predictive models to cross-analyze everything against each other and make stronger recommendations.

Connecting to ROI directly

Many businesses have revolved their AI projects around generative AI for efficiency gains in producing content or developing software for instance. But not only are these task-level initiatives harder to prove a measurable impact from, in some cases they end up hurting productivity in the long run with added time spent reviewing and editing AI outputs.

When it comes to analyzing data for forecasting and predictions, AI has shown immense value and is tied to more tangible business outcomes. Now, forecasting real time insights tied directly to revenue can help leaders remain adaptable in an increasingly unpredictable economy, offering a fast track to ROI on these projects.

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For years, the central question around AI was whether it really works the way much of the market’s hype says it does.

That debate is settled, and we’ve seen what AI can do. The more important question now – and one most organizations still aren’t asking – is whether we’re working alongside it the right way.

That shift in framing matters. Budgets have been allocated, tools deployed, and pilot programs have graduated into full-scale production.

And yet, something is still missing.

IBM’s Institute for Business Value found that only 25% of AI initiatives have delivered their expected ROI, and just 16% have successfully scaled across the business despite years of investment and genuine enthusiasm for what the technology can do.

The problem isn’t AI itself; the bottleneck stems from what organizations have or have not built around it.

That’s a leadership problem, and fixing it will require more than buying better tools or scheduling more trainings.

Stop Investing in the Wrong Places

The instinct for most organizations has been to buy the latest platforms, stand up a few pilot programs, and bring in a vendor to train their workers. That approach addresses the surface-level challenge, but misses the greater underlying issue. The truth is the greatest barrier to AI maturity is the lack of investment in the human infrastructure needed to support it.

The companies seeing the strongest AI outcomes are rarely those with the most sophisticated or expensive models. They’re the ones that have fundamentally rethought how their people work. Among organizations that Boston Consulting Group designated as AI leaders, roughly 70% of resources went towards people and process changes, 20% to IT infrastructure, and only 10% to the AI models themselves. Most organizations have that ratio backwards.

When leaders become hyper-focused on deploying the right tools and launching the right uses cases, they neglect the organizational muscles that are essential to using AI responsibly and consistently. All the tools in the world won’t close that gap without the right training, guardrails, and policies to back them up. And building that support structure must be a leadership priority, not an afterthought.

The Productivity Gains Are Real, But Fragile

None of this is to say AI isn’t creating real value. It absolutely is – at least, for the companies using it well. But those gains are more fragile that many leaders realize. They evaporate when companies lack support for their employees across their interactions with the technology, or when they fail to clearly communicate where human judgement and critical thinking are still essential.

The data here is hard to ignore. Among employees who use AI on the job, less than 8% report receiving extensive training with their tools. And that number has barely budged despite a sharp increase in daily usage. Moreover, 60% say it often takes them longer to figure out how to accomplish a task with an AI tool than it does to simply do it themselves.

Companies are deploying AI faster than they are enabling people to use it, and in doing so, may be creating exactly the friction and confusion they were trying to eliminate.

What Leaders Owe Their People

This is where leadership has to show up differently. The gap between AI potential and AI reality isn’t going to close through procurement decisions or new rollout announcements. It closes with deliberate, ongoing investment in people. That means three things:

Focus trainings on people, not just tools: AI is evolving faster than training curriculums can keep up. Invest in training role-specific judgements, helping people understand where AI makes them faster and where it introduces challenges or risks.

Move beyond adoption rate metrics: If 80% of your organization is using AI and productivity is still flat or declining, adoption is the wrong metric. Measure time-to-completion on real work tasks and be honest about what you find. Some use cases that are slowing people down simply shouldn’t be using AI.

Stop treating AI policy as a compliance checkbox: Companies getting this right have built AI governance into how they plan and execute work daily, not appended into and acceptable use document. That means leaders who model where they use AI and where they don’t, and who are willing to explain why.

Making AI Potential a Reality

The technology is ready. But leaders need to be able to do more than allocate budget and monitor usage.

They need to decide when AI should and should not be used, what to rebuild rather than automate, and how to support their teams throughout all of it.

Those are the questions most organizations are still failing to ask, and until they do, the ROI gap isn’t going anywhere.

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The National Cyber Security Centre (NCSC)’s recent advisory detailing how the Russian cyber actor APT28’s exploited vulnerable routers to enable DNS hijacking highlights a dangerous blind spot in enterprise security today.

Traditional perimeter-based security models are increasingly failing at the edge, and this should be uncomfortable reading for any security leader still anchoring their strategy to perimeter defense.

This is the same story we saw play out in 2024, when Chinese state sponsored bad actors linked to Volt Typhoon exploited an unpatched, end-of-life FortiGate 300D firewall to compromise a domain admin account, escalate privileges, create a new user, and establish persistence deep enough to survive a device restart.

One unpatched edge device. One breach vector. Total access. The perimeter didn’t just fail; it handed attackers the keys. According to the FBI, this breach remained undetected for over 300 days, and the same exploit was used to gain access to over 100 separate utility companies across the US.

These two incidents aren’t outliers. They’re a pattern. And the pattern is telling us that the old model is finished. Add to this the recent news about the power and sophistication of the latest generation of agentic AI tools such as Anthropic’s Claude Mythos, and it really is time to act quickly.

The problem with perimeter thinking

Legacy security was built on a simple assumption: draw a line between trusted internal systems and untrusted external ones, defend the line, and you’re protected. It made sense when company data lived in on-premises data centers and employees showed up to the office. The boundary was real and defensible.

That world is gone. Edge nodes are now everywhere; in factories, retail stores, utility substations, and customer premises, and the clean network borders of the past have either blurred or disappeared entirely. Edge devices themselves rarely have dedicated security capabilities. IoT and OT systems in particular frequently lack the robust features needed to detect and resist advanced threats.

Worse, software-based management tends to fail precisely when it matters most. When the software layer is compromised or unresponsive, organizations lose visibility and control at the exact moment they can least afford to.

Then there’s credential theft. According to Verizon’s 2025 Data Breach Investigations Report, the human element is a factor in 60% of breaches and attackers have become highly effective at exploiting it. They don’t need to break in, they walk in, using legitimate credentials, through the front door.

Once inside, now often augmented by AI capabilities, they move laterally from a single-entry point across operational systems, compromising entire environments in minutes. A security architecture built around password authentication offers almost nothing against this type of attack.

What resilience actually looks like

The security conversation has shifted. For years the focus was on keeping attackers out. That is still necessary, but it’s no longer sufficient. What organizations are increasingly recognizing is that resilience depends just as much on what happens after a compromise; specifically, whether security teams retain visibility and control when it counts.

Lost visibility during an incident, even a contained breach, can quickly spiral and escalate. This is driving a serious rethink of how distributed infrastructure is managed, particularly as edge environments extend further across factories, retail locations, utilities and a myriad of remote sites.

Out-of-band management (OOBM) is one approach gaining real traction here. Rather than relying on the production network for management traffic, OOBM operates on an entirely separate, highly secure parallel path. That means that even when the main network is compromised or down, edge devices remain manageable, visible and controllable.

Crashed devices can be remotely rebooted, or even re-configured. Powered-off devices can still be reached. And critically, administrative access is separated from the primary production network targeted by attackers, thereby reducing exposure to the credential-based attacks that are now the most dominant breach vector.

The operational benefits are real too: fewer costly emergency site visits, faster data recovery times, and preserved control during high-pressure incidents when software management tools have gone dark.

The reckoning

What’s becoming clear is that perimeter security, on its own, is no longer a viable strategy. The edge has expanded too far, credentials are too easily stolen, and attackers are too fast once they’re in.

The organizations that will weather the next wave of incidents aren’t necessarily those with the most sophisticated perimeter defenses. They’re the ones that have accepted the compromise will happen and built the visibility, control and recovery capability to deal with it when it does.

Everyone else is building blind spots. And attackers, increasingly, know exactly where to look.

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In February 2026, the California Attorney General announced a $2.75 million settlement with Disney DTC and ABC Enterprises, the largest in the history of the California Consumer Privacy Act (CCPA).

Most of the noise has focused on the legal and interpretive dimensions: how the settlement reads against the statute, what it signals about enforcement posture, and what it adds to an enforcement record that has been building for more than three years.

Since the first major CCPA action against Sephora, in 2022, for failing to honor opt-out requests, settlements with DoorDash, Tilting Point, Healthline, Sling TV and Jam City have progressively expanded the technical specificity of what compliance has to look like in practice.

For privacy leaders trying to decide what to do on Monday morning, the most useful lessons from the Disney settlement are technical. In many enterprises, there is a gap between two initiatives that operate in parallel: a privacy tech layer focused on cookies, cookie banners, and webforms, and a data layer that runs on identity graphs, pseudonymous profiles and cross-system data flows.

The four technical claims leveled by the California Attorney General: gaps in how opt-outs reach logged-out users, disconnected opt-out tooling, missing cross-brand propagation, and absent opt-out functionality on apps and connected TV, are four symptoms of this misalignment

These challenges and claims are not unique to Disney, nor do they reflect negligence. They are consequences of timing. The privacy layer in most enterprises is built to satisfy a regulatory model that took shape between 2018 and 2022, when "consent management" largely meant deciding which cookies fired on a webpage.

The data layer it was bolted onto had been evolving for a decade by then, towards exactly the kind of cross-device, partner-dependent identity resolution that makes modern advertising and personalization possible. The two were never properly integrated in most enterprise environments because, for several years no one seemed to mind.

What the recent enforcement record establishes is that they do now.

One principle, four symptoms

The settlement's central principle is unusually direct: "If a business can associate a consumer's devices with the consumer for advertising purposes, it can and must associate those devices with the consumer for purposes of honoring the consumer's opt-out rights."

In other words, the scope of your opt-out obligation is defined by the scope of your data monetization, not by the scope of your consent management platform. If your advertising stack resolves an anonymous device signal to a known profile to target a consumer, you have, for the purposes of the law, identified that consumer.

The obligation follows the use of identity capabilities, not who built them. If identity is being used to target a consumer with ads, the same identity must be used to exclude them once they opt out.

That single principle unifies the four technical points of the settlement.

The first: identity parity concerns whether opt-outs cover consumers who aren't currently logged in. Many enterprise programs apply opt-outs only to authenticated users, on the reasoning that the business doesn't know who a logged-out user is. The settlement reframes that. If your advertising infrastructure uses pseudonymous profiles—the device-level identifiers that ad-tech systems use to recognize the same person across visits without a login - to target that user, then for the purposes of the law, you have identified them. The opt-out has to reach the same identity.

The second: architectural fragmentation concerns how opt-out requests travel through the systems meant to enforce them. Most enterprise privacy programs run two separate products: a consent management platform (CMP) that governs which trackers and tags fire on the website, and a data subject rights (DSR) tool that handles webform submissions like Do Not Sell or Share requests. When those products aren't integrated, a consumer who submits a webform may stop appearing in certain backend records, but the CMP keeps firing the same tags on every page they visit, and data sharing continues. The webform captured the request. The collection infrastructure never received it.

The third: cross-brand propagation concerns whether an opt-out submitted on one property reaches the others that share its data infrastructure. If a media company runs three streaming services on a single advertising stack, and a customer opts out on one, the law treats that as an opt-out across all three because the data is monetized across all three. The technical capability to propagate the signal usually exists; the compliance logic that ties opt-outs to it rarely does. The same point extends downstream: if ad-tech partners already hold a consumer's data, blocking tags on your own site doesn't reach what they have. They need to be actively notified, through a workflow that runs every time an opt-out is processed.

The fourth: non-browser surfaces concerns the consumer-facing channels that aren't a website. Cookie-based consent tools were built for browsers. They do not, by default, extend to mobile apps, connected TV environments, or any other surface where data is collected outside the browser. In Disney's case, consumers on a connected TV app could only opt out by going to a webform on a different device - a webform that had no effect on the code transmitting data from the TV app to its ad-tech partners. The mechanism existed; the obligation it was meant to satisfy went unmet.

The difficulty is structural

The four technical gaps above share a common root, and fixing them points to something more fundamental than patching individual systems. None of this is straightforward to operationalize.

Modern data infrastructure is genuinely complex and rebuilding how the privacy and data layers of an enterprise stack actually communicate is not the kind of project that gets completed in a quarter. But the structural difficulty points to something deeper than implementation effort: a flaw in how most organizations have framed the problem from the start.

Privacy as a data infrastructure question, not a regulatory one

Privacy programs built around regulations are, by design, reactive. A new law passes, a settlement lands, an enforcement sweep reveals an unexpected gap, and the program scrambles to respond. That cycle is the predictable outcome of treating privacy as a compliance checklist rather than a capability embedded in how data actually moves through the organization. Regulations will keep coming, and they will keep changing. No program designed around any single regulatory framework will stay current for long.

The more durable approach starts in the data infrastructure itself. When privacy controls are built into the data layer, tied to identity resolution and data flows rather than bolted onto individual regulatory requirements, they adapt. A new regulation adds a specific obligation, but the underlying framework for honoring consumer choices across identifiers and systems is already in place to absorb it. The work becomes configuration, not reconstruction.

The path forward is not another tool layered on top of existing infrastructure to satisfy the next regulation. It starts by embedding privacy controls into the data layer itself, tied to how data actually flows through the organization. That foundation does not need to be rebuilt every time the regulatory landscape shifts. It absorbs change. For privacy leaders looking to get off the reactive cycle for good, that is where the work begins.

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The disclosure-to-exploit window used to be measured in weeks; for weaponized vulnerabilities, it now runs in hours. Out-of-cycle patches that used to be exceptional have become routine across enterprise environments of meaningful size.

This pattern now has a name. You might have heard it already: the Patch Apocalypse. Sounds a bit dramatic, but the impact warrants the drama.

It describes something measurable — software flaws are being discovered, disclosed and weaponized faster than most patch management programs were built to handle.

Several factors are converging at once. Frontier AI models are accelerating vulnerability research — Anthropic's Project Glasswing and comparable initiatives have produced thousands of high-severity findings in compressed timeframes.

Attackers are using the same class of tooling to reverse-engineer patches far faster than previously thought possible. Public disclosures are arriving on shorter cycles.

For any team responsible for keeping production systems patched, this all translates to a backlog that grows faster than the available maintenance management windows can drain it. And “drain” is the right word here, because that’s also the impact on the team: it’s very, very draining.

This is far from an anecdotal observation. The workforce cost is already visible.

Recent UK data shows what’s happening at the personnel level: 42% of UK IT professionals report high levels of stress from their jobs, and 76% say that stress is affecting their physical and mental health. 30% report difficulty concentrating, 35% report trouble sleeping and 30% report increased anxiety and depression.

Why traditional patching is breaking down

Patch management was built around predictability. Vendor releases on a known schedule. A defined maintenance window. Manual testing in a staging environment. Communication, approval, deployment, verification.

The model worked when most enterprise software was released on predictable monthly or quarterly schedules, when threat actors needed weeks to weaponize a disclosed CVE, and when out-of-cycle patches were rare enough that a program could absorb them without restructuring. When those scenarios change, the model’s validity changes.

Two structural changes have done most of the work:

Volume is the first. When a single AI model can autonomously surface thousands of high-severity vulnerabilities across major operating systems and browsers — as Project Glasswing did within weeks of its April launch — the downstream effect is more CVEs arriving sooner, with public patches available, all flowing into the same backlog the IT team was already trying to clear.

Velocity has compounded that. Attackers have access to the same class of capability. Patches can be reverse-engineered in as little as 72 hours, sometimes far less. Any system unpatched in that window is exposed to working exploits.

Combine the two, and a patch program that was already running near capacity has to absorb a step change in volume, with shorter deadlines and less predictability about when the next critical disclosure will land. That’s a lot of pressure, and it’s what’s driving the stress data up.

Automation is taking center stage

As an operating model, automation is far better-equipped to survive a Patch Apocalypse than previous iterations. Three important principles underscore the model’s efficacy:

1. Continuous, risk-based triage. The CISA Known Exploited Vulnerabilities list is the non-negotiable top tier. An Exploit Prediction Scoring System threshold appropriate to the environment can drive priority for everything else. Below that threshold, work waits for the maintenance ring.

2. Automated test and deployment rings. The test cycle has to compress to fit the exploit window. Even with top-tier skills and best intentions, a human checklist cannot move at that speed. The familiar sequence — test ring, early-adopter ring, broad production, mission-critical — has to be instrumented and capable of running without manual coordination at every stage.

3. Closed-loop verification. A patch isn’t deployed until the install is confirmed on every endpoint, and a CVE isn’t closed until a rescan confirms it. Compliance evidence is produced as a byproduct of the workflow, not assembled from a spreadsheet the week before an audit.

Industry data points in the same direction. 67% of IT professionals say AI tools and automation will free up their time for more interesting, fulfilling work. 66% say the same tools will help them provide better service to end users. Less than one in three organizations report having fully embedded automation in their IT workflows.

Who’s paying the cost

Any cost considerations regarding patch programs need to take the human cost into account. A patch program running on legacy assumptions will absorb the Patch Apocalypse by burning out the team running it. The stress figures are showing the early signs. The downstream cost includes attrition, error rates, lost productivity and the slow erosion of the institutional knowledge that holds a program together.

On the other hand, programs where automation runs the workflow have the potential to absorb the same volume without requiring the team to absorb it personally. Continuous prioritization, instrumented rings and verification embedded in the workflow take variable, manual work out of the critical path.

Two-thirds of IT professionals see AI and automation as a route to better work — fewer frantic escalations and more time on the problems that need human judgement.

The Patch Apocalypse is very much here, and is poised to reach every program. Is the workflow underneath built to absorb the impact? If not, consider the whole scope of what — and who — is at stake.

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For as long as there's been software there has been tension between open source and closed source. The creator of the Linux kernel, software engineer Linus Torvalds, has long been an outspoken advocate for open source environments – while putting the boot into closed source corporate entities who have tried to exploit the ecosystem.

Open for business

During a Q&A at Aalto University, an attendee complained that the Nvidia Optimus chip – a processor that switches between integrated graphics and dedicated graphics – was no longer being supported on Linux.

That meant that this expensive chip fitted into her Linux laptop was basically useless unless developers worked on a fix by reverse-engineering the process. Torvalds answered by giving Nvidia both barrels – complaining that the company was profiting from the Linux Foundation ecosystem through products like the Tegra chips for Android devices.

At the time, he complained that competitors like Intel and AMD were happy to co-operate with Linux to build native drivers whereas Nvidia would treat its proprietary code as classified material. This exchange culminated with an angry Torvalds flipping Nvidia off to camera.

Turning the tables

The situation today is almost entirely different from that we found nearly 15 years ago. Nvidia has embraced open source with open arms, and has also fully transitioned to open source Linux GPU kernel modules as being the default options as of 2022, pushed under a dual GPL/MIT licence.

With the AI buildout also fully underway, Nvidia has crafted the Linux-based DGX OS for its personal mini supercomputing DGX Spark products. This Ubuntu-based system is designed to work with the entire Nvidia software stack – with this AI layer (including software like CUDA, cuDNN and NCCL) being closed.

Although the battle between open and closed source remains active, Nvidia's reversal signalled that the future of enterprise and AI-centric computing was to be found in open source environments.

Artificial intelligence has quickly become the boardroom's favorite solution. From forecasting and reporting to scenario planning and budgeting, finance leaders are under growing pressure to demonstrate how AI can improve efficiency and drive better decisions.

But against the rush to adopt AI, many organizations are overlooking a fundamental truth: that AI is only as effective as the systems, processes and data that support it.

This is particularly true in finance, where many teams continue to rely on fragmented technology stacks, disconnected data sources and spreadsheet-heavy workflows. While AI promises to automate analysis and surface deeper insights, it cannot compensate for weak foundations. In fact, it often does the opposite, exposing issues that previously remained hidden beneath layers of manual work.

The reality is that many finance functions are less prepared for AI than they realize.

The spreadsheet problem AI cannot solve:

Spreadsheets remain deeply embedded within enterprise finance. They are familiar, flexible and accessible. However, they were never designed to serve as the backbone of modern financial planning and analysis for large enterprises.

In many organizations, critical forecasting models, budgeting processes and reporting workflows are still maintained across countless spreadsheets, often with limited governance and varying levels of accuracy. Data is copied between systems, formulas evolve over time, and key assumptions can become difficult to trace.

Introducing AI into this environment does not eliminate these challenges. It amplifies them.

If an AI model is drawing insights from inconsistent data sources or outdated spreadsheets, it will simply generate the wrong answer faster. Automated recommendations may appear sophisticated, but their reliability is ultimately determined by the quality and integrity of the underlying information.

This is why the familiar principle of ‘garbage in, garbage out’ remains so relevant to finance teams today.

Why finance teams may be overestimating their AI readiness:

Many organizations assess AI readiness by evaluating tools. They ask whether they have access to the latest models, whether employees are using generative AI and AI agents, or whether automation opportunities exist within their workflows.

Far fewer assess the quality of the infrastructure feeding those systems.

True AI readiness starts with questions such as:

• Is financial data consistent across systems?
• Can teams trust the numbers they are working with?
• Are planning, reporting and forecasting processes standardized?
• Is there a single source of truth for business performance?

If the answer to these questions is unclear, AI adoption risks introducing new complexity rather than delivering meaningful value.

The challenge is not a lack of ambition; most finance leaders recognize the potential of AI. The challenge is that many organizations are attempting to build advanced capabilities on top of foundations that were never designed to support them.

Data quality is becoming a strategic priority:

As AI becomes more embedded within finance operations, data quality is shifting from an operational concern to a strategic business priority.

Finance teams have long spent significant amounts of time gathering, reconciling and validating data before analysis can even begin. AI has the potential to reduce that burden, but only when the underlying information is accurate, connected and accessible.

Organizations that invest in modern finance infrastructure gain a significant advantage. Centralized platforms, integrated data environments and standardized planning processes create the conditions necessary for AI to deliver meaningful outcomes. They also improve transparency, governance and trust in financial decision-making.

Without these foundations, AI initiatives risk becoming expensive experiments that fail to deliver lasting value.

Building the foundations before scaling AI:

The future of finance undoubtedly involves AI. The technology's ability to improve forecasting, accelerate reporting and support more strategic decision-making is too significant to ignore.

However, the organizations that realize the greatest benefits will not necessarily be those that adopt AI first. They will be those that prepare for it properly.

Before automating processes or deploying new AI capabilities, finance leaders should take a closer look at the systems supporting their operations. Are they creating a trusted, connected and scalable environment for decision-making, or are they simply digitizing existing inefficiencies? AI is a powerful multiplier, but multipliers work in both directions.

For finance teams still relying on fragmented systems and spreadsheet-driven processes, the priority should not be adopting AI faster. It should be strengthening the infrastructure that allows AI to succeed.

Because AI will not fix weak finance foundations. It will expose them.

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Researchers at the University of Toronto have demonstrated a computer worm that reasons its way across a network, working out a different attack for each machine it lands on, with no humans involved.

Much of the coverage has called this a breakthrough in demonstrating how the AI worm can target any online device, but that capability has been visible on the horizon for anyone watching the criminal underground.

What the paper does is settle the argument about whether it is buildable and, in doing so, reopens three issues most internet security teams would rather not confront directly.

The first of these is cost, and this is the story that matters. Building attacks aimed at specific systems used to be slow and expensive, requiring skilled operators.

That expense is one reason many mid-sized organizations were largely ignored - they were not worth the engineering effort. The Toronto worm removes that constraint by running an open-weight model on the GPUs of machines it has already compromised.

Devices too weak to host a model send their reasoning upstream to an infected node elsewhere on the network. The attacker’s compute bill is paid by the victims, and each captured machine extends the worm’s own infrastructure.

Once tailored attacks cost almost nothing, being uninteresting stops protecting you. Being reachable starts to matter far more than being interesting.

Patching complications

The next issue, patch management, is where things become a little more complicated. Traditionally, when a conventional worm would ride a specific vulnerability, the conventional response would be to patch it, and the worm would die. WannaCry spread across more than 150 countries in 2017 on a single flaw, reinforcing the lesson that rapid patching limits damage.

This worm, however, does not give defenders a single flaw to patch. It reasons out a different route per host, and in one experiment, when copies repeatedly failed on older systems because of a detection bug, the parent process identified the failing check, removed it, and tried again. You cannot rely on closing a single door against something that rewrites its approach as it goes.

And finally, and more worryingly, the worm was able to consume newly published security advisories during execution and generate attacks against vulnerabilities that did not exist when the underlying model was trained. That challenges the assumption that knowledge cutoffs meaningfully constrain offensive AI.

If the model can read today’s advisory and reason from it, the training date matters far less than many assume.

Working, with limitations

However, its limitations deserve to be stated plainly. Exploitation attempts succeeded 44 percent of the time, and the researchers noted that most failures were malformed payloads rather than flawed reasoning.

It was also slow. Yet according to the researchers, across fifteen experiments the worm obtained elevated access on about 74 percent of hosts, replicated onto roughly 62 percent, and reached seven generations of self-replication within a week.

A 44 percent success rate that continually retries is not a wall the threat runs into. It is a baseline, and that baseline tracks what open-weight models can do today, a capability that has so far moved in one direction.

Another underappreciated point is that the model runs inside an environment the attacker controls. That makes many of the safety mechanisms discussed by AI vendors (refusals, filters, and rate limits), largely irrelevant. There is nothing to rate-limit when inference is occurring on infrastructure the attacker already owns.

If your AI risk model assumes a platform provider will enforce guardrails, this is a scenario that will bypass them entirely.

Plan around the attacker

What to do about it is less dramatic than the threat itself. Keep patching. The problem is that patching was already a treadmill against capable adversaries, and this only makes the treadmill faster.

The more effective solution is to plan around an attacker that eventually gets in and adapts once inside. Segmentation and containment, for instance, should take priority over chasing individual vulnerabilities. If the entry point can be almost anything, then the critical question becomes how far an intrusion can spread.

There is also the issue of exposure. A worm like this will seek the easiest foothold first, just as human operators do. These can include leaked credentials, forgotten infrastructure, internet-facing services, or access already circulating in criminal markets.

Monitoring external exposure is primarily about buying time, rather than preventing compromise entirely, which is precisely what an autonomous attacker is designed to take away.

The worm remained in the lab, and the code is gated for defensive researchers, which was the right decision. But academic containment is not quite the same as reassurance.

The economics that once shielded many organizations are weakening, the patch-it-and-move-on model is losing effectiveness, and some researchers and practitioners are placing operationally relevant autonomous attacks within roughly twelve to eighteen months.

Therefore, the question we should be asking is whether to take this seriously now, on your terms, or later, on an attacker's.

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As enterprise AI systems evolve, the limiting factor is shifting. Model quality still matters, but it’s no longer the main issue holding systems back. Increasingly, what constrains performance, scalability, and cost is context.

Large language models are now expected to support long conversations, multi step reasoning, and complex workflows that span time, users, and systems.

Every one of those interactions generates tokens, and those tokens produce key value (KV) cache — the working memory that allows models to reason efficiently without constantly recomputing prior steps.

Most AI architectures still treat this context as temporary. KV cache typically lives in GPU memory, is tied to a single inference process, and is discarded as soon as resources are exhausted.

That approach might be acceptable for small scale experimentation, but it quickly breaks down in enterprise environments where context lengths grow, concurrency increases, and recomputation becomes expensive.

Inference context has quietly become one of the largest bottlenecks in enterprise AI.

KV cache as AI native data

To understand why this matters, it helps to stop thinking about KV cache as “just a cache.”

Enterprises have spent decades building strategies around structured data and unstructured data, but AI introduces a third class that deserves just as much attention: AI native data. This is data generated by model execution itself, and KV cache is one of its most important forms.

KV cache directly determines inference latency, throughput, energy consumption, and cost. As context windows get longer and reasoning chains become deeper, the volume and importance of this data grow faster than token counts alone. When KV cache is constantly thrown away, systems pay for it through rising latency, lower GPU utilization, lost reasoning context, and higher inference costs.

At scale, this inefficiency becomes structural rather than incidental.

Why existing infrastructure assumptions don’t hold

KV cache also exposes a mismatch with traditional infrastructure design.

GPU memory delivers exceptional performance, but it is scarce and local to a single server. CPU memory extends capacity but remains volatile. Local NVMe storage adds scale yet keeps context trapped at the node level. Traditional shared storage provides durability and resilience, but it wasn’t designed for highly dynamic, inference time state.

This leaves enterprises with a fragmented memory hierarchy where context is either fast but fragile, or persistent but difficult to access efficiently. No amount of tuning can fully resolve this, because the problem isn’t optimization — it’s architecture.

What enterprise AI needs is a way to treat inference context as system memory rather than disposable state.

Introducing an inference context memory layer

That shift is what we describe as an inference context memory layer.

Instead of forcing all KV cache to live and die inside GPU memory, this approach allows context to be created close to the GPU for low latency, then managed across a hierarchy of memory and storage tiers designed explicitly for inference workloads. Inactive context can move out of high cost memory without being discarded, while relevant context can be restored on demand without recomputation.

This changes the behavior of inference systems in a fundamental way. Inference is no longer a series of isolated executions that start from scratch each time. It becomes a continuous, stateful process where knowledge accumulates, moves, and is reused across sessions, agents, and nodes.

When storage becomes part of AI memory

Making this work places new demands on storage.

Inference context is large, mostly immutable, and technically recomputable — but regenerating it at scale is costly and inefficient. A storage architecture for inference context must preserve locality when performance matters, enable sharing without manual replication, and provide resilience so context isn’t lost when hardware fails.

When storage is designed this way, it stops being just a place to store data and becomes an extension of AI memory itself. That shift has real economic consequences: faster time to first token, higher GPU utilization, support for much longer sessions, and dramatically lower cost per query.

For enterprise workloads like tax advisory, legal analysis, healthcare reasoning, financial planning, and customer support, this is critical. These systems depend on preserving reasoning history and conversational context, not repeatedly rebuilding it from scratch.

Context is now infrastructure

Enterprise AI is entering a new phase.

Models will continue to advance, but the systems that scale successfully will be defined by how well they manage the intelligence those models produce. Tokens are no longer fleeting artifacts, and context is no longer something enterprises can afford to lose.

KV cache is AI native data. It represents system state. And increasingly, it must be treated as infrastructure.

The architectural principle is simple: generate context once, manage it intelligently, and reuse it wherever possible. That shift is foundational to making enterprise AI reliable, efficient, and scalable — and it’s why storage once again plays a central role in the future of computing.

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London Tech Week’s focus on AI - from a £12 million investment in AI for SMEs to AI bootcamps for graduates and more - has reflected the pressure to compete in an AI-era.

As this digital revolution progresses, the job economy is changing, but the mantra that AI is taking our jobs is simply not correct and potentially fueled by an undercurrent of classicism.

When the Luddites famously started to break the first machines of the industrial revolution in 1811 in England, fearing for their job as textile artisans, the “Bourgeoisie” would describe them as “ignorant workers”, with no understanding of basic economics.

More than two hundred years later, with the rise of GenAI, it is no longer the blue-collar workers who fear for their job, but the white-collar workers. This time it is the “bourgeois” who live in the anxiety of an uncertain world.

Since ChatGPT introduced AI into the everyday lexicon, it has been clear that we would experience an unprecedented revolution. The rhetoric that immediately began to dominate social discourse has been that AI tools would render most jobs insignificant.

Furthermore, whilst other technological revolutions ended up being creative destruction, ‘this time it was different’.

But is that really the case? Or are we more fearful, more concerned about destroying the status quo, because this time it’s a different ‘class’ of people being impacted? This time it’s the desk workers, not the physical laborers, who risk losing jobs, and suddenly there is alarm.

Artificial Intelligence relies on humans - and more humans than ever

AI is a human creation and still relies on humans to evolve. First, we have those who build the infrastructure, like data centers, which accounted for almost all of the United States’ GDP growth in the first half of 2025 (according to Harvard economist Jason Furman).

Then, we have those who train the models, which still need to be constantly retrained. Even if models are able to train themselves eventually, there is no consensus that human intervention in training will become obsolete, because human behavior and the entropy of organizations are in a constant state of flux and evolution.

And even when trained, AI constantly needs to also understand the “context” in which it is prompted to perform efficiently. AI then needs to be deployed. Managing security, defining guardrails for agents, understanding how to use AI and tracking agentic AI’s actions, all comes with inherent challenges.

The CIOs of the largest global corporations are already investing hundreds of millions of pounds to understand this. Startups based in San Francisco - a city I recently visited where 95% of out-of-home ads were about AI agents - are focused entirely on resolving these problems for large enterprises.

The fact that both Anthropic and OpenAI have launched their own consulting companies is proof that managing AI complexities in the coming years will be the biggest source of growth for all consulting and outsourcing companies of the world.

Sourcing the right human talent in the AI era is the biggest challenge

Software engineering is a job category where GenAI - perfectly trained on open-source code and GitHub repositories - can now code better than even the most experienced developers.

Additionally, developers in AI labs - with privileged access to “tokens” on Claude Code or OpenAI Codex - now develop 100% of the time without writing a single line of code. Nonetheless, when asked about their biggest challenges, all AI startups would point to recruitment.

A report by the UK's National Foundation for Education Research showed a 50% increase in tech job adverts between 2019/20 and 2024/25, with entry-level roles particularly affected. However, we’re now seeing a surge in demand driven by Gen Z, according to Employment Hero’s March Jobs Report.

This demand for AI expertise is reflected in a new Malt Tech Trends Report, which analyzed 1.2 million searches of tech freelancers in 2025. It reveals that AI is now the second most-in-demand skill, irrespective of company size, industry, or project type. More specifically, demand for freelancers with agentic AI expertise exploded by 5,800% in just twelve months.

Observer of the AI revolution, Andrew Ng, explains that if, for example, a team of 3 developers builds 10 times faster, then they need more designers or product managers to fuel the creative process. Doing more faster, with fewer people creates more work to fuel and execute the output.

More people are echoing the same rhetoric as Ng, calling out the phenomenon of ‘AI washing’, whereby companies have justified mass redundancies with AI disruption. In reality, in many cases, they were either adapting to geopolitical and economic uncertainties or had simply employed too many in the crazy post-COVID bull market.

The AI job apocalypse is not yet here… Still, the fear is real and needs to be understood

Software engineering is a perfect example of a job category that has constantly evolved. Since the inception of computer science, programming has become progressively more about “natural language”. Whilst there were 50,000 developers worldwide in the 1960s, today there are almost 50 million. Undoubtedly, the eradication of barriers to entry to build software increases that number tenfold.

History, data, and observation shows us that the AI job apocalypse is not yet here. Still, the fear is real and needs to be understood. The reason every science fiction novel paints an inhospitable world and unattractive paradigm is because the human mind always fears change. We assume the worst.

AI transformation, like all transformations, will be a cultural change first. And it’s companies, not professionals, who are most at risk if they fail to adapt. If one thing will be different in this digital revolution, compared to the last (arguably comparable is the advent of the internet), it’s the rate of change.

CEOs will have to be imaginative, change org charts and processes, admit they are not omniscient, take risks, and invest in training. Schools and universities also face the challenge of teaching soft skills: how to adapt to live and work in a more uncertain world. Because we can only harness top-tier AI talent if we understand how to truly adapt to change.

Independent professionals - those who create their own roles - from freelance developer to fractional manager and strategic consultants - have already redefined work.

On average, freelancers spend 4 hours a week on upskilling and keeping up with the job market and already have the habit of switching from one client project to another. They were the first to adapt to AI and realize that a job is more than just a bundle of tasks.

As Jensen Huang, CEO of Nvidia, recently said, if someone were to observe him at work, we would conclude that his day consists of tasks like making hundreds of calls and sending emails. AI will replace, augment, and improve these tasks. But it will not take Jensen’s job.

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In the early 2000s, IP VPN was the enterprise networking technology of choice for IT leaders.

MySpace was the go-to social network, we used Skype for video calls, we listened to music on our new MP3 players and the Nokia 1100 was the most popular mobile handset.

It feels like a different era entirely, yet many businesses are still running on legacy networks that were perfect for their needs back then but are now holding them back.

By today’s terms, networks were built for low levels of traffic. Cisco estimates global IP traffic levels were around 175 petabytes per month in 2001. Compare that to today’s figure, which is around 522,000 petabytes per month, or approximately 3000 times higher than 2001 levels, and you can understand why 87% of businesses in an Accenture study believe their legacy network is compromising their ability to advance on cloud, data and AI and digital transformation.

Untangling and replacing the complex web of enterprise networks built up over years is an unavoidable and costly necessity. It’s a bit like replacing the windows in your home - you know you’ll improve security, stormproof your home and cut energy costs by upgrading, but the process feels like a hassle.

Today, IT leaders aren’t just ‘replacing the windows’ by modernizing outdated networks; they’re going further and building high capacity, low latency, secure architectures designed to withstand the explosive demands of AI.

Making the move to SD WAN

Millions of businesses are switching from IP VPN to Software-Defined Wide Area Networks, or SD WAN. Strong market growth is forecast in SD WAN, with one market forecast anticipating SD WAN CAGR of almost 40% (38.9%) from 2023 to 2030.

This growth is being driven by multiple factors including a shift to cloud-native architectures; a change in workplace practices and rise in remote working environments; and strong demand for network architectures that can manage current and future AI-related applications and services.

SD WAN is faster, more cost effective and more secure, with built in zero trust protection. It’s purpose built for distributed users and for managing cloud, AI workloads, data flows, and SaaS traffic.

But, to be truly AI ready, IT infrastructure must be software driven, and this is where SD WAN excels: it gives your business the security, flexibility, and reliability needed to operate confidently in an AI driven future. Here are five ways switching to SD WAN will help you build an AI-ready network:

1.Built for AI-scale performance

High-bandwidth, low latency SD WANs are critical for the delivery of AI workloads, particularly as businesses move towards AI inference. They provide fast access to cloud services and dynamic bandwidth allocation as they monitor network conditions and reroute over the best available path.

For example, imagine a drive-through restaurant that uses an AI voice to take and relay orders or a supermarket that uses an AI model to scan shelves in its store, to detect gaps in stock, alert staff and predict which items will run out next. A high-performance, low latency network is essential here to guarantee a seamless customer experience.

SD WAN’s application-aware routing levels this up even further, prioritizing AI traffic and deprioritizing the transfer of, for example, bulk file transfers or back-ups.

2.Security that matches today’s threat landscape

The global cyber attack surface has expanded dramatically. AI now plays a dual role, enabling more sophisticated attacks while also powering new, advanced defense capabilities. Traditional IP VPNs offer traffic encryption but lack native security features. In contrast, SD WAN is built to protect modern networks from today’s high volume, highly sophisticated cyber threats:

- Zero trust access protects users, devices and applications

- Traffic is encrypted end to end, so that all data between sites, platforms and applications is secure

- Threat prevention at the edge protects core infrastructure from threats, with features such as intrusion detection and prevention, malware scanning and DNS security

- Automated real-time security updates with threat intelligence pushed globally within minutes

3.Cloud connectivity without compromise

SD WANs provide direct, optimized access to major cloud environments, such as Microsoft Azure, AWS, and Google Cloud, by using automated secure tunnels and intelligent path selection.

This ensures cloud and AI services run with lower latency, higher performance, and more reliable connectivity. Also important to note is that SD WANs provide high levels of autonomy and automation, so it’s easy to make changes quickly and easily as businesses navigate dynamic market conditions.

4.Data insights that power automation

SD WAN captures real-time data including latency, packet loss and application usage patterns – data which can be fed into AI-based network monitoring, automation and predictive maintenance management tools, so that networks become self-optimizing, self-healing and proactively secure.

5.A foundation ready for SASE and Zero Trust

When combined with Secure Access Service Edge (SASE), SD WAN creates a single, secure, high performance network foundation that’s built to drive AI opportunities while protecting against cyber risks with integrated security solutions including zero trust, secure web gateways and cloud firewalls.

SASE is a cloud based networking and security framework that combines SD WAN with integrated security services (like Zero Trust, secure web gateways, and cloud firewalls) into a single unified architecture. It’s the gold standard of AI-ready architecture.

As enterprises accelerate toward an AI driven future, the networks that once served them well are now becoming a barrier to progress. SD WAN offers a clear path forward: a software defined, secure, high performance foundation built to handle the scale, speed and complexity of modern cloud and AI workloads.

By making the shift now, businesses can replace aging infrastructure with an agile, intelligent network that not only supports today’s demands but unlocks the full potential of tomorrow’s AI innovation.

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Somewhere right now, a customer is repeating themselves. They are explaining their problem for the third time, to the third person, because the organization on the other side has no shared memory of the previous two conversations. It is an infrastructure problem that AI is making harder to ignore.

It is also becoming impossible for policymakers to ignore. Just in April, the Mayor of London launched a new AI and Jobs Taskforce to examine how AI is changing work across the capital, signaling that the conversation has moved well beyond investment announcements and into the harder question of what AI does inside organizations.

It is also shining a spotlight on a memory crisis inside modern business.

AI is accelerating work, not clarity

As UK organizations rush to deploy AI in the workplace, many are layering it onto fragmented systems that were never designed to preserve institutional memory in the first place.

According to research published in Harvard Business Review, knowledge workers toggle between applications and tools roughly 1,200 times per day, a pattern known as "toggling tax". That figure alone tells the story: we aren’t short of tools, but there is no coherence among them.

The result is a new kind of productivity paradox. Work is moving faster, but clarity is not improving.

This is where much of the current enterprise AI conversation unravels. A surprising amount of what is marketed as AI today still relies on humans to do the synthesis work themselves. The system retrieves documents. It summarizes conversations. It surfaces links. But employees still carry the burden of reconstructing meaning, and so do the customers and end-users waiting on the other side of those decisions.

Notably, when these types of AI tools do the retrieval, but humans skip the synthesis, the output feels hollow. That creates a trust and credibility problem - not just for the individual, but for AI as a category. People start associating "AI-assisted" with "low-effort".

When context is lost internally, the effects aren't invisible. They surface as slow responses, repeated requests for information that customers already provided, support experiences that feel fragmented, and sales teams reconstructing account history manually before every renewal, escalation or executive review.

Stateless systems cannot preserve organizational memory

The AI models themselves are becoming more capable, but the organizational foundation beneath them remains fragmented.

Most AI systems today are fundamentally stateless. They generate outputs based on temporary context windows rather than durable organizational memory. Every interaction requires the system to repeatedly reconstruct understanding from fragments.

Consider how databases work. We do not recompute everything from scratch every time a query arrives. We cache and index, then preserve relationships between entities, because continuously recomputing context is computationally irrational.

Yet much of enterprise AI is still being deployed exactly this way and the industry has started mistaking activity for intelligence.

What I believe organizations should focus on is whether they have structured, durable memory that lets AI and humans reason from the same shared context. Without that foundation, AI outputs remain generic.

Most collaboration systems multiply this problem in two ways. First, they encode knowledge into naming conventions and tribal memory – the kind that lives in channel names nobody can decode and folder structures only three people understand. New employees are not learning the business, they are learning the conventions.

Second, even when information exists, it remains inaccessible. The same decision appears as "PostgreSQL migration", "database move Q3", and "backend infrastructure change" across three different channels. They are semantically identical but textually invisible to any system trying to surface it.

This problem becomes even more acute in distributed organizations. I don’t believe you can build modern global companies on a "you had to be there" culture. Yet many businesses still operate as though important context naturally transfers through proximity and synchronous communication.

Search is not the same as understanding

Search was designed to discover information, whereas modern enterprise work requires systems that understand the relationships in data.

A customer escalation is not just a support ticket. It is connected to product decisions, engineering discussions, account history, contractual obligations, and revenue impact. A sales opportunity is tied to customer sentiment, historical support patterns, product usage, and internal stakeholder alignment.

Traditional collaboration systems flatten these relationships into disconnected channels and documents, whereas AI knowledge graphs preserve them.

Researchers call this a transactive memory system: the collective understanding of who knows what, how decisions were made, and how work is coordinated across teams. The same logic now extends to AI. Intelligent systems can participate in that process too by encoding context, surfacing relevant history, and routing knowledge to the right people at the right time.

Britain's productivity problem is becoming an AI problem

The Office for National Statistics has consistently flagged weak productivity growth as one of the UK's most persistent economic challenges. Since 2010, UK productivity has grown at 6.2%, compared with roughly 10% across the euro area and nearly 15% in the United States over the same period. AI is increasingly being positioned as a mechanism to help close that gap.

But productivity does not improve because your business has added more AI agents to the workflow. If every important decision still requires humans to manually reconstruct fragmented context, organizations just accelerate confusion.

What UK businesses need are systems capable of preserving context, maintaining institutional memory, and grounding AI systems in trusted organizational knowledge. Better AI infrastructure starts with a simple question: Does your organization remember anything? For most, the honest answer is no.

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Spend any amount of time reading about AI and you'll quickly notice a pattern. Stories about new capabilities and investment are rarely far removed from questions about what the technology means for jobs.

It's not difficult to see how that has shaped public perception. Much of the conversation around AI tools continues to be framed through the lens of workforce reduction, creating anxiety about what the technology might take away rather than what it could enable.

Research from Acas reflects that unease, with more than a quarter of UK workers identifying job losses as their biggest concern about workplace AI.

Those concerns matter because across sectors, organizations are introducing AI at a time when employees are already navigating economic uncertainty, budget pressures and increasing workloads. In that environment, fears about replacement can shape how new technologies are received long before people experience their benefits.

Yet, focusing on jobs alone risks missing a much more important conversation. For many organizations, particularly those delivering essential public services, the challenge isn't a lack of work. It's a lack of capacity. The real question is whether AI can help people work more effectively in increasingly challenging circumstances.

The reality of frontline work

That challenge is particularly visible across public services.

Housing officers support residents through difficult circumstances, social workers assess risk in complex situations, and customer service teams help vulnerable individuals access essential support. In each case, outcomes depend on judgement, context and human relationships so these aren't environments where technology can simply step in and take over.

What many frontline staff struggle with isn't a lack of expertise, but the admin burden that prevents them from applying their expertise where it matters most.

Critical information is often spread across multiple systems. The platforms that underpin these services were rarely designed to share information easily. In practice, they are often a mix of the ERP system and older line-of-business applications, each holding part of the picture.

As a result, staff spend valuable time searching for records, reviewing case histories and piecing together information before they can take action. These challenges may sit behind the scenes, but they consume significant amounts of time.

Supporting expertise, not replacing it

AI delivers the greatest value when it gives people faster access to information and more time to focus on the decisions that matter.

A housing officer could quickly surface relevant tenancy history from multiple systems, while a social care worker could be alerted to emerging risks or significant changes that warrant attention. These case workers might use AI to identify patterns in service demand and prioritize their approaches more effectively.

Crucially, this works by drawing on the systems they already depend on, the ERP and case management platforms that hold their data, rather than buying additional solutions and adding to the fragmentation. The value comes from connecting and making sense of information that already exists, not from adding to the systems of record.

In each case, the case worker remains responsible for the decision, with AI helping them reach that point faster and with better information.

For many teams, demand already outstrips capacity. The challenge is creating enough time for skilled professionals to focus on work that requires experience, judgement and human interaction.

Used effectively, AI can help create that capacity. By reducing administrative friction and making information easier to access, it allows people to spend more time applying the skills that organizations depend on.

This principle extends well beyond the public sector. Whether in business, financial services, customer operations or government, the greatest value often comes from making expertise easier to access and apply, rather than attempting to replace it altogether.

Why perception matters

When AI is introduced alongside discussions about efficiency savings and workforce pressures, it's easy for people to view it through the lens of cost reduction rather than capability building. That perception can create resistance before new tools have had the chance to demonstrate their value.

This is why successful AI adoption requires more than technical implementation. One of the clearest lessons from digital transformation is that technology alone doesn't drive change, people do.

Anyone who has worked on a major ERP program will recognize this. The systems that succeed are rarely the ones with the most sophisticated functionality, but the ones that teams understand, trust and actually use day to day.

Organizations that make progress tend to focus on practical outcomes rather than the technology itself. They engage teams early, involve them in how tools are deployed and demonstrate how AI can help address genuine operational challenges.

When people can see the impact on their day-to-day work, whether that's reducing admin, improving access to information or supporting faster decision-making, adoption becomes far more natural.

Trust develops over time through involvement, transparency and tangible results. People need to see how technology supports their work before they are likely to embrace it.

Moving beyond the replacement narrative

The debate around AI has become disproportionately focused on what work might disappear. That focus is understandable, but it risks obscuring a more immediate challenge facing organizations today: how to help people manage growing workloads, increasing complexity and rising expectations.

For the public sector in particular, the opportunity is far more practical than a distant vision of fully automated services. It lies in helping frontline staff spend less time searching for information, enabling them to identify risks more quickly and giving them better access to the insights they need to act. In most cases, that means getting more value from core systems rather than chasing wholesale reinvention.

Those may not be the stories that generate the most attention, but they are the ones most likely to determine whether AI delivers meaningful value.

If organizations want employees to engage with AI, they need to move beyond conversations centered on replacement and focus instead on the problems the technology can solve at grassroots. This cohort is less interested in what the AI is capable of doing, but rather how it helps them to do their jobs better.

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The global digital economy runs on a thriving ecosystem of third-party vendors, enabling organizations to scale and innovate faster than they possibly could do on their own.

This digital ecosystem is teeming with software suppliers, not just business software that you can buy but also a vast array of software libraries that are embedded in third-party products.

Speed, however, can sometimes be the enemy of risk, as many organizations have not adequately validated whether these third-party technologies are sufficiently safeguarded against cyber threats and other digital risk.

So, while software is a great enabler, it also brings risk, given that it often is built with frameworks and libraries that are not known or well supported.

Consider that companies employ an average of 106 SaaS apps within their IT environments , and the picture becomes quite clear: software supply chain security is a serious concern.

It’s no wonder that half (51%) of participants in the latest Supply Chain Risk Survey ranked software vulnerabilities in supplier products as the most disruptive cybersecurity threat to their organization’s supply chain, behind only data breaches (64%) and malware or ransomware (52%).

An ever-changing attack surface that comprises cloud services, micro-services, APIs, SaaS platforms, third‑party services and now AI agents has expanded well beyond what once was an understood perimeter before widespread digital transformation took hold.

How secure is your own extended digital ecosystem? If this question makes your heart race, then take a closer look at three key considerations for addressing software supply chain security.

1. Visibility: Determine what’s actually in your multi-layered supply chain

Since the software supply chain is part of a vast, interconnected digital ecosystem, organizations likely do not have full visibility of what and who make up their third-party providers. Recent high-profile incidents have signaled just how fragile supply chains can be.

Assuring business continuity requires organizations to scrutinize partners before placing such deep trust in them. That effort starts with knowing who is in your interconnected digital ecosystem before you can start to manage the risk.

Understanding risk across a supply chain is conceptually easy, but it is practically difficult. While clearly outlining security parameters and requirements in supplier contracts is a great starting point, it is not enough, as contracting is generally a point-in-time activity and should be paired with monitoring. You must be able to see and measure software assets so you can better manage them.

After all, you can’t protect what you can’t see, and many businesses still don’t have a complete, accurate asset inventory, meaning that their vulnerability exposure is incomplete. If you don’t know what systems, apps, devices and libraries are in your environment, vulnerability management is supposition, inference and guesswork.

It is crucial to understand what your suppliers are doing both upstream and who you supply downstream, because their decisions are now part of your organization’s own risk profile. Software often presents the biggest blind spots in asset management, thanks in large part to a lack transparency in software build and dependencies, shadow IT, shadow AI and unmanaged endpoints.

An organization's exposure is tied directly to the security posture of every supplier they rely on. Attackers know this, increasingly targeting upstream or downstream partners. You can secure your own environment perfectly and still be vulnerable through others’ oversight. Tools that can profile, quantify and score risk across the supply chain, therefore, are essential, as is tooling that monitors for unusual activity.

2. Vigilance: Prioritize the security of AI integrations across your software supply chain

Threats can lurk anywhere and everywhere across your supply chain. But there’s a new kid in town: AI. The software supply chain has expanded to include the unique risks of AI ecosystem, such as reliance on external foundational models and highly connected agents.

This escalating integration of AI tools makes the multi-faceted software supply chain even more of a concern. Cybersecurity professionals who participated in the latest Cybersecurity Workforce Study revealed a troubling AI-related security event their organization experienced in that prior year: data poisoning (cited by 11%).

Data poisoning happens when bad actors intentionally insert corrupted, misleading or malicious data into the training dataset of a machine-learning model. Even a small amount of poisoned data can change the model’s behavior, in turn resulting in misclassifications, degraded accuracy or malicious outcomes. So suddenly that seemingly helpful ChatBot that is embedded in your CRM, CMS or other purpose-driven enterprise software may not be so friendly after all!

Indeed, organizations simply have little / no control over the software that suppliers are using, making it much more difficult to ensure vulnerabilities are identified before widespread rollout, as well as supported and patched once deployed, but they do have control over scrutinizing suppliers.

Therefore, the people on your security team and the processes they follow matter more than ever. Technology accelerates both sides of the fight, so your real advantage comes from having skilled practitioners who understand how AI changes your risk profile, attack surface and can put the right controls in place to compensate.

Cybersecurity professionals who specialize in software supply chain security can quantify the risk of model poisoning / steering, prompt injection and model inversion, and assess the inherent bias of pre-trained open-source models, protecting the integrity of software and services from upstream vulnerabilities. Such a holistic approach ensures that every component, from third-party libraries to the training data itself, meets the organization’s security and ethical standards.

In addition, reviewing and evaluating vendor agreements is an important task for cybersecurity teams and stakeholders. Think of these disciplined actions as a necessary stress-test meant to identify and address weaknesses and changing needs. A good contract with clear deliverables and expectations is part of a cybersecurity defensive strategy alongside your people and your defense technologies and ongoing monitoring of systems and services.

3. Validation: Adopt skills frameworks and codes of practice for software supply chain security

No organization must stand up against the heightened threat of software supply chain security alone. Take advantage of existing guidance such as the U.K.’s Software Security Code of Practice to follow when you’re trying to batten the software hatches at your own organization.

Not only does this code support software vendors as they adopt secure software lifecycle development practices; it also supports software customers in mitigating the likelihood and impact of software supply chain attacks.

In addition to following code and other guidance frameworks, organizations can look to skills frameworks and vendor-neutral certifications to validate that their cybersecurity professionals demonstrate certain skills needed to build and strengthen supply chain security and resilience.

Skills development in the disciplines of governance, risk and compliance (GRC), secure software development and AI skills better enable cybersecurity and risk professionals to make informed decisions regarding software supply chain security and risk management.

From complexity to better security

Supply chains are complex, longer than you think and multidimensional. Organizations must place much greater focus on stress-testing the resilience of software suppliers and continuously evaluating exposure.

This approach goes well beyond being careful about what software makes it all the way to procurement. The potentially more damaging layer to address in the macro supply chain involves the embedded software and integrated AI tools that other suppliers are using.

The question is not whether your digital supply chain will face disruption. It's whether you have the visibility, vigilance and validation to operate when it does. That’s resilience: the north star of software supply chain security. Without question, transparency has to run through supply chains instead of just sitting inside organizations.

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Many consider two of the most valuable companies in the world, Microsoft and Apple, to be at different ends of a spectrum. While different in their broad target market, they compete intensely in similar markets like operating systems, consumer hardware and enterprise software. It's little surprise, then, that their respective leaders have looked at each other with disdain throughout history.

Big Macs and chips

Apple's co-founder Steve Jobs compared Microsoft to the fast food chain McDonald's in an interview for the 1995 PBS documentary series 'Triumph of the Nerds'.

In a set of scathing comments, he said the company that Bill Gates founded was only able to enjoy its reach and success due to a "Saturn-5 booster called IBM". Gates used the opportunity granted by IBM outsourcing the operating system for the first IBM PC to Microsoft, called MS-DOS, with Gates retaining the rights to license the software and "create more opportunity", as Jobs phrased it.

Although he praised this aspect of the business – reiterating that Microsoft deserves its success – Jobs was adamant the firm doesn't add any originality into the broader ecosystem and isn't interested in the user experience. They have no "spirit" he said, and described their products as "very pedestrian".

From enemies to friends

Despite the decades-long sparring that Gates and Jobs engaged in, they eventually underwent a multi-stage reconciliation process that eventually led to a very different kind of relationship before Jobs' death in 2013.

In today's technology landscape, the two companies enjoy less of a heated rivalry that peaked during the operating system wars in the 1990s – with each enjoying a moat (Microsoft in the enterprise space and Apple in the consumer space).

Instead, they now compete in domains like AI and cloud computing alongside plenty of other Silicon Valley rivals. However, that doesn't mean there isn't the occasional jab – like outgoing Apple CEO Tim Cook dunking on the Surface Pro.

Something shifted in early 2026. In the space of just a few months, HSBC named David Rice as its first Chief AI Officer, Lloyds appointed Sameer Gupta as Chief Data and AI Officer, and the UK government hired Kalbir Sohi to the newly created role of Chief AI Officer, the most senior AI leadership role in the public sector. This is just the tip of a massive iceberg.

Nearly half of Britain's biggest companies have now appointed dedicated AI leaders, with 42% of those appointments made in just the last year alone.

For organizations yet to act, the more pressing question is what is actually holding them back, and whether they have thought clearly enough about what the role needs to look like to be worth doing at all.

In fact, Thoughtworks research found that 69% of UK financial services organizations have already appointed a Chief AI Officer, well above the 46% average across industries. A further quarter are actively recruiting.

In its early form, the Chief AI Officer was often more of a coordinator aligning teams, reassuring regulators and shepherding pilots. In some organizations, that model still exists, with the role functioning more as a symbolic PR appointment than a genuine driver of change.

The Chief AI Officers who are making a real difference today look very different. They are evolving into P&L owners: controlling budgets, shaping investment decisions and being held directly accountable for business outcomes.

They sit at the heart of commercial strategy rather than on the edge of it, directly influencing how AI drives revenue, product innovation and customer experience.

A broader shift

This evolution reflects a broader shift in what boards are actually asking for. For several years the AI conversation centered on efficiency, covering automation, productivity and cost reduction. Those gains are now largely understood and, in many cases, already realized. The harder question dominating boardrooms now is growth.

Growth is a completely different brief. It requires calculated risk, long-term investment and a genuine rethink of how value is created. It also demands faster decisions and clearer accountability. Committees and distributed ownership models struggle with this, particularly when AI initiatives cut across technology, data, operations, legal and customer-facing functions simultaneously.

That is where empowered Chief AI Officers earn their place. Backed by a mandate to prioritize initiatives, a strong Chief AI Officer will halt projects that are not delivering and scale the ones that are, bringing focus to what can otherwise become a sprawling, fragmented AI program.

They also play a critical role in balancing ambition with responsibility, embedding governance and ethics into delivery from the start rather than bolting them on at the end.

Outside of financial services and professional services

For UK organizations outside financial services and professional services, the recent wave of appointments should land as a practical prompt. Across HSBC, Lloyds and the government, this is no longer a sector-specific story. To organizations that have just appointed this role, make sure your new leader is given the genuine authority, budget and accountability to actually matter.

Timing matters too. Lloyds reported that generative AI delivered around £50 million of value in 2025, with more than £100 million in additional value expected this year. These are the returns that flow from having leadership in place early. The gap between organizations experimenting with AI and those scaling it with clear ownership is already widening quickly.

Lessons learned

The lesson from this wave of appointments is not to appoint a Chief AI Officer at all costs, but rather to be clear-eyed about what specific problem the role is meant to solve.

First, audit how AI decisions are actually being made today. If ownership is split across innovation teams, data functions, risk committees and product leaders, your AI strategy is almost certainly moving more slowly than the business expects. At scale, it becomes a liability.

Second, get explicit about outcomes, not activity. AI roadmaps that focus purely on use cases and tools miss the point. The question boards are now asking is how AI will drive measurable growth, new revenue, better customer retention and faster product cycles. If AI leadership cannot be tied directly to those levers, the role will never carry the authority it needs.

Third, embed AI leadership alongside financial and product strategy, not beside it. The most effective Chief AI Officers sit with CFOs and CPOs, rather than operating as a technical advisory layer. Budget ownership and commercial responsibility matter far more than job titles.

The cost of delay

Finally, move. The cost of delay is rising. Organizations that clarify ownership now will be better placed to scale responsibly and competitively. Those that wait risk discovering, too late, that they have a huge leadership gap.

What is often underestimated is how far the CAIO role stretches beyond technology. The most fundamental challenges presented by AI are not purely about bringing in new tools. They are about people, practices and the processes that underpin everyday business. In that sense, the role has as much in common with a Chief People Officer as it does with a senior technologist.

Building organization-wide AI literacy, managing cultural change and setting clear guidelines around what tools staff can use and what data they can use them with are just as central to the job as any technology decision.

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There used to be a time when you’d buy an Apple app by paying for it once and never again. Then along came the idea of app subscriptions, and slowly but surely, Apple has started selling more and more of its software products through recurring payments. From iCloud+ to Apple TV, there are many ways to spend your cash on Apple’s wares on an ongoing basis.

Today, Apple flogs bundles of its own apps to its users, with Apple One in particular offering a cluster of apps for a single monthly price. And Apple hasn’t stopped there, with the Creator Studio package being introduced a few months ago for users of its artistic apps.

But while these app groupings ostensibly give you a way to get the software you need without paying multiple subscription fees, I’m starting to worry about how they restrict your choices and force you down avenues you might not want to travel.

Not only can they be expensive, but they’re also inflexible, giving you very little ability to customize them to your needs. If subscriptions are the future of app monetization, then Apple needs to do a whole lot better.

Enter the Creator Studio

Apple likes to portray itself as the company of creatives — think about its “Here’s to the crazy ones” or “I’m a Mac” commercials of days gone by. And the firm already sells apps made specifically for this demographic, including Logic Pro, Final Cut Pro, Pixelmator Pro, and more. So it makes sense that Apple would want to offer a software suite that grants interested users access for a single fee, instead of requiring several payments, whether one-off or ongoing.

That’s what you get with the Creator Studio, which is priced at $12.99 / £12.99 / AU$19.99 a month or $129 / £129 / AU$199 a year. In return for your cash, you get a pass for the following apps:

• Final Cut Pro
• Logic Pro
• Pixelmator Pro
• Keynote
• Pages
• Numbers
• Motion
• Compressor
• MainStage

And yes, some of these apps — like Keynote, Pages, and Numbers — are already free, but pay the Creator Studio entry fee and you get “a library of high-quality, royalty-free photos and graphics, and powerful intelligence features” in addition to what comes with the toll-free editions, Apple says.

Considering Logic Pro alone costs $199.99 if you want to buy it outright (rather than pay an ongoing subscription), this package might seem like a rather good deal. But it’s not the price that I’m interested in — it’s the way app bundles like Creator Studio work.

Right now, there are two main app collections available from Apple: Creator Studio and Apple One. While Creator Studio contains the aforementioned creative apps, Apple One starts at $19.95 / £18.95 / AU$24.95 a month and comes with 50GB of iCloud+ storage, plus memberships to Apple TV, Apple Music, and Apple Arcade.

There are two additional tiers, with the most expensive costing $37.95 / £36.95 / AU$49.95 a month and throwing in 2TB of iCloud+ storage, Apple Fitness+, and Apple News+, in addition to what’s already in the entry-level option.

But what if you want to mix and match your apps and services from Apple? What if you want, say, Logic Pro and Final Cut Pro from Creator Studio, plus Apple Music, Apple Fitness+, and Apple News+? Well, you’re going to have to either take out the top Apple One bundle and separately buy those creative apps outright, or you’ve got to pay for memberships of two distinct ongoing subscriptions.

In this instance, combining Creator Studio with the most expensive edition of Apple One means paying $50.94 a month when all you want are five apps. The rest of the content you pay for is superfluous. Throw in AppleCare+, and you’re forking out even more.

Apple gives practically no flexibility or customization with its app collections. You can’t mix and match apps and services, and if there are some you want from both subscriptions, you’ve got to pay for both. That makes it an incredibly expensive way to go.

But with Apple seemingly getting evermore enthusiastic about subscriptions — and the revenue they bring in — don’t be surprised if this state of affairs continues.

Is there a better way?

You might feel that, since Creator Studio is relatively new, Apple is still working out the logistics of how to sell the apps and services it contains. But Apple is no stranger to subscriptions. It’s been offering the likes of Apple Music and Apple Arcade for years now, so it knows a thing or two about how to structure subscription offerings.

Is the lack of flexibility here and the absence of any way to pick and choose apps a deliberate policy on Apple’s behalf? We can’t know for sure, but it’s not a good look.

While the Creator Studio might seem like a decent bargain on its face, it becomes considerably less so if you’re not interested in everything it offers and want to pair its apps with those from the Apple One bundle.

Considering how keen Apple seems to be on subscriptions, I’m surprised it doesn’t offer individual memberships for each component app within these packages. For instance, why is there no individual subscription for Pixelmator Pro? Instead, you’ve got to choose between a one-off fee or the full Creator Studio.

If Apple were to go down this route — and it's what I'd do if I were CEO-to-be John Ternus — it would instantly solve the problem of pairing different apps from different collections. You could simply add whichever apps you wanted to your own “build a bundle” package and get exactly what you need for a more acceptable price.

But that kind of process doesn’t seem to be anywhere on the horizon. There are no rumors saying it’s coming, no telltale clues nestled in Apple’s code. Don’t hold your breath for its imminent arrival.

Until we do get something like that, Apple customers are going to continue to feel ripped off by a rigid, inflexible system that is happy to take their cash in return for apps they don’t want or need. It’s a situation that’s in desperate need of a rethink.

Across 100 engineering organizations, 61% already run AI agents. Yet almost none trust them enough to bring them into production.

There are a few reasons for this. Firstly, agents are prone to making mistakes that humans know to avoid through experience. They move at a much faster pace, and by their nature operate autonomously. If something goes wrong, there’s often no audit trail or visibility into what they are doing across the organisation.

This is problematic when a rogue agent inevitably leaks credentials, hits unauthorized repositories, or burns through cloud budget before anyone notices.

This unreliability is why most organizations keep agents away from anything that matters.

However, the real problem is not the agents. It’s the absence of governance around using them. The controls already exist, and have for a long time.

They are the same principles that have been applied to human engineers for years: minimal privileges, audit logging, identity management, and scoped access. Though familiar, companies struggle to apply these fundamental rules.

To ensure organizations get off to a good start with deploying AI agents, they should implement three simple governance controls: isolate, scope, and approve.

Isolate by default

Start with isolated, ephemeral workspaces. Every task that is spun up from a clean template should be erased the moment it is completed. No shared state or bleeds between runs. This means that should something go wrong, the damage will be confined to that task, and nothing beyond it.

Then from that workspace, all agents should have zero outbound access by default. Not limited. Zero. An explicit ‘allowlist’ then defines exactly which domains, methods, and paths an agent can touch. Everything else gets blocked. And even with narrow access, sensitive systems such as GitHub or Salesforce should get read-only access also.

Your agent is in an isolated workspace. Network connectivity is limited. Now it’s time to control tool calls. Model context protocol (MCP), for instance, is how agents invoke external tools, including file systems, databases, and APIs. Without airtight guardrails, an agent can reach any MCP server available to it.

The danger is that this is a significant and largely invisible attack surface. Proxies that sit in front of those tools allow administrators to define an approved list, filter at the per-tool level, and log anything that tries to reach outside the boundary.

Scope permissions and real-time monitoring

Even a well-isolated agent can cause problems if it’s running with more permissions than it needs. Agents should never inherit a user’s full credentials. API keys should only carry the permissions required for that specific task, and nothing more.

In addition to scoped identities, there must be clear visibility. Streaming every request through real-time monitoring and alerting teams when an agent makes 10x its typical API calls, contacts a new domain, or starts burning tokens unusually fast will provide timely, and actionable signals.

Identity-aware LLM routing ties it all together. Authenticating users through existing SSO, and routing all model requests through centrally managed keys solves the problem of API key sprawl. Without this measure of control, hundreds of loose keys with no attribution or clear revocation path quietly accumulate.

Approve agentic tasks with a human in the loop

Think of an agent as a talented junior employee. High output, eager to help, but not yet trusted to push to production unsupervised. AI agents should generate output but not decide what ships. This is where human approval gates, backed by existing role-based access controls, come in. Humans must be held mutually accountable for their agent’s output. This is as much a mindset as a technical control.

However, there is one area where you should assist human oversight: model selection. Not every task should go to the same model. For example, regulated data has different requirements than content generation. Routing by policy ensures that compliance requirements are met without relying on individual developers to make the correct call every time.

Full audit trails make the whole lot accountable. Every prompt, every tool call, every model interaction should be mapped to an authenticated identity and exportable to whatever observability stack is already in place. It should always be clear which authenticated user sent which prompt to which model, and why.

Embed governance in your infrastructure, not your apps

Overall, the goal is to empower organizations with the confidence to deploy their AI agents safely without slowing down their developers, agents or the business. Ensure this happens by laying down a governance layer to make AI agent adoption sustainable. But there’s a catch. Implement governance at the infrastructure layer. There are two reasons why.

Firstly, it avoids duplicating policy for every AI stack you deploy. Secondly, it ensures a consistent governance across a very fast-changing world where agents, models, and harnesses will leapfrog each other monthly. You do not want to rewrite governance policies at the pace of AI change.

Done right, infrastructure-lever governance makes AI production ready. Security teams with visibility over what agents are doing will be more inclined to approve more, compliance teams with clear audit trails will sign off faster, and developers who trust the guardrails will be able to push further.

Those enterprises that build in governance now will not just have better security posture, they’ll outpace competitors waiting for sign-off to run their first pilot.

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“Don’t open suspicious emails.”

This used to be the baseline mantra with cybersecurity training. Spotting a counterfeit data request was once simple: poor spelling, questionable email addresses, or a direct request for cash accompanied by an incredible story of a prince or ageing millionaire. But those days are over.

Generative artificial intelligence (AI) makes prepping sophisticated attack flows (which would have taken months to code) available with just a few keystrokes: as a result, spoofing or phishing emails today are often compelling, topical and personalized, making them hard to spot.

With widely available phishing kits (like Evilginx), threat actors can create fake login pages or even CAPTCHA pages with a planted JavaScript injection attack with ease. In other words, there are now even more intelligent ways for threat actors to penetrate a system and steal data quickly, all with the help of AI.

What types of AI-enabled attacks are on the market?

Many AI-powered attacks aim to trick people into revealing sensitive information. The top three types of AI attacks that business leaders in the UK are concerned about are AI-generated phishing, business email compromise, and malicious AI agents. Of these, AI-generated phishing is one of the most concerning, and with good reason.

There are multiple types of phishing, including deepfake video calls and vishing (voice phishing), a tactic that uses phone calls or other voice messages to impersonate a person or organization.

In recent years, there have been high-profile successful deepfake attacks like the $25 million heist on the engineering firm, Arup, where an employee was tricked into giving away millions of dollars via a fake internal meeting that seemed real.

If a business’s infrastructure simply relies on an employee’s ability to spot a fake without proper training, this opens it to the inevitable reputational damage and financial loss resulting from an attack.

Cybersecurity skill gaps need to be addressed: Security teams need to train their workforce on why phishing continues to be a serious threat and how AI is being used by threat actors to enhance those attacks, because even with AI-driven defenses, employees who are poorly or inconsistently trained could unwittingly lead to devastating outcomes.

The future of cyber resilience with AI: Fighting AI with AI

Despite the malicious use of AI, AI is an excellent ally in combating cyber-attacks. Next-gen data security and cybersecurity solutions use AI to process large amounts of logging and monitoring data to find anomalies and outliers, block threats, and make recommendations or adjustments to security controls.

AI is also very good at spotting behavioral indicators of compromise (IoC) and correlating seemingly unrelated security activities, making it a must-have capability in the AI era.

The message has never been clearer: today, businesses need to embrace product offerings that apply AI in cybersecurity because the attackers already have.

Additionally, to achieve strong cyber resilience, cyber awareness must be a top priority. AI-powered training solutions can further enhance this by automating, adapting and personalizing the experience for employees, based on their role and their response to ongoing phishing simulations, making it more engaging, efficient and effective.

The double-edged sword of AI presents a conundrum for professionals across sectors. AI is a good investment for security, but a majority of businesses are not deploying it effectively enough yet, which allows attackers to gain an advantage.

AI is here to stay and will continue to impact cybersecurity for the foreseeable future. As organizations embrace the benefits of AI in their day-to-day operations, it becomes even more imperative for them to safeguard against malicious exploits to ensure the integrity and reliability of their business systems in an increasingly interconnected world.

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Whether it’s a new agent, a new gateway, or a new monitoring layer, every time a security gap appears, the instinct is to close it with a new tool. It seems sensible in the short term, but over the years, the layers build up until enterprise security becomes one of the most complex and costly parts of running a business.

The result is a fragmented stack that is expensive to license, difficult to manage, and hard to justify to the board. Security teams are left with overlapping controls, integration headaches, and a user experience rife with friction. And to make matters worse, the whole bloated stack might well be pointed in the wrong direction.

Work moved, but security didn't

For most knowledge workers, the browser is where the working day begins and ends. From CRM to email, a growing number of enterprise applications are best delivered through a browser tab. Adding to this is the rapid adoption of generative AI tools, which employees use daily for drafting, analysis, and workflow automation. The browser is not a gateway to work but has become the workspace itself.

Security architecture has not kept pace with this shift. The controls most organizations rely on were designed for a different era, one built around corporate networks, managed devices, and applications behind a firewall. That model has not existed in its original form for years, yet it still underpins traditional security strategies.

As a result, there is a significant blind spot: Network and device-level controls can determine whether a user is allowed to reach an application, but they cannot see what happens once they are inside it.

When the stack stops at the door, critical information never crosses the threshold. For example, organizations may never see data is being accessed, how it is being handled, and whether it is copied into a personal email or pasted into a public AI tool.

The browser is the primary attack surface

Most workers use consumer browsers designed for the broadest audience possible. Because they’re not built for the rigors and needs of enterprise use, they’re soft targets.

Savvy threat actors can harvest credentials inside the browser session, and malware targets locally stored cookies and passwords. Data exfiltration increasingly happens not through network breaches but through routine user actions such as copying a customer record, downloading a report, or sharing a file with the wrong destination.

The rapid rise of AI tools has exacerbated the problem. Employees are pasting sensitive business data into generative AI platforms without understanding where it goes or how it is retained. Agentic tools that take actions inside business systems on a user's behalf are especially risky. They introduce a category of risk that most security teams have limited visibility into, and those interactions leave no trace on the network perimeter.

Legacy security tools were not built to see inside a browsing session. They inspect traffic, scan endpoints, and flag anomalies after the fact. By the time a control triggers, the action has often already happened.

Control needs to move to where decisions are made

With enterprise activity concentrating in the browser, the enforcement layer needs to follow suit.

The most important thing to reframe is moving from managing access to managing behavior. Traditional security tends to focus on whether a user is permitted to access an application.

That still matters, but the more consequential question is what happens inside the application once access is granted. A file downloaded to a personal device or a customer record pasted to a private email are the moments when data movement can turn into data loss risk. But most security and data protection stacks can’t see them.

Embedding security and data protection into the browser makes these moments governable because policy applies at the point of action, in real time, without disrupting the workflow. A user working with sensitive data can move it freely between authorized applications, while controls prevent it from reaching an unauthorized destination.

This is also where zero trust can be properly realized. Most implementations evaluate identity and device posture once, at login. A browser-native model assesses session context continuously, adjusting controls as circumstances change without interrupting the user. Enforcement follows the work, rather than waiting at the edge of the network.

Consolidation is the only way forward

Managing browser risk isn’t simply a case of throwing more tools into the stack. Many enterprises will find their stack is already full of layers that were built to compensate for consumer browsers. You have a virtual desktop interface (VDI) to control application access, and VPNs maintain network tunnels for SaaS tools that need a very different method of connectivity and protection.

Data loss prevention (DLP) solutions attempt to intercept data movement after the moment it happens, and cloud access security broker (CASB) layers mediate cloud access that the browser already intermediates.

Each is a reasonable response to a security gap, but together they establish an unwieldy infrastructure that was never designed to work as a whole. These systems force 100% of data through choke points (SASE cloud proxies) for inspection, creating performance bottlenecks and a poor user experience.

And as encryption cyphers strengthen toward post-quantum encryption standards, a significant portion of this traffic is blind to these break and inspect architectures.

Closing them from the inside by making the browser itself the primary point of governance removes the need for many of them entirely. The stack got this big by solving the wrong problem, and solving the right one starts with the browser.

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For many small and medium-sized businesses, cybersecurity conversations center on endpoints like laptops, servers and cloud platforms.

Yet one category of device continues to sit quietly outside that focus: the printer.

Despite being deeply embedded in day-to-day operations and handling highly sensitive information, print infrastructure is still widely overlooked.

This lack of scrutiny is part of a broader challenge. New research shows 73% of UK SMBs fear data privacy issues in their current document management processes, highlighting widespread concern about how sensitive information is handled across both digital and physical workflows.

Yet 55% of UK SMBs consider printers a low priority in their cybersecurity strategy.

This worrying disconnect between the critical data printers handle and their low security prioritization creates a vulnerability that attackers can exploit to access sensitive workflows, intercept documents or move laterally within a network.

A growing blind spot in hybrid working environments

As we know, work no longer sits neatly inside the office. It happens across home networks, personal devices and shared spaces – environments that SMBs struggle to monitor or control. Today, print and scan workflows routinely handle sensitive data, from payroll and contracts to customer records.

Without visibility into who is printing what and where, organizations have no way of protecting that sensitive data. And it doesn’t take much – just one misdirected scan or a print job left in a tray can expose confidential information, often without any obvious sign that something is wrong.

According to Quocirca, 74% of SMBs have experienced a print-related data loss incident in the past year. A further 33% say documents printed on employee-owned home printers are now a top factor in data loss.

Behavioral factors further amplify the problem. Research shows 47% of UK SMBs believe that employees try to bypass their organization's print security guidelines. At the same time, 63% assume their printers are secure because they sit behind a firewall, and half do not consider them a security threat at all.

Together, these trends point to a clear security gap: while print workflows are handling increasingly sensitive data across distributed environments, they are not being secured, monitored or governed to the same standard as other endpoints.

Real risk in outdated hardware

Almost three-quarters (73%) of UK SMBs frequently worry about the risk posed by their outdated systems. Older printers running unpatched firmware and default credentials quietly process and store sensitive data while remaining unmanaged.

A compromised printer can serve as an entry point into wider business systems. If laptops and servers require active monitoring, printers do too.

Concerns are not limited to hardware. They also span cybersecurity exposures linked to connected printers, vulnerabilities introduced as scanned documents move through the cloud, and the risk of unauthorized access to print queues.

Alongside this are more visible, day-to-day issues, such as confidential documents being left unattended and the difficulty of tracking information once it exists in physical form.

These risks can be managed, but only if SMBs treat print and scan as part of the security perimeter.

Without this, resilience across hybrid workflows becomes guesswork. The future of work is not only about cloud and AI; it is also about securing the everyday document processes that move sensitive data across physical and digital environments.

Building control through visibility and smarter printing

Despite low prioritization, almost six-in-ten (63%) SMBs in the UK acknowledge print security needs improvement. To secure the future of work, organizations need secure print hardware foundations and protection that keeps pace with evolving threats.

Smart printing builds on these secure hardware foundations by embedding visibility, policy enforcement and audit trails directly into print and scan workflows.

Of UK SMBs that have adopted smart printing, 89% say it provides clearer visibility into printing and scanning activity across users and locations, 86% say it helps them meet compliance and security standards, and 85% say it improves enforcement of rules and restrictions.

Rethinking the role of print in cybersecurity

As we delve deeper into the future of work, printers need to be brought into the wider security strategy. A data breach originating from a printer can be just as damaging as one involving a compromised device or network, with the potential for significant financial loss, regulatory penalties and reputational harm.

Printers are not just office equipment. They are part of the digital infrastructure that supports modern work. As print and scan workflows become more digital and cloud-connected, printers deserve the same security attention as any other endpoint.

In practice, that means SMBs need three things: secure hardware as a foundation, security that keeps pace with new threats, and the visibility and control to maintain resilience at scale. Bringing printers into the security strategy is a practical step towards protecting sensitive data in a world where both work and risk are distributed.

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IDC projects that AI infrastructure costs at Global 1000 companies will run 30% higher than current budgets by 2027. That gap shows a mismatch between how AI workloads behave in production and how enterprise IT has historically planned for capacity.

The pattern repeats across industries. A pilot project validates an AI model on a controlled dataset, and budgets are created around those economics. When the system moves into production, the bill often outpaces what anyone originally modeled.

The natural instinct is to blame the size of the model or the cost of using tokens, but that’s not where the money goes. The cost lives in the data layer, driven by how often the system reads, how many services it touches, and how continuously those operations run.

What pilots don’t show you

A pilot runs against a narrow dataset, with a handful of concurrent users, on a request-response cadence familiar to anyone who has shipped a web application. Production looks nothing like that.

Consider a generative AI customer support agent in production. A single user prompt can trigger simultaneous lookups across session activity, CRM records, inventory systems, retrieved manuals, and other sources before the model produces a response. All of this happens under sub-100ms latency budgets, with the slowest lookup gating the rest. The operational problem becomes tail latency across many small parallel lookups.

Now layer agentic workflows on top. A user request decomposes into a plan, then into a series of steps that each issue their own lookups, write intermediate state, and read it back. What starts as one inference expands into tens or hundreds of data accesses, with the system holding session and memory state across the entire arc. The cost profile that emerges is nothing like what the pilot priced.

Where the 30% comes from

The overrun comes from a series of defensive choices made under uncertainty. When teams can’t see how data flows through a single request, they over-provision to absorb spikes. When they can’t predict cache behavior under shifting context, they duplicate data across systems to reduce dependency risk.

When one downstream service slows down, they layer another service on top to insulate against it. Each choice is locally rational. The aggregate is a system that costs 30% more than the workload requires, and that’s before anyone adds a new use case.

The underlying problems are usually the same. Fan-out per request goes unmeasured end-to-end. Context gets fragmented across feature stores, session stores, user profile systems, vector indexes, and third-party APIs. KV cache and prefix reuse get left on the table because the inference layer can’t hold or share state across calls.

Replication and tiering decisions get made per system rather than per access pattern. None of these show up in a pilot. All of them show up in the production bill.

What the AI data tier has to deliver

AI in production is a continuous, distributed system whose hot path is context assembly — many small reads per request under tight latency budgets — combined with writes that must keep multiple representations of the same entity consistent.

These systems need two things at the same time: predictable low-latency reads under high concurrency and consistent writes across the data path. The infrastructure underneath has to be sized and shaped accordingly.

A few architectural decisions end up driving most of the outcome:

Match the data tier to the access pattern Session state, agent memory, feature lookups, retrieved context, and KV cache reuse all have different read patterns, freshness requirements, and durability needs.

Treating them as different data tiers — or laying them on whatever database happens to be in the stack — is the most common source of overrun. The session store and the system of record have different access pattern demands from the same data tier.

Engineer for fan-out and predictable tail latency Throughput is the wrong primary metric for an AI data tier. The right one is the predictability of many small reads triggered by one request. A batch of parallel lookups is only as fast as its slowest member, and a single slow lookup stalls the entire context-assembly step.

Storage systems optimized for write throughput pay a read amplification penalty under this access pattern. Systems that keep the primary index in memory and resolve point lookups in a single I/O behave differently at the tail.

Treat write consistency as a correctness requirement When updates across user profiles, embeddings, feature vectors, and session state aren’t synchronized, downstream context assembly reads a mix of versions and the model produces confident output grounded in contradictory data.

These are hallucinations that have nothing to do with sampling or model probability, and they don’t yield to better prompts or bigger models.

Treat inference-time data reuse as infrastructure. KV cache reuse, prefix sharing, and agent memory persistence are first-class infrastructure concerns. Teams that figure this out early run the same workloads at lower GPU utilization than teams that haven’t. This is the largest leverage point that doesn’t appear in most AI cost models.

Where to start

The most useful first step is to trace a single production request end-to-end — counting lookups, logging sources, and measuring tail latencies. That exercise reveals more than any architectural review. Once teams can see how data moves through one interaction, they can categorize data accesses by tier and verify that each is running on infrastructure suited to its pattern.

From there, the next question is what’s being recomputed that could be reused, particularly across inference calls and agentic steps. Fan-out per interaction should become a metric teams watch as closely as p99 latency — because at scale, it drives cost just as directly.

AI cost in production is a design discipline. Teams that address it early have far more control over performance and spend than teams that wait until the bill forces the issue. In many cases, the 30% gap is the cost of learning these lessons too late.

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IBM’s launch of its AI coding assistant “Bob” points to a much bigger shift in enterprise modernization. Across the industry, AI tools are being positioned as a way to make legacy systems easier to understand, assess and eventually modernize. And there is real value there.

Some of these tools can read thousands of lines of legacy code, identify deprecated APIs, summarize business logic and surface technical debt in minutes. For organizations carrying decades of operational history, that kind of visibility is a big step forward - but let’s not confuse visibility with modernization.

Understanding how a system works is necessary. It is not sufficient. I have seen teams produce clean dependency maps, detailed code summaries and impressive technical assessments, only to realize the hardest part starts after the AI has finished scanning the code.

Legacy estates rarely sit neatly off to the side. They are woven into the operating model of the business. They reflect years of process decisions, integration choices, compliance requirements, customer-specific exceptions and institutional knowledge that is often scattered, tribal or barely documented. Lovely little treasure hunt, except the treasure is risk

An AI model may identify an ageing integration point or highlight an application that supports a critical business process. That is helpful. But the real challenge begins when teams realize how many other systems, workflows and operational teams are connected to what looked like a straightforward change.

In many large organizations, legacy systems are still in place for a very simple reason: they work. They continue to perform reliably under demanding conditions, even if parts of the surrounding environment have evolved, degraded or become harder to support over time.

That is why modernization is not just a technology exercise. It is a sequencing exercise. It is a risk exercise. And, done properly, it is a business decision.

The multi-layer challenge

Every technical decision inside a legacy estate has consequences somewhere else. A change to one application can affect recovery procedures, audit requirements, licensing agreements, batch schedules, integration layers or support processes that have been stable for years.

This is where many modernization programs stall. Teams underestimate how interconnected these environments have become. AI can accelerate the technical assessment, but its real value comes when those insights are connected to the operational and commercial context around the system.

That distinction matters. Enterprises are moving away from broad “replace everything” strategies and becoming more selective. Not every legacy platform needs to be ripped out. Some systems need restructuring. Some need better interfaces. Some need to be moved. And some, frankly, should be left exactly where they are because they are doing their job reliably at scale.

Workload placement has become much more nuanced. Moving a service to public cloud may improve scalability and speed up software delivery, but it can also introduce data sovereignty concerns, latency issues, cost variability or new support dependencies.

At the same time, keeping workloads on modernized IBM Z or Power environments may provide more predictable performance for applications that already run effectively at scale.

The real question is not, “How do we get everything off legacy platforms?” The better questions are, “Which systems genuinely benefit from relocation, which need to be modernized in place, and which can be extended through modern interfaces?”

Without that context, organizations can spend a lot of money moving systems around without actually fixing the underlying problem. Congratulations, you now have the same complexity in a newer location.

We are already seeing this play out in enterprise environments where legacy platforms still sit at the center of high-volume operations. In one recent assessment, AI coding assistants were used to analyze more than six million lines of RPG code running on IBM Power systems, processing roughly 30 million requests a day.

The work surfaced technical debt and concentrated areas of complexity in weeks, giving the organization a clearer basis for deciding what to modernize, where to start and how to sequence change without disrupting core operations.

That is the practical value of AI in modernization: not magic, but better visibility, faster assessment and smarter prioritization.

Why enterprise AI deployments are becoming more specific

This broader shift is also showing up in how hyperscalers talk about enterprise AI adoption. Microsoft CEO Satya Nadella has described the market as moving from “discovery” into “widespread diffusion.” In plain English, the challenge is no longer just building impressive models.

It is embedding AI into real workflows, real operations and real business systems at scale. That is much closer to how modernization actually works inside large enterprises.

The same shift is happening with AI models themselves. The industry still loves to talk about scale, but most enterprise teams are not sitting around hoping for a trillion-parameter model to save them. They need tools that help engineers solve very specific problems inside environments that are already complicated enough.

In many cases, smaller, specialized models are proving more useful because they can be deployed in controlled ways, focused on specific tasks, and governed more tightly.

That governance point matters. Bringing AI into infrastructure operations raises very practical questions: What data can the model access? What systems can it touch? Can it recommend changes? Can it execute them? Who approves movement toward production?

That is another reason task-specific models are gaining traction. Teams can define exactly what the model is allowed to do, where human approval is required and how changes move through existing controls. In enterprise environments, that kind of control is not bureaucracy. It is how you avoid turning a productivity tool into tomorrow morning’s outage bridge.

Where AI is delivering practical value today

The organizations getting real value from AI are usually not the ones making the loudest claims about it. They are applying AI to engineering and infrastructure work that already consumes huge amounts of time: investigating incidents, mapping dependencies, validating changes, supporting regression testing and understanding how complex systems actually behave.

A lot of that work comes down to giving engineers better visibility and helping them get to root cause faster.

AI models can help connect runtime anomalies to recent code changes. They can reduce the time teams spend manually tracing incidents across hybrid environments. They can support regression testing around older applications and surface integration dependencies that were previously difficult to visualize across multiple infrastructure layers.

That becomes especially important in environments where cloud-native services sit alongside long-established mainframe and midrange systems. In many organizations, the hardest problems show up in the seams between those environments, particularly when different teams manage different parts of the estate with different tools, different metrics and different operating rhythms.

That is why the most useful AI deployments tend to focus on practical engineering work, not grand attempts to automate everything at once.

Organizations are seeing value in areas that are repetitive, complex and difficult to scale manually. Automated test generation can reduce regression risk around legacy applications. AI-supported observability correlation can shorten incident investigation cycles. Dependency analysis can help teams prioritize infrastructure work that removes bottlenecks affecting service delivery.

In most cases, AI is not replacing engineering judgment. It is improving the work engineering and infrastructure teams already understand well. And that is where the expectations need to be clear.

AI can absolutely speed up discovery. Work that once took weeks of manual assessment can now happen much faster. But that is usually the point where the real work starts.

A model can tell you how systems connect. It cannot tell you how much disruption the business is prepared to absorb. It cannot decide which customer commitments matter most. It cannot magically unwind 25 years of operational dependency while everyone politely keeps breathing.

Technology leaders should view AI coding assistants as decision-support tools for broader infrastructure and modernization strategies, not as stand-alone solutions to legacy complexity.

IBM’s Bob announcement shows how quickly these capabilities are advancing, especially when it comes to understanding legacy code and helping teams work through large, complex estates. But visibility only matters if organizations can turn it into practical change without creating instability elsewhere.

AI can help you read the legacy estate. It can help you understand the risk. It can help you move faster. But modernization still requires judgment, sequencing and operational discipline.

That part is still very human.

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AI investment is accelerating across enterprises.

Budgets are increasing, boardrooms are asking hard questions, and the answer they’re getting back is token costs, prompt counts, and copilot deployment numbers.

Those aren't business metrics. They're activity logs.

Most organizations have settled on those numbers because they’re easy to track and show up well in reports. The problem is that those numbers measure the activity, not the results.

They show how much AI is being used, not whether the business is doing better since implementing it.

This is where token maxxing starts. It happens when organizations begin rewarding those who use AI the most, rather than what it actually delivers.

By optimizing for the wrong thing, we’re quietly setting AI programs up to fail.

When metrics become the mission

There’s a simple truth in business: people optimize for what gets measured, and we’re seeing that with AI right now. Understandably, because teams begin to generate more output when that is what leadership tends to see and reward.

Over time, the measure becomes the mission, and the focus on real outcomes starts to drift. The result is a surge in output that looks impressive on paper but has little to do with faster decisions or better products.

Teams with the highest usage or spend start to become the benchmark that others feel they need to match. But metrics like token spend should be evaluated the same way any other business investment is.

If we committed the same budget to a new sales tool or a go-to-market campaign, the first question from the board would be: how did revenue increase, and did it move the business forward in a measurable way?

High token consumption with no clear line to a business outcome is not a success story. Without that connection, it is just an additional cost. Meanwhile, the organizations that figure this out first will move faster with less spend — and that gap compounds.

AI did not create the problem; it exposed it

We’ve seen this pattern before. In the early days of cloud computing, companies moved fast and invested heavily without rethinking how work was structured. Costs went up, but the outcomes didn’t always follow.

That wasn't a cloud problem. And this isn't an AI problem. It never was.

The reality is that most companies already had a structural issue long before AI arrived. The work that matters most doesn’t live in a single system. It stretches across teams, tools and departments, held together by people stitching context together and filling in the gaps between systems. That’s always been the case. What AI has done is expose it, and this is where the problem runs deeper than just metrics.

AI makes individuals faster within the tools they already use. But when that work still sits inside disconnected systems, speed doesn’t fix the problem; it makes the gaps more visible. Teams move faster, but not necessarily together, and that’s why usage metrics can look so strong. More prompts, more output, more activity. On the surface, it looks like progress, but without a clear connection to outcomes, that activity can quickly become noise.

The problem isn’t just that we’re measuring the wrong thing. It’s also that most organizations don’t have a clear view of outcomes in the first place.

What good measurement looks like

Stop asking how much AI is being used and start asking what has changed because of it. A few shifts that will follow:

Output volume does not equal business value. Treat the two as interchangeable, and your AI program is already starting to drift.

Start with the outcome, not the tool. Instead of asking, “How many prompts did we run?” ask, “What did we achieve?”

Measure what crosses teams. If impact stays inside a single function, you have a useful tool — not a competitive advantage.

Here are a couple of examples: for engineering teams, this could mean focusing less on lines of code or pull requests and more on what actually reaches production and delivers value to customers. For marketing, it might be focusing on whether campaigns launch faster or land better.

Ultimately, AI that makes one person’s morning easier is a useful tool, but real value comes from AI that changes how the whole company gets work done. Without that, you end up with intelligent tools that operate in isolation. Helpful in moments but limited in impact.

Redefine what good looks like

Right now, most of the AI conversation is still focused on individual productivity — how much faster one person can write, code, analyze, or create. That matters. But it’s only part of the story.

The bigger opportunity sits at the organizational level and looks at how work actually moves across teams, systems and the company as a whole. AI can absolutely improve how businesses operate, but only if we are willing to ask harder questions about what we are actually measuring and why.

The companies that pull ahead won’t be the ones using AI the most. They’ll be the ones who changed what they measure when AI showed up. That’s the only metric that matters.

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Sun Microsystems was a huge force in the technology landscape, with its co-founder and CEO Scott McNealy an outspoken and brash maverick in the early Silicon Valley ecosystem. The company had just launched a new system, and McNealy was quick to push back on any critique centering around the implications for user data.

The smart home dream

During an informal Q&A session with reporters, McNealy slapped down concerns that the newly launched Jini platform could pose a risk to user privacy.

The system, as it was engineered, was a revelation – but ultimately failed to catch on due to some pretty significant hardware hurdles. Designed to allow devices to communicate with and share resources, the Jini network architecture allowed unadulterated communication without configuration, driver installations, or human intervention.

It was an early and ambitious effort to establish a vision for smart homes and offices. The trouble was that it required devices to continuously upload data and lease space on networks, with the system creating a massive digital footprint.

Erosion of privacy

McNealy's comments, unsurprisingly, drew immediate and sharp criticism from privacy advocates and campaigners. Lori Fena, then chairman of the board of the Electronic Frontier Foundation, said the comments were "completely irresponsible", for example.

There is, however, a kernel of truth in his remarks – with the direction of travel in the tech industry geared toward the collection of vast amounts of user data, with companies using this data to build profiles primarily for advertising and marketing purposes. In some cases, such as the Cambridge Analytica scandal, user data had been used for political advertising.

Today's technology landscape is now dominated by web-scraping generative AI technologies, massive data collection and monetization as well as IoT systems that dominate cities and offices.

Some also argue that surveillance capitalism has become a dominant force that the world's biggest organizations are tapping into, with consumers simply expected, for the most part, to "get over it" and carry on as normal.

Generative AI has given rise to a new breed of business that can generate synthetic content, be it video or music, and Suno AI is among the biggest names in this category.

The startup's CEO, Mikey Shulman, however, landed himself in hot water when he made comments about the joy and fulfilment that musicians get from practising their craft.

Making music 'enjoyable' again

Shulman was speaking on the 20VC podcast in January 2025 when he remarked that making music isn't something that most people enjoy doing.

He framed his words in such a way that would suggest that his platform, Suno, cuts out a lot of these steps – largely centered around mastering the skills that you need – which would, in turn, lower the barrier to entry for those who aren't naturally gifted or have the time to 'get good' at making music.

In his words, he wanted to "[give] everybody the joys of creating music" which he deemed a huge departure from the status quo. His remarks, however, drew the ire of countless working within the music industry as well as regular people on social media.

Struggling artists

The threat of AI to the lives and livelihoods of those working in the music industry is very worrisome, according to research, with workers standing to lose 25% of their income over the next four years. It's no surprise, then, that Shulman's comments instigated such a fierce and violent backlash – forcing the CEO to row back and apologize a couple of months later.

His critics also suggested that the comments fundamentally misunderstood the nature of art and working in a medium such as music, where the hours of toil, practice and refinement are, in and of itself, part of what makes it such a fulfilling endeavor.

However, generative AI is still a new phenomenon and companies like Suno have only just entered the arena. Although the nature of the existential threat to the creative industries is clear, the specific economic impact on artists in the years to come remains unclear – especially in an uncertain landscape in which a strong backlash against AI art is brewing.

Here at TechRadar, I test some of the best 4K Blu-rays each month as part of the Blu-ray Bounty. This is where we look at the latest 4K releases from that month and judge whether they’d be a worthy addition to your collection, focusing solely on the video and audio quality of each disc.

Every so often, a disc wows me so much that I add it to my rotation for testing the best TVs and best soundbars. 4K Blu-ray is my go-to source for AV testing, as it delivers a higher video bit rate, resulting in better picture quality than streaming, and uncompressed soundtracks for the best audio.

Some of the discs from the near-100 discs I’ve tested as part of the Blu-ray Bounty that have joined over the years include Wicked, The Mask, The Sound of Music and Dark City, to name just a few.

As part of the most recent June 2026 Blu-ray Bounty, there’s another disc that will definitely be joining the testing rotation, as it performs at a reference-quality level. And that disc is Speed Racer.

Breathtaking color

My go-to 4K Blu-rays for color testing have been Wicked and The Sound of Music. Wicked features an oversaturated, candy-color style that really generates a lot of pop on the right screen, while The Sound of Music delivers still-vibrant but more authentic and realistic colors.

Speed Racer falls in Wicked’s camp, but takes it to another level. Throughout the movie, there are some seriously vivid colors. Red is a prominent feature throughout the movie, as it's the color of Speed Racer’s ‘M’ logo and the interior of his car, both of which are often front and center on screen. It’s also the color of the Racer garage at home and Papa Racer’s shirt, which he wears throughout the movie.

When I tested the Samsung S95H/S99H and LG G6 side-by-side, both TVs interpreted the red differently: the G6 gave it a much deeper red hue, while the S95H/S99H made it look paler, almost orange in some places. Both of these TVs, likely to be two of the best OLED TVs I’ll test in 2026, captured the visual punch of this red, but it was useful to see how each TV interpreted the color.

There are plenty of scenes where a single color dominates. As the Racer family arrived at Royalton Industries, there were a lot of bold purples, including the airship's color, the walls, and even the transport. On the OLEDs I tested the disc on, these purples had a beautiful richness to them that again was displayed in different ways by the S95H/S99H and the G6, with the latter again adding that bit more depth.

This trend continued throughout the movie, whether it was the neon greens of a grass field in a flashback, the fantastic pink/purple/orange gradients in the sky during sunset or the lush blues of the sky during daytime race sequences (or Speed’s iconic blue and white shirt.) Even the white of the Mach 5 / 6 and Speed’s helmet had a glorious clarity to them, really dazzling on the S95H/S99H and G6.

The use of color is fantastic in Speed Racer, and if you have a TV with Dolby Vision, you’ll be rewarded with the most breathtaking colors, some of the best I’ve ever seen on a 4K Blu-ray. But it’s not just color reproduction where this disc is useful.

A Dolby Atmos showcase

When it comes to testing soundbars and TV audio, my go-to 4K Blu-rays have been The Batman, namely the Batmobile/Penguin car chase scene for its detail and bass, and Top Gun: Maverick, for its all-around expansive Dolby Atmos soundtrack.

Speed Racer has proved to be a far more compelling disc, audibly, than I ever thought. While I anticipated some hefty bass from the car’s engines, I didn’t anticipate the level of detail and sound mapping I would hear.

During the opening race, there are plenty of demonstrations of pinpoint accuracy as cars swerve wildly through the corners of the winding track. The sound of squealing tires demonstrates excellent directionality and is fully connected with the action on screen. As a car moves from side to side on screen, you can be sure to hear it in the front channels.

One moment in this scene, in particular, really grabbed my attention, though. As Speed makes his way around the track, he has to grind the rear axle of the Mach 5 against the edge of the track and as I was watching, I heard the sound of a grind come through crystal clear in the left rear speaker of the Samsung HW-Q990C soundbar I was using (an 11.1.4 channel Dolby Atmos system comprising of a soundbar, subwoofer and two rear speakers). This was the moment that made me realize just how precise and detailed the Dolby Atmos mix of this disc is.

There’s good use of Atmos effects and height channels too. As Speed uses his spring-loaded chassis to jump over a rival racer, a cut to said rival’s cockpit reveals a burst of not only bass but also the sound of the wind from the Mach 5 as Speed flips over. This sounded immersive, clean, and incredibly precise. There are plenty of helicopter sequences too, which are great for showing what Atmos can do.

There were plenty of highlight moments throughout the movie, but it is the race sequences that showcase the clarity and depth of this Dolby Atmos soundtrack. But there’s one final area where this disc is a perfect testing disc.

Always in motion

Motion handling is a key component of any TV. If a TV can’t accurately handle fast-moving images, then action movies and sports won’t look good. While motion interpolation settings, such as judder and blur reduction, can reduce these, they can introduce unwanted side effects, such as the ‘ghosting’ of a ball in a soccer game (where a trail of the ball appears as it travels across the screen).

I anticipate that Speed Racer is going to act as somewhat of a ‘torture test’ for some TVs (like The Batman can be due to its low brightness), as there are not only plenty of fast-paced driving sequences with plenty of quick shifts in direction, but one other real challenge: slow panning shots.

Frequently throughout the movie, commentators from various countries slowly track across the screen from side to side, and even on the S95H/S99H and G6, which have proven to have great motion handling with the right settings, these shots still struggled at times. The commentators sometimes showed judder as the TV’s tried to interpolate the motion, meaning this is something I’ll be using in future tests. With these panning shots, however, there is sometimes natural judder.

Again, though, it’s the fast-paced race sequences that can make or break a TV’s motion. Chaotic driving as cars swerve to avoid debris, smash into one another, or pull a tight turn can look unnatural if a TV’s motion isn’t right. Too much blur and judder reduction, and it’ll look artificial; not enough, and it’ll look like a juddery mess.

Speed Racer really is one of the best Dolby Vision and Dolby Atmos 4K Blu-rays I’ve ever tested and is a triple threat disc for color, motion and sound, so if you’re a regular reader of our TV and soundbar reviews and comparisons here at TechRadar, expect to see Speed Racer appear very soon.

Samsung has officially confirmed that the Galaxy Ring 2 is in development. Dr. Hon Pak, Senior Vice President and Head of Samsung’s Digital Health Team, recently told Forbes that the company is “working on the next generation” of its smart ring.

Beyond confirming that a successor ring is on the way, Pak offered only a few hints about what we might expect. But the smart ring market looks different than it did when the original Galaxy Ring launched in 2024.

Competition is growing rapidly, with new smart rings appearing almost every month. While many still struggle to match the devices in our best smart rings guide, the bar has been raised considerably. The original Samsung Galaxy Ring remains our top overall pick, while the newly launched Oura Ring 5 is one of its strongest rivals.

I’ve tested many of the latest smart rings, and if Samsung wants its next ring to remain at the top, these are the upgrades I’d most like to see.

1. Resist the temptation to add a subscription

Even though interest in smart rings has surged over the past few years, I think we can still say they’re a relatively niche category. And I think one of the biggest barriers to entry is the ongoing subscription fee some brands charge.

For example, Oura requires a monthly membership of $5.99/£5.99 per month to unlock its full experience, and subscriptions — optional or otherwise — are on the up with fitness apps. Meanwhile, rivals like Ringconn have built some of their appeal around offering a similar health tracking experience without that additional fee.

Samsung certainly got this right with the original Galaxy Ring. And there are currently no signs a subscription is coming. But maintaining that approach feels increasingly important. We’ve already seen some companies introduce subscription-free products only to later place some features behind a paywall.

If Samsung wants to keep the Galaxy Ring truly competitive, it must make core health insights available without a monthly fee.

2. A slimmer and lighter design

Designing a smart ring has always been a challenge. Manufacturers need to pack in sensors, batteries and processing power all while making the ring feel as comfortable and jewellery-like as possible.

That’s one reason the new Oura Ring 5 has generated so much attention. It’s managed to become smaller and lighter while still adding some new capabilities.

Now, the Galaxy Ring 2 doesn’t necessarily need to be the smallest ring on the market. But even a slight reduction in size and weight would demonstrate that Samsung understands just how important comfort is when you’re asking people to wear something 24 hours a day.

3. Better battery life

Battery life is such an important part of the smart ring experience. The more I take off a ring, the less likely wearing it everyday will become a habit. And the more likely I become to forget about it. It also makes monitoring for trends and average vitals more tricky and less accurate.

As I write this, I’m testing the RingConn Gen 3, which promises significantly longer battery life than many of its rivals at 12 days on average. Other manufacturers are also exploring new battery technologies and the Samsung Galaxy Ring 2 is rumored to be getting a powerful solid-state battery upgrade

The original Galaxy Ring already offers a good battery life, but expectations are rising fast. And again, Samsung doesn’t necessary need to beat every rival. But if competing rings continue pushing to longer runtimes, the Ring 2 will need to remain competitive.

4. iOS support

I think we can safely say this one feels less like something I’d like to see in the next generation of Samsung Galaxy Ring and more like something that must be a necessity.

The Samsung Galaxy Ring was only for Android users. Now, reports already suggest Samsung is working towards iPhone compatibility. If that’s true it could dramatically increase the Galaxy Ring’s appeal.

Because at the moment, limiting the ring to Samsung’s ecosystem excludes loads of potential buyers. Sure, Samsung users will continue to benefit from deeper integrated and additional features. But core functionality across both Android and iOS feels like a necessity.

However, we know Apple's already filed a patent for ring technology — see the above diagram — so even if it never comes to fruition, it's certainly a form factor Apple is mulling over.

5. Add a compelling new health feature

Most smart rings track the same core metrics, like sleep, heart rate, activity, stress and recovery. That means Samsung may need to offer something genuinely distinctive to stand out.

I say that because I’ve been testing the RingConn Gen 3 over the past few weeks, which adds features like vascular health insights and sleep apnea monitoring. Now, whether those features prove valuable to most users over the long term remains to be seen. But, they give the ring a clear point of differentiation.

I don’t think Samsung needs to copy those exact features. But offering something beyond the standard collection of health metrics could help the Galaxy Ring 2 feel like a meaningful upgrade over the original and a solid choice in a sea of similar offerings.

6. AI that isn’t annoying

I’m generally sceptical about AI chatbots in wearables because most implementations I’ve tested simply repeat the same information that’s visible in the app or provide really generic recommendations.

That said, some of Pak’s comments to Forbes do suggest Samsung is thinking beyond a basic execution of AI. Explaining that people have different motivational styles, so the approach needs to be tailored accordingly: “Over the next two to three years, the AI will be able to say, based on this person's characteristics, I'm going to nudge them this way, and 70 or 80 per cent of the time I can predict that person is going to exercise more or sleep more.”

I’m still cautious, especially as Samsung has already added some AI features to Samsung Health. But many wearable brands underestimate how much language matters when it comes to behavior change. The best implementation isn’t just telling users to sleep more, it might identify useful patterns, changes or spot warning signs and deliver advice in a way that’s genuinely personalized.

If Samsung can achieve that, AI could become one of the Galaxy Ring 2’s most compelling features rather than another box-ticking exercise.

There’s a good chance we’ll learn more at Samsung’s next Galaxy Unpacked event, which is scheduled to take place in London on July 22, 2026.

Lately, I've noticed a growing number of celebrities and influencers are talking about AI. Some seem to be partnering with tech companies. Others have positioned themselves as AI evangelists, encouraging their audiences to embrace the technology before they get left behind.

Among those who have generated significant attention are Reese Witherspoon, Mel Robbins, Sandra Bullock and Matthew McConaughey. Their messages do all differ, but they tend to orbit the same idea, which is that AI is here, it's important and you'd better get on board fast.

The thing is, the more a celebrity tells me I need to use AI, the less I want to. And judging by public sentiment, I'm really not alone.

I don't think everyone talking positively about AI is acting in bad faith. Some may genuinely believe it will improve people's lives. Others may have investments, partnerships or financial incentives tied to the industry's success. (And that's hardly unusual — celebrity endorsements have always existed.) Some may simply be repeating the dominant narrative without stopping to consider how influential they are.

But rather than try to understand their personal motivations, what interests me more is the growing gap between the way AI is being promoted and the way many people actually feel about it.

Because while some public figures seem convinced that widespread AI adoption is inevitable, public trust in the technology remains surprisingly low. Survey after survey finds that many people are cautious, sceptical or actively worried about AI. And it's not difficult to understand why.

A growing disconnect

Conversations about AI have moved really quickly over the past year. We now find ourselves debating copyright, creative labor, deepfakes, misinformation, surveillance, environmental costs, job displacement and the growing concern that outsourcing too much thinking to machines may come with cognitive consequences of its own. On the morning I'm writing this, there are fresh headlines about the grim realities of data centre expansion.

At this point, there are so many legitimate concerns surrounding AI that it's difficult to keep track of them all.

Meanwhile, some of the companies that initially appeared determined to replace workers with AI have been rowing back their plans. We've seen reports of AI-generated content requiring extensive human correction, customer service experiments failing to meet expectations and organizations discovering that replacing people is a lot harder than they first imagined.

That's why I find the celebrity enthusiasm so fascinating. Because while some public figures are urging people to embrace AI before they get left behind, many people seem to be moving in the opposite direction.

The comments beneath posts promoting AI are often filled with scepticism. Articles about AI backlash are becoming increasingly common. And when I asked my own social media audience how they felt about celebrity AI endorsements, many expressed similar concerns.

Becky Hughes told me: "All of this makes me more reticent than ever to use social media or adopt new technologies, because the safest option seems to be not to engage at all."

Jay Vera Summer said: "When I see celebrities do things like this, I wish I knew more about their stock portfolio. Especially when it's coming from people who usually don't give financial or career advice."

Whether or not all of our suspicions are fair, I think they suggest something really important is happening here, which is a growing trust gap.

At least from where I’m sitting, it seems people no longer automatically assume that enthusiasm for AI is neutral. They're increasingly wanting to know who benefits from the pro-AI messages, who profits and whose interests are being served when the technology is being promoted so aggressively.

Fear over specificity

If you actually look at what much of the celebrity messaging amounts to, it's surprisingly hollow. Learn AI. Use AI. Don't get left behind. It’s inevitable.

And what fascinates me is how little specificity there tends to be alongside it. What exactly should people be using AI for? Which tools? In what contexts? For what benefit? What are the trade-offs? What are the risks?

I haven't seen many celebrities get into any of that. To be fair, even many of the people building, investing in and advocating for AI rarely spend much time on the details. Instead, the conversation often just gravitates towards fear.

The fear of becoming obsolete. The fear of missing out. The fear of being left behind by a future that everyone else supposedly understands. As someone who has spent years covering technology, that kind of rhetoric always makes me uneasy.

And that's not because I think AI won't have a place in the future. I think it almost certainly will, for better and for worse. But because "you'll get left behind" isn't really an argument. It's an appeal to our anxieties so that you’ll act fast without thinking. And it’s encouraging adoption without fully engaging with the reasons people might be hesitant in the first place.

AI as a feminist issue

I find this particularly interesting when AI is framed as a feminist issue. Earlier this year, The Cut described this phenomenon as the "girlbossification" of AI, giving a name to the growing trend of influential women encouraging other women to embrace the technology or risk falling behind.

Several prominent women have made versions of this argument. And they’re sort of right. In some studies, women have adopted generative AI more slowly than men. But the gap appears to be driven partly by risk, ethics, and workplace factors, not just technical ability. And women have plenty of reason to be concerned about the risks.

We know that women and girls have been disproportionately affected by some of AI’s most disturbing uses, including deepfake pornography, AI-generated image abuse, and sextortion. In one UN estimate, up to 95% of online deepfakes are non-consensual pornographic images and 99% of those targeted are women. I know this isn't theoretical because I've experienced a version of it myself.

Against that backdrop, telling women they simply need to embrace AI can feel completely disconnected from reality. It risks treating healthy scepticism as ignorance when, in many cases, it seems to me that it’s a response to genuine concerns and lived experience.

The recent partnership between Kylie Jenner and Meta feels particularly relevant here. The campaign positions AI-powered glasses as fashionable, desirable and aspirational. And in some ways that's exactly what celebrity endorsements have always done, take a technology and make it feel culturally normal.

But that's exactly why these messages deserve scrutiny. At the same time women are being encouraged to embrace AI-powered devices, there have already been multiple stories of women being unknowingly recorded by smart glasses. Which to me highlights the very real concerns around privacy, consent and surveillance that often get overlooked in conversations about the latest cool new tech on the block.

More informed than you think

I've seen some people online brush all of this conversation off and argue that we shouldn't be taking celebrities seriously anyway. But like it or not, they do help shape public narratives. They influence what people pay attention to, which questions get asked and which concerns get ignored.

And right now, many of those narratives seem to be built around this strange assumption that resistance to AI exists because people don't understand it. Well, I've spent the past year talking to people about AI, and I suspect the opposite is often true.

Many people understand enough to have concerns. They've tried the tools. They've seen both the benefits and the downsides. They're making conscious decisions about how much of their work, creativity, relationships and attention they're willing to hand over to AI systems.

That's why I find so much of the celebrity messaging unconvincing. The more people tell me I have to use AI, the more I want to pause and ask: okay why? And I know I'm not alone.

And that’s not because people are afraid of the technology. If anything, that framing completely misses the point. What I see instead is caution, scepticism and a willingness to actually ask the difficult questions about where this technology is taking us.

Because I think whenever someone insists a certain future is inevitable, our alarm bells should start ringing. That's when we need to ask: okay, whose version of the future are you trying to sell?

American businessman and venture capitalist Marc Andreessen is an individual who was among the earliest to be involved in the early internet boom. Now, the founder of Netscape and co-author of Mosaic – two iconic web browsers – has fully committed to AI.

AI maximalism

One year after OpenAI launched ChatGPT, the technology veteran Andreessen published an essay that advocated for a fully-fledged acceleration of the AI buildout.

In this overwhelmingly optimistic manifesto, he argued that should AI mature and become a more sophisticated and universally utilized technology, machine intelligence that surpasses human capabilities would save countless lives.

For example, medicine is in the "stone age", he wrote, and a combined machine and human intelligence would be able to work on new cures. There's also the scope for improving the lot of mankind by, for instance, using AI to solve problems around nuclear fusion – bringing clean and cheaper energy to people around the world.

AI realism

His essay is certainly a full-throated attack on all those who have (and continue to) advocate for a pause to AI development so that scientists can properly assess and mitigate any risks. And there are plenty of those.

He describes his enemies as "bad ideas" rather than "bad people" and highlights various labels used, in his view, to stagnate progress – including "existential risk" and "tech ethics".

Despite progressing at pace in terms of capital investment and the expansion of the technology into businesses and into day-to-day life, the AI buildout has reached something of a plateau.

Not only are there arguably diminishing returns on most public-facing models, but critical bottlenecks like energy – and also components like memory – might mean deceleration is an inevitability, whether or not Andreessen or others like it.

Restaurant owners are feeling the pinch, through consistently rising costs of ingredients, labor and energy bills.

There is however a less visible profit drain: the silent loss of the phone ringing unanswered as the kitchen struggles to cope.

It’s lost business in disguise, and another area where the customer experience can let down a restaurant in its busiest moments.

Despite the growth in online orders, phone ordering still happens. But the point is when it occurs: phone orders spike at the exact moment a restaurant is slammed during service.

A quick-service restaurant that faces heavy load during its peak hours is likely to see the greatest inbound volume of calls at the same time: Friday or Saturday night.

The owner is forced to choose between answering the phone and dealing with existing customers and orders.

Neither scenario is ideal, nor does it do anything good for the customer experience.

Why traditional workflows fail to scale

Many solutions deployed in the hope of managing phone orders have failed to scale or were prohibitively expensive. Hiring additional personnel to attend to phone lines is costly, and the employee still cannot take more than one call at a time.

Even in this scenario, callers may have to wait on hold, leave voicemail, or receive a busy signal. Then, it becomes frustrating to try and connect to the restaurant again when waiting in line.

There’s also the question of precision. The most mistakes happen under pressure: misunderstood orders, incorrect delivery locations, allergy-related instructions missed. While some of those mistakes may only constitute bad customer experience, others lead to waste, losses, refunds, and sometimes, lost customers.

Finally, the opportunity cost of having employees talk to customers instead of doing other things is huge: each minute they spend talking on the phone is a minute spent not preparing meals, dealing with issues of in-store customers, or completing deliveries on time.

What do AI voice agents bring to the table?

AI voice systems can significantly improve the customer experience across a few critical use cases:

Availability: the system is available to pick up every phone call, no matter how busy the restaurant may be inside. There’s no on-hold messaging queue, no hold music, voicemail message, and most importantly, no missed calls.

Concurrency: a human agent can focus on one call at a time. The AI system, however, can manage multiple calls simultaneously, which is especially useful during the moments of peak demand.

Accuracy through confirmation: if done right, AI systems repeat the order before confirming it to eliminate errors and mistakes from the process entirely.

Intent-based conversations: not everyone calls in with the intention of placing a new order. Some have questions, want to update their existing order, or do something else entirely. A properly managed implementation will ensure that the phone system knows what the caller intends to do and collects the information necessary to complete the request successfully.

It also guarantees that every interaction with the phone agent goes through the same steps: identifying intent, gathering data, fulfilling the request, confirming its completion, and providing a smooth user experience.

More precise recommendations: AI-powered recommendations are always relevant and consistent throughout the entire conversation. They are not distracting or annoying and help to boost orders’ value.

One useful way to sanity-check the value is simple volume. One recent rollout reported an AI phone agent processing over 4,000 orders in three months, and capturing thousands in orders from the busiest sites.

More importantly, calls were answered immediately even at peak, and concurrency stopped being the bottleneck.

Why is the technology finally practical?

Voice automation technology is no novelty anymore, but the recent advances in the field are all about speed and intent recognition. More advanced algorithms mean fewer misunderstandings. Improved integration with ordering systems helps to implement voice as a channel of communication, allowing businesses to serve more customers during peak times.

Two other changes are making a difference in real deployments: localization of voice, and better menu understanding. This means when a customer does not know the exact name of a dish, the phone agent can still find what they mean without turning the call into a guessing game.

Practical implementation: where to deploy voice systems

The strongest signals here are the number of incoming calls and a complicated multi-site operation. But even the smaller restaurants with a handful of calls per day can be good candidates for voice automation due to the inefficiencies associated with having an employee to attend to the phones. Automation replaces a fixed staffing cost with something you can scale up and down.

The following are the guidelines for responsible deployments:

Structure around intents: not everyone calls in for an order; therefore, the phone agent should handle ordering, updating the order, and answering questions efficiently, offering clear prompts and a consistent user experience at each step of the interaction.

Transparency: the system should notify callers about the automation process and inform them if the conversation might be recorded.

Over time, the most useful systems also get more flexible around how orders are completed. For example, enabling cash ordering where it makes operational sense, or sending an SMS link if someone abandons the order mid-call, with the basket pre-filled so they can finish on their phone. Those are small touches, but they remove friction in the moments where customers typically drop off.

Implementing voice automation in the restaurant operation allows managers to scale easily, provide consistent customer experience, and generate more orders in the process.

Not futuristic, but practical

Framing voice technology as a future-state project does not benefit the hospitality industry. Voice technology is available now and brings a significant ROI from the revenue you are already generating.

The telephone line has become a nuisance in many restaurant operations for too long. Implementing a proper phone management strategy turns it into an operational channel you can manage.

We've listed the best POS systems for restaurants.

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