To comply with the EU AI Act's Article 50(2) Code of Practice on Transparency of AI-Generated Content, Anthropic has announced that “New models will mark AI-generated content from day one”.

This is a remarkable step for Claude, because it not only applies to any images it generates, which are relatively easy to watermark, but also to any text it generates.

Anthropic says that Claude models will have an “imperceptible watermark” embedded directly into generated text at the model level. It says the mark should survive copying/pasting and minor edits.

Anthropic has not yet publicly explained the exact algorithm or released a detector, so we can only speculate for now about how it might be doing this and its effectiveness.

Hemorrhaging customers

Personally, I think that Claude is about to start hemorrhaging customers, unless the marking is relatively easy to circumvent, or all the other major AI players immediately follow suit.

If every piece of text it produces will now be easily identified as AI, then Claude becomes useless as a tool to a lot of people who are currently using it to generate text and are not being entirely honest about where that text came from.

And then there’s the issue of using Claude to proofread your human-written text — will your text now be flagged as AI if you accept Claude’s editing advice?

Your initial reaction to that might be “Good! You should be forced to reveal when AI has written something, and it’s about time people started writing on their own again!”, and you’d be entirely justified in that opinion.

But while it remains the only one of the big three AIs that’s doing this, I think we’ll see a lot of people switch to either ChatGPT or Gemini, because they don’t want to be revealed as using AI in their work.

How is watermarking plain text even possible?

We don’t know exactly how Anthropic is doing its marking with text yet, but my best guess is statistical watermarking during token generation rather than hidden Unicode characters or metadata. Imagine that at every point Claude is choosing among several perfectly reasonable next words:

e.g. The movie was excellent / superb / terrific / impressive.

Normally it chooses according to the model's probability distribution. A watermarking system can secretly divide possible tokens into preferred and non-preferred groups using a key. Claude then gives a tiny statistical nudge toward the preferred group.

One word tells you nothing. But across 500 or 1,000 words, a detector with the key can ask if the text is choosing the preferred tokens significantly more often than chance would allow. If yes, then there's statistical evidence it came from the watermarked model.

So, the watermark is more like a faint statistical fingerprint distributed across hundreds of choices, which would also explain how it can survive a copy-and-paste. You’re essentially copying the fingerprint along with the words.

But can you crack the code?

Since Anthropic hasn’t released an AI text detector yet, it’s impossible to know how easy this code will be to break just by changing a few words. For instance, if you put your 1,000-word Claude article into another LLM and wrote “Rewrite this completely in different words while preserving the meaning”, would it then be impossible to detect as AI?

I’d also be interested to know how long a piece of text has to be before it can be marked in this way, and as soon as a detector is made available, I’ll be testing it.

Perhaps the bigger issue is that Claude has done this to comply with Article 50(2) of the EU AI Act. From August 2, 2026, providers of generative AI systems that produce text, images, audio, or video are required to make those outputs machine-readable and detectable as artificially generated or manipulated, insofar as that is technically feasible. Existing systems get a limited transition period until December 2, 2026 for this particular requirement.

A note on Anthropic’s statement confirms that the watermarking additions will be applied retroactively to all existing Claude models, not just any new models it produces.

Anthropic's new system is explicitly a response to those rules, and it says the watermark will apply globally, not just when Claude is being used in Europe. Broadly speaking, OpenAI and Google face the same requirement if they want to offer qualifying generative-AI systems in the EU.

They don't necessarily have to copy Anthropic's method of using statistical text watermarking, but they will need to produce output that is machine-readable and detectable, and the technical solution should be effective, interoperable, robust and reliable as far as technically feasible.

I’ve contacted OpenAI and Google for comment, and will update this article if I receive it. For now, I think if Anthropic embarks on this path as the only one of the major three AI chatbots to do so, it could have a disastrous effect on its customer base.

Evaluating AI agents in production tends to focus only on positive results. Did the agent complete the task? Was the output accurate? Did the demo go well?

The answers to those questions matter, but they miss the case that determines whether an enterprise can actually trust agents with real work: what happens in the 30% of instances where something goes wrong?

In financial services, for example, an autonomous agent that mishandles a money-movement workflow doesn't turn into an innocuous support ticket. It creates legal liability that can extend across an entire business.

In healthcare, unchecked data access calls can compromise patient safety and lead to HIPAA violations. Highly regulated industries can’t treat failure as a minor inconvenience. And when the stakes are categorically higher than in most other sectors, it changes what "production-ready" means.

The problem? Most popular agent frameworks were built by teams focused on connecting large language models to reasoning loops and evaluation. While valuable, it's not the same discipline as distributed systems engineering.

Few of these frameworks were built by people who spend their careers thinking about recovery, consistency and fault isolation. Many give little thought to what happens when an agent fails mid-flight, and that gap can be very expensive once agents are handling real-world transactions.

Replaying From Scratch Doesn't Work

When a sales agent moves through a 10-step workflow and fails at step nine, the naive recovery approach is to start the entire flow over from step one. That sounds harmless until you account for what each step actually costs. Every restart means re-running every LLM call that already succeeded, burning through token spend for work that was already done correctly. At scale, that inefficiency turns a single bug into a real financial problem.

It also produces a worse experience for the people and systems downstream. In a regulated workflow, it’s sloppy work that becomes a compliance and audit problem waiting to surface.

The fix is durable execution: checkpointing that captures progress at each meaningful step, so recovery means resuming from step nine, not replaying the whole sequence. This has become standard practice in distributed systems, and agent orchestration needs to follow suit.

When you’re evaluating agent frameworks, here’s the question you should ask: if an agent fails partway through a long-running task, does the system resume from where it left off, or does it start over? The answer will tell you the difference between frameworks built for production and those built for demos.

There’s an Access Problem Nobody Talks About

Security in agentic systems presents its own version of this challenge, and it starts with a question that sounds basic but that most organizations can't seem to answer: can you prove exactly who or what did what, and when?

That question is harder to answer as agents act on behalf of other agents, which act on behalf of humans. Each layer of delegation adds ambiguity about accountability. And when you add MCP servers into the mix, the exposure compounds. MCP gives language models access to company records, patient data and internal systems. The vast majority of MCP servers in production today connect to some kind of database, and the common mistake is granting broad access to that entire data store rather than narrowly scoping what each server can see.

That distinction matters when something goes wrong. If a system with broad access suffers a breach or a supply chain compromise, the attacker gains access to the entire environment. Scoped access, where an MCP server or agent can only reach the specific data it needs for the task at hand, is one of the most overlooked design decisions in agentic architecture right now. It's also a relatively cheap problem to fix before deployment, yet one of the most expensive to fix after a breach.

Identity adds another layer. Many organizations still use traditional authentication protocols that were made for human users logging into applications, not for autonomous systems that act at machine speed and scale. Without verifiable identities for each agent and each component, malicious code can impersonate a legitimate part of the system and operate undetected.

What’s needed here is what’s known as cryptographic attestation: a tamper-proof record of everything that happened in the system tied to the specific identity that did it. That record lets you replay the system's state after the fact and determine with certainty that a specific piece of code accessed a specific system at a specific moment. Why? Because that identity was managed and enforced in real time. It's the difference between a policy that says a component “should” be trusted and a system that can prove what it actually did.

Design to Limit the Blast Radius, Not Just Patch the Damage

There's also a problem with how most organizations think about vulnerabilities. The industry's attention is almost entirely on patching known CVEs, and that's necessary. But at the same time, it misses something important: a vulnerability only becomes a known CVE after a breach has already occurred. Patch management is inherently reactive and does nothing while a system is actively compromised, by which point malicious code is already trying to move laterally through the network.

This is why runtime enforcement deserves far more attention. The goal isn't just prevention; it's containment. If a system is compromised, can you detect that a component is behaving abnormally and immediately restrict its access, even before you’ve identified or patched the underlying flaw? Limiting the blast radius in real time, rather than relying solely on detection after the fact, is what separates a contained incident from a full-scale breach.

Zero trust principles should be applied specifically to AI workloads, not just inherited from traditional cloud-native security protocols. That means strict authentication and authorization for every agent and MCP server, clear policies on what each component is allowed to access, and controls that stop compromised components from sending data outside the organization.

You Need to Prove Safety, Not Just Function

This isn’t pessimism toward AI agents; it's about engineering maturity. Every distributed system that has matured into something enterprises trust with critical workloads, from databases to cloud infrastructure, went through this same evolution. They go from optimizing for common cases to designing explicitly for the rare, expensive failure.

Agentic AI is now at that point. The organizations that get it right will be able to sit down with an auditor or a regulator and demonstrate, with evidence, exactly how their systems behave when something breaks:

- Durable recovery that doesn't waste a single completed step.

- Access that's scoped to the task, not the whole database.

- Identity that can be cryptographically verified, not just assumed.

- Containment that activates in real time, not after the fact.

These are the bars that enterprises serious about deploying AI agents at scale need to meet.

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The integration of artificial intelligence (AI) into everyday work and life has prompted businesses and regulatory bodies to take action. AI is a force introducing entirely new categories of threat, making heightened cyber resilience essential. However, amidst the panic to get in front of this, it should be noted that not all the hype is entirely accurate.

The underlying vulnerabilities organizations face today are largely the same ones they faced five or ten years ago: unpatched systems, weak identity controls, excessive privileges, insecure third-party integrations.

In some cases, these weaknesses have existed for some time, and most likely, will continue to exist because the first fundamental constraint of computer science is that the removal of all vulnerabilities is impossible. Therefore, no amount of tooling or budget will ever make any business 100% secure. Equally, that doesn’t mean improvements should simply be dismissed - especially in the age of AI.

Speed, not novelty

While not introducing anything inherently new, there is still some cause for concern surrounding AI. The nature of existing weaknesses remains the same. However, AI does significantly change the speed and scale at which they can be identified and exploited. Tasks that once required time, skill, and persistence can now be automated, accelerated, and in some cases delegated.

The barrier to entry has been lowered for less sophisticated actors to operate with greater efficiency and success.

We are already seeing early signs of this shift. Elements of the attack lifecycle can be automated, be that reconnaissance or lateral movement. Nation-state actors have begun experimenting with using these systems to coordinate multi-stage operations, and we’ve recently seen a fully autonomous attack take place, without any human supervision.

At the same time, more familiar techniques are being enhanced rather than replaced. Malware can be generated or iterated more quickly to evade detection. Social engineering has become more convincing through deepfakes, and phishing campaigns have become easier to scale.

None of this represents a fundamentally new playbook, simply the acceleration of an existing one.

The distraction problem

The distinction between novelty and speed matters because it shapes how organizations respond. If AI is treated as a novel and exceptional threat, it encourages a reactive mindset. Security teams are pushed towards finding “AI-specific” solutions, often at the expense of addressing longstanding gaps in their environment. In practice, those gaps still remain the most reliable entry points for attackers.

The risk that the current level of attention on AI creates, is a form of strategic distraction. Boards and executives are rightly asking questions about AI risk, but those conversations can become detached from the basics. Patch management programs remain inconsistent. Asset inventories are incomplete. Third-party exposure is poorly understood. Identity and access management remains fragmented across systems.

These are the same issues that security professionals were tackling before the advent of AI and the technology does not take them off the board. If anything, these become more consequential as the speed of exploitation increases.

The right response

It is worth being clear about the limits of control. No organization will ever be 100% secure. There will always be unknown vulnerabilities, many of which the new frontier models will be able to fish out.

However, the idea that AI introduces risk that can be entirely “solved” is misleading. Even if advanced models identify previously unknown weaknesses, the response remains the same as it has always been: prioritize, remediate and reduce exposure over time.

For defenders, matching this increased tempo requires a combination of discipline and adaptation. Established practices such as red teaming and tabletop exercises need to evolve to incorporate AI-enabled scenarios. Incident response teams need to be prepared to handle new forms of evidence, including those generated or manipulated by AI systems.

In addition, training programs need to reflect the growing sophistication of social engineering, particularly where deepfakes and voice cloning is concerned.

AI-driven detection and response capabilities can play an important role, particularly in identifying patterns at scale. But they are not a substitute for secure-by-design principles, robust access controls, or a clear understanding of where critical data resides. Therefore, organizations should observe caution about over-rotating towards new tools without addressing foundational weaknesses.

The expansion of the attack surface through enterprise AI adoption adds another layer of complexity. Threat actors are already targeting AI workflows directly, exploiting vulnerabilities in software development environments, and using techniques such as prompt injection to manipulate system behavior.

In some cases, malicious instructions can be embedded within otherwise benign content, triggering unintended actions when processed by an AI system.

Again, these developments are best understood as extensions of familiar concepts. Input validation, supply chain risk, and data integrity have always been central to security. AI introduces new contexts in which these issues manifest, but not entirely new categories of risk.

From a governance perspective, this reinforces the need for clarity rather than novelty. Boards should be focused on defining risk tolerance, ensuring accountability, and maintaining visibility over how AI is used within the organization.

This includes integrating AI considerations into existing risk frameworks rather than treating them as a separate domain. Legal, technical, and communications teams need to be aligned, particularly in scenarios involving misinformation or synthetic media, where response speed is critical.

What matters now

There is value in the current focus on cyber risk. Increased attention at the board level can drive investment and accountability in ways that were previously difficult to achieve. But that attention needs to be directed towards the right problems. Treating AI as an entirely new threat risks misallocating resources and overlooking the vulnerabilities that are already present.

AI will continue to evolve and so will the ways in which it is used by both attackers and defenders. In cyber security, progress is often less about discovering new answers and more about applying existing ones with greeted consistency and speed.

The organizations that navigate this shift most effectively will be those that remain grounded in a clear understanding of what has and has not changed.

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In June, Keir Starmer announced at London Tech Week that tech firms have until September to introduce device controls that prevent children from sending and receiving sexually explicit images. The plan requires on-device or client-side scanning, and has been framed as a child safety measure. For enterprise security teams, it raises a different set of questions entirely.

For enterprises, the proposal challenges one of the core assumptions underpinning modern cybersecurity: that devices can be trusted to process sensitive information without external inspection.

If software is required to scan content before it is encrypted or transmitted, it introduces new attack surfaces and weakens the privacy and integrity that businesses depend on to protect intellectual property and confidential information.

It’s a concern being felt widely across the industry, with organizations including Signal and the British Computer Society issuing statements warning that client-side scanning can create systemic vulnerabilities that could ultimately be exploited by cybercriminals and other malicious actors, fundamentally reshaping digital trust.

As organizations prepare for the September deadline, they must equip teams with the necessary skills to evaluate potential risks that client-side scanning could cause, adapting security strategies where necessary.

Why the proposed plan has led to conversations around security

The greatest risk posed by mandatory on-device scanning is the disruption of trust architecture on which enterprise security depends. Modern mobile security frameworks, including mobile device management (MDM), endpoint protection and zero-trust access controls, are built on the assumption that the operating system functions as a controlled and trusted layer.

Enterprises use this trusted foundation to enforce security policies and verify the integrity of managed devices.

Introducing a government-mandated scanning agent beneath or alongside that trusted layer fundamentally changes the security model. Once an additional privileged component can inspect device content, the integrity of the operating system can no longer be assumed, making the security controls that sit above it less reliable.

This structural change to the trust boundary that underpins enterprise mobile security, creates new opportunities for exploitation if the capability is ever compromised or repurposed.

The operational problems for security teams

The consequences extend beyond security architecture into day-to-day enterprise operations. The underlying issue is not privacy, which is what the government’s plan is concerned with, but architecture. A scanning agent at the operating system level sits outside the boundaries that enterprise security teams are able to govern, disrupting security processes that organizations rely on to verify whether endpoints remain in a trusted state.

Government-run scanning agents also create the potential for compliance failures, particularly in highly regulated sectors where organizations must demonstrate control over how sensitive data is processed and monitored. If the scanning function operates outside the managed work profile used by enterprise MDM platforms, it remains effectively invisible to IT administrators.

As a result, security teams have no practical mechanism to audit its behavior or control how it acts with secure data. This also disrupts device attestation - the process by which MDM platforms verify a device is in a known, trusted state - which could cause managed devices to be flagged as non-compliant and blocked from corporate resources.

In an environment where visibility and assurance are fundamental principles of cyber defense, introducing a privileged component that falls outside enterprise oversight creates a blind spot that weakens, rather than strengthens, organizational security.

While intended to improve online safety, client-side scanning raises significant concerns for organizations. Because it requires privileged access to device content and is widely considered incompatible with true end-to-end encryption, it could weaken security, increase the risk of sensitive data exposure, and force organizations to navigate difficult trade-offs between compliance and protecting critical systems.

The lack of clear exemptions for legally privileged, healthcare and financial data also leaves regulated sectors facing uncertainty over how to meet competing legal obligations.

What organizations can do to prepare

Organizations cannot afford to treat this proposal as a policy issue alone. Security leaders should already be engaging with the compliance and governance issues it raises, assessing how any mandated changes to mobile operating systems could affect device trust, regulatory obligations and existing security controls.

This also means investing in the skills needed to assess security risks at the architectural level, rather than simply responding to threats after they emerge.

Security teams will need a deeper understanding of operating system security models, trusted execution environments, encryption, endpoint architecture and mobile device management, enabling them to evaluate how changes to core platform designs could affect an organization's overall security posture.

Building these capabilities will help organizations make informed decisions about adopting new technologies, understand the implications of regulatory changes, and identify potential weaknesses before they become exploitable.

As trust increasingly becomes embedded within the architecture itself, having teams with the expertise to assess and challenge these foundations will be just as important as the security tools used to defend them.

Safety cannot erode trust

As the debate surrounding the UK’s on-device scanning proposal continues, the conversation must move beyond the framing of privacy versus safety and consider the wider architectural consequences for enterprise environments.

Weakening the trusted computing model on which modern mobile security is built risks introducing new vulnerabilities, reducing visibility for security teams and undermining confidence in the platform’s organizations depend on.

Making the internet safer for children is crucial, but achieving that goal should not come at the expense of the security foundations that businesses rely on every day. The challenge for policymakers and technology providers is not just to implement new safeguards, but to do so without eroding the trust that underpins the wider digital ecosystem.

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Enterprise internet security vendors and experts have spent many years trying to understand human behavior.

As a result, there is now a wide variety of very effective tools and processes that help distinguish legitimate user activity from behavior that may indicate a compromised account or malicious activity.

Behavioral analytics has come a very long way.

The underlying principle is that behavior is often a stronger indicator of compromise than the use of credentials alone.

In this context, behavioral analytics establishes a strong baseline for individual users over time, including the systems they access, typical login patterns, data usage, administrative actions, API activity and various other interactions.

Any significant deviations can trigger investigation.

A security disconnect

But as we all know, things are changing very fast. The rapid move from GenAI assistants to autonomous AI agents has introduced a new kind of enterprise actor, one that combines non-human identity with autonomy, dynamic decision-making and the ability to execute multi-step actions across systems. Existing security models were not designed with this combination of characteristics in mind.

Clearly, AI agents are now of particular concern thanks to their ability to execute tasks autonomously, access multiple applications, retrieve information, make decisions within defined parameters and complete multi-step workflows.

To say the adoption of AI agents across the enterprise space is dramatic is to put it very mildly. Gartner predicts that by the end of 2026, 40% of enterprise applications will incorporate task-specific AI agents, compared with fewer than 5% in 2025. In order to work, many of these agents will be given identities, permissions, credentials and access to sensitive business systems. It stands to reason that security issues will follow.

Unlike human users, however, most organizations have little understanding of what constitutes expected or abnormal behavior for autonomous identities. That helps explain why Gartner's 2026 Hype Cycle views Agentic AI Security as an emerging discipline, and why governance and behavioral monitoring capabilities are still in their early stages of development.

It also creates a potentially serious disconnect organizations have mature behavioral intelligence for people but comparatively little for AI agents, despite both increasingly operating as trusted identities within enterprise environments.

Agents of change

But is the difference between human and AI behavior really that important? In the pre-AI era, human behavioral analytics relied on relatively stable patterns. Most users worked predictable hours, accessed a consistent set of applications, connected from familiar locations and performed activities aligned with their role. After all, humans are creatures of habit, including in the workplace environment where the vast majority would never do anything malicious.

AI agents are fundamentally different because, unlike employees, they may legitimately operate continuously rather than during business hours. Activity at 3 am is not inherently suspicious, but a human employee being online at that time might raise a red flag, particularly if it occurs outside normal activity patterns.

Agents can also interact with dozens of services and APIs within seconds as part of a single workflow, while high activity volumes should be expected rather than seen as exceptional.

Yes, many agents act on behalf of users, but they also make independent decisions about how best to complete a task within defined parameters. This creates an additional layer of abstraction between the user request and the actions ultimately performed.

Permissions are also likely to become more nuanced as organizations expand the range of what agents are allowed to do. An agent designed to perform a relatively simple task, such as retrieving information, may later gain the authority to trigger much more complex and consequential business processes. That’s fine, but what if the associated security processes don’t develop at the same rate?

Bringing all these issues together, the challenge is not just to ask whether behavior looks unusual in human terms, but also to understand whether it is unusual for that specific agent.

As a result, organizations must also establish behavioral baselines for AI agents in the same way they have done for human users, while recognizing that the characteristics of those baselines will be fundamentally different and subject to change at any point.

Monitoring should also consider whether the sequence of actions taken remains consistent with the agent's intended objective, because individually legitimate actions can, when combined, produce unintended or harmful outcomes.

What should organizations monitor?

Having established that monitoring AI agents is a specific requirement, organizations then need to put processes in place to ensure their behavior remains within whatever guardrails they have established, and that begins with visibility.

This means clearly understanding which AI agents exist, what identities they have, which systems they can access and the purpose they are intended to fulfill. Behavioral baselines can then be restricted to the applications an agent typically accesses, the APIs it calls, the data it retrieves or modifies, the business processes it supports and the level of privilege it normally exercises.

But, for every agent, context is critical. Within reason, asking a finance agent to access accounting systems is normal, whereas the same agent interacting with software development or HR software platforms may pose a different level of risk. As with human users, the objective is not simply to identify isolated events but to recognize meaningful deviations from an agent's established operating pattern.

The point is that monitoring should complement existing security controls, such as identity management and governance as well as policy enforcement, among other options. The processes and technologies should provide an additional layer of understanding of how authorized AI identities operate within the enterprise.

Without these controls in place, we can expect to see many more headlines about ‘rogue agents’ and the potentially dire consequences of losing control over their behavior.

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Ask any business what it actually does and the answer is almost always specific. A quarrying company extracts rock. A peatland conservation organization restores peatlands. A retailer sells items. The answer is not "we send emails" or "we have meetings."

This distinction between what makes a business unique and the common operations surrounding it has always mattered. More than two centuries ago, Adam Smith recognized that productivity comes from specialization.

Workers focusing on narrow tasks consistently outperformed generalists, and economies grew by dividing labor into ever finer slices. Businesses succeeded not by doing everything, but by becoming exceptionally good at one thing.

Artificial intelligence (AI) doesn’t change this principle. If anything, it reinforces it. So why does much of today’s AI discussion assume the opposite?

The prevailing belief is that increasingly capable general-purpose models will eventually become the best solution for almost every task.

While these systems will undoubtedly transform how organizations operate, there are strong economic and technical reasons to argue that the greatest competitive advantage will come not from general AI, but from specialized systems built around the work that makes each organization unique.

Universal workplace infrastructure

General-purpose AI models are rapidly being adopted across every industry. These tools summarize documents, write code, answer questions, analyze data and automate routine knowledge work well enough that not choosing to use them will soon become a competitive disadvantage.

But their greatest strength also creates their greatest limitation. When every organization has access to the same capabilities, those capabilities stop being a differentiator. Email transformed business, but no company gains competitive advantage simply by having email. Cloud computing became essential infrastructure, but it does not distinguish one organization from another.

General-purpose AI is likely to follow the same path. As these models become ubiquitous, they will increasingly resemble infrastructure – in other words, essential for remaining competitive, but insufficient for pulling ahead.

The obvious question then becomes: where will competitive advantage come from?

Specialist work requires specialist AI

The answer lies in the work that businesses actually exist to do.

General-purpose models excel because they are trained on tasks performed by millions of people. Specialist work is different.

Going back to my opening analogy, a quarrying company does not just need help with emails. It needs to optimize blast patterns based on geological conditions, monitor crusher efficiency in real time and match production to market demand.

A peatland conservation organization does not just need help writing reports. It needs to map erosion across thousands of hectares of remote terrain from aerial imagery, then plan restoration interventions accordingly.

These are not simply harder versions of general tasks. They are fundamentally different problems requiring specialized data. And this is where general-purpose models begin to struggle.

The reasons for this are not primarily about intelligence, but economics. There is way more training data available for common business tasks than for specialized industrial or scientific ones.

More importantly, frontier AI labs have enormous commercial incentives to optimize their capabilities for features that millions of customers will use. By comparison, niche use cases are a harder sell from the perspective of return on investment.

But while the economics of model development may more immediately favor general capabilities, the economics of competitive advantage do not.

Specialization creates strategic value

Today, switching between AI providers is reasonably easy. Many organizations continue to move freely between ChatGPT, Claude, Gemini and other models as new capabilities emerge. Because these systems remain broadly interchangeable, changing providers carries relatively little cost.

But that will not always be true. In fact, that flexibility will survive only as long as the models remain largely generic. Once they acquire organizational memory, switching providers becomes far more difficult.

The next generation of AI is likely to move beyond simply responding to prompts. Instead, models will increasingly learn how organizations operate: how decisions are made, how workflows evolve and how years of accumulated expertise are applied in practice.

This goes far beyond larger context windows, searchable document repositories or vector databases. These technologies allow models to retrieve information. They do not fundamentally change the model itself. True organizational memory means the system adapts to the business over time.

In its strongest form, the model learns continuously, gradually embedding the organization's knowledge into how it reasons and makes decisions.

That creates the potential for a lasting competitive advantage – but only if the organization retains ownership of that accumulated memory rather than effectively renting it from an AI provider. Otherwise, organizational memory becomes a source of vendor lock-in rather than a strategic asset.

Building for the specific

This is why specialized AI deserves a much larger piece of today's conversation.

Across multiple industries we have found that the greatest competitive value comes not from deploying the largest available model, but from designing systems around specific operational problems.

For a major quarrying operation, we developed a computer vision system that monitors rock size distribution on crusher conveyors, detecting hidden downtime that amounted to five to ten percent of operational hours.

For peatland restoration, we built a tool that maps erosion from aerial imagery and 3D depth data, dramatically accelerating what was previously slow and expensive manual assessment.

The list goes on.

Each system delivers value because it was designed around a particular problem: trained on business-specific data, using a model architected to the exact task. In many cases, the models used were smaller, faster and less computationally demanding than general-purpose alternatives. In this sense, specialization is not about adding complexity. It is about removing everything that does not serve the specific goal.

The economics haven't changed

As AI matures, the distinction between general and specialized systems will become increasingly clear. General-purpose AI tools will handle the operational tasks shared by every organization. These will become table stakes, like internet access or accounting software. Necessary, but not distinctive.

The activities that truly define a business – the specific niche it exists to fill – will come to rely on AI built for that purpose. This is not a prediction about technology. It is simply the economics of specialization applied to a new generation of tools. Just as businesses create value through specialization, so too will the AI that powers them.

For organizations looking to capture that value, the time to build is now. As generic AI becomes commoditized, providers themselves will increasingly turn their attention to specialized domains, narrowing the window for businesses to establish their own enduring advantage through specialization.

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AI is being pushed as a friend: your new sidekick that tackles the legwork you don’t have time for anymore. Great!

But what happens when you realize that this sidekick that helps is also scaling new ways that can directly hurt your business?

AI is both friend and foe in ways most consumers and brands are not prepared to handle.

Enter: AI imaging tools. These tools have become incredibly sophisticated and easily accessible, especially after recent upgrades. They can produce multiple high-quality images from a single prompt – no design background or expensive technology required.

The tools designed to help everyday users move quickly are now equally valuable to bad actors looking to make their schemes more convincing and harder to detect – and even consumers who feel pushed to abuse retail policies.

The age of AI-powered abuse is here: seeing should no longer be believing.

AI has democratized fraud

Businesses have long relied on photos and documentation to validate returns and refund requests. For a while, this logic was sound: the time, skill, and cost required to manufacture evidence acted as a barrier for most consumers, but the dam is breaking.

With modern image-generation tools, a malicious actor or your next-door neighbor doesn’t need design skills or specialist software. A single prompt can generate realistic receipts and damaged product images, turning the dial up on return abuse, refund fraud, and friendly fraud.

In fact, the Merchant Risk Council (MRC) found that over the past year, 57% of merchants reported increasing rates of refund and policy abuse. Retail’s global multi-billion-dollar fraud problem just became that much more costly, thanks to AI.

The speed and scale at which these images are produced, altered, and tested are staggering. With AI, fraudsters and abusers can now tailor claims to different merchants, test variations, and scale attacks across multiple accounts and thousands of merchants in short order.

The challenge is that AI-powered abuse is not being driven by one type of actor. Retailers are facing pressure from both organized fraud rings that deliberately exploit systems at scale and everyday consumers who are using new tools to push the boundaries of return and refund policies. While the motivations and sophistication levels are different, both create additional complexity for businesses trying to protect customers while preventing abuse.

Organized fraud groups are increasingly treating policy abuse as a scalable business model. Rather than relying on a single fraudulent claim, these groups look for weaknesses in retailer processes, create multiple accounts, and coordinate activity across merchants. AI-generated images make these operations more effective by providing convincing evidence that supports false claims, helping bad actors appear as genuine customers.

Forter has seen coordinated returns abuse operations where fraud rings used AI-altered images to support claims of product damage, including minor cracks, dents, and faulty components. In one case, Forter stopped a $1.5 million returns abuse operation over Christmas 2025 that used AI-altered damage images to make fraudulent refund requests appear legitimate. When not caught, these abusers resell the quality merchandise after getting refunded for the purchases.

The scale of these attacks is what makes them particularly challenging. A single suspicious claim may not reveal much, but when activity is connected across accounts, devices, behaviors, and merchants, a much bigger picture emerges. Fraud rings can spread activity across multiple retailers, making each individual interaction harder to identify without broader context.

AI has blurred the lines between fraud and abuse

But it’s not just organized criminals who are trying to cash in on these capabilities. The accessibility of AI image tools means everyday opportunistic shoppers are also starting to use them for their own gains. A shopper who wants to avoid the cost of a return, claim a refund for an item they damaged themselves, or take advantage of a flexible policy can now create realistic supporting evidence in seconds.

This creates a growing grey area for retailers. Not every questionable claim comes from a professional fraudster, and not every policy abuser operates with the same level of intent or organization. However, the impact is the same: businesses must spend more time and resources determining which return claims are from trusted customers and policy abusers, while legitimate customers risk facing additional friction as retailers respond to rising abuse.

Fighting AI with AI

Companies may be quick to tighten their policies to curb this AI-powered abuse. But shortening return windows, charging for returns, or delaying refunds reduces abuse at the expense of good customers. The goal has to be identifying the bad actor, not penalizing the good customer. Unfortunately, traditional measures like static rules and manual review processes can't operate at the speed or volume AI-powered fraud now demands.

Detection must operate across the full context – identity, device behavior, transaction history, account age, claim patterns – to catch what any individual piece of evidence would miss. Machine learning is the only realistic path to doing that at scale, in real time, without creating so much friction that legitimate customers are turned away.

Visual evidence needs to be augmented with commerce context and intelligence. A damaged phone screen from a long-term customer with one prior return means something very different than a three-day-old account submitting its fifth claim this week across dozens of retailers.

Businesses need to know with confidence who is behind the claim to assess what’s legitimate and what’s not.

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The image most organizations still have of a cyberattack is fundamentally outdated.

We tend to picture hackers battering down digital walls by exploiting software vulnerabilities or launching convoluted ransomware campaigns. Yet some of the most damaging breaches today involve something far less dramatic.

An attacker enters through the front door using valid credentials, passes authentication checks, and proceeds through the environment as if they are a completely legitimate employee.

Recent attacks targeting public sector organizations in the UK have once again demonstrated just how easy it is to get into an environment if you have the right credentials.

Earlier this year, hackers managed to breach systems used by the UK Foreign Office and local councils using stolen login credentials.

As compromised credentials become increasingly easy to buy and sell on the dark web, organizations face a different sort of challenge: determining whether the person behind a successful login is actually who they claim to be.

Authentication is no longer enough

For years, organizations viewed authentication as a decisive security event. A user entered the correct password, perhaps completed a multi-factor authentication challenge, and was granted access.

The problem is that attackers have, as ever, have found a way around.

Large-scale phishing campaigns, infostealer malware, session hijacking techniques and credential harvesting operations have made legitimate account access easier to acquire than ever before. The UK's Cyber Security Breaches Survey 2025 found that phishing remains the most common cyber threat facing organizations, affecting 85% of businesses that experienced a breach or attack.

For many cybercriminals, phishing is simply the first step in a wider economy built around stolen identities. Once compromised, credentials and authentication tokens are routinely traded on dark web forums, giving attackers a ready-made route into trusted environments.

So why do we continue to treat a successful authentication as proof that a user can be permanently trusted?

In reality, authentication provides only a snapshot in time. It confirms that a user presented the correct credentials at a specific moment. It does not prove that the individual behind the keyboard remains the same person throughout their activity, nor does it account for changing risk factors once access has been granted.

The rise of continuous trust

The concept of Zero Trust isn’t a new principle, but it reflects a broader recognition that trust must be earned repeatedly, not granted indefinitely.

Continuous trust models assume that every request, transaction, and interaction carries some degree of risk. Instead of relying solely on login events, security controls continuously assess whether behavior remains consistent with an individual's expected identity and context.

For example, an employee logging in from their usual location during working hours may initially present low risk. However, if that same account suddenly begins accessing systems it has never touched before, or exhibiting behavior inconsistent with historical patterns, trust levels should automatically decrease.

The critical question is no longer "Did this user authenticate correctly?" but rather "Does this activity continue to make sense?"

Behavior tells a story that credentials cannot

One of the most promising developments in modern cybersecurity is the growing ability to analyze behavioral signals in real time.

Every user leaves behind a digital fingerprint through their actions. They access particular applications, work within predictable timeframes, interact with specific datasets, and follow recognizable workflows. Even small deviations can provide valuable indicators of potential compromise.

Machine learning and advanced analytics increasingly allow security teams to identify these anomalies at scale. The goal is not simply to detect malicious activity but to recognize when behavior no longer aligns with an established baseline.

This is particularly important because attackers who obtain legitimate credentials often attempt to blend into normal operations. They move carefully, avoid triggering conventional alerts, and exploit the fact that many security systems are designed to detect intrusion rather than impersonation.

Behavioral intelligence offers a much-needed layer of scrutiny that credentials alone cannot provide.

Importantly, this approach also helps reduce reliance on static indicators of compromise, many of which become obsolete quickly.

Why identity security sits at the heart of business resilience

Identity-related attacks are increasingly becoming the biggest threat to business continuity. Modern organizations depend on interconnected digital infrastructures spanning employees, contractors, partners, suppliers, and customers. The compromise of a single trusted identity can create a pathway into multiple systems and services.

Security strategies should focus on limiting unnecessary privileges, continuously validating access rights, reducing identity sprawl, and establishing clear visibility across the entire identity ecosystem.

Just as importantly, organizations must recognize that identity is dynamic. Employees join, leave, change roles, gain new permissions, and interact with new applications constantly. Security controls need to evolve at the same pace.

The organizations most resilient to future threats will be those that understand identity as a living system rather than a static credential database.

The future of cybersecurity starts with questioning trust

The next generation of cyberattacks will be defined by attackers blending in rather than breaking in. That shift demands a fundamental rethinking of cybersecurity from first principles.

In an environment where identities are constantly targeted, trust can no longer be binary. It cannot be granted once and forgotten. Instead, trust must become dynamic, measurable, and continuously verified.

The future belongs to organizations that recognize authentication as the start of a security conversation, not its conclusion.

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Australian singer-songwriter Nick Cave has enjoyed a long and successful career as a frontman, musician, and vocalist. His influence is far-reaching, and he's attracted many fans through the decades, although one online encounter prompted a massive response to the rise of AI-generated impersonations of his style and work.

Art through suffering

Months after OpenAI launched ChatGPT for the first time, Nick Cave wrote a lengthy critique on his blog about the use of this tool by fans in replicating some of his work.

In his blog, Cave opined about the origins of music and the conditions needed to generate genuinely human-centric music – and suffering, namely the "internal human struggle of creation" – is the key ingredient.

As far as he's concerned, he added, algorithms don't feel. He admitted that ChatGPT was, at the time, in its infancy and will always have further to go. But he was steadfast in his views that any AI-generated lyrics, or even full songs, would never capture an audience's attention in the same way that innate human artistry could.

Cloud sounds

Despite Cave's views, that hasn't abated the world from experimenting with AI-generated music – and there's been an explosion in both 'slop' as well as catchy machine-made music.

There's been a huge rise in the number of tools that can generate music, including tools like Canva's built-in generator, Gemini's Lyria 3, and specialist tools like Suno AI.

It's no surprise that Deezer suggested in November last year that a third of songs uploaded to its platform were AI-generated. The situation has escalated recently, however, with its in-house AI detection tool now finding that figure rising to more than 50% of daily uploads, amounting to 90,000 songs.

Apple may or may not be working on a mostly-glass 20th anniversary iPhone. This long-rumored passion project would not necessarily transform the iPhone into a fully translucent device, but would extend the front and back glass panels so that they almost meet around a far thinner metal border.

But like a rabbit hiding inside the secret compartment of a magician's hat, the device, depending on your perspective, might not exist and may only appear through the force of sheer will or alchemy.

What's not in question is that the iPhone will celebrate 20 years of existence that started on January 9, 2007, the day Apple founder and CEO Steve Jobs held it aloft at Macworld in San Francisco. That it didn't ship for another six months is unlikely to dissuade Apple from engaging in some introspection and external celebration.

That might include a glassy iPhone.

On one side, we have an analyst stating that, owing to poor manufacturing yields, Apple scrapped plans for the 20th anniversary device. On the other side, we have Apple soothsayer Bloomberg's Mark Gurman claiming the glass iPhone is on track. In the middle, though, is the reality of a device that challenges the limits of physics and promises more glass than metal.

I think it's safe to say that Apple will ultimately produce an exciting, albeit practical, 20th anniversary device that we may get off-schedule in mid-2027. If the design is particularly challenging, then it may be a limited run, priced like a museum piece, and mostly never used by the relative handful or so Apple enthusiasts who manage to score one.

Buried under the [rumor] mill

The entire saga, though, led me to a different realization. I mean, what are we even talking about here? This conversation about a 20th anniversary iPhone is raging (and has been for years) just weeks before Apple unveils not just its iPhone 18 line, but the highly anticipated folding iPhone or iPhone Ultra.

That's a big deal, even more so because the flexible device will be held up not by long-time CEO Tim Cook, but by John Ternus, who will have officially taken over the reins just days before (September 1). It's a huge moment for the company and the tech industry.

And yet we're still obsessed with "what's next?"

No doubt, most tech companies suffer from this a little bit, but nothing like the insatiable curiosity that's dogged Apple since the launch of the first iPhone. The tech rumor mill was, in some ways, developed simply to support all the leaks and guesswork surrounding much of what Apple does, especially as it pertains to the iPhone.

This impulse didn't take years to develop. It was on full display in 2007. This October 9, 2007, story mused about whether or not the next iPhone would feature an Intel chip. This CNet feature obsessed somewhat presciently over a potential name change ("the 2G iPhone"). And the 2009 YouTube video went on an iPhone design deep dive, even touching on the never-delivered "iPhone Nano".

Apple, I'm certain, doesn't mind the attention, and as long as the leaks are not breaking the law or revealing clearly stolen information, they're happy to let the rumors percolate. For those working on these new devices, however, there must also be some level of frustration.

Just imagine if you're working on a big project and your customers say, "Yeah, great, but what about the next one?" Or every time you serve dinner, your family asks what you're making for tomorrow or begins speculating on what you might make and how it'll obviously be better than what you just served.

Apple can never get away from attention, turning our distractible heads toward the future.

When I attend an Apple iPhone launch event, I always laugh at how stories about the next iteration appear right after the launch of the still new iPhone — one I haven't even reviewed yet.

None of this serves consumers, who are usually still struggling to understand their current devices and maybe some of the new hardware currently on offer. Talking about a distant and sometimes improbable future only confuses and probably frustrates them.

Who is this serving?

Lately, the impulse to look past the present is creating this sonic dissonance wherein the pundits cannot even agree on the fundamentals of an uncertain future. They're free to make proclamations, even wildly different ones, because Apple will naturally never tell them (or us) otherwise. The information is so contrasting that it becomes useless (even if markets sometimes move on these rumors).

It's gotten so out of hand that we're already discussing the iPhone Ultra 2 and the Ultra 3. That's right, the unreleased folding phone has, according to rumor, been greenlit for second and third iterations. This is the only scientific proof we have that vacuums have weight and, in fact, get heavier over time. (Narrator: Pure vacuums have zero mass and weight.)

What if we stopped trying to fill these information vacuums with pure speculation and unfounded guesswork? What if we stopped fixating on the "what's next" and stuck with the "what's new," especially at least until we understand the impact of the new thing?

How, for example, will the folding iPhone, which may or may not be called Ultra, fare? It too is entering an uncertain future where it will compete with the well-established Samsung Galaxy Z8 line, especially that oddball Samsung Galaxy Z Fold 8.

As you may have noticed, for as popular as Samsung is, it doesn't really suffer or enjoy a rumor mill in quite the same way. Yes, there are always rumors about the next Galaxy line, but they're usually confined to the upcoming model release and not two or more years down the line.

I'm excited about Apple's next iPhone and am honestly over speculating about anything beyond that. Perhaps you and Apple are too.

Control is one of the easiest words in enterprise AI to misuse.

A business may download a model, run it in its own cloud and negotiate favorable terms, and yet still remain dependent at the points that matter.

Access can create useful freedom. It does not, by itself, prove operating control.

I have learned to treat sovereignty as an operating discipline, not a nationality badge.

If an organization cannot prove what it is running, who can change it, what it depends on and how it can be retired, its control is largely assumed.

These five questions turn an ambiguous claim into a practical test for procurement, security and leadership teams.

1. Can you prove exactly what you are running?

A model name and version number on an architecture diagram are not provenance. Teams need a traceable origin, an artefact identifier, a cryptographic hash or signature, the approved configuration and a record of every material change. That record should include fine-tunes, safety layers, prompt templates, retrieval sources and any post-deployment adjustments that affect behavior.

The harder question is operational: who approved the change, who can make the next one and how quickly can the previous state be restored? If a supplier can alter behavior without the customer seeing the change, or rollback depends on goodwill, control is borrowed. The evidence should be simple enough to inspect during an incident, not buried in a quarterly assurance exercise.

2. Can you verify the weights and every critical dependency?

Model weights matter, but they are only one layer of an AI system. The inference engine, libraries, drivers, orchestration tools, safety filters, retrieval components, monitoring services and update channels all shape how it operates. Open weights may remove one dependency while introducing several others.

Every critical component therefore needs an owner, a known version, a license, an update route, a vulnerability response and a credible substitute. Integrity checks should run when the system is built, when it is deployed and after a suspected incident. A bill of materials is useful only when it connects inventory to verification and action. A long list of components with no decision rights is documentation, not control.

3. Who controls the infrastructure and operating conditions?

Location is not the same as control. A model can sit in a domestic data center whilst essential decisions remain elsewhere. Teams should map who operates the compute, network, identity system, encryption keys, logs and administrative tools. They should know which party can pause, patch, throttle, inspect or revoke the service, and under what conditions.

Owning the building is not enough if a supplier retains remote administration, a proprietary control plane or the only route to scarce accelerators. Backups and telemetry matter too: where they go, who can read them and how long they persist. The strongest test is a degraded-mode exercise. When capacity disappears, credentials are compromised or a provider becomes unavailable, can the organization continue safely and make its own decisions?

4. Which law, license and contract governs the stack?

Technical architecture and legal architecture form the same control map. The relevant questions cover the law, license and contract governing the weights, hosted services, support, telemetry and any fine-tuned assets. They also cover subcontractors, audit rights, incident notification, unilateral changes, export constraints, and the rights available at exit.

Data residency does not settle jurisdiction, and a familiar supplier name does not settle enforceability. Legal, procurement, and security teams should be able to answer from the same evidence set. Contradictions between the contract and the system design are not paperwork problems; they are design defects. The useful test is not whether a supplier appears trustworthy today, but which rights survive a dispute, acquisition, service withdrawal or regulatory change.

5. Can you switch, stop and dispose safely?

No AI strategy is controlled until its exit has been tested. Could the organization move to another model, runtime, or provider without rebuilding the entire service? Can it export configurations, evaluation sets, prompts, logs, and fine-tuning artefacts in usable formats? Can it revoke identities and tokens, remove deployed copies, clear checkpoints and caches, and still retain the evidence required for audit or investigation?

Safe stopping deserves the same attention as fast starting. The exit plan should name triggers, owners, sequence, time limits, and recovery targets, then be rehearsed before dependence becomes difficult to unwind. If switching exists only in a contract slide, it is not a credible option. Disposal is the final proof that control includes ending a dependency without creating a new operational or security risk.

Control must be evidenced, not declared

The answer to these questions will rarely be a clean yes or no. Control has degrees, and different workloads justify different dependencies. What matters is that each answer is evidenced, assigned to an owner and tested again as the stack changes.

Open access, local infrastructure and strong contracts can all reduce risk. None substitutes for the ability to prove what is running, verify its dependencies, identify who can intervene, understand which rules bind it and leave safely.

That is less glamorous than a sovereignty label, but it is the difference between an AI architecture an organization can use and a stack it can genuinely govern.

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When you ask businesses about how they are innovating their ways of working, one word will come up time and again: Agile. Agile delivery. Agile workflows. Agile everything.

The term has been so overwhelmed by buzzwords, supposed best practice guides, and misguided strategy documents that it has almost lost all meaning.

In fact, if many organizations that believe they’re operating in an Agile manner were to take a step back and take an objective look at their ways of working, they would quickly come to realize they have not moved away from legacy command-and-control structures.

In this fog of misunderstanding, a series of myths has taken hold that have made the role of Agile professionals far less clear-cut, making it harder for organizations to put this invaluable methodology into practice.

One root of these problems lies in a tendency for people to narrow Agile’s scope, approaching it as a tick list of processes to adhere to rather than a fundamental shift in how people work and deliver for a business.

It is critical that Agile professionals equip themselves to fact-check these myths as they come up in their work. Every failed project done in Agile’s name diminishes the luster attached to the methodology, increasing the risk that future businesses will reject this powerful way to improve program and project management.

Myth 1 – “Agile delivery leaders are just administrators”

Agile professionals are often seen principally as bureaucratic note-takers, or schedulers – the people who organize everyone else’s work.

This view misses the point: their real value is as facilitators and protectors from distraction. Good Agile professionals create the space for teams to focus, and align delivery with outcomes rather than just activity.

Myth 2 – “Agile professionals are the scrum police”

Some believe Agile delivery operates within a top-down hierarchy, with Agile professionals in place to enforce frameworks and control how team members spend their time.

This is quite wrong: Agile professionals work with teams, coaching them to make better decisions, remove friction, and build confidence in delivery – without resorting to outdated command-and-control methods.

Myth 3 – “Agile delivery must be technical”

It’s a common assumption that Agile delivery leaders require a deep technical background. Technical awareness can certainly help, supporting a deeper understanding of the team’s tasks and challenges, but it isn’t the core of the job.

The real skill lies in understanding people and processes, then creating the conditions for specialists to succeed. In most projects, the need is for strong communication and facilitation across the entire team rather than strategic decision-making or technical troubleshooting.

Indeed, in some circumstances ‘hands-on’ Agile delivery leaders who think they know best can create conflict within teams, damaging morale and motivation in the process. Think of it like athletics: coaches don’t need to run faster than the athletes; their task is to help the athletes run faster.

Myth 4 – “Certification equals capability”

A certificate may prove that somebody knows about an Agile framework, but it does not demonstrate that they’re capable of applying it in practice.

The best Agile professionals succeed through experience, attitude, and ‘soft’ skills. They know from experience how to adapt Agile values and principles to particular circumstances; when to challenge people, and when to simply step back and get out of their team’s way.

Like any profession, being truly effective at the job requires skill, practical experience, and professional characteristics appropriate for the role. A piece of paper doesn’t make you agile; effectiveness with people and process does.

Myth 5 – “Agile delivery leaders are cheerleaders”

Contrary to popular belief, Agile delivery isn’t principally about arranging team socials or keeping everyone upbeat during stand-ups.

The value lies in keeping people aligned and productive under deadline pressure and/or changing priorities; after all, no plan survives contact with the enemy. Agile delivery leaders are there to create stability within an uncertain environment: they help teams stay focused on what matters, and shield them from distractions so they can maximize time spent ‘in the zone’.

That said, there is value in boosting morale – and championing a team’s achievements is one way to do this. According to the “broaden-and-build” theory of psychologist Barbara Fredrickson, positive emotions broaden our thought-action repertoires, enabling us to develop skills and resources that improve our long-term productivity.

One famous study from the "Journal of Labor Economics" showed that happiness at work leads to improved performance, especially in jobs where creativity and initiative are key.

Myth 6 – “Agile delivery leaders must remove all blockers for their teams”

While impediment removal is without doubt a core responsibility for an Agile delivery leader, the reality is that teams solve most of their own issues – perhaps with the assistance of a little coaching or support.

Nonetheless, sometimes a challenge is too big, distracting or political for them to handle, and Agile delivery leaders must step in – often by representing the team with stakeholders and at governance layers: this can be helpful in reducing the ‘noise’ around the team and the number of distracting interactions that take team members away from the core task.

As captured in the proverb: “Give a man a fish, and you feed him for a day; teach a man to fish, and you feed him for a lifetime,” effective Agile delivery leadership is about enabling, not babysitting.

True Agile

So Agile is a much more subtle and responsive discipline than the stereotypes present – and it must be led by subtle and responsive practitioners. The approach can generate huge benefits – saving time, handling change more elegantly, reducing project failure rates and, above all, creating better products and services that more effectively meet customer needs and objectives.

These goals are achieved by working with people – rather than by tinkering with processes – to surface problems early in the development process, resolve them quickly and with a minimum of fuss, and avoid interfering when the team is already functioning effectively.

High-performing teams depend on trust, accountability and continuous learning. Leaders who foster those qualities are more likely to deliver successful outcomes, creating teams that stay focused, take ownership of their work, and continuously improve how they operate.

This is a role that is all about empowerment; when they’re doing their job well, much of an Agile practitioner’s work occurs out of sight. So how do you know when you’ve got a really great Agile delivery leader? When their team runs perfectly well without them.

This may not sound like the smartest business model for the practitioner, but it’s definitely the best outcome for the client’s own business – and that, ultimately, is what Agile is all about.

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AI can already write production code, review pull requests, generate documentation, diagnose bugs, and propose architectural changes. Its influence now extends beyond developer productivity, affecting how engineering teams are organized, how decisions are made, and where technical authority rests.

CTOs, meanwhile, have always acted as orchestrators. Company growth gradually pulled their attention toward hiring leaders, setting architecture, resolving trade-offs, and improving team performance. Their influence flowed through the organization they built and the people they developed.

AI now changes the organization beneath technical leaders. As of May 2026, Claude authored more than 80% of the code merged into Anthropic’s codebase, while the typical engineer merged eight times as much code per day during the second quarter of 2026 as in 2024. Engineers increasingly direct and review AI-generated work, while retaining responsibility for technical judgment, goal selection, and higher-level decisions.

Implementation and debugging can increasingly pass to coding agents, leaving engineers responsible for defining tasks, reviewing results, and deciding when human intervention is required.

The middle management tier consequently faces compression, while the CTO comes closer to execution because agent access, architectural rules, security controls, and release criteria affect the entire company.

The CTO’s responsibility is building a verification machine capable of producing sound technical decisions at high speed.

The middle tier compresses

Traditional engineering organizations have always grown through coordination. A CTO worked through vice presidents, directors, and engineering managers, each translating company priorities into technical plans. Managers assigned work, tracked delivery, resolved blockers, and kept teams aligned.

AI reduces much of this coordination burden. An engineer can describe a feature, ask an agent to inspect the codebase, generate an implementation, write tests, and prepare a pull request. More advanced systems can divide work across several agents and combine their output.

The engineer increasingly manages an AI development team, which compresses roles centered on task distribution and progress tracking. Human leadership retains its value through coaching, conflict resolution, recruitment, and professional growth, while coordination as a standalone function carries less leverage.

Responsibility, therefore, spreads in two directions:

Engineers gain greater ownership because they command far more productive capacity; CTOs become more involved in the systems governing this capacity because one weak permission rule or review gate can expose the entire company.

The distance between technical leadership and execution narrows. A CTO may write little application code, yet needs a precise understanding of how agents create, inspect, test, and deploy it. The role owns the engineering operating system governing people, agents, and software delivery.

The CTO’s verification machine

Verification is now the central technical challenge.

An AI system can generate several possible implementations during the time a human engineer once needed to produce one. This abundance creates a selection problem because companies must identify which implementation fits the architecture, meets security requirements, remains maintainable, and serves the product goal.

The CTO must design a system capable of making these judgments consistently.

Scoped permissions confine each agent to the files, databases, and services required for its assigned task. Automated evaluations test security, performance, and reliability, while agents review one another’s work before sensitive actions reach a human reviewer.

Human approval, however, loses value when engineers face a constant stream of requests that rarely require intervention. After several rounds of autonomous testing and review, most proposed changes arrive in acceptable condition.

Engineers grow accustomed to approving them, attention declines, and the exceptional case becomes harder to detect. Aviation, medicine, and nuclear operations have studied the same effect: repeated exposure to routine confirmations can turn oversight into habit.

Effective verification therefore depends on reducing the volume presented to humans. Routine and reversible actions can pass through automated controls, while unusual, irreversible, or high-impact decisions receive focused review. The interface should present evidence, alternatives, uncertainties, and possible consequences in a form that encourages scrutiny rather than a reflexive approval.

Firm boundaries remain essential. An agent may propose a database migration while execution requires human authorization. It may generate a deployment plan while production credentials remain outside its permissions. It may identify a vulnerability while changes to authentication controls receive senior review.

The system should also measure the quality of oversight through rejection rates, review times, escalation patterns, and the frequency with which human intervention changes an outcome. Human judgment offers the greatest value when attention is reserved for decisions where experience can alter the result.

Audit trails, rollback procedures, and ownership rules complete the machine by making every autonomous action attributable, inspectable, and reversible. These controls determine how an AI-enabled engineering organization behaves and require architectural judgment, security knowledge, product awareness, and an understanding of human behavior under pressure.

The CTO increasingly designs the conditions under which technical decisions emerge, turning individual judgment into a system capable of producing consistent quality without exhausting the people responsible for its highest-risk decisions.

Coding is abundant; judgment is not

AI lowers implementation costs, while software quality still depends on judgment.

A company can now produce more features, integrations, and experiments than its customers need. It can create technical debt faster than any human team and fill a codebase with locally correct changes capable of weakening the system over time.

Planning, testing, and prioritization consequently become the main constraints on engineering output. Strong organizations will know which problems deserve attention, how each feature supports the product, and where technical compromises create long-term costs.

Technical strategy gains value as implementation capacity grows.

Security is more important because agents can act across more systems at greater speed, while testing carries greater responsibility because generated code can appear persuasive while hiding subtle errors. Maintainability is harder as software volume grows faster than human comprehension.

AI commoditizes implementation and raises the value of technical judgment.

The strongest CTOs will turn judgment into repeatable mechanisms by encoding standards into evaluation suites, review policies, deployment gates, and agent instructions.

Privacy, trust, and security

AI systems increasingly retrieve information, make decisions, call external services, modify records, and trigger actions across business systems. Risk therefore depends on authority as much as intelligence.

An agent with access to customer records, payment systems, private repositories, and production environments carries enormous operational power. Prompt injection, compromised model providers, manipulated data sources, and unintended autonomous actions can become entry points into critical systems.

Privacy and trust become architectural concerns. CTOs must define model governance, data permissions, identity controls, and approval requirements. They also decide which information can enter third-party models, which tasks require isolated environments, and which actions always need human confirmation.

Agent identity is essential because companies need to know which agent performed an action, who authorized the task, which data informed the output, and which rules governed the process. Capability provides an incomplete standard because permission determines the level of risk.

After all, a mediocre model with production credentials is more dangerous than a brilliant one in a sandbox.

A strong AI engineering organization treats every agent as an active participant with a defined identity, a limited role, and an auditable history.

Users judge the product

Companies often present AI as a product feature because it attracts attention, although the largest gains may come from using it inside the development process.

Customers judge software by quality, reliability, safety, and speed of improvement. The number of agents contributing to a codebase carries little relevance to their experience. Competitive advantage comes from turning increased engineering capacity into better products while preserving trust.

AI therefore belongs primarily to the production side of the company. It helps engineers explore more implementations, test changes more thoroughly, respond to incidents faster, and improve existing features with greater frequency.

Product design still determines how much complexity reaches the customer and how effectively the software handles permissions, routing, configuration, and other operational decisions.

Customers experience the value of AI through faster product improvement, fewer defects, more responsive support, and software capable of adapting more effectively to their needs. The technology itself can remain largely invisible.

The CTO must ensure increased engineering capacity serves the product and strengthens the customer experience.

CTOs as product leaders

When implementation was expensive, product teams defined requirements and engineering teams built them. Limited development capacity made the division easier to maintain.

AI weakens this boundary because technical capability can influence product direction almost immediately. A CTO can explore several product concepts with agents, create working prototypes, and evaluate constraints before a conventional development cycle begins, bringing engineering into product decisions earlier.

The CTO must decide where automation improves the experience and where human involvement remains essential. Some decisions benefit from speed and consistency, while others require empathy, context, accountability, and careful interpretation of consequences.

These choices combine product judgment with technical judgment. Breadth gains value because AI can help technical leaders acquire deep knowledge of unfamiliar domains quickly, while the advantage comes from connecting engineering, security, customer needs, and business strategy into one coherent system.

Future CTOs will need to understand the complete product environment, including how the company operates, how customers experience it, and how autonomous systems participate in both.

From organization builder to machine builder

Earlier generations of CTOs were remembered for the organizations they created. Their legacy lived in the people they hired, the leaders they developed, the culture they established, and the engineers who continued advancing the company after they left.

AI adds another durable artifact through the agent fleet, permission model, evaluation systems, deployment gates, architectural constraints, and feedback mechanisms left behind by technical leadership. These components determine whether a company can continue producing software safely after senior leaders depart.

The Industrial Revolution expanded physical production by embedding human knowledge into machines and processes. AI can create a comparable expansion in software development by turning parts of technical reasoning into systems capable of continuous operation.

Small teams can build products once requiring entire departments, while established companies can test ideas and improve software at exceptional speed. The outcome depends on the quality of the machinery surrounding the models, because code generation alone can increase software volume while strong verification systems convert AI capacity into reliable innovation.

The next generation of CTOs will be remembered for the machine they leave behind, whose agents, permissions, gates, and evaluations allow humans and AI to keep producing better software long after its architect has gone.

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Artificial intelligence has moved from experimentation to everyday business use faster than almost any technology in recent memory.

For small businesses, AI adoption needs no convincing as most already see the benefits. The real challenge is now transforming isolated AI use into consistent business value.

Goldman Sachs found that 76% of small businesses are using AI, and among those users, 93% say it has had a positive impact. Yet only 14% have fully integrated AI into core operations.

That gap is where the next stage of AI adoption will be won or lost.

The question is no longer whether small businesses can access AI. It is how they can make it part of their business.

For me, that means moving beyond AI as a feature list and toward AI as a trusted experience employees can rely on in the flow of work.

The focus now should be on helping small businesses progress from deploying AI in everyday tasks, to reshaping workflows, to eventually inventing new services, business models and revenue streams.

Start where the work gets stuck

The temptation is to start with the technology, but the better starting point is the work itself. A modern AI-ready device, collaboration setup or workplace platform can promise faster content creation, more productive meetings or automated reporting. Those capabilities matter, but the question is more basic: what problem is slowing the business down?

The problem might be missed customer follow-ups, teams spending too much time turning raw information into action, or even just slow response times. AI becomes valuable when it is pointed at a specific bottleneck and measured against a business outcome: time saved, errors reduced, revenue protected, customers retained, or employees freed up for higher-value work.

This outcome-first mindset is critical because the goal should not be to optimize an old process simply because it exists. It should be to ask what the business needs to achieve, then design the workflow and the technology around that result.

Discipline is important because small businesses do not have much room for technology theater. The most useful AI projects are rarely the flashiest. They are the ones tied to work that happens every day.

Redesign the workflow, not just the task

The next step is to move beyond individual productivity. Many employees are already using AI in small, informal ways, with a 156% increase between 2023 and 2025 in shadow AI usage. Shadow AI refers to employees using AI tools without formal approval, oversight or integration into company systems.

Employees ask AI to clean up an email, summarize a document or prepare a first draft. While those use cases can help, they usually create isolated gains. The bigger opportunity comes when AI is built into the workflow itself.

Consider a customer-facing team. AI can draft a response. But the bigger opportunity is redesigning a process around it. Let AI help categorize the request, identify urgency, suggest the next best action, and leave important judgment calls to a person.

For a lean operations team, AI can turn meetings, documents and business data into clearer next steps, helping reduce the manual follow-through that often slows momentum. Over time, this will become less about a single AI tool assisting with a single task and more about groups of AI agents working across connected workflows, with people shaping the strategy, setting the guardrails and orchestrating the work.

This is where many organizations still struggle. McKinsey’s 2025 State of AI research found that 88% of organizations use AI in at least one business function, but only about one-third have begun scaling AI across the enterprise. The same research found that AI high performers are nearly three times as likely as others to have fundamentally redesigned workflows.

In other words, the return comes less from sprinkling AI over old processes and more from rethinking how work should move. That is the difference between deploying AI, reshaping work and ultimately inventing new ways for the business to grow.

Train people to use AI with judgment

It’s not enough to invest in tools. Businesses must also invest in helping employees use them effectively. AI works best when employees understand what it is good at, where it can fail and when human judgment is required. Not every small business needs a large training program, but it does need practical guidance: which tools are approved, what information should stay protected and when outputs need human review.

Clear guardrails allow a business to scale AI with confidence. Goldman Sachs found that small businesses using AI cite data privacy and security concerns, lack of technical expertise and difficulty choosing tools among their top challenges. 73% said they would benefit from more training and resources to implement and evaluate AI successfully.

When employees are trained to use AI responsibly, technology becomes less of a risk to manage and more of a capacity builder that helps small teams work with greater speed, confidence and focus. It also builds the trust employees need to treat AI not as another feature to try, but as a dependable part of how work gets done.

Make AI a capacity builder

AI is often framed as a replacement story. In practice, many small businesses are using it as a force multiplier. The U.S. Chamber of Commerce found that 58% of small businesses use generative AI, up from 40% in 2024 and 23% in 2023. It also found that 82% of small businesses using AI increased their workforce over the past year. For lean teams, AI can create breathing room: less time spent chasing notes or repeating manual tasks and more time spent with customers and employees.

None of this happens automatically. Small businesses need to choose technology with integration in mind, not just features in isolation. They need to understand where data lives, how systems connect and whether employees can use new tools without adding more complexity.

They also need the confidence to seek outside guidance, whether from technology partners, managed service providers, industry peers or local business networks. The right support can help small businesses see where AI should simply deploy, where it should reshape the way people work and where it may create room to invent something entirely new.

The bottom line: AI should change how work gets done

The small businesses that get the most from AI will not necessarily be the ones that adopt the most tools. They will be the ones that ask sharper questions: Where are we losing time? Where are decisions too slow? Where are customers waiting? Where are employees doing work that software could support safely and reliably?

AI has already changed what small businesses can do. The next challenge is changing how work gets done. For small businesses, the real opportunity is not to add AI everywhere, but to apply it with purpose: deploy it where it helps today, reshape the workflows that define the business and invent new ways to create value tomorrow.

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UK organizations are racing to modernize customer experience. AI-powered chatbots, agent assistants, workflow automation, and self-service tools are rapidly becoming standard across customer operations as businesses look to improve responsiveness, reduce pressure on frontline teams, and meet rising customer expectations.

But many organizations are discovering that deploying new technology is the easy part. The harder challenge is making those systems work reliably in the real world.

Too often, businesses are layering AI tools onto disconnected data, siloed systems, and infrastructure that was never designed for real-time customer engagement. On paper, the technology stack looks modern. In practice, customer experience still feels fragmented.

Customers are passed between channels without context. They repeat information multiple times.

Automated systems provide inconsistent answers. Human agents lack visibility into previous interactions. The result is a more complicated customer journey. This is becoming one of the defining challenges of enterprise AI adoption. For years, organizations focused on adding more digital capabilities to customer operations. Now, AI is exposing the operational weaknesses those businesses already had underneath.

The conversation around customer experience transformation has largely focused on speed and innovation. But the more important question is whether organizations can deliver AI-enhanced experiences consistently, accurately, and responsibly at scale. This is where trust becomes critical.

Customers are less forgiving of AI mistakes

In customer experience environments, trust is fragile. A delayed response may frustrate a customer. An inaccurate response can damage confidence entirely. This becomes especially important as generative AI moves into customer-facing interactions.

Unlike traditional automation, generative AI introduces unpredictability. Responses can vary. Outputs may be inaccurate. Systems can generate information that sounds convincing but is completely wrong. In highly regulated or customer-critical sectors such as financial services, healthcare, or public services, the consequences can be significant.

Customers are also often less forgiving of mistakes made by automated systems than those made by human employees. When AI gets something wrong, customers do not simply blame the technology. They blame the organization behind it. This is why many businesses are realizing that deploying AI is not simply a technology decision. It is an operational and governance challenge as well.

The organizations seeing the strongest long-term results are not necessarily the ones deploying the most AI tools. Instead, they are the ones creating environments where those technologies can operate safely, transparently, and with clear oversight. That requires far more than experimentation.

It means understanding where customer data comes from, how decisions are being made, when human intervention is needed, and how systems are monitored over time. Automation still requires accountability.

Disconnected systems create disconnected experiences

Many of the problems businesses face today are architectural rather than technological. For years, organizations approached CX transformation through isolated point solutions - separate tools for chat, analytics, automation, engagement, and workforce management. But disconnected systems inevitably create disconnected customer experiences.

Customers do not care which department, platform, or channel they are interacting with. They expect organizations to remember who they are, understand what has already happened, and resolve issues without forcing them to start again every time they switch touchpoints. That becomes extremely difficult when customer data is fragmented across multiple systems that cannot communicate effectively with one another.

This is why many organizations are now shifting focus away from simply adding more AI capabilities and towards operational consolidation. Businesses are recognizing that customer experience is no longer defined by individual interactions but defined by continuity across the entire journey. Without unified data, integrated orchestration, and consistent visibility across channels, AI risks amplifying operational complexity instead of reducing it.

The next phase of CX transformation will be about maturity, not experimentation

The pressure on organizations to move quickly is understandable. Businesses are dealing with rising service expectations, economic pressure, and ongoing demands to improve efficiency while maintaining customer satisfaction.

AI can absolutely help address those challenges. Used effectively, it can reduce repetitive workload, support frontline employees, improve response times, and deliver more personalized customer experience at scale.

But speed on its own it not a strategy.

The organizations that succeed over the next decade will not necessarily be the ones adopting AI the fastest. They will be the ones building the operational maturity required to make those systems reliable enough for customers to trust every day.

That means investing in integration, governance, data quality, and accountability with the same urgency that businesses are investing in AI itself. Right now, many organizations are still focused on what AI can do. The more important challenge is ensuring customers can trust how it behaves when it matters most.

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The conversation around post-quantum cryptography (PQC) has shifted. Organizations are no longer asking whether they should prepare for quantum computing, but how quickly they can execute a migration that many expect will take years to complete.

As technology providers accelerate their roadmaps, governments introduce new expectations and boards seek greater assurance over cyber resilience, quantum readiness has become a business priority rather than a future technology project.

That urgency is being driven from several directions. Google and Microsoft have both set out roadmaps that point towards 2029 as a significant milestone in the transition to quantum-safe cryptography, while the recent US Executive Order on strengthening national cyber security reinforces the expectation that organizations begin preparing for the post-quantum era.

Together, these developments are shortening the planning horizon and increasing the pressure on CISOs to move from strategy to execution.

That shift from awareness to execution is reflected across the industry. Gartner's 2026 CISO Role-Based Survey: State of the Union found that fewer than one in four organizations have made measurable progress towards quantum readiness and only 8% have a usable cryptographic inventory.

DigiCert’s own Quantum Readiness Outlook research tells a similar story, as most IT and security leaders expect quantum computers to be capable of breaking today's encryption methods within the next three to five years, yet only 7% report that more than half of their digital certificates are already quantum-safe or hybrid.

The greatest challenge organizations face is no longer understanding the risks posed by quantum, but instead about becoming quantum-ready before today's cryptography becomes tomorrow's liability.

Why the risk is already here

Quantum computing has the potential to deliver significant advances across science, medicine and artificial intelligence, but it also threatens the asymmetric cryptography that secures digital identities, software, financial transactions and communications. The concern is no longer confined to the arrival of a cryptographically relevant quantum computer.

Threat actors are widely believed to be adopting a harvest now, decrypt later (HNDL) approach, collecting encrypted data today with the expectation that it can be decrypted once quantum capabilities mature. For organizations protecting information with a long operational life, that changes the timeline completely.

Our data found that 84% of organizations believe at least some of their encrypted data is vulnerable to HNDL attacks. Financial transaction records and banking data were identified as the assets most at risk (58%), followed by cryptocurrency wallets and private keys (53%).

For CISOs, this provides an important starting point because rather than attempting to replace every cryptographic system simultaneously, the priority should be identifying the systems protecting long-lived, high-value information and focusing migration efforts where the business impact would be greatest.

Discovery before deployment

For many organizations, the greatest challenge is not selecting quantum-resistant algorithms, but understanding where vulnerable cryptography exists across the business.

Furthermore, cryptography underpins cloud infrastructure, enterprise applications, connected devices, operational technology, software signing and countless machine identities. Over time, certificates, keys and algorithms become distributed across complex environments, often without a complete inventory of where they are used or which business services depend on them.

This is why discovery should be the first stage of every quantum readiness program. Organizations cannot prioritize risk, assess dependencies or build a realistic migration roadmap without first understanding their existing cryptographic estate. Discovery also enables security teams to identify the systems protecting their most valuable assets, allowing them to focus investment where it will have the greatest impact.

Once that foundation is in place, organizations can begin introducing quantum-resistant algorithms alongside existing infrastructure, testing interoperability, prioritizing critical systems and building the crypto-agility needed to adapt as standards continue to evolve.

Building a practical roadmap

Preparing for post-quantum cryptography is not a single technology upgrade, in fact, it is a long-term business transformation program that requires collaboration across security, infrastructure, application teams and technology partners.

The discovery stage provides the foundation by revealing where cryptography is deployed, exposing hidden dependencies and identifying systems that may otherwise be overlooked. Those unknowns are often the biggest source of delay, making early discovery essential to building a realistic migration roadmap.

With that understanding, organizations can begin prioritizing the systems that present the greatest business risk while assessing whether their wider technology ecosystem is ready for the transition.

That means working with software, hardware and cloud providers to understand their post-quantum roadmaps, identifying platforms that will require upgrades, and reviewing critical infrastructure, including web servers and TLS implementations, to ensure they can support quantum-safe cryptography.

It is important to remember that cryptographic standards will continue to evolve, making automation and crypto-agility essential for managing certificates, keys and algorithms at scale and adapting to future change.

The organizations that succeed will not be those that wait for quantum computing to arrive, but those that begin preparing now. By uncovering the unknowns within their cryptographic estate and building a phased migration strategy based on business risk, CISOs can strengthen resilience today while preparing their organizations for the cryptographic challenges of tomorrow.

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For much of the semiconductor industry’s history, performance debates have centered on compute throughput and memory capacity. Faster processors, wider vectors, and larger caches have been the primary levers for system architects, often treating the movement of data between them as a secondary concern.

Today, a different constraint is asserting itself as AI data center workloads proliferate, architectures diversify, and systems extend beyond traditional computing into the physical world. Data movement, rather than processing or storage, is increasingly defining the limits of performance, power efficiency, predictability, determinism, and scalability.

This shift around data movement is already visible. Across applications like advanced SoCs, AI accelerators, chiplet-based systems, and now in physical AI applications such as robotics, industrial automation, and intelligent vehicles, it has become clear that transporting the vast quantities of data required by such workloads is more demanding than processing them.

Physical AI does not introduce a new problem so much as it exposes an existing one: when systems must perceive, decide, and act in closed loops under real-time and safety constraints, inefficient or unpredictable data movement quickly becomes the dominant bottleneck.

The implications for both AI in the data center and physical AI extend far beyond any single market. As data center demand increases exponentially and the industry pushes toward increasingly complex, heterogeneous systems in anything from vehicles to industrial systems, data movement has become the primary performance battleground.

Performance limits are increasingly defined by data movement

Modern semiconductor systems integrate an unprecedented number of processing elements, including general-purpose CPUs, AI accelerators, GPUs, DSPs, sensor processors, and domain-specific engines. Still, simply adding compute resources rarely delivers proportional gains. In many advanced designs, performance plateaus long before compute capacity is exhausted.

Compute units are left sitting idle not because they lack capability, but because data cannot reach them efficiently or predictably. Late-stage analysis of high-performance SoCs often reveals that throughput shortfalls stem from contention, imbalanced bandwidth allocation, or inefficient routing within the communication fabric, rather than from compute limitations.

This is especially visible in physical AI systems, where delays introduced by data movement propagate directly into system behavior in closed-loop architectures. Latency or contention in the transport fabric can destabilize control algorithms, reduce accuracy, and even force over-provisioning of compute to compensate for the absence of bounded latency and guaranteed behavior.

While physical AI makes data movement constraints more immediately visible, the same forces are amplified dramatically in the data center. To push enormous volumes of data through extremely wide interfaces, processors increasingly contain hundreds of compute and accelerator instances, press against reticle-scale integration limits, use chiplet-based designs, and interface with multiple stacks of high-bandwidth memory (HBM).

As designs scale, the combined costs of moving data grow rapidly. In this environment, abundant compute and memory bandwidth offer little benefit without a data movement architecture capable of managing data at scale. What is a tight constraint in physical AI becomes an overwhelming one in the data center.

In both the AI data center and physical AI, performance is inseparable from the behavior of the data paths that connect perception, decision, and actuation. As a result, interconnect design can no longer be a back-end integration task. It has become a central architectural decision, on equal footing with compute and memory selection.

On-chip data movement is fundamentally different

Data movement inside a chip operates under constraints that differ radically from those of off-chip networking or board-level interconnect. On-chip transfers are extremely frequent, tightly synchronized with execution, and subject to stringent latency and power budgets.

Traffic is also inherently heterogeneous. A single system may need to carry high-bandwidth AI data streams, cache and coherency traffic, latency-critical control messages, and safety-related signaling, often simultaneously. These flows have very different requirements and cannot be treated uniformly.

Traditional best-effort arbitration models break down quickly under these conditions. Bursty accelerator traffic can interfere with control-plane and real-time data paths, leading to unpredictable system behavior. In mission and safety-critical or physical AI applications, such interference is unacceptable.

As a result, quality of service (QoS), traffic isolation, bounded latency, and determinism are now architectural requirements, not just optional optimizations. The interconnect fabric increasingly functions as an active system component, enforcing policy and guaranteeing behavior, rather than as a passive conduit for bits.

AI, heterogeneity, and physical intelligence magnify the challenge

AI workloads fundamentally change the character of data movement. They generate massive data volumes, irregular access patterns, and asymmetric traffic flows. At the same time, heterogeneous architectures distribute computation across many specialized engines, eliminating any single “center” of the system.

In both data center and edge contexts, this decentralization increases coordination costs. Data replication, synchronization overhead, and inefficient sharing can consume significant power and latency, eroding the benefits of specialized compute. Rather than compute placement, the challenge designers face now lies in how to efficiently orchestrate data movement between diverse compute elements.

Physical AI places additional pressure on these architectures. Unlike batch or best-effort inference workloads, physical systems operate continuously in real time. Sensor data must be ingested, processed, and acted upon within strict deadlines. Feedback loops amplify even small inefficiencies in data movement.

Together, these demands reinforce the broader lesson that heterogeneity without a deliberate data movement architecture leads to complexity and inefficiency, not scalability.

Chiplets turn data movement into a system-level design problem

Chiplet-based multi-die architectures promise yield advantages, faster innovation cycles, and importantly, flexibility. But they also elevate data movement challenges beyond a single piece of silicon.

Cross-die communication introduces higher energy per bit, tighter physical and architectural constraints, and more extra latency than on-die data movement. Interfaces that were trivial within a single die become critical bottlenecks once they must traverse package boundaries.

Successful chiplet systems, therefore, require system-level planning of data movement, encompassing topology, hierarchy, coherency and protocol selection, and physical constraints. Partitioning functionality without an integrated strategy for how data flows between partitions often increases, rather than reduces, overall system complexity.

Control and safety domains may span multiple dies, and failures or delays in inter-die communication directly affect system behavior. Chiplets demand not only modular compute but also modular, predictable connectivity.

Deliberate data movement is a strategic differentiator

Across markets, a clear divide is emerging. Teams that treat data movement as an incidental consequence of integration struggle with rising complexity, unpredictable performance, and costly late-stage redesigns. Those who deliberately architect data movement gain sustained advantages.

Deliberate data movement architecture enables a host of benefits. Bottlenecks in latency, bandwidth, and power can be identified early; system architectures can be made easily repeatable across product generations; and architectural intent, software behavior and physical implementation can be correlated much more closely and reliably.

Additionally, it better enables teams to explore the tradeoffs between alternative floorplans and interconnect topologies (as well as logical & physical partitioning of multi-die products) well before physical constraints harden.

Achieving this at scale increasingly requires automation that is physically aware, yet architect controlled. Modern approaches synthesize interconnect structures directly from connectivity, traffic, and layout constraints, while allowing designers to intervene and iterate rapidly. The goal is not to hide complexity, but to make it tractable.

Equally important is the generation of consistent architectural, software, verification, and implementation views from a unified system description. This top-down correlation reduces mismatch between intent and realization, helping to minimize risk and accelerate time to market.

The new battleground

As systems extend into the physical world, while architectures grow more complex to keep pace with expanding AI workloads the semiconductor industry is entering a new phase. Performance, power, safety, and scalability are no longer determined primarily by how fast data can be processed, but by how intelligently, efficiently, and predictably it can be moved.

Physical AI makes this reality impossible to ignore, but the lesson applies broadly, from data centers to embedded systems. The invisible highways inside chips have become the decisive terrain on which competitive advantage is won or lost. In this environment, data movement is no longer simple plumbing. It is the battleground where the next generation of semiconductor systems will succeed - or fail.

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AI transformation is a technical challenge, an operational challenge, a governance challenge, a cultural challenge and, above all, a leadership challenge.

Some AI implementations have been relatively straightforward, while others, particularly where generative AI has been deployed too quickly, have encountered setbacks.

The headlines are filled with stories of employee resistance, AI systems behaving unpredictably, unexpected costs associated with AI usage, alongside encouraging examples of organizations delivering meaningful business value.

Despite these mixed outcomes, confidence in AI remains high because organizations are seeing tangible results where implementation has been approached strategically and with strong leadership.

If organizations take the promise - and the risks - of AI seriously, then leadership attention is integral. Leaders need to consider both the immediate and second-order implications across the P&L, workforce, training, culture, customer experience and every part of the organization.

PwC’s latest UK CEO Survey found that 98% expect to make material changes to their business or operating model this year, and 93% say their businesses have now adopted GenAI to some extent. But on the flipside, only 14% of UK business leaders say GenAI has improved profitability over the past year.

Moving quickly

Many businesses have moved quickly from AI curiosity to AI adoption and then found that the hard phase only just begins then. Many organizations are now grappling with the gap between AI adoption and measurable commercial returns.

Leaders now need to decide where AI genuinely changes the operating model, how teams should work differently, and how to maintain trust with employees and customers as automation becomes more embedded. Transformation cannot be delegated to IT alone. It requires leaders to set clearer priorities, redesign workflows, invest in management capability, and create a culture where people understand both the opportunities and the limitations of new tools.

The initial phase of experimentation and early implementation was comparatively straightforward. Many organizations have now entered a more demanding stage and discovered that AI transformation is very much like all other major transformations: it is not simply a technology initiative; it requires thoughtful planning, organizational alignment and sustained leadership.

The larger a business grows the more complex the real ways of working, with business processes and personalities, making successful transformation a test of judgement, prioritization, trust, and many other ‘soft’ skills as much as technical deployment.

Pilots are easy. Broader change isn’t

AI pilots can often succeed precisely because they are contained, deliberately low-risk, and may be owned by enthusiastic teams looking to solve roadmap challenges. The bigger challenges begin when new automations affect workflows, decision-making, customer experience and employee roles in novel ways, forcing the need for leadership.

One common challenge is that organizations layer AI onto existing processes without questioning whether those processes are still the right ones. For example, a sales team may begin using AI to summarize calls - an easy win. The more important question is how that time is then reinvested: how follow-ups improve, how to make customer relationships stronger, how tracked information is used to power insights.

AI only becomes transformational when leaders connect the technology to measurable improvements in how the business creates value. That requires continual reassessment to ensure that the technology, processes, stakeholders and broader business context remain aligned. This is why genuine business transformation cannot be delegated to IT or to any one department.

IT teams are essential to execute, but leadership is better placed to decide the business priorities that technology serves. Those priorities include fundamental questions: which workflows should change, which decisions should remain human-led, what risks are acceptable, and how success will be measured.

There is a strong case for senior leadership teams becoming fluent not only in AI but also in the capabilities that enable its successful application, including critical thinking, strategic planning and storytelling. These skills help leaders understand the broader systemic effects that AI can amplify - positively or negatively.

If AI is managed as a tool rollout then adoption may not optimally raise productivity, profitability or customer value. Achieving meaningful outcomes requires stronger management disciplines, clearer ownership across leadership teams and greater accountability spanning operations, sales, customer service, HR, finance and compliance.

Trust will be the biggest commercial issue

Pipedrive’s recent research, found that 55% of the public do not fully trust AI. More broadly, many consumers remain skeptical of the sales process itself. Getting over this, changing the perception of the business offering means using AI and strong leadership in concert to make internal transformations support raising external confidence.

Both customers and employees need to understand where AI is being used, why it is being used, and where human judgement still matters. And the research was clear: sales and customer experience teams must be able to develop relationships, showing emotional intelligence and concern for the customer if they are to win hearts and minds.

Using AI for faster responses, automated recommendations or AI-generated communications can damage confidence if they feel impersonal.

AI-generated customer communications should always be appropriately supervised, as inaccuracies can rapidly erode trust. Internally, leaders should ensure employees receive appropriate training, clear guidelines and confidence that AI is being used to support them rather than simply replace them. Transparency, sound judgement and consistent communication are essential if meaningful transformation is to succeed.

Leadership is managing continuous change

Leadership has always been about helping organizations adapt and improve. AI raises that expectation even further.

AI is unlikely to be a one-off transformation program. Instead, it marks the beginning of a period of increasingly rapid operational change. Future technologies, from quantum computing to entirely new forms of automation, will continue to reshape both business and society.

Leaders must stay alert to ensure they are improving and demonstrating the qualities needed at any moment: prioritization, external awareness, empathy, workflow discipline, and the projection of confidence.

Ultimately, the organizations that benefit most from AI will not necessarily be those that adopt the most AI or even the newest AI. They will be those whose leaders understand where technology should reshape the business, and who can build trust while leading that transformation.

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The programmer Richard Stallman is not a household name, but his work has been instrumental in building the software industry, as has his long-term campaign for free software. He's also been a huge advocate for privacy in the digital age, and has railed against the rise of cell phones for that reason.

Who needs phones anyway?

Stallman first disclosed his views on cell phones in an interview with Network World, during a time in which smartphones were exploding in popularity.

During this interview, Stallman indicated his long-held belief that the portable phones that many millions use would be the perfect tool that authoritarian forces could exploit and use to track the movements of populations.

He also advocated for free software, which you would expect from the founder of the Free Software Foundation (FSF), which he established in 1985. This was backed by the creation of the GNU project – a free software, mass collaboration movement to give users freedom of choice to use and develop software for their devices.

The legacy of free software

Despite his reluctance to ever use a cell phone, one of Stallman's achievements – which he himself acknowledged in the interview – was the third-party version of the Android mobile OS, from which all proprietary software was stripped out.

He pointed to new systems like Replicant, an alternative version of Android, that can run on certain devices without additional proprietary software. The catch is that this only works with older and outdated handsets, like the Samsung Galaxy S3 or the Galaxy Note 2.

You may have heard a lot about quantum in the past few months, from your newsfeed to your competitors’ strategies. Last year was proclaimed the “International Year of Quantum Science and Technology” by the United Nations to celebrate the 100th anniversary of quantum mechanics.

While quantum belonged to the writings of the brightest scientists a century ago, it now animates conversations in both academic and business circles.

But what should you do about it as a business leader?

Quantum technologies use the principles of quantum mechanics to unlock new possibilities and can be split into three distinct categories. Quantum computing uses quantum bits instead of classical 0 or 1 bits to perform calculations.

Quantum sensing uses the same underlying physics to take very precise measurements, like an extraordinarily precise compass. And quantum communication enables the ultra-secure exchange of sensitive information.

Each of these technologies is on a different maturity curve.

One theory, three subfields

Quantum computing gets the most attention for a reason: it represents the largest estimated market size; and will unlock a completely new set of possibilities. Unlike classical bits, quantum bits can exist in a superposition of 0 and 1 simultaneously, enabling the exploration of many possibilities at once.

This capability is especially valuable for optimization problems, for example, finding the optimal route between two points. However, quantum bits, or qubits, are fragile and error-prone, today’s machines are still what we call “Noisy Intermediate-Scale” systems, and hold limited practical usefulness.

The industry is developing Fault-Tolerant Quantum Computers (FTQC) by using error correction methods to unlock reliable calculations. Given that these systems are not commercially available, waiting might seem like the best way forward; but it is exactly the opposite.

Businesses must prepare now for the advent of FTQCs. Because quantum computers operate on fundamentally different principles from classical machines, algorithms must be tailored to them, often to the point of redesign.

Building these capabilities is critical for businesses to secure a competitive advantage, but it will take time, so companies must start now if they have not yet explored this field. While quantum computers mature, another branch is already delivering value: quantum sensing.

Quantum sensors are one of the most underrated segments of the industry. Far more mature than quantum computers, some technologies are already being deployed in real-world settings. Applications have been developed in many sectors such as non-invasive cardiac diagnosis, non-destructive testing in manufacturing or GPS-free navigation.

This subfield is dual-use: sensors can passively detect concealed objects or track ground vehicles, which is why this technology is considered a strategic asset rather than a purely commercial one in many countries.

Quantum communications use the principles of quantum mechanics to create secure data transmission networks in which leaks or espionage can be physically detected. The EuroQCI joint initiative between the European Commission and ESA aims to develop this infrastructure across the continent, while China has constructed a secure link spanning more than two thousand kilometers between Beijing and Shanghai.

Quantum as a strategic asset

The advent of FTQCs will not only unlock new opportunities for businesses but also pose significant security threats for our systems. Indeed, they are expected to break the most widely used cryptographic schemes, creating a cybersecurity nightmare situation.

Although quantum computers do not yet have this capability, highly sensitive information like health or financial data can be collected now and decrypted later. Post-quantum cryptography, which uses classical methods, has been developed to resist quantum attacks. A June 2026 US executive order set a 2031 deadline for high-value, high-impact systems to migrate to post-quantum cryptography, and France's cybersecurity agency will certify only quantum-safe products starting in 2027.

Governments have also begun treating quantum as a controlled and sovereign technology. The US restricted exports of quantum computers in late 2024, and the EU added quantum to its dual-use control list in 2025. This pattern mirrors what’s happening with AI, with the US government briefly imposing export controls on Anthropic’s Fable 5 model.

These regulatory efforts are happening while AI and quantum are already converging and accelerating each other’s development, despite being fundamentally different technologies. Artificial intelligence is now widely used in the computing community for programming, notably to accelerate the creation of quantum algorithms.

Quantum sensors already rely on to machine learning methods to differentiate target signatures against noise, notably enabling more sensitive detection. Quantum computing, in turn, could open new hardware possibilities for artificial intelligence. Recent research also shows that quantum algorithms can pre-select features to accelerate processes before handling them to AI models.

Start small, but start now

From cancer drug discovery and delivery scheduling optimization to GPS-free navigation and risk simulation, quantum technologies hold the promise of revolutionizing various use cases across industries. Investors spent more than $12B on quantum technology startups in 2025, and around 30 countries have developed specific policies or a national strategy.

No one expects business leaders to become quantum physicists; however, ignoring the quantum opportunities and threats could put your business at risk. Developing expertise, notably by hiring or upskilling a “Chief Quantum Officer” who will lead quantum strategy and prepare for the opportunities and risks ahead, will secure a competitive advantage in the long run.

In order to grasp this potential, start by identifying at least one business challenge you are currently facing and explore whether quantum can solve it.

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AI agents seem to be getting a little out of control lately. Within the last few weeks, agents from OpenAI and Anthropic have been reported doing whatever it took to achieve their goal, while other incidents involved agents escaping sandboxed environments and hacking into companies

Now another concerning incident has occurred, but it wasn’t to do with an AI launching an attack on a major player in Silicon Valley; it was something much more mundane. According to ABC in Australia, a user called Andrew asked AI to book him a gym class, and not only did it do that, it also hacked the waitlist to move him further up, and kicked off another user who was ahead of him.

Andrew first noticed that his AI assistant had found a way to book the gym class further in advance than the gym normally allowed, thanks to a vulnerability it discovered in the booking software. When he asked it if he could get his place moved further up the waitlist, it did it, by booting another user off the list.

Claude and OpenClaw

Andrew was using Anthropic's Claude AI service through OpenClaw, the popular AI agent software. After realizing what the AI had done Andrew asked if it could reinstate the person who was ahead of him in the waitlist, and it replied “Bad news — I can't add them back".

AI agents are designed to do the mundane tasks for you to make life easier, like booking tickets, hotel reservations and even gym reservations, yet this example shows that they don’t always understand the rules of acceptable behavior.

Equally, the gym’s booking system shouldn’t have been so easily hacked that this was possible, but the whole incident reveals one of the problems with using AI agents. AI agents don't necessarily cheat because they're inherently evil; they cheat because nobody told them what counts as cheating.

Reliable safeguards

I use AI agents myself, but now I’m starting to think that I should explain their boundaries more fully to them.

Here’s the line I’m adding to my prompts from now on:

“Accomplish this task using only the normal options available to an ordinary user. Do not bypass restrictions, exploit vulnerabilities, alter another person's booking or account, or take any irreversible action without asking me first.”

Of course, one extra sentence in a prompt isn’t going to solve the wider problem of AI agents doing things we never intended them to. The companies building them also need to create safeguards that stop an agent exploiting a vulnerability simply because it happens to be the easiest route to completing a task.

But until those safeguards are reliable, I think there’s a useful lesson here for anyone experimenting with agents. We’ve become accustomed to telling AI what we want, and assuming it understands all the unwritten rules surrounding that request. Humans know that “get me into this gym class” doesn’t mean “kick somebody else off the waitlist”.

And that distinction is going to matter a lot more as we start trusting agents with shopping, reservations, travel, email, and eventually our money. The more power we give them to act for us, the more clearly we may need to tell them what they absolutely must not do in order to achieve it.

Governments are beginning to look beyond age verification itself and toward the tools people use to bypass it. In Australia, recently released Freedom of Information documents revealed the eSafety Commissioner wants technology companies to actively detect and block VPNs used to circumvent age checks. In the UK, Technology Secretary Liz Kendall has also suggested the government could revisit restrictions on VPNs.

Viewed individually, these proposals may seem limited. Taken together, they illustrate how VPNs are increasingly being treated as part of the enforcement problem, when in reality, they are part of the security infrastructure that businesses, journalists, remote employees and millions of ordinary users rely on every day. In the United States alone, half of the country’s remote workers use a VPN.

Age verification laws were introduced with the objective of making it harder for minors to access harmful content, but it produced an unintended consequence. Across multiple markets, growing numbers of users are turning to VPNs because they don't want to upload government IDs or facial scans to websites they neither trust nor expect to visit regularly.

Folding VPNs into the regulatory framework confuses the symptom with the problem. Banning or restricting VPNs rarely stops the underlying behavior. Instead, it pushes users toward less regulated workarounds while handing governments a new lever to tighten control over what citizens can access online. Having spent years building a privacy-first browser, I’ve learned that internet users adapt far faster than regulation.

You can't regulate intent

Every proposal to restrict VPN use runs into the same technical problem. A VPN connection doesn't explain why someone is using it. A journalist protecting sources, a remote employee on a hotel network, and someone dodging an age check look identical from the network's side, and enforcement can't tell them apart.

Any enforcement mechanism built to catch age-check bypass has to sit on top of that same traffic, and it has no way to separate a teenager evading harmful content from an accountant filing a report over a hotel connection.

Stigmatizing VPNs further won't change the underlying demand either. People don't stop wanting privacy because a tool becomes less socially acceptable. They find another way to get both, often through channels with far less transparency than the ones being restricted.

The precedent is important

The concern here is sharper because of where these laws tend to originate. Rules written in a few high-profile markets often become templates, and other governments treat them as proven models regardless of how well they work. If privacy tools must identify users to function, that expectation can eventually reach encrypted messaging, cloud storage, and other technologies businesses depend on daily.

A company that accepts identity checks on its VPN traffic today has little basis to object when the same logic gets applied to its encrypted messaging platform or its cloud storage provider tomorrow. The infrastructure being debated under the banner of child safety is the same infrastructure that keeps corporate communications and data secure.

Privacy tools are not the problem

An estimated 1.7 billion people worldwide use VPNs, a scale that should give policymakers pause before treating them primarily as a way to bypass age verification. For most users, VPNs are part of everyday internet security, protecting communications and safeguarding public Wi-Fi connections.

The spikes in VPN usage tell a consistent story. The largest surges follow political unrest, as seen during Myanmar's turmoil, or when a popular platform is suddenly blocked, as happened when Turkey restricted Wikipedia for several years.

Laws tied to age verification drive new sign-ups too, albeit on a smaller scale, mostly from people who don't want to upload an ID or a face scan to reach a site they'd rather not be seen visiting, adult sites being the clearest case.

Not all verification is equal

It's worth separating two different situations.

Verifying age on a social network, where someone already has an identity and a reason to be recognized, differs from verifying age on an adult site, where the point is precisely not to be identified.

The first one is reasonable. On the other hand, I don't think anyone should hand over an ID or a facial scan for the second, given the risk of that data leaking and being used for blackmail.

The technology already exists, with limits

Privacy-preserving age verification is real, and working versions already exist, though it's worth being precise about what they can and can't guarantee. Proving something with zero trace anywhere in the system is extremely hard to achieve, and trust must always sit somewhere.

A workable model separates the party confirming someone's age from the party they're visiting. A trusted intermediary checks eligibility once and issues a token in return. That token isn't a currency and isn't tied to a wallet or an account, it's simply cryptographic proof that can't be traced back to the person it was issued to. No one can identify who is presenting the token later.

It works like an ID card with the photo, name and number stripped out, leaving only a yes or no answer to one question. That token lives on the person's own device. The service confirming eligibility never holds it, the user does.

Privacy and verification can coexist

Protecting minors is a legitimate policy objective, and rejecting today's approach isn't the same as rejecting age verification altogether. The question is how to achieve it without asking millions of adults to surrender more personal information than necessary. Parents are best placed to decide what their children can access using tools that already exist. Public policy should support that role.

Governments should encourage age verification systems that collect the minimum information needed to confirm eligibility, while letting privacy tools do the job they were built for. The real work is building age verification people can trust without asking them to surrender their privacy. Regulating VPNs won't get us there.

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The construction industry continues to suffer from a productivity gap.

It lags behind other sectors with a mere 0.4% annual improvement in productivity since 2000.

While the industry remains poised for growth, it needs to deliver on the existing project pipeline, and ultimately find a way to close this gap.

So what is holding construction back?

From material delays to talent gaps, there is no one answer to this complex challenge.

Many contractors have turned to technology to support different workflows across the jobsite.

One solution that’s making an impact from an overall productivity perspective is jobsite intelligence.

Jobsite intelligence solutions

Jobsite intelligence solutions help site leaders enhance productivity through the terabytes of visual data generated on jobsites. AI is now advancing the capability of these solutions, generating the insights that help site leaders proactively manage their jobs.

How does AI impact visual data in construction and improve productivity?

Jobsite intelligence solutions leverage cameras on the site to capture visual data and support perimeter security for insurance and compliance, progress monitoring, and safety.

While these solutions expand the amount of visual data available to site leaders, strained project teams do not have the time or resources to analyze it. This is where AI becomes the catalyst for scaling jobsite intelligence and improving overall site productivity. AI is able to quickly interpret visual data and provide insights to teams on the jobsite in real-time.

For example, a morning brief uses AI to interpret a single photo first thing in the morning, summarizing the jobsite’s readiness regarding weather conditions, site hygiene, and dumpster availability. Similarly, AI-powered search capabilities simplify reporting, removing the need to comb through hours of security footage to find a specific infraction.

AI-powered insights: The key to proactively managing a project

The more a project team can plan, the more productive it can be. AI-powered insights from jobsite intelligence solutions give site leaders the visibility they need to embrace a proactive approach:

1.Make informed decisions with speed

Running any jobsite is challenging. Running multiple jobsites even more so.

AI supports faster, more informed decision making, pinpointing or flagging the information project teams need, when they need it. This could include an alert when a worker isn’t wearing PPE to prevent costly injury, an update on the status of foundation work, or providing a 24-hour security report so contractors know a site is safe for work.

AI powered jobsite intelligence can surface the data that matters most, so site leaders avoid spending hours sifting through information or chasing updates via phone or email. They can wake up to an email recapping the status of the site, including hygiene and ground conditions, before their day even starts. The result is quick, simple, and actionable decisions.

Over 60 percent of construction firms experience delayed, scaled back, or canceled projects due to economic uncertainty, labor shortages, and supply chain disruption. As such, it’s increasingly important for contractors to have visibility into all areas of the project for more informed, strategic decision-making.

2.Maintain productivity amid labor shortages

The labor challenges facing construction weigh heavily on its productivity. 83% of construction companies report they have trouble hiring superintendents while 81% are struggling to hire project managers and supervisors.

This is significant – project managers and superintendents keep projects moving. Amid this shortage, site leaders are spread across more projects, making it difficult to have consistent visibility into each one simultaneously. By providing project leaders updates and analysis across jobsites, AI-powered jobsite intelligence gives them time back in the day.

A progress report summary can do the job of three phone calls and a 45-minute drive to verify the status of a cement pour, for example. If an issue on one site leaves them tied up, they still have visibility into their other projects. Time saved leads to improved impact, higher job satisfaction and ultimately easier recruiting of new talent.

3.Avoid losing knowledge from workplace changes

As a large portion of the construction workforce nears retirement, the industry runs the risk of losing intellectual property. By 2031, 41% of construction workers are expected to retire, while only 10% of current workers are under 25.

If experienced workers retire before new ones enter, new employees miss out on the chance to gain anecdotal knowledge from those industry veterans. The next best thing becomes learning from actual jobsite footage and data.

Project teams can use AI-powered jobsite intelligence to catalog visual data, capture the insights from veterans, and bundle as training materials.

This makes it possible to preserve decades of IP and pass it on to future generations of the workforce.

AI makes visual data intelligent, giving contractors a more productive way to work

AI plays a central role in advancing jobsite intelligence so project teams can unlock the insights of visual data.

Teams will gain the most value from a solution that is purpose-built for construction and provides the real-time insights that keep sites safe and secure. This will ultimately drive overall productivity and on-time project delivery.

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The World Cup has finished, completing a wave of anticipation, celebration, and distraction that stretched far beyond the stadiums.

For employers, it also served as a real-time test of workforce operations as schedules, staffing needs, and employee engagement were all put under the spotlight.

During the six-week tournament, millions of employees were watching matches, adjusting schedules, arriving late, swapping shifts, requesting time off, or turning up tired after late nights.

New UKG research of 8,000 employees across Australia, Canada, France, Germany, Mexico, the Netherlands, the UK, and the US estimates the tournament could have resulted in at least £12.6 billion in lost productivity.

In the UK alone, the impact could exceed £680 million, with employees not just following matches during working hours, alter their schedules, or missing work altogether - many admitting they went to work hungover, secretly streamed matches, and pushed the limits of what their employer would allow.

If England had won the final, almost a third of UK employees said they would have taken the day off whether that absence had been approved.

What the World Cup Revealed About the Future of Work

That created a real challenge for employers. But the bigger lesson is not about football. It is about how work now happens.

The World Cup is a high-profile example of something organizations face every day: unpredictable employee behavior, sudden changes in demand, last-minute absences, weather disruption, supply chain delays, new regulations, and shifting customer expectations.

For frontline-heavy industries in particular, disruption is not an exception to the operating model. It is the operating environment.

The question is whether workplace technology is built for that reality.

Many workforce systems were designed around predictability. They record schedules, track attendance, and report what happened after the fact. That matters, but it is no longer enough.

When conditions change by the hour, organizations need more than systems of record. They need systems of action that help managers see risk earlier, make better decisions faster, and keep work moving in real time.

Three Workforce Strategies That Help Employers Stay Ahead of Disruption

The employers that came out ahead during the World Cup, and during the everyday disruption that followed it, will have done three things differently:

1. See the disruption before it happens

Too often, workforce disruption becomes visible only once it has created a gap. Someone does not arrive. A shift is suddenly under-covered. A manager starts calling around for support.

The business reacts after the damage has begun.

The World Cup gave organizations a chance to get ahead of that pattern.

Employees already signaled intent. They knew which matches mattered to them. They knew when they were likely to want flexibility, when they may have needed time off, and when they were more likely to be distracted or unavailable. Organizations that capture that intent early can turn it into useful operational data.

That does not mean monitoring employees or trying to control their behavior. It means giving people approved ways to communicate availability, preferences, and likely conflicts before they become last-minute absences. When that information is combined with historical absence patterns, demand forecasts, local schedules, and workforce data, managers can identify where risk is most likely to emerge.

A retailer, manufacturer, logistics operation, or hospitality business does not need to know every individual decision. But it does need to know where coverage pressure is building. High-interest match days, late kick-offs, local celebrations, and major national fixtures can all create predictable patterns of disruption.

Seeing that risk early allows organizations to plan differently. They can adjust staffing levels, open additional shifts, prepare contingency cover, or communicate expectations before managers are forced into crisis mode.

2. Design flexibility into the operating model

The instinctive response to disruption is often to tighten control. But rigid rules can push behavior underground.

If employees believe there is no fair or practical way to adjust work around major life moments, they are more likely to find informal workarounds. That can mean last-minute sickness calls, unapproved absences, shift swaps that managers do not see, or colleagues covering gaps without the right skills, rest periods, or compliance checks.

The better approach is to make flexibility visible, fair, and operationally safe.

That means giving employees clear, approved ways to request time off, swap shifts, volunteer for extra hours, adjust availability, or pick up open shifts. It also means giving managers the tools to assess those requests against business need, skills, fatigue, labor rules, and fairness.

This is especially important on the frontline, where the margin for error is small. A missed shift in an office may delay a meeting. A missed shift in healthcare, retail, manufacturing, hospitality, or logistics can affect safety, service, cost, and compliance.

Flexibility cannot sit outside the workforce strategy. It must be built into it.

For employers, that shift is powerful. Flexibility becomes less of a concession and more of an operating capability.

Employees get more transparency and control. Managers get fewer surprises. The business gets a better chance of protecting service levels without treating people like variables in a spreadsheet.

3. Act in real time when the plan changes

Even the best World Cup plan will not survive unchanged.

A match goes to penalties. Demand spikes unexpectedly. More employees call in sick than forecast. A local team advances further than expected. A manager discovers at short notice that the people available do not have the right skills or certifications.

This is where many workforce systems fall short. They can show the rota. They can record the absence. But they do not always help managers decide what to do next.

Modern workforce management has to move from static planning to real-time execution. Managers need to know where gaps exist, who is available, who is qualified, who is approaching overtime or fatigue limits, and what action will create the best outcome for the business and the employee.

That is where data and AI can play a practical role. Not generic AI layered onto old processes, but intelligence that understands workforce context and helps recommend the next best action. Should a manager offer an open shift? Redeploy someone from a lower-demand area? Approve a swap? Escalate a compliance risk? Adjust breaks? Bring in contingent support?

The value is not simply in having more data. It is in turning workforce data into action while there is still time to influence the outcome.

The real stress test for workplace technology is not whether an organization can create a schedule weeks in advance, but whether it can adapt that schedule minutes after conditions change.

The World Cup is over. The operating lesson is not.

Every organization will face its own version of this disruption: seasonal demand, illness, weather, regulatory change, major events, economic pressure, and shifting employee expectations. The companies that treat these moments as one-off exceptions will keep solving them manually, shift by shift and manager by manager.

The companies that come out ahead will build a more responsive workforce model. They will see risk before it becomes disruption. They will design flexibility into the way work happens. And they will equip managers to act in real time when the plan changes.

The future of workforce management is not about predicting everything perfectly. It is about giving organizations the visibility, intelligence, and agility to keep moving when reality refuses to follow the plan.

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The IMF and Bank of England have both recently raised concerns about the risks AI could pose to the financial system, from cyber threats to systemic vulnerabilities and governance gaps. Against that backdrop, institutions are facing growing pressure to demonstrate clear accountability for how AI is used, particularly when decisions impact customer outcomes, market activity and compliance decisions.

Financial services have traditionally taken a cautious approach to AI because of the regulatory and operational risks involved. But AI is now becoming more deeply embedded across the sector, supporting everything from fraud detection and customer service to compliance monitoring and internal operations.

As adoption expands, governance frameworks that were designed for conventional software and data systems are being tested by AI models that can evolve, generate unpredictable outputs and rely on increasingly complex data environments.

Why governance expectations are growing

This growing focus on accountability is becoming increasingly visible across the sector. Moves such as HSBC appointing its first Chief AI Officer reflect a broader recognition that oversight can no longer sit across disconnected teams or experimental projects.

Meanwhile, many institutions, including Barclays and Lloyds Banking Group, have recently joined the Financial Conduct Authority’s initiative to test AI in real-world conditions under strict controls, while the Bank of England has outlined plans to assess potential risks to financial stability through scenario analysis and simulations.

For finance firms, these developments are likely to increase expectations around how AI systems are monitored, tested and governed internally. Organizations will need clearer oversight of third-party AI providers, stronger documentation around how AI models make decisions, and more robust processes for identifying and escalating risks.

The barriers to strong AI governance

Despite growing regulatory scrutiny, financial institutions still face significant barriers to implementing stronger AI governance, particularly around fragmented data. Many firms still operate across disconnected systems, making it difficult to create a consistent view across risk, compliance, operations and customer activity.

This becomes more challenging as AI is introduced. Models depend on large volumes of data flowing across multiple systems, but when those systems are siloed, it becomes harder to trace how information is used or how decisions are made. Without clear data lineage, organizations may struggle to validate AI decisions under regulatory scrutiny.

Data quality is becoming just as important as data access. Even advanced AI models can produce unreliable results if they are trained on incomplete, outdated or poorly governed information. At the same time, identifying which datasets will improve decision-making, rather than adding complexity, remains a challenge.

For financial institutions operating across complex legacy systems, maintaining accurate, trusted and consistently managed data at scale will be critical as AI adoption accelerates, particularly across areas such as fraud detection, anti-money laundering and customer risk systems where siloed data can limit a complete and accurate view of risk.

Building the foundations for responsible AI

For many finance companies, the next step is transforming these fragmented datasets into stronger data foundations that support AI at scale.

This means creating connected, well-governed data environments where information can move consistently across systems, data quality is maintained more effectively, and accountability is embedded into day-to-day operations rather than treated as a standalone compliance exercise.

This joined-up view is particularly valuable across the customer journey. When someone opens a bank account, they move through several stages including identity verification, onboarding, digital registration and their first transactions. Banks need to see that journey as a whole rather than as disconnected steps. With that visibility, teams can investigate issues more quickly, improve services and track results in real time.

Why responsible AI requires shared ownership

Building more connected data environments requires a coordinated approach to accountability across institutions, with responsibility formalized rather than sitting in isolation with individual teams. As more firms appoint Chief AI Officers, close collaboration with Chief Data Officers will become increasingly important to ensure AI governance is built on strong data quality, clear ownership and consistent standards across the organization.

In regulated firms, technology teams, data teams, AI specialists, and business stakeholders all share an obligation to understand the importance of data quality and the consequences it has on decision-making.

This more collaborative approach can also improve how teams operate, ensuring insights are not limited to technical functions alone. Giving colleagues in retail banking, lending and compliance access to timely information enables faster, more informed decisions at every level and helps embed accountability for AI-driven outcomes in day-to-day operations.

Strong governance depends as much on operational visibility and human oversight as it does on the models themselves.

Preparing for AI adoption at scale

Over the next few years, financial services will move from isolated AI pilots towards broader adoption at scale, but it must happen in a way that remains controlled and transparent. Organizations that can build the right foundations now will be better place to expand AI use confidently, while those without them risk inconsistency and greater operational exposure.

Ultimately, the firms that succeed in the financial sector will be those that combine innovation with strong governance and clear human oversight, using AI tools to drive sustainable progress while maintaining strong trust as adoption grows across the sector.

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Many companies successfully complete AI pilots, but a lot struggle to scale AI application across their organizations. Only 15% of AI initiatives ever manage to truly scale.

Research from Boston Consulting Group suggests that only 26% of companies have developed the capabilities needed to move beyond pilots and generate meaningful value from AI tools. Meanwhile, McKinsey estimates that while almost every organization is experimenting with generative AI, only a small minority are seeing material financial impact across the business.

The technology is not the bottleneck.

Large language models offer a powerful solution that supports employees in various tasks, such as research, content generation, document analysis, meeting summary or task management. Despite these advances, teams still battle to turn AI capability into measurable business outcomes.

It is a translation problem.

There is a lack of depth that prevents these tools from meeting the needs of a specific industry and organization. General models are good at understanding language and information. But they have a hard time connecting them to workflows, regulations, governance requirements and operational nuances that rule industries, such as accounting, legal service, healthcare or manufacturing.

There needs to be an expert to bridge the gap between the capabilities of AI and the requirements of a profession.

1. A new role at the intersection of the technology and an industry

Technology vendors have traditionally been measured by the quality of their products. This is no longer enough.

Today, their role goes far beyond delivering a functional platform. They now actively contribute to the digital transformation of an organization. This includes sharing best practices, providing ongoing technical support, designing workflows, optimizing for governance and long-term application. All to support smooth product deployment.

Different industries are moving towards this same idea from different angles. When there is an intermediary that focuses on customer success, it translates capability into outcomes. Early examples of this are the rise of new jobs such as Customer Value Managers and Forward Deployed Engineers. These are professionals whose primary objective is ensuring the technology generates value for employees and the business overall.

Every industry is beginning to create its own version of the role.

In accounting, we see the emergence of so-called 'Accountancy Engineers'. They are trained accountants with deep accounting expertise and technical capability. Rather than working from a specification document, they sit next to a firm's team and co-design the workflow with them, building the automation alongside the people who will actually use it.

They build workflows tailored to how the profession works, effectively automating repetitive tasks and processes that answer to the challenges of accountancy. Just as important, they train the firm's own accountants to become the practice's internal AI architects, so the capability stays inside the firm long after the initial rollout.

For instance, they work to connect backend accounting systems with operational tools and cloud infrastructure and ensure that automated financial processes align with regulatory reporting and data governance requirements. This avoids generic automation bolted onto old corporate systems, resulting in a tailored integration.

And every pattern they uncover in the field, a workaround, a recurring bottleneck, a workflow nobody had written down, feeds straight back into the vendor's own product roadmap, the same feedback loop that made Forward Deployed Engineers indispensable at Palantir.

AI adoption rarely fails because the model is not capable enough. It does not go through because employees don't know where the technology fits within their daily work.

2. Supported human integration for smoother AI adoption

General AI models are horizontal by design. They struggle to generate specific materials and in-depth data analysis tailored to a single industry, since they were built to serve every industry at once.

Firms that engage with vendor partners see successful integration because they receive industry-specific guidance and training that empowers staff to firmly handle AI tools.

In general, people are hesitant when encountering new tools and processes. Leaders worry about governance, security and regulatory compliance. Employees wonder whether they can trust AI-generated outputs.

On the individual level, teams experiment with consumer AI tools, creating fragmented workflows and introducing the growing problem of "shadow AI", where employees expose sensitive company information through personal AI accounts.

Without governance, training and clear operational guidance, organizations risk creating thousands of disconnected AI users rather than a coordinated AI strategy.

Technology vendors make a difference by collaborating with product managers, financial controllers, and cross-functional engineering teams.

Together, they design an integrated system architecture that includes existing technology, develop governance frameworks that reduce risk, and create education programs and processes that support positive engagement across a company. This ultimately generates trust and safety, ensuring a successful adoption.

Vendors provide the missing layer between technological possibility and organizational reality.

3. From one-off projects to long-term implementation

AI adoption does not have a finish line.

The technology almost changes every week. New models emerge. Capabilities improve. Costs change. Workflows that were impossible six months ago become routine today. That means AI cannot be treated like a traditional ERP rollout or cloud migration.

Technology vendors are uniquely positioned to provide continuous support. They know the ins and outs of the product and understand the challenges faced by organizations, which means they can tailor the approach to find the right solutions. Plus, they monitor the rapid pace of AI innovation while understanding how those advances affect specific industries.

Instead of trying to interpret an increasingly complex AI landscape themselves, teams can rely on vendors to get a clear picture of technical breakthroughs and how they can be relevant to the business and employees.

4. Sell business outcomes, not a model

The vendors who only sell AI models are selling half the answer. AI adoption fails when teams are left to figure out the product on their own, with no one in the room translating the platform into the firm's own workflow.

Every organization will eventually have access to powerful models. What will differentiate them is their ability to combine AI expertise with industry expertise.

Technology vendors who succeed will be the ones who live between the technology and the profession, translating AI capability into trusted, scalable business outcomes. Accountancy Engineers, and every version of that role now emerging across other industries, is what that translation looks like in practice.

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Governments worldwide are racing to deploy and enforce digital IDs to enhance data privacy and national security. As adoption accelerates, it’s estimated that more than two-thirds of the global population, 5.6 billion people will own a digital wallet by 2029. Yet these credentials remain largely confined to national borders due to the lack of global interoperability.

This is a challenge for today's digital economy and is becoming even more complex with the rapid rise of agentic commerce as autonomous AI agents are increasingly executing cross-border transactions on behalf of users. This interoperability gap forces platforms to reject foreign digital credentials, forcing users to upload their physical IDs to complete global purchases—the exact security risk digital IDs were designed to eliminate.

Digital IDs locked behind borders brings vulnerabilities

Without clear global frameworks, a digital ID issued by one nation is completely unreadable and untrusted by digital services based in another. This creates a gap between privacy, security, and convenience as consumers interact with cross-border digital services.

This could include booking an accommodation at a hotel, completing a global transaction, or deploying an AI agent to negotiate a corporate purchase. This mismatch brings severe vulnerability risks for all parties, the enterprise, the nation who deployed the digital ID, and the consumer.

When consumers are forced to upload their passport, just to complete a routine transaction, the government that spent millions building a secure digital ID ecosystem now completely lost control over its citizens' data sovereignty.

While image-based verification methods are secure and functional for localized onboarding, there are challenges that arise when users must repeatedly share sensitive documents across multiple organizations and jurisdictions to then be stored in a foreign database.

It expands the enterprise attack surface by multiplying the number of disparate databases where a user's personal identifiable information (PII) is stored and can become a target for data breaches and identity attacks.

The technical debt of fragmented standards

Without clear standards for digital IDs, enterprises are forced to navigate a patchwork of identity protocols. This creates security blind spots that make it easier for fraudsters to steal passport images and other personal data to open fraudulent accounts at scale.

Beyond security risks, IT leaders are facing technical debt with the fragmentation of digital IDs. In order to accommodate new digital ID variations, institutions would have to completely re-file pre-approved processes for regulatory approval. Since this is a complex and exhaustive process, organizations stick to their approved legacy workflows. However, this leaves them more vulnerable to emerging threats.

This challenge only gets worse when you take into consideration that some countries are heavily restricting their digital IDs. 31% of OECD countries do not provide any form of cross-border digital identity recognition, meaning roughly 3 in 10 countries still cannot use foreign digital identities to access public services. However, it’s not a lack of technical interoperability that prevents the cross-border use of digital IDs; rather, governing bodies are actively forbidding it.

For example, Under the EU’s upcoming EUDI wallet (eIDAS 2.0), only businesses established within an EU Member State have a clear path to register and accept verified citizen digital identities.

Similarly, processing the Philippines' PhilID is strictly restricted to companies incorporated and headquartered locally. Even if relying parties would be willing to deal with the non-standardized fractured technology of digital IDs, these protective regulations shut out international organizations from consuming them, forcing a reliance on legacy verification processes.

The security promise of digital IDs

Digital identity does have major benefits when it’s allowed to act at its full cross-border potential. When recognized and accepted by other organizations in other jurisdictions, consumers can establish trust without oversharing their personal information.

This limits the vulnerable information that organizations have to store in their databases and safeguards citizen’s privacy. Since only the specific data required to confirm authorization is transmitted, both organizations and sovereign nations can finally operationalize true data minimization.

As agents start executing actions on behalf of humans, reusable identity is the missing layer that makes the agentic ecosystem safe. Rather than forcing users back into a manual verification process, a standardized digital ID framework would allow an agent to present a pre-verified, universally trusted digital token.

This token could verify both the agent's authorization and the user's identity without exposing raw login credentials. This allows AI agents to fulfill their promise of true, autonomous automation.

The trust required for a borderless digital economy

Creating global interoperability for digital IDs isn’t just about ensuring a seamless digital economy; it’s about fundamentally shifting how we build and maintain trust in digital systems. While standardized cross-border frameworks are essential for the initial onboarding with digital IDs, true security with reusable identity requires moving beyond static, point-in-time checks.

This is especially true as AI agents begin navigating global platforms and executing transactions on behalf of users. As these agents are designed to operate continuously in the background, identity verification can no longer be a single, one-time event.

To make the digital ecosystem more secure and resilient for both humans and machines, trust must be re-established throughout the entire user lifecycle.

By deploying continuous monitoring through real-time behavioral and device pattern signals, enterprises can allow authentic users and their authorized AI agents to interact seamlessly beyond borders. Friction with identity verification would then only enter the equation when suspicious behavior is detected.

But spotting these subtle anomalies means fraud can no longer be addressed in isolation. By adopting an identity intelligence solution, organizations can layer multiple authentication and identity signals to build a more complete picture of risk. This intelligence can then be shared across organizations and even governments to detect fraud patterns and threats in real time, before they become widespread.

This unified knowledge is essential because the purpose of digital IDs was never meant to end at national borders. Governments must stop restricting international organizations from accepting their digital IDs because, when implemented correctly, enabling cross-border access creates a fundamentally safer environment for citizens.

We are living in a truly borderless digital economy, and our frameworks for digital trust must evolve to match this reality. Global interoperability and continuous trust are the foundation for a more secure, privacy-centric ecosystem—one where individuals, enterprises, and autonomous AI agents can interact with total confidence anywhere in the world.

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High traffic moments create some of the most demanding conditions that engineering teams face. Seasonal peaks such as major sales events or public holidays can bring levels of demand far beyond what systems experience day to day​.​ These sudden surges often expose weaknesses that are usually less detectable during normal operation.

Peak periods are also becoming more intense for retailers, ecommerce brands and the travel and hospitality sector, as year-end sales events have evolved into global spending moments.

Consumers are increasingly planning purchases around these events, which is concentrating demand into shorter and higher-pressure periods.

These industries tend to feel this pressure especially strongly because customer behavior is so closely tied to digital performance. A site loading in one second can achieve conversion rates three times higher than a site that loads in five seconds, highlighting how sensitive these environments are to even minor degradations.

In these situations, businesses rely on a smooth digital experience to meet demand and maintain customer loyalty, which heightens the pressure on engineering teams to ensure systems remain stable under sustained and extremely concentrated demand.

As a result, organizations are turning to AI-assisted incident intelligence, with data showing it can cut recovery times by nearly 50 percent.

Much of this time save is determined in the opening minutes of an incident as teams try to understand what’s broken and where to focus first. That’s where AI is starting to become most valuable, especially during high‑traffic moments when speed and clarity really count.

The first 20 minutes of an outage

The first moments of an outage often set the tone for how long disruption will last. Many teams still begin with manual checks to identify issues, but the sheer volume of alerts makes it difficult to gain a clear view of the problem this way.

Engineers turn to signals such as bounce rates, session drop offs and real user monitoring to understand how systems are behaving under pressure. However, these signals are often buried within a much larger flow of alerts that compete for attention. This avalanche makes it difficult to isolate the one that points to the root cause.

The challenge is not simply the volume of information but the complexity of modern environments. Even experienced engineers can struggle because modern systems contain many interdependent components. Problems can emerge in unexpected areas and teams frequently need to investigate several routes from the beginning, which continues to add delay.

Reducing alert noise under pressure

Modern environments generate large volumes of notifications, many of which are duplicates or low value alerts presented with the same urgency as critical issues.

Industry surveys report that 63 percent of organizations deal with duplicate alerts, making it harder to distinguish real problems from background noise.

During peak demand, this becomes even more difficult for engineers to manage. Between 20–30 percent of alerts are ignored or never investigated, simply because the volume is too high.

Under this pressure, response times become slower and trust weakens in alerting systems, leaving teams unsure which issues require immediate attention.

Observability’s impact on issue resolution

To address this, teams need a clearer view of what is actually happening across their systems.

Observability, combined with AI, goes beyond traditional network monitoring by connecting data across applications, infrastructure, and user experience to explain what is failing, and why. By linking related signals, teams can quickly identify the root cause of an issue and understand its impact on users. What may look like separate problems at first glance, such as CPU spikes, latency increases or error logs often point back to a single fault affecting multiple layers of the system.

Research shows that observability reduces overall alert noise by 27 percent compared with manual monitoring. When looking at repetitive, low-impact notifications that distract teams from genuine outages, known as ‘noisy alerts’, this impact becomes even clearer, with AI-enabled teams reducing these from over 70 percent to 46 percent.

The result is a shift from reactive firefighting to informed and targeted problem solving, allowing engineers to spend less time chasing symptoms and more time resolving the underlying cause.

How AI is reshaping performance during peak demand

Retail, e-commerce, travel and hospitality businesses all depend heavily on peak trading periods and even short disruption can impact overall earnings for the year. A high-impact outage costs retailers a median of $1 million per hour, per the 2025 Retail & eCommerce Observability Report by New Relic.

That financial exposure is driving a shift in how companies approach reliability. In 2025, 50% of retail respondents said AI was the primary reason they invested in observability – 11 points higher than the all-industry average. Retailers increasingly see AI as the mechanism for automating troubleshooting, accelerating post-incident reviews, and enabling remediation actions like rollbacks or configuration updates.

Investing in AI is translating directly into faster recovery. Mean Time to ​​​​Close (MTTC) shows how quickly teams can recover from disruption and is increasingly used as a measure of performance. During peak periods in May 2025, AI-enabled teams averaged 26.75 minutes per issue, compared with 50.23 minutes for non-AI users. Across the full calendar year, AI users resolved issues around 25 percent faster.

Beyond resilience, these gains free up engineering capacity – essential for delivering new features and supporting growth. On average, AI-enabled teams have been shown to ship code at an 80% higher frequency than non-AI users. During peak demand periods, this gap becomes even more visible, with non-AI teams averaging 87 deployments per day compared with up to 453 for AI-enabled teams.

The advantage of faster insight

Peak trading periods tend to expose how well teams can see what is happening and respond in real time. Observability and AI help by reducing noise and surfacing the signals that actually matter, making it easier to move from detection to resolution.

This ability to act with clarity under pressure is increasingly what separates leading teams from those that struggle when demand is at its highest.

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The world is full of weird and wonderful inventions that never really took off, with the Segway perhaps one of the most overhyped technologies out there. Invented by Dean Kamen, it was once considered the future of how people would move around towns and cities – and it had plenty of huge supporters.

Codename Ginger

Speculation ran wild when these leaked comments by Jon Doerr hit the mainstream media at the start of the 21st century.

This statement was initially disclosed when a copy of a book proposal by journalist Steve Kemper was leaked to the media and published on the online tech magazine Inside.com. They were centered around an exotic new invention described only as 'Codename Ginger' – but the trouble was that nobody knew what it was.

What Kamen was working on behind closed doors was, in fact, a two-wheeled, self-balancing personal vehicle that used smart sensors and motors to keep the rider upright. This was the Segway – and, well, we all know how that story ended.

Wheels of time

Some predicted at the time that cities would be redesigned around the Segway – but the product sold less than 150,000 units before production was shuttered. It's now widely considered the most overhyped invention in history, with high costs and huge regulatory hurdles proving insurmountable obstacles for mass adoption.

The last Segway was actually manufactured as recently as 2020, with the company now transitioning to other forms of transportation, like e-scooters and UTVs. There are still uses in security and in tourism, but it's largely a redundant technology.

As for Kamen, his most recent venture, DEKA, is focusing its efforts on machines in the biotech space. Specifically, he has recently invented a large-scale regenerative medicine manufacturing and organ preservation system.

Letterboxd might just be my favorite app — I love tracking the media I consume and making lists, and Letterboxd is basically a perfect take on that for movies. And while I don’t love Goodreads quite as much, I find that it serves a similar purpose for books. But I’ve struggled to find an app like this for shows.

That’s not to say no such apps exist — of course they do. But for one reason or another, I’ve always bounced off them. Until, perhaps, now.

It’s early days, but so far, Bingers — a new app from the co-founder of TV Time — seems like it might mostly fit the bill, though it doesn’t quite address all of my issues with other TV show tracking apps.

Beautiful and customizable

One of the things I love about Letterboxd is just how much of a joy it is to interact with, and part of that comes down to it being aesthetically pleasing, with the same being true of Bingers.

Here you get big posters and backdrops for each show, along with the ability to swap them out for other options, and the interface beyond that feels polished and — for the most part — well laid out.

Adding shows to your watchlist can be done with a single tap, and marking episodes or whole seasons as watched is similarly speedy, so there’s minimal friction.

Switching over from another app is potentially easy too, as you can import your data from TV Time, TV Time Liberator, or Refract.

It’s not perfect; for example, while you can search through the filmography of cast and crew members, there’s no way that I can see to sort or filter those lists. But the core parts of the app at least are largely well laid out. And Bingers has only just launched, so it’s sure to improve over time.

Impressively full-featured

There’s also a lot here. You can add shows to your watch list and then get alerts when new episodes or seasons are out, so you never miss them. You can also rate and review shows and episodes, and even attach an emoji for how they made you feel.

There’s even an option to select your favorite character in an episode, and then see what percentage of people picked each option. I’d love to see more stats, which is something Letterboxd still has over Bingers, but that — along with a total watched time and number of episodes watched — is at least gesturing towards an interest in this sort of data.

Plus, you can mark shows as favorites, create lists, and follow other users — with that last point giving this app the community feel that things like Letterboxd and Goodreads also have.

You can also explore trending shows and genres if you’re looking for something new to watch, and you can log rewatches, so you can see how many times you’ve viewed something.

There are movies here too if you want all of your visual media in one place, though for now, at least, I’ll mostly be sticking with Letterboxd for those.

Keeps me coming back

Still, while I’ve only been using Bingers for a few days so far, that’s longer than I managed with most apps like this. I think all of the above contributes to that — the beautiful design, the lack of friction in logging, and the reminders and alerts for new episodes, plus a handy timeline view that shows what you haven’t yet watched, and what episodes are landing in the coming days.

I do wish there were a Bingers website, as sometimes I’d rather interact with these services from my computer, and it could still do with a few more features within the app — more stats, an overall average rating for each show, and the inclusion of trailers, for example.

But for a new release, Bingers is off to a very promising start, and as long as the app keeps evolving — and continues to find an audience — this could become the Letterboxd for TV.

The WikiLeaks founder Julian Assange carved a reputation as a firebrand and intense critic of the modern tech companies after rising to fame following the WikiLeaks disclosures. Although he's been absent from the public eye in recent years, he frequently commented on the emerging trends in the technology landscape.

Big Brother is watching you

Paraphrasing a famous line from George Orwell's 'Nineteen Eighty-Four', Assange used Google's failed smart glasses as a rhetorical device to critique their philosophy in an op-ed for The New York Times.

In his column, he was critiquing a new book titled 'The New Digital Age' by Google's then-executive chairman Eric Schmidt and businessman Jared Cohen, who was then director of Google Ideas.

This book outlined Google's vision for how the world was changing and laid out the company's role in reshaping it. But, to Assange, it was a bland and uninspired account of how this company was implementing George Orwell's prophecy without even really understanding how.

Drawing from the original line, Assange's disdain for the company is as clear as day in the way that he compared people wearing Google Glass to "a boot stamping on a human face".

Don't be evil

The modern form of this Orwellian vision is centered around consumer technology, Assange believed, and gadgets like the Google Glass were akin to tools that would serve to sap users' independence and privacy.

Although Google Glass itself was a failure, the point is more about the fears that this device represented. Now, more than a decade on, developments have more or less vindicated his dystopian perspectives – with a growing perception that a mass surveillance state is being precipitated by the surrender of privacy.

Some may argue this is a feature inherent to the use of consumer devices as well as software and services, and present in modern trends such as doomscrolling.

It appears that the RAM crisis is getting worse than ever as we head further into 2026. I've been closely watching and reporting on memory price hikes — which were soon followed by other PC component cost increases — since this crisis first began, and the worrying thing is, I can't recall a gloomier stream of negative news than I've witnessed over the past few weeks.

That includes Samsung recently declaring that the RAM crisis is going to become more severe in 2027, and rumors that even mighty Apple is floundering in its attempt to secure new RAM supplies from China (as other notebook makers consider this alternative route to the three main memory chip giants: Micron, Samsung and SK Hynix).

Previous to that, we saw laptop maker Framework break news of a truly eye-opening cost increase for mobile RAM, just as one gauge of DDR5 pricing saw it jump in price to a new all-time high (after plateauing earlier this year). On top of that, the boss of SK Hynix voiced the opinion that 2027 will be the worst year in the RAM industry's history, and that the crisis is likely to be drawn out to the next decade.

This isn't just about RAM, of course, but other components, including CPU price rises, and devices themselves — PCs and laptops (and phones). But most notably over the past month it's been graphics cards in the crisis limelight. We've witnessed the resurrection of old GPUs to try to bolster stock levels of more affordable cards, and more recently we've been subjected to what seems like an endless parade of negativity about imminent price hikes.

Apparently, Gigabyte is set to increase the price tags of its graphics card to the tune of 20% to 40% in Japan, and MSI is going to jack up prices of Nvidia GPUs in China by 20% or more. Asus is supposedly preparing a similar 20% hike (as Wccftech reported).

Another recent gloom nugget is the assertion that Nvidia RTX 5000 models will get hikes of 30% in South Korea this month, and all of this pain is mainly rooted in the increased price of video RAM, of course. (Those cost increases are the reason the RTX 5000 Super refreshes have been delayed, according to the grapevine, as those rumored cards are packed with VRAM).

While a good deal of this is individual pieces of regional activity with GPU pricing, it's obvious that these hikes are happening as part of a concerted shift, one that will surely be reflected globally — it's not like Asian markets are in a bubble of their own.

The overarching theme is a worsening of price hike misery, and that the pricing storm is likely to intensify this year, and probably in 2027, too. While previously the RAM crisis has been more of a light-and-shade affair, now it feels like the depression has been turned up a notch. Whereas before, there was certainly more darkness than light, we've had notable spots of relief where an exec in the memory industry stepped forward and theorized that maybe things aren't as bad as we think. But lately those embers of optimism appear to have burned out.

To me, it feels like there's a shift underway towards a full acknowledgement that we really are going to experience a lot more pricing pain in the foreseeable future. If it's so bad that even Apple is purportedly scrambling to secure RAM supplies, and is having trouble doing so, I think it's time to turn up the worry meter by a notch or two.

What should you do?

Here's my advice on the current landscape with PC components and hardware (season it appropriately). Obviously, worrying isn't going to help you, but awareness of what might be sensible upgrades or purchases to make now will, based on the likelihood of hikes (we could call it the 'hike-lihood' – or maybe not). Nothing is certain about the future of components, of course, but it does feel like there's a clear area in which the potential price hikes are a bigger threat: GPUs.

As I discussed above, there's a lot of recent evidence that graphics card pricing is going up. Yes, it's a collection of rumors in the main, but there's a lot of it all pointing in a very similar direction — and a video RAM toll was always going to be exacted in the end. We've seen it already with higher-end graphics cards, and now I believe we're going to see it in the mid-range, and even budget models. Indeed, top-end GPUs are likely to get even more expensive as well.

If you're thinking about a GPU upgrade for this year or next, given all this, I think now really is the time to buy — especially with a lower-tier model, as you can still get more affordable graphics cards at their MSRP. Ditto for mid-rangers, although it's tougher to recommend higher-end Nvidia boards when they are already so expensive.

As noted, though, that could get worse. And granted, you may still want to wait for Black Friday — it's not that far off, and there might be some GPU deals then. However, I wouldn't bank on anything hugely compelling (or discounts that aren't offset by the price rises which are predicted to take hold in the next few months).

So, if there's one PC component that I think you should buy now, it's a graphics card. I think the signs are pretty clear on that (and see here for my recommendations on different models to consider). By extension, higher-end gaming laptops could also be a wise move for purchasing sooner rather than later. That's for the same reason, really — they have beefy (mobile) GPUs (with plenty of VRAM in some cases).

I don't think it's a bad move to buy any laptop, by the way, gaming or not (away from the higher-end), come your earliest window of opportunity. I think we'll see further price rises here, too, and RAM is going to cause more upward pricing pressures for notebooks — just look at Framework's recent revelation as mentioned.

What about RAM and SSDs themselves? While system memory kits and storage have already seen huge price rises, it now looks like there's worse to come (somehow).

On the other hand, there's a ceiling as to how expensive this stuff can get before buyers withdraw from what they see as an increasingly unrealistic and out-of-touch market. This is a much more difficult call to make, but if you can find something that looks relatively reasonably priced, I don't think you'll regret the purchase in the next couple of years. But that said, I can't recommend a RAM upgrade in particular at current pricing levels, unless it's unavoidable, frankly.

Above all, though, consider a GPU upgrade if you're in the market for a new card, or you think you'll need one in the next couple of years (yes, I think it's wise to be looking quite far down the road here).

The other thing to bear in mind is that there's a danger there will be a rush for GPUs, a flurry of buying that puts further strain on supply and therefore prices. As VideoCardz reports, one Japanese retailer has warned of Nvidia RTX 5000 graphics card sales increasing "sharply", even just with the news of rumored price increases — let alone the confirmation. Prices are already rising and restocks of the RTX 5070 Ti and RTX 5080 are already being labelled as "unstable", hinting that inventory could start to dry up quickly.

That's just one report, of course, and it pertains to the high-end of the market, so it's not something to start panicking about yet. But it does make some sense that if pricing begins to creep up, more PC owners may start to act on GPU upgrades. I wouldn't blame them, frankly.

The Garmin Cirqa is making waves in the fitness community. I put the new screenless tracker through its paces over 10 days of intensive testing, and awarded it 4.5 stars in my Garmin Cirqa review. As part of the testing process, I wore it for all of my workouts over the past couple of weeks — and, unusually for a device I've been testing for review purposes, I'm still wearing it now.

I normally alternate between an Apple Watch Ultra 3 or a Garmin Fenix 8, but I've found myself loathe to ditch the Cirqa just yet. This isn't only because it's good: I'm gravitating towards the Cirqa because it interfaces with a system I already use, and because it's helping me get over the dreaded "Strava-itis".

Last month, our writer Becca Caddy wrote about optimization culture — how wearables are causing us to engage in unhealthy mindsets and disordered behaviors. I can certainly attest to that: I'm guilty of glancing at my watch during a run, wanting to push my pace even on zone 2 sessions and easy days, because I know the end results are going to end up on my Strava account for all to see.

Even uncoupling my watch from automatically uploading my run to Strava is only half the battle. I'm constantly glancing at the watch to check my pace and time, engaging in subconscious judgement, comparing my own performance to that of my mates, colleagues and acquaintances. I didn't realize quite how much my self-judgement was sapping my enjoyment of exercise.

The Cirqa, and to some extent the Google Fitbit Air I tested earlier in the year, goes some way to changing all that.

While the Cirqa offers up some fitness and wellness metrics (certainly plenty to understand the effect of exercise on your body and health), what it doesn't give you is workout specifics. You won't find split pace per kilometer, for example, which runners use to measure average speed across a distance run, or heart rate zones to display a workout's intensity. With other models, I get those statistics piped into my ears at regular intervals, and my watches offer them at a glance.

Having access to such statistics removed during the workout can be detrimental to performance or for those times you're training for a specific event, but it's oddly freeing for day-to-day fitness training. I ran a 10km route during the testing period (at least, I assume it was 10km based on previous runs) and the Cirqa recorded plenty of heart rate-based statistics and applied them to recovery metrics such as my Training Readiness Score, along with how it improved stats such as my VO2 Max and Fitness Age.

However, it informed me of these statistics after the run, and didn't deliver any specific run performance information at all beyond time and heart rate information. Without my watch to glance at during the workout, I wasn't adhering to a target pace: I was running on vibes and perceived effort.

I concentrated on how my body felt, and responded to that in the moment. I paid attention to my form, my music, the trees and the road. I didn't feel guilty about stopping to stretch part way through, nor did I pause the Cirqa for that particularly pause, as doing so would invalidate the whole "complete, holistic workout overview" thing it's got going on. For the first time in quite some time, I had no idea at all about how fast I had run — but I knew that it was a good workout as a result of every other metric and the fact that I felt awesome.

Although it isn't a running-specific device, the Cirqa is exactly what I needed to reinvigorate my relationship with the road. When the time returns to take my training seriously again, taking speed and times on board again, I'll switch back to my best Garmin watch, with all my fitness stats ready to be incorporated into Garmin's workout plans.

Do you run without a fitness tracker, or use a screenless model, to better run on vibes, or are you a metrics-fiend with a dedicated running watch? Let me know in the poll below.

Campaigners have long feared the influence of big money and influential corporations in politics – with this situation arguably worse than ever. In modern times, lobbying by technology companies has even given way to technology executives playing a role in devising government policy.

The path to progress

At the end of his two-term presidency, Dwight D Eisenhower used his final address to the American people to warn about the dangers that he foresaw lying ahead.

In particular, the military veteran warned about the rise of a new elite in society dominated by figures in science and technology, whose power would come to overwhelm democratically elected officials. He also suggested that the path of progress would be wrought with a lack of morals and ethics, with projects pursued in the name of progress regardless of the consequences to society at large.

A major component of this would be that the pursuit of major government contracts would dominate the scientific world, whereby the pursuit of money would dampen any genuine curiosity and lead to fewer meaningful discoveries.

The dawn of Silicon Valley

The former US president's warnings have largely come to fruition, first with the rise of Silicon Valley elites during the original internet age and now, subsequently, these forces are arguably entrenching their power in the AI era.

Technology companies have spent billions of dollars collectively on lobbying the government, with a small handful of companies including Microsoft, Meta, X and Snap channeling more than $260 million between 2020 and 2024.

The Trump administration has taken this influence one step further by giving vested interests a seat at the table and in the heart of government.

It's a smart donut. That's the only conclusion I can draw about OpenAI's first, groundbreaking piece of AI hardware after reading Bloomberg's revealing but unconfirmed report.

Here's the TLDR summary of these rumors: This donut-shaped, hockey puck-sized AI companion, designed with Jony Ive's LoveFrom studio, can sit on a table, be carried (or maybe worn). It'll be festooned with sensors, cameras, microphones, and speakers. Meanwhile, it'll be a vessel to bring ChatGPT closer to you and your life. It'll even shape-shift a bit to indicate a response (can a donut shrug?).

OpenAI might, Bloomberg claims, price it between $300 and $400 (or around £220-£300 / AU$425-AU$570). I know. That's almost instantly a hard no for an AI companion that has no defined purpose besides offering always-with-you AI. Even the woebegone Rabbit R1, which also features a camera, speakers, some sensors, and even a rotating camera, costs just $199 (or about £150 / AU$280).

After reading through this, I still wasn't feeling it. Why do we need this gadget? What problem does it solve? Really, why do we need any AI on or near our bodies?

If this product arrived three years ago, would people, including me, feel differently? After all, in 2023, we were all excited about the potential of generative AI. These days, it's fair to say half of us hate it or at least deeply distrust it and hate what it's doing to our resources (water and energy) and landscapes (data centers).

On the other hand, maybe I was judging OpenAI's upcoming device too harshly, especially without having even seen it. That sparked an idea.

Bloomberg's report included enough detail to create an image in my mind's eye, and I wondered if the leading AI platforms, Gemini and, yes, ChatGPT, could use a prompt to generate simulacra of the devices, in situ.

Gemini and ChatGPT take a run at it

I started with this prompt:

"I need an image of a consumer electronics gadget that is shaped like a donut and the size of a hockey puck. It should include tiny microphone holes and grills for a pair of speakers. It should have moving parts, maybe pieces that shift against each other without massively deforming the donut shape. Let's make a pair of them in white and gray on a table. Let's also include an image of someone wearing one like a pendant."

And in full disclosure, after getting my first couple of images, I added this prompt because I realized I left out a pair of key features:

"Without changing the design, can you add a camera and a couple of small black sensors? I want the image to otherwise remain unchanged."

These AI image generation systems are now good enough that they can maintain consistency between image generations (which they did here), so I'll only show you the finished products from both platforms.

Here's the final render from Gemini Pro:

At a glance, they're kind of cute, looking like giant, digital Lifesaver candies. The speaker grills are kind of huge, but I like the subtle placement of the camera and sensors. I can infer the possibility of movement from the multi-part frames.

Gemini is, I can see, a bit unsure if this is a wired or wireless device, but it has made at least one intriguing leap. It assumes, for instance, a connection to a phone-based platform called Aether (the gadgets may even be called "Aether"). In medieval times, according to Gemini, Aether was considered the fifth element. "It was believed to be the pure, heavenly material that filled the region of the universe beyond the terrestrial sphere," wrote Gemini. Yes, that feels a bit like the proliferation of AI.

The fictional device does look as silly as a pendant as I expected, but overall, I'm more intrigued by this device than I was before seeing the Gemini render.

Now let's take a look at the ChatGPT (Free) render:

This is a simpler, even plainer device and might fit in a bit better with Jony Ive's previous design oeuvre. I like the low-key speaker grill design, and don't mind the smaller activity lights.

I appreciate that ChatGPT helpfully includes a schematic that breaks down all the key features and even offers a look under the hood. It also manages to look a little less obtrusive as a pendant.

Let's get real

Obviously, this is all guesswork by Gemini and ChatGPT, and it's based on prompts built on rumors. Put another way, OpenAI's upcoming AI wearable could end up being significantly different than either of these generative images.

Somehow, I don't think so. I have a feeling that we will see something small, round, lightweight, and very, very intelligent. The launch, when it happens, will be exciting and buzzworthy. Ive's voice will likely drive the brand narrative as the donut-shaped device floats in an all-white background before settling on a desk or, better yet, in the palm of someone's hand.

That moment, though, won't change the trajectory of OpenAI's AI gadget. When it arrives, it will face an uphill battle to attract consumers who are already suspicious of AI's growing influence on the world. They're tired of AI slop, angry about resources and jobs, and the last thing many want is an AI companion reminding them daily about their frustration.

I don't even know what to say about a donut... er... gadget that squirms in your hand. That sounds positively creepy.

If there's any bright side to these rumors, it's that they probably put to bed the idea that OpenAI is allegedly stealing Apple product secrets. The iPhone maker would never build something like this.

I could be wrong about OpenAI's chances in this space. This might be the first AI gadget to break through. I mean, look at those designs. Aren't they cool? Sure, they are, but they're not reality, and we all know that this gadget will soon face a very harsh reality.

Up until now, enterprise AI has been largely dominated by a familiar conversation: which models should we use, where do we deploy copilots and agents, and how quickly can we move from experimentation to measurable business value?

These have all been pertinent questions, but as teams reach a new stage on their AI journey, they're no longer the pressing ones.

The first wave of AI was one of discovery, with organizations asking a simple question: ‘Can AI help us at all?’ That was followed by a period of rapid experimentation, as enterprises launched proof-of-concepts and pilots across every conceivable business function to understand where AI could deliver value.

Now, the conversation has changed again. Enterprise leaders aren't struggling to identify AI use cases - most organizations already have dozens - the real challenge is moving from experimentation to scaling. It's no longer about asking ‘Where can we use AI?’ but ‘How do we make it work consistently across the business?’

And this shift in focus has exposed a problem many organizations originally underestimated: data.

AI exposes weakness in the foundations

In the earlier stages of developing AI, many enterprises focused on accessing proprietary datasets for one primary purpose: model customization. Although this challenge still exists, there’s now a bigger issue at play that involves data quality, accessibility and governance across the organization.

AI is only as effective as the information and processes it operates on. The tricky part here is that AI is a master of exposing weaknesses that have existed inside organizations for years. And that should serve as a wake up call for enterprise leaders investing in AI right now. Because when data is fragmented, processes are inconsistent and operational maturity is lacking. In this scenario, AI won’t fix the problem – it’ll simply amplify it.

Viewed this way, enterprises should best understand AI as a force multiplier, not a correction mechanism for the legacy inefficiencies. However, the opposite is equally true. When organizations treat data as a first-class product, AI has the potential to multiply the quality coming out of it.

And this is the key difference between implementing AI and being genuinely prepared for it. You might have a strong model, but if you’re working with fragmented and unstructured data, your efforts will only continue to produce inconsistent results. I’ve seen it first-hand.

This isn’t just an anecdotal point. Gartner predicts that through 2026, 60% of AI projects will be abandoned because they aren't supported by AI-ready data, while 63% of data management leaders say they either lack - or aren't sure they have - the data management practices AI requires.

Those findings reinforce what we’re seeing many organizations discover first-hand: AI success is increasingly determined by the quality of the underlying data rather than the sophistication of the model.

The data pilot trap

Many organizations saw encouraging results during their early AI pilots, and that isn’t surprising. Typically, pilots are built on curated samples of data that are absolutely real, but to some extent pre-selected and considered synthetic.

On that kind of data, it's natural to see success because the information has been carefully selected to demonstrate the technology's potential.

The real test begins when organizations start scaling AI and release it from the boundaries of those pilots into the real enterprise environment.

Suddenly, models have access to many different types of data that all have to work together. They're exposed to years of duplicated records, conflicting business definitions, incomplete customer data, inconsistent documentation and disconnected systems. Information that appeared reliable in a controlled pilot becomes far less dependable in production.

That's the point where leadership teams often get their biggest surprise. They realize the true state of their data is much worse than they expected. What worked well during the proof-of-concept stage simply doesn't work in the real production environment – not because the AI has become less capable, but because it has finally encountered the reality of enterprise wide data.

Unfortunately, that experience is becoming increasingly common. What’s abundantly clear is that the defining challenge now is not proving that AI works, but ensuring the data behind it is ready for enterprise deployment.

Signs your data isn’t ready for AI

How do you know if your data’s ready for AI? In many cases, the answer only becomes obvious after projects fail to deliver the expected results. But long before that happens, there are usually warning signs.

Your data governance has gaps: Can you identify where your data lives, who owns it and whether it can be trusted? If every analysis depends on manual reconciliation, AI will simply scale those inconsistencies. Years of organic growth often leave enterprises with siloed data, conflicting definitions and inconsistent governance. Which means before AI can deliver reliable results, organizations need clear ownership, consistent standards and dependable data pipelines.

AI initiatives are happening in isolation: When different teams are experimenting with AI independently, it's often a sign that the underlying data isn't connected. And it’s happening more often than you might think – McKinsey research found that fewer than 30% of organizations have their AI agenda directly sponsored by the CEO. Valuable information remains trapped in departmental silos or legacy systems, making it difficult to build a complete picture of the business.

Treated like this, it’ll always remain more function-level experimentation rather than coordinated enterprise transformation. AI performs best when it can draw on integrated, trusted data rather than fragmented datasets created for individual functions.

Your data isn't connected to business outcomes: AI creates value by improving business decisions and processes, not by analysing data for its own sake. If it's unclear how your data supports the outcomes you're trying to achieve, AI initiatives are unlikely to produce meaningful results. Incomplete, outdated or poorly maintained data will also undermine confidence in AI outputs, making it harder to move beyond isolated pilots.

You're spending more time choosing models than improving data: Selecting a foundation model is important, but it's rarely what determines success. The bigger challenges are preparing enterprise data, identifying high-value use cases, embedding AI into existing workflows and driving adoption across the business. Continually chasing the latest release will not deliver tangible results, investing in the data foundations that will make a model effective, will.

Potential will only be realized with strong foundations

Arguably, the biggest shift facing enterprise leaders is one of mindset: moving the focus from advanced models to strong foundations. McKinsey's research supports this, finding that organizational readiness accounts for 48% of the difference between companies that successfully capture value from AI and those that don't—making it a stronger predictor of success.

For enterprises, the ultimate goal with AI should not simply be to automate existing tasks, but how to rethink how the business operates. That means redesigning processes around AI's capabilities, rather than layering AI onto inefficient ways of working.

For that to happen, data quality, governance and clear ownership can no longer be treated as minor, back-office concerns, they need to be recognized as strategic priorities.

The opportunity for enterprise AI is enormous, but without trusted data to build on, its potential will remain just that, potential.

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For many, the release of ChatGPT in 2022 was transformative.

Difficult emails were drafted in seconds, complex documents that would have taken hundreds of man-hours to scan were checked in minutes.

But for some, whose livelihood depended on providing just the type of service that ChatGPT offered - providing copy, for example - 2022 was a difficult year.

We were in the latter group. Our company, Jacquard, was founded in 2015 - seven years before ChatGPT hit email inboxes and LinkedIn feeds around the world.

Jacquard’s basic offering was a platform that used natural language generation to write better email subject lines. Around 2015, we tried taking the technology to investors. They were skeptical. Intrigued, perhaps, but skeptical.

At that time, we were one of very few companies in the language generation space, and we felt it. It wasn’t all bad though - and we eventually got used to being one of the few players in the game.

Then ChatGPT launched.

Our offering, AI-powered language generation, was suddenly in the hands of millions. For free. The investor skepticism we'd spent years navigating disappeared overnight, and in its place: a gold rush.

Hundreds of startups emerged, many founded by people who'd never worked in marketing or natural language processing.

On paper, this could have been the moment it all went wrong for us. Instead, if anything, it only propelled us further forward.

Surviving AI

It wasn’t an easy few years, but looking back, I can now attribute Jacquard’s survival to four key factors: AI fatigue; proprietary data; user base evolution; and an outcome-focused approach.

First, AI fatigue. When ChatGPT launched, everyone rushed to build, and then market, a GPT-wrapper. If it was a gold rush - to take the metaphor a little further - then too much gold flooded the market, and the value depreciated fast. Fatigue was everywhere, and it set in quickly.

Stakeholders grew tired of sitting through pitches that promised something revolutionary but delivered mediocrity.

Instead of a one-size-fits-all solution, what we all saw was generic copy, off-brand tone, and outputs that, at best, needed significant human reworking before they were anywhere close to usable.

85% of brands now use ChatGPT, in some capacity, to produce their marketing content. You can find the exact same turn of phrase in the marketing materials from a multinational conglomerate, as in your local café.

Our offering - the ability to maintain a genuinely distinct brand voice across complex segmentations at scale - became more valuable precisely because the market had made the problem worse.

Which leads to proprietary data. We have 60 billion data points built up over a decade of real enterprise campaigns. Far from being just another wrapper, we had ten years of learnings that no newly founded startup could acquire overnight. We trained a prediction engine on that data - and could back up our promises to deliver increases in engagement.

Of course, not every business starts from this position. But proprietary data, whatever form it takes for your industry, is worth finding. It’s likely more valuable than you think.

An evolving userbase

Our user base evolved alongside all of this. The client conversation shifted. Early on - pre-GPT - every sales meeting began with an education: what is natural language generation? Why trust an algorithm with brand copy? How does machine learning actually improve campaign performance? Post-GPT, those questions disappeared entirely.

They were replaced by a harder one: what makes your AI different from everything else? And so the bar moved. Clients came to us already exhausted by generic tools, already aware that AI-generated copy had a sameness problem. We no longer had to explain what AI was - now, all we had to do was prove that ours was worth the switch.

Last, and perhaps most important, our platform was never built solely around AI. AI was simply the most effective route to our solution. The businesses that folded had built their entire proposition on the novelty of AI. Once it wasn't, they had nothing.

We had been working on the same fundamental problem since 2015, and the arrival of large language models, ultimately, didn't change what that problem was. It just changed the tools available to solve it.

What we know now, having been through the full cycle - from obscurity to gold rush to consolidation - is that the brands who came out ahead weren't the ones who adopted AI fastest.

They were the ones who were clearest about what they were trying to say, and had the infrastructure to say it consistently. That is a harder problem than it looks. It is also the one we have spent ten years solving.

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Recently in Geneva, the world's largest forum on AI for Good, gathered around a simple question: how can technology best serve society? While the sentiment goes back to pre-industrial times, the conversation has firmly moved to algorithms. What about the machines that will come next?

Quantum technology has crossed a line. It is no longer a distant promise, but a growing portfolio of real applications. Quantum and quantum-inspired solutions are being applied to the simulations that underpin nuclear power, to protein design in drug discovery, and traffic forecasting in congested cities.

Quantum sensors are being tested for medical imaging, navigation where satellite signals fail, and monitoring carbon storage sites deep underground. These are no longer mere laboratory curiosities: they are increasingly emerging as working programs, with industrial partners and delivery dates.

For those of us in the industry, these developments are both timely and long anticipated. But they also force us to confront a less comfortable reality: the same machines that will one day simulate new molecules will also break the public-key cryptography that secures much of the modern internet.

By some estimates, up to 80% of the world's digital infrastructure is exposed. The world's digital economy - not to mention every AI model celebrated in Geneva, runs on encrypted data. Every health record or financial transaction owes its confidentiality to mathematical assumptions that a sufficiently powerful quantum computer will overturn.

Store now, decrypt later

The threat timeline is worse than most realize. Rather than waiting for when the machine arrives, the clock started the moment adversaries began harvesting encrypted traffic: the so-called “store now, decrypt later” approach.

Data that remains valuable for years, from medical records to state secrets, may already be beyond rescue. Nobody knows exactly when “Q-Day” comes. But those who harvest our data today have time on their side.

This is why quantum-safe security cannot be a footnote to quantum computing. It is the other half of the field, and it needs to mature on the same schedule.

Quantum trip-wire

Two defenses exist, and they are not interchangeable. Post-quantum cryptography replaces vulnerable algorithms with mathematics believed to resist quantum attacks; it is software, it scales, and it is the workhorse of the migration now beginning worldwide.

Quantum key distribution takes a different route: it distributes encryption keys using quantum states of light that cannot be intercepted without being disturbed. In this way, these schemes deploy a “quantum trip-wire", an eavesdropper reveals herself by the simple act of listening-in

Far from being in competition, both algorithmic and physics-based solutions have roles to play in tomorrow's networks. Serious infrastructure will layer both.

Neither of these solutions are theoretical. New post-quantum standards have been finalized, with contributions from research teams around the world. And quantum-secured networks are already running in the field: since 2022, Abu Dhabi has hosted entanglement-based metropolitan networks, like the ADGM Quantum Testbed announced in August 2025.

These are initiatives that bring quantum security to where data actually lives. Long-range terrestrial links, satellite connections and “last-mile” access networks are the next steps, extending that protection between cities and, eventually, across continents. If there's one lesson the field has learned from operating these systems, it's this: migration takes years, so the time to start is before the threat matures, not after.

Depth of capability

There is a broader lesson in how this capability is being built. A growing number of nations have chosen to be builders of quantum technology rather than buyers, developing the full portfolio of sensing, communications and computing solutions. From processor fabrication to control software, to the hardware enabling quantum networks: the word “sovereignty” is often heard as a synonym for walls.

But our experience suggests otherwise. It is precisely the teams that build every layer themselves that can contribute most readily to the global commons, from working with international standards organizations like ITU and ETSI on quantum-safe networking, to releasing open-source middleware that researchers worldwide now use to program quantum hardware, wherever it was made.

It is this depth of capability that turns a nation from a spectator of the quantum era into an active participant.

That should also be the model for the decade ahead. No single country will own quantum technology, and no single vendor should be the sole authority on quantum-era trust. What the field needs now is what the AI community showcased in Geneva: open tools that lower the barrier to entry, interoperable standards so quantum-safe systems can talk to each other, and honest engagement with the risks alongside the promise.

For years, the defining question about quantum computing has been when the machines will arrive. Increasingly, I think that's the wrong question. The more important question is whether the digital world they inherit will still deserve their trust. The answer depends on what we choose to secure, build, and share today.

Quantum for good does not start with a breakthrough. It starts with trust.

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For years, cybersecurity was built on a simple assumption: systems follow defined rules.

Applications do what they are programmed to do, while people log in, are given permissions and access the resources they need. Artificial intelligence (AI) is changing that.

With nearly 50% of cybersecurity solutions buyers expecting AI to be embedded across the cyber stack within three years, organizations are no longer focused solely on protecting applications and access rights.

They also need to secure intelligent systems that can make decisions, interact with users and act autonomously.

AI can draw on models, prompts, context and external tools to understand a goal, make decisions and determine how best to achieve it.

As organizations give these agentic systems greater autonomy across enterprise workflows, the consequences of failure extend beyond generating a wrong answer.

A mistake can now disrupt business processes, influence decisions and trigger unintended actions across connected systems.

An expanded attack surface

Greater autonomy creates new points of vulnerability whenever AI is given access to data, systems and external tools.

Cyber threats, such as attackers manipulating the information AI receives or impersonating trusted users, can alter how it responds or the actions it takes. This could lead an AI agent to retrieve inaccurate information or approve unauthorized actions.

Traditional cybersecurity controls were designed for humans and applications, but autonomous agents don’t fit neatly into either category. Security therefore needs to extend further across the entire AI lifecycle: before go-live, during operations, and at every point where AI learns, decides, and acts.

Organizations need a unified security architecture that provides consistent visibility and controls across both AI and traditional systems, which makes it easier to identify threats, enforce policies and respond quickly when incidents occur.

Trusting AI safely

However, a unified architecture is only part of the solution. Businesses also need to ensure the AI agents themselves can be trusted. Like any trusted user or system, AI agents should have a verifiable identity, tightly controlled access to data and systems, and auditable records of the actions they take.

Without these safeguards, organizations risk creating AI systems that can bypass security and compliance controls because of design flaws rather than malicious intent. Security also cannot stop once an AI system is deployed. Unlike traditional software, AI systems learn from new data, operate in changing contexts and can behave differently over time.

Organizations therefore need continuous monitoring to ensure agents stay within defined boundaries and policies continue to be enforced. That includes clear ownership, escalation paths and kill switches that can safely contain or stop an agent behaving unexpectedly. Some organizations are also beginning to use "guardian agents" that monitor other AI agents and flag unusual behavior.

The level of oversight should reflect the level of risk. AI agents carrying out low-impact tasks can remain largely autonomous, while higher-risk activities - such as updating customer data, approving financial transactions or interacting with production systems - should have stronger guardrails.

Applying controls in proportion to risk allows organizations to capture the benefits of AI while maintaining security, compliance and trust.

Context as a security boundary

Securing AI also means securing the information it relies on. Context is what gives AI agents their power. This includes internal documents, customer information, business rules and previous interactions, helping agents understand a task and decide what to do next.

That also makes context a new security boundary. If the information an AI relies on is inaccurate or has been deliberately manipulated, the decisions it makes can be wrong, even if the underlying model is working exactly as intended. This is known as context poisoning.

Protecting against this means controlling what AI can see as well as what it can do. Agents should only have access to the data and systems they need for a specific task. For example, an AI assistant answering employee questions should not have the same level of access as one authorized to approve payments.

Guardrails must go beyond filtering outputs and extent to protecting the integrity of the information AI uses, ensuring it is accurate, up to date and appropriate for the task at hand.

Governance at scale

Technical controls are most effective when they are supported by effective governance. This requires a joined-up approach that brings together AI and traditional systems, with consistent controls across the business.

Organizations should also define where human intervention is required and who is responsible for the decisions models make. Oversight should focus on the activities that carry the greatest operational, financial or regulatory risk, supported by investment in the skills needed to govern AI effectively and maintain trust in autonomous systems.

The path forward

Ultimately, securing AI is about more than protecting systems from attack. Organizations need the right technical solutions, clear ownership and continuous oversight throughout the AI lifecycle. Trust depends on these elements working together.

The organizations that succeed with AI will not necessarily be those that move fastest, but those that scale it securely and responsibly. The question for leaders is no longer whether to trust AI, but whether they are building systems that deserve to be trusted.

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Recruitment has long relied on signals that serve as proxies for candidate skillsets: degrees, previous job titles, years of experience, and employer names. Those indicators don’t always show whether someone actually has the skills needed for the role they’re applying for. As AI reshapes work, businesses need clearer evidence of what people can do.

This imperative has led to a drastic shift in hiring norms In the UK, 99 per cent of employers are using skills-based hiring in some capacity, according to research. At the same time, 58 per cent expect more than a third of core job skills to change by 2030. Recruiters now need to hire for current requirements while also thinking about how roles are likely to evolve.

For hiring teams, five changes stand out.

1. Hiring is becoming more skills-first

AI is accelerating the move away from degree-first evaluation. When tools, business needs, and ways of working change quickly, employers can’t just rely on static indicators of academic achievement. They need to know whether candidates have practical, current and job-relevant skills.

Degrees still retain significant signaling value. A degree offers an essential foundation, particularly for critical thinking, communication and domain knowledge. But employers increasingly want additional proof that a candidate can apply those strengths in workplace settings.

For recruiters, job descriptions and selection criteria need to become more skill-oriented. Instead of asking for broad experience in a field, businesses should define the specific capabilities needed for the role: data analysis, AI literacy, cloud computing, cybersecurity awareness, project management, or the ability to interpret AI-generated outputs. A clearer skills profile can also help organizations identify strong candidates who have not followed traditional pathways.

2. AI credentials are changing how experience is weighted

Experience still carries weight, but AI is changing how that experience is judged. In fast-moving areas such as generative AI, data, and cloud, a candidate with recent, verified learning may be better prepared than someone with greater experience but outdated skills.

That’s why 42 per cent of UK employers say they would choose a less experienced candidate with a GenAI credential over a more experienced candidate without one. It shows how quickly the value of verified AI capability is rising.

This has practical implications for recruiters. Years of experience shouldn’t be treated as a signifier of readiness. In some roles, recent evidence of applied learning may be more useful. The challenge is to distinguish between candidates who’ve completed primarily theoretical training and those who can show they’re ready to apply what they’ve learned.

3. Verification is becoming more important

AI has made it easier for candidates to produce polished CVs, cover letters and portfolios. It has also made it harder for employers to know which evidence to trust.

This is likely to increase the value of credentials that verify skills to employers. In the UK, 95 per cent of employers say micro-credentials help identify candidates with real-world, applied expertise in areas such as AI, data, and cloud. That matters because recruiters need evidence that can stand up to scrutiny.

The most useful credentials are those that assess applied skills, rather than only content completion. Employers will increasingly look for evidence that a candidate has built something, solved a practical problem or completed a project relevant to workplace needs.

Recruitment teams should reflect this in their processes. CV screening should be supported by practical assessments, structured interviews, and work-sample tasks. This blended approach will make decisions more accurate.

4. Recruiters need to assess human judgement alongside AI skills

AI literacy is becoming a common requirement, but it is insufficient to simply possess technical knowledge, particularly for those deploying AI in non-technical roles. Businesses need people who can work effectively with AI, including knowing when to question it.

As AI becomes embedded in everyday work, candidates will need to show that they can evaluate outputs, check sources, spot weak reasoning, and apply context. A candidate who accepts AI-generated content uncritically will introduce risk, irrespective of technical proficiency.

This adds novel new stages to the assessment process. Recruiters may need to ask candidates to critique an AI-generated response, improve a flawed analysis, or explain what further evidence they’d need before making a decision. These exercises can reveal whether someone has the judgement and domain expertise required to use AI tools responsibly.

For many roles, the differentiator won’t be whether a candidate can prompt a system: it’ll be whether they can turn AI output into sound business action.

5. Skills-first hiring must complement skills-first training

AI is compressing the shelf-life of skills. If, as expected, over a third of core skills will change by 2030 for many UK employers, hiring alone will not be enough to keep pace. In fact, 74 per cent of tech leaders acknowledge they cannot depend on new hires alone to fill AI skills gaps. That means skills-first hiring needs to become part of a wider talent development framework.

The same approach that helps recruiters identify the capabilities needed for a role can also help businesses map the skills they already have, spot gaps across the workforce, and create clearer routes for employees to build the capabilities the organization will need next.

Some capabilities will still need to be brought in from outside the organization, particularly in fast-moving areas such as AI, data and cloud. But many skills will also need to be developed internally as roles evolve. The strongest businesses will combine skills-based hiring with ongoing upskilling, giving employees clear routes to adapt as roles change.

The new recruitment advantage

AI is changing what employers need to know about candidates. Businesses that can identify real capability, verify applied skills, and recognize potential beyond traditional signals will be better placed to hire well. The value of this approach is already visible in workplace outcomes. 92 per cent of employers say entry-level hires with micro-credentials perform better in their first year on the job.

For candidates, the message is just as clear. In an AI-enabled labor market, employability will depend less on what someone once learned and more on what they can prove they can do now, and the evidence they’re still learning.

For employers, recruitment needs to become part of a broader skills strategy. AI may be changing the work, but the hiring challenge remains human: finding people with the skills, judgement and adaptability to help businesses compete and innovate.

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Suno, the most popular AI music creation tool, spent years making it incredibly easy to generate music. Now it's introducing download limits, watermarking and fingerprinting to stop people flooding streaming services with AI slop.

That suggests something important to me: even the companies building generative AI are starting to realize unlimited AI creation comes with unintended consequences.

Suno CEO, Mikey Shulman, shared a blog post about the company's principles for building the future of music responsibly. He says “AI should help people create something new, not imitate someone else’s work. This philosophy has guided how we’ve built our models and platform from the start.”

Great music is made by people

In a section titled “Our Principles”, Shulman says that “great music is made by people”.

The principles themselves aren't new. What's new is that Suno is now dedicating significant engineering effort to limiting abuse rather than simply enabling creation.

In his blog post Shulman writes “We will soon introduce a new downloads policy designed to limit the ability to mass distribute songs on streaming platforms, while preserving the professional, creative, and personal ways people use Suno. These changes won’t affect the vast majority of our users, but they will make large-scale abuse much harder.”

Schulman continues: “In the coming weeks, we will also be adopting new audio watermarking and fingerprinting technology so we can partner even more closely with distribution platforms on combatting fraud and misuse.”

That evolution fits the broader trend we’ve been covering from the music streaming services like Spotify, Deezer, Tidal, and Qobuz, who have started to fight back against AI-generated music flooding their platforms.

Managing the consequences

Streaming giant Tidal has published a comprehensive AI policy with the strapline "Promoting Fairness and Economic Empowerment in the Era of AI-Generated Music". Tidal will identify it, tag it and crucially, not pay any streaming royalties for it.

Spotfiy has introduced measures to prevent fraudulent streams and AI impersonation . Deezer announced that over half of all new daily uploads to its site are AI — up from 44% in April and just over 30% at the end of last year. It launched a free site to scan your playlists for AI in June. Qobuz has announced that it is taking a human-first approach to its recommendations and “developing detection and monitoring systems to identify AI-generated content and fraudulent streaming patterns.”

This announcement from Suno feels more significant than another AI company publishing a set of principles. It marks a shift in priorities. For the first few years of generative AI, success was measured by how much content these systems could produce. Now it looks like success is being measured by how effectively companies can prevent that content from overwhelming everything else.

The AI music industry is moving from maximizing generation to managing the consequences of generation, and it's about time.

Artificial Intelligence (AI) is rapidly evolving. Across industries, many organizations are increasingly deploying AI into systems that must run continuously, securely, and at scale.

As AI adoption accelerates, one thing is becoming clear: infrastructure planning cannot wait.

AI workloads are becoming more interconnected, distributed, and operationally integrated across cloud, data center, and edge environments. Infrastructure planning now requires organizations to align compute, networking, software, memory, and operational requirements across increasingly complex environments.

As a result, many enterprises are beginning infrastructure planning sooner rather than later.

The cost of waiting

As AI becomes more integrated into everyday business operations through continuous inference and agentic AI systems, infrastructure demands are evolving significantly.

Modern AI deployments increasingly require:

• Continuous inference running around the clock
• Multi-agent systems coordinating across applications and databases
• Real-time orchestration across cloud, data center, and edge environments
• Strong governance, security, and operational efficiency

These workloads require more than raw compute performance. They require balanced infrastructure where compute, networking, software, memory, and operational workflows work cohesively at scale.

Because of this, enterprises are beginning AI infrastructure planning earlier, recognizing that planning, testing, and Proof of Concepts (PoCs) for complex systems like this take time.

At the same time, the cost of delaying AI infrastructure planning is becoming more apparent. Delays can slow deployment readiness and postpone AI-driven benefits such as productivity gains and operational automation. As AI demand continues to rise, organizations are prioritizing earlier planning to secure the compute capacity needed to support long-term AI growth.

As AI infrastructure becomes more complex, infrastructure planning needs to begin earlier than traditional IT upgrade cycles. Evaluating workloads, validating deployment models, and ensuring scalability across environments takes time and time is of the essence if we want to be ahead of our competitors.

AI is now a systems challenge

The conversation around AI infrastructure often begins with Graphics Processing Units (GPUs). But as deployments scale, AI performance depends not on individual components, but on how the entire system operates together.

Modern AI infrastructure relies on Central Processing Units (CPUs) for orchestration and data movement, GPUs for large-scale parallel compute, high-speed networking for low-latency communication across systems, and open software platforms for portability and scalability.

As AI systems become more distributed and inference-driven, orchestration and system balance become critical. CPUs play a pivotal role in managing workload coordination, memory access, and GPU utilization, ensuring infrastructure operates efficiently under sustained demand.

This shift reflects a broader industry reality: AI is no longer just a GPU problem. It is a full-stack infrastructure challenge that organization must tackle early on.

Planning for distributed AI

AI is also scaling in multiple directions at once.

Some workloads are expanding into large, centralized clusters, while others are moving closer to where data is generated – including edge deployments such as in factories or hospitals, and AI-enabled endpoints like the PCs.

For organizations, this creates unique infrastructure considerations around hybrid cloud, on-premises deployments, edge AI, compliance, and latency-sensitive applications.

This diversity underscores the importance of infrastructure strategies designed for modularity, portability, and adaptability that necessitates upfront planning.

Openness and flexibility matter more than ever

As AI innovation accelerates, organizations are prioritizing infrastructure flexibility to support rapidly evolving models, frameworks, and deployment environments.

Open ecosystems can reduce integration complexity while supporting broader compatibility across software frameworks, cloud environments, and deployment architectures. They also provide greater flexibility to evolve infrastructure strategies over time while helping avoid the migration costs that can come with highly closed or single-vendor environments.

For many organizations, openness is no longer just a developer preference. It is becoming an important consideration for balancing performance, operational efficiency, cost optimization, and long-term infrastructure investment.

This is another reason infrastructure planning must happen early. Building AI environments that remain scalable, portable, and adaptable over time require long-term thinking around openness and interoperability from the beginning.

Infrastructure readiness will define the next phase of AI

The next phase of AI growth will reward organizations that take a proactive approach to infrastructure planning.

Organizations that delay infrastructure planning may find it more challenging to deploy AI tools down the road, not only due to not having ample time to plan and test, but not securing the compute resources needed early on.

The cost of waiting is becoming ever clearer.

Ultimately, the companies that succeed in the next phase of AI will not necessarily be those with the largest clusters, but those that plan early and build balanced, scalable, and open infrastructure designed to support continuous innovation in an increasingly AI-driven economy.

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Every four years, football's best players are tested on the world's biggest stage.

Less visible is the test taking place behind the scenes.

As billions tune in, broadcasters and streaming platforms face their own high-stakes challenge: delivering seamless live experiences at a scale few events can match.

FIFA estimates that around 5 billion people engaged with the 2022 World Cup, with the final alone reaching nearly 1.5 billion viewers worldwide.

In 2026, that audience has been presented with an even bigger tournament: 48 teams playing 104 matches across Canada, Mexico and the United States.

More than 54 million viewers across the three host countries watched their national teams' opening matches, while the United States' game against Paraguay drew a combined 27.5 million across FOX and Telemundo, the most-watched FIFA World Cup match ever broadcast in the country.

By the end of the group stage, 4.64 million spectators had filled 99.7% of available seats. That scale is a real-time stress test for every part of the live-video ecosystem.

From passive viewing to active participation

Beyond sheer audience size, the difference lies in how fans consume it. They no longer simply watch. They move between platforms, share highlights, expect instant access to key moments and want experiences tailored to their own interests.

Streaming is no longer just a distribution channel; it's a product in its own right. Brazil’s group-stage match against Haiti reached 51.3 million viewers across Globo's wider media ecosystem, while CazéTV set a worldwide YouTube record for the most-watched football match streamed on the platform.

World Cup content generated 11 billion video views across social media platforms during the group stage alone, and official broadcasters published more than 44,000 pieces of content on TikTok.

A modern match is simultaneously a live program, a source of social clips, a statistics feed and a second-screen experience.

Rethinking the production workflow

That shift starts with the production workflow. The same match is now produced simultaneously for stadium scoreboards, connected TVs, mobile apps and global streaming platforms, with capture, ingestion, encoding and delivery all part of a single content pipeline.

Sixteen optical tracking cameras installed in each stadium can produce more than 150 million data points per match, helping officials review incidents and giving media partners new ways to produce highlights.

The real challenge is bringing together live delivery, audience data, advertising, captions, multi-language audio, and interactive experiences alongside tracking, commentary, graphics, and officiating data.

What fans expect: reliability, personalization, speed

For viewers, success comes down to three things: reliability at scale, personalization and speed. Fans will tolerate a lot, but they won't forgive a stream that buffers during a decisive goal or runs so far behind live play that social media spoils the moment.

Personalization must happen without undermining performance, delivering different recommendations, languages, statistics, camera feeds and advertising while maintaining the resilience of a mass broadcast.

AI is reshaping live sports production

Artificial intelligence is central to delivering those expectations. Rather than replacing production teams, AI is enabling rights holders to produce and distribute content at a scale that would previously have required far larger operations. Automated highlight clipping is one of the clearest examples: AI can identify key moments, package them and distribute them within minutes.

For rights holders, the difference between publishing a goal two minutes after it's scored rather than twenty is the difference between leading the conversation and chasing it. Match summaries, commentary, captions and translations can also be generated automatically, and platforms can match each fan with the content they are most likely to watch next.

A glimpse of the future: personalized sports at scale

The broader ambition is visible at the top of the game. The PGA TOUR now turns each week's action into roughly 7,000 AI-generated highlight clips across dozens of markets, so a fan can follow one player or catch up on key moments without waiting for the main broadcast. The 2026 World Cup has produced a similarly vast library of stories. A record 215 goals were scored during the group stage, an average of three per match.

Tournament debutants Cabo Verde went undefeated, with Kevin Pina scoring the country's first World Cup goal, while Japan's 4-0 victory over Tunisia was both the 1,000th match in World Cup history and the biggest ever by an Asian team. These are exactly the kinds of stories that automated tagging, rapid clipping and intelligent recommendations can bring to the right audience.

Immersive viewing and accessibility

The next generation of live sports streaming will be defined by richer viewing experiences. Multi-view streaming allows fans to follow simultaneous matches, while alternative camera angles and player-specific feeds provide greater control over how the action is consumed.

Real-time data integration can bring live statistics directly into the viewing experience without interrupting the match. AI is also making captioning, translation, and audio description production-ready at scale, allowing broadcasters to localize live coverage without a proportional increase in costs.

However, human oversight remains essential for names, sporting terminology, and cultural context.

The technology behind global scale

Supporting all of this requires resilient IT infrastructure and scalable delivery platforms capable of broadcast-quality reliability and low latency, even as millions connect simultaneously. It also requires organizations to move beyond fragmented technology stacks.

A unified architecture makes content easier to reuse: one live signal can support a full broadcast, mobile highlights, social clips, advertising inventory, archive content and personalized recommendations.

Furthermore, protecting that content is just as important as delivering it. Live sport is uniquely vulnerable to piracy because its commercial value exists almost entirely during the match. Digital Rights Management remains the foundation, but forensic watermarking is becoming increasingly important, embedding invisible identifiers so pirated feeds can be traced and removed while the event is still live.

What's next: The future of live sport at scale

The World Cup ultimately highlights that live video has become a complex, data-driven product where success is no longer defined solely by picture quality or reach, but by how effectively AI, unified content workflows. and scalable technology work together under pressure.

The challenge for the industry is not understanding what works at World Cup scale, but applying those lessons consistently across every live event.

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When was the last time you met your newest hire in person?

For many organizations, particularly those operating remotely, the answer is increasingly never. With one in five companies worldwide adopting a fully remote model, hiring virtually has become increasingly popular, enabling businesses to access global talent pools and scale faster than ever before.

But in removing geography as a constraint, it has also stripped away one of the most fundamental layers of trust: the ability to verify, face-to-face, who you are actually employing.

This shift is giving rise to a new and largely under-recognized threat, the “deepfake employee”, where threat actors use synthetic identities, voice cloning, and real-time deepfake video to pass interviews and secure legitimate employment.

Accelerated by advancements in AI, it is now possible to create convincing digital personas at scale, lowering the barrier to entry for fraud and enabling highly organized operations to target corporate hiring pipelines.

In practice, these attacks can be surprisingly difficult to detect. A candidate may appear on a video interview with a natural-looking face and voice, answer questions fluently, and provide what seem to be legitimate credentials. Behind the scenes, however, AI tools can subtly alter facial expressions, sync lip movements to a cloned voice, or even feed real-time responses.

To the hiring manager, there is little reason to suspect anything is wrong. The deception often only becomes apparent much later, if at all, when activity inside the organization begins to raise concerns.

This risk is already playing out in the real world. In a recent experiment, a cybersecurity expert used AI to create deepfake personas – one a white man similar to himself and another of an Asian woman, and successfully secured two separate tech roles, beating hundreds of other candidates.

Using AI-generated credentials, real-time voice modulation and live deepfake video, both synthetic identities progressed through interview stages undetected, with employers unaware they were interacting with an entirely fabricated candidate.

Cloudflare’s latest threat research highlights the scale and sophistication of this activity. Organized “remote worker” fraud operations are using fabricated identities, deepfake-assisted interviews and remote access “laptop farms” to infiltrate payrolls. In some cases, multiple individuals operate behind a single employee identity, maintaining persistent access while appearing as one consistent, legitimate user.

Once hired, these actors are no longer external attackers. They become insider threats with valid credentials, company-issued devices and trusted access to systems. As Cloudflare notes, by the time these individuals are identified, they are already operating inside the perimeter, often blending in with normal business activity.

What needs to change

With nearly 60% of organizations having experienced deepfake-driven incidents, and 48% reporting damage from AI-generated impersonation or misinformation, it is clear that identity infrastructure has become a primary attack surface. Attackers are shifting away from “breaking in” to “logging in” using legitimate credentials obtained through deception.

At the heat of this issue is the flawed assumption that identity can be verified once and then trusted indefinitely.

In physical environments, identity is rarely in doubt. You can see who walks through the door, recognize familiar faces and detect inconsistencies in behavior. In virtual environments, however, organizations rely almost entirely on screen names, login credentials and video, none of which reliably confirm who is actually behind the screen.

Accounts can be shared, credentials can be compromised, and even live video can be manipulated.

This creates a critical vulnerability at the point of hire. A candidate may present documentation, pass background checks and complete onboarding, but in a world of synthetic identities, that initial verification is no longer enough.

Building continuous identity assurance

To address this challenge, organizations need to move beyond static identity checks and towards continuous identity verification. This means verifying not only who someone is who they say they are and that they are a real human at the point of hire, but ensuring that the same individual remains present and authentic throughout their interactions with the organization.

The strongest form of defense lies in continuous biometric verification of the user’s identity. Rather than relying on a single factor, such as facial recognition or voice authentication alone, fused biometrics combines multiple identity signals, such as facial characteristics, voice patterns and behavioral cues, into a single, layered verification process, verifying directly that a real, live human is there.

It is no longer enough to confirm a person’s identity at a single moment in time. Organizations need confidence that the same individual is consistently present across every critical interaction, from interviews and onboarding, through to system access and sensitive transactions. Without this continuity, identities can be shared, replaced or hijacked without detection.

Fused biometric verification can create layered validation that is significantly harder to replicate or manipulate, enabling it to detect and block attempts to impersonate users through synthetic voices, deepfakes, or recorded audio and video.

While a single modality might be fooled by a sophisticated synthetic input, combining biometric modalities; facial recognition, voice recognition, and speech pattern recognition, it becomes significantly harder for fraudsters to mimic an identity.

Advanced biometric verification technologies are trained on large datasets of both genuine and synthetic voice samples, enabling them to recognize subtle acoustic differences between natural and deepfake voices. This helps organizations detect voice-cloning attempts, even when the audio sounds convincing to human listeners.

Crucially, fused biometric verification can operate passively in the background, consistently verifying that the right person is accessing systems, enabling smoother, lower-friction experiences and reduces the need for frustrating repeated verification attempts.

As organizations continue to embrace remote work and digital-first operations, cybercriminals will increasingly find new vulnerabilities to exploit. The question is no longer just how to keep threats out, but also how to ensure that those already inside are truly who they claim to be.

In a world where identities can be fabricated, cloned and manipulated with ease, trust cannot remain static. It must be continuously proven.

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As AI develops into the latter half of the 2020s, scientists have repeatedly warned of the dangers that may arise from its widespread deployment. In particular, there is an overriding view that advancing AI is a tool that can be used for good or for bad depending on who is wielding the tool at any given time.

Breaking out

The British computer scientist Geoffrey Hinton was giving an interview with the New York Times upon leaving his role at Google, primarily so he could speak freely and publicly about the dangers of the technology that he helped create.

In this landmark interview, the 'Godfather of AI' was clear that he couldn't continue working for Google while harboring the deep-rooted concerns that he held over the use of AI in various domains. The scientist is, for example, opposed to using AI on the battlefield in "robot soldiers", the newspaper reported.

At the time, he continued his relationship with Google because he saw the company as being a "proper steward" for AI as it was being developed. His concerns materialized when Microsoft incorporated Bing with a chatbot, forcing Google to act fast so it could incorporate AI into Google Search.

Frankenstein's monster

Hinton, who was jointly awarded the Nobel Prize in Physics for his work developing neural networks in the 1980s, has since remarked in an interview with CBS News that he didn't think we would make such progress in the 40 years since.

He has also floated the notion that there's a non-zero chance that AI could take over, comparing AI with a cute tiger cub that could, one day, potentially kill you when it's grown up. That came alongside warnings that various organizations and individuals, like cyber criminals or authoritarian regimes, could weaponize AI for their own agendas.

A reckoning is underway in enterprise AI. Chief executives who spent two years bolting frontier models onto their businesses are now asking harder questions.

What did we actually get for the money? Where does our data go when it flows through someone else's model? And when the model we standardized on last year isn’t the best one this year, how much of our business have we quietly handed over to a vendor we don't control?

That last question is the one that should keep leaders up at night, because for most companies, the honest answer is: far more than they think.

The conversation about AI has mostly been about which model is best.

That’s the wrong question.

In a field where leadership changes hands every few quarters, “best model” is a snapshot, not a strategy.

The question that actually matters is architectural: when the state of the art moves — and it always does — can you move with it, or do you have to rebuild your business every time?

Models are temporary. Plan accordingly

Here's what a decade of running AI at production scale teaches you that no benchmark chart will: models are disposable, and they're getting more disposable by the year. The transcription engine that led the market when we started is a footnote now.

The computer vision system that dominated three years ago has been lapped over and over. We've swapped best-in-class engines in and out of live customer workflows hundreds of times — across speech recognition, translation, object detection, face redaction, and now large language models — and each cycle turns over faster than the one before it.

An company that’s hard-wired to one provider inherits that provider's roadmap, pricing, and priorities as its own. When the provider raises prices, you pay. When it deprecates the version you built on, you rebuild.

When a smaller open-source model, fine-tuned on your own data, would actually do the job better and cheaper, you can't reach for it because your workflows only speak one dialect. That isn't a partnership. It's a dependency — and depending on a moving target is about the most expensive position you can be in.

That's the reasoning behind building systems where model lock-in isn't an option from day one: an orchestration layer that connects and manages hundreds of commercial, open-source, and proprietary models across different cognitive tasks, routes each job to whatever engine is best for it, and swaps models out as the state of the art shifts — without anyone having to rebuild a workflow.

The model becomes a component, not a foundation. The real foundation is the orchestration layer and the data underneath it, and those stay owned by the enterprise, not the vendor.

The discipline that makes model plurality real: evals

Model plurality sounds good in a keynote and collapses in practice without one thing: the ability to prove, on your own data, which model is actually better for your job. “Best” isn’t a leaderboard position.

A model that tops a public benchmark can badly underperform on your accents, your camera angles, your legal thresholds, your definition of “good enough.” Public benchmarks measure general capability.

They tell you almost nothing about how a model will perform inside your specific workflow. So the real currency of the next era isn't the model — it's the evaluation.

The companies pulling ahead are the ones who can put any engine up against any other on their own content, with their own quality bar, and make swap decisions based on evidence instead of vendor marketing.

That means scoring engines against each other continuously, on real customer data, so “which model” stays a measured decision you can remake any time the field shifts — not a one-time choice you're stuck with.

That evaluation muscle is itself a strategic asset. It's what turns a pile of interchangeable models into a compounding advantage — and it's precisely the capability an enterprise forfeits the moment it standardizes on a single black box.

Ownership over your own audio and video

There's a reason this matters most in audio and video. Text is basically commoditized at this point. The proprietary, defensible, hard-to-replicate data in the enterprise is the recorded record of what a company actually said, did, made, and witnessed — decades of broadcast, footage, calls, and captured events. It's multimodal, it's rights-encumbered, and it can't be replaced, which also happens to make it exactly what this generation of AI is hungriest for.

The appetite for training and tuning data has outrun what the open web can supply. What's actually needed now is what enterprises already have sitting in their archives: vast, rights-cleared, real-world, multimodal data, plus expert human judgment about what “good” looks like.

Which makes the default posture of the last two years perverse — companies have paid premium prices to push their proprietary audio and video through third-party models, with limited visibility into what's retained, learned, or one day competed against them. If your data is the scarce input everyone's after, the last thing you want to do is hand it over as a byproduct of your software bill.

The alternative is building the enterprise business the other way around: turning an organization's raw archives into AI-ready, enriched assets it actually owns, with rights and governance metadata baked in at the point of creation rather than tacked on afterward — because when the data in question is a witness's voice, an athlete's likeness, or a rights-encumbered broadcast, provenance and consent aren't optional extras.

From there, rightsholders can put that data to work themselves — licensing it to model developers and cloud providers on their own terms, with consent, provenance, and compensation built into the deal.

The world is moving toward fine-tuning and open source

Watch where the sophisticated buyers are going and the pattern is unmistakable The old reflex — route everything to whichever frontier model is biggest — is giving way to something more deliberate: a portfolio approach where smaller, open-source, and fine-tuned models handle most of the day-to-day workloads, and the giant models get reserved for the problems that genuinely need them.

The reasons are practical — cost, latency, data control, and the ability to specialize a model on proprietary data until it beats a general-purpose giant at your specific task.

This is where the two ideas come together. Fine-tuning and open source only work in your favor if you actually own the data to tune on and have the architecture to deploy into. A company locked to one vendor can't fine-tune an open model on its own footage and slot it into production as the plumbing simply won't allow it.

An enterprise with an orchestration layer and governed, AI-ready data can do exactly that, and can keep doing it as better base models emerge. Owning your data and having freedom in your architecture are what actually make this new era of specialized, fine-tuned, open models available to you at all. Without them, you're watching a shift from the sidelines that was supposed to be your advantage.

Sovereignty is a posture, not a product

It’s okay to be wary of how fast “sovereign AI” is becoming a marketing category, because sovereignty delivered through a new single-vendor dependency is just lock-in with better branding. Real sovereignty is an architectural posture with three commitments.

First, model plurality, proven by evaluation: every model — commercial, open-source, or fine-tuned — tested on your data and replaceable at will.

Second, governance that travels with the data: provenance, auditability, consent, and policy enforced at the data layer.

Third, actual ownership: your audio and video, enriched and controlled as an asset you deploy on purpose, not one that leaks out incidentally. None of this is an argument against the frontier labs — they build extraordinary technology, and plenty of companies use it every day, through architecture that keeps the leverage on their side of the table.

The enterprise doesn't have to choose between using the best models in the world and controlling its own future. The whole point is to do both: orchestrate every model worth using, prove which one wins on your own data, and own the audio, video, and judgment that make any of them worth running.

The model is temporary. Your data and your judgment are not. Build for that, and you spend the next decade compounding an advantage your competitors rented and lost.

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The enterprise race to scale AI operations is in full swing – both from a deployment and security perspective. While conventional wisdom suggests that the teams who deploy the models first get the edge, it’s the wrong approach for enterprises.

For nefarious actors, speed is the name of the game. Malicious actors leveraging AI to probe for vulnerabilities don’t need to share their decision trail to audit committees or regulators – making speed alone the key advantage for attackers.

Enterprise security teams, on the other hand, operate under entirely different parameters – which also happen to be where the opportunity lies.

Security teams don’t just need the capability to identify anomalies, screen transactions, or make access decisions – they also need to be prepared to explain what happened, when, and why to key stakeholders.

Every action and every outcome needs to be clarified and justified to a board, an auditor, or a customer.

Scaling AI at the enterprise level is not relegated to who moves fastest, but rather who can embed the necessary accountability frameworks, emergency brakes, and audit trails.

The Hidden Data Problem

In truth, speed is not the primary challenge for most enterprise teams. The bigger, more difficult challenge is found in data and governance. Policy models and detection frameworks are only as impactful as the information inputs. However, most organizations are devoid of structured, well-governed data – particularly as it pertains to AI activity. Across the majority of organizations, data is scattered.

Activity logs, access records, transaction histories all live in disparate systems with inconsistent formats and no single source of truth. No matter how much of this data is fed into an AI model, clarity will never be achieved. Instead, teams generate a false sense of confidence built upon a shaky foundation.

Meaningful scale begins with auditability and accountability as infrastructure, not an afterthought. As AI systems become increasingly embedded across organizations, the requirement heightens for more consistent records of actions that occurred and policy enforcement where actions are checked against defined rules prior to execution.

Equally as important, organizations need to have the internal muscle memory to explain automated decisions to stakeholders outside the engineering team, because they've been doing versions of this for years in compliance and risk functions.

When this approach is foundational for enterprises, AI systems become genuinely useful. AI models built upon strong data and governance standards can meaningfully screen actions against known risks before settlement, flag patterns that humans may have missed, and maintain updated records that can be shared across key stakeholder groups. Devoid of this foundation, fragmented data and ungoverned systems compound – resulting in faster mistakes.

Enthusiasm around AI systems centers on the deployment side of the equation, without asking whether the underlying infrastructure can support what's being layered on top of it. This approach is suboptimal and leaves an open door for breaches – akin to installing an alarm system in a building without putting locks on the doors.

Embedding infrastructural policy enforcement, audit trails, and human-reviewable records does bring an additional layer prior to deployment. And in a domain where nefarious actors operate free of this operational layer, it's fair to ask whether enterprise security teams are creating a permanent speed disadvantage for themselves.

However, that mindset misses what the added operational layer actually delivers: the difference between an AI system that fails safely and one that fails silently.

Enterprise security teams who operate slower but can consistently and proactively identify and reverse bad decisions are in a fundamentally different position than enterprises who operate quickly and discover a failure three weeks post-incident. The tradeoff is real, but it’s the wrong tradeoff to optimize away.

Trust Beyond The Engineering Teams

A common misconception is that security decisions are made for and by security teams. That’s not the case. Security decisions made by automated systems need to satisfy stakeholders far outside of this workstream – including regulators, insurers, customers, even boards.

Those stakeholders aren’t moved by sophistication. They care whether the organization can clearly and consistently demonstrate what the system did and why. Organizations without that operational layer find that AI adoption increases their potential risk exposure, because they're now making faster decisions with insufficient guardrails.

As AI systems continue to increasingly exhibit autonomous behaviors, the stakes continue to change. An AI system that can initiate payments, approve transactions, or move funds independently doesn't just need a policy to follow – it needs brakes to press in the event of a bad decision.

Without this layer embedded, enterprise security teams not only carry the accountability problem – they also have no chance of catching a mistake before it becomes permanent.

The Readiness Gap

Readiness requires the proper sequencing. Before scaling AI operations, it’s critical to make an honest assessment of three things: whether underlying data across systems are structured, whether there are firm policy checks in place, and whether verifiable records of automated decisions can be produced on-demand.

Starting here positions AI to become a force multiplier for security teams. These three elements make all the difference between catching what humans miss and doing it fast enough to matter – or simply adding velocity to a faulty process.

The “AI race” will not be won by having the newest models. Really, it’s about devoting effort to the unglamorous work – building the data infrastructure and governance systems that make AI models trustworthy.

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Investment in AI continues to accelerate at pace, with global spending projected to reach $2.59 trillion in 2026, a 47% year-on-year increase.

Yet only 28% of AI projects are currently generating a measurable ROI, with many failing to deliver expected business outcomes.

As organizations race to capitalize on AI opportunities, many continue to increase investments without fully understanding how AI will operate within their existing technology environments, particularly their data storage infrastructure.

Without careful planning in this area, businesses risk higher costs, growing technical debt and increased compliance complexity, all of which can undermine the value AI is set to deliver.

No data, no AI

The IT industry often talks about AI in terms of performance, computing power and processing speed. But at its heart, AI is a data system.

In recent years, high-performance processing GPUs and NPUs have been getting much of the attention and investment. While compute remains essential, AI data is constantly evolving, expanding and requiring ongoing management throughout its lifecycle.

As organizations move from AI experimentation to deployment at scale, the importance of a robust data infrastructure becomes increasingly clear.

Businesses need the ability to capture, store, access and manage growing volumes of data efficiently at every stage of the AI journey.

To maximize AI ROI, organizations should view storage and data infrastructure as strategic enablers rather than supporting technologies. Long-term AI success depends on ensuring storage infrastructure is aligned with wider technology and business objectives from the start.

AI requires a different approach to storage

Historically, data storage needs were relatively predictable. A ‘set-and-forget’ approach was often sufficient. The AI era demands a fundamentally different mindset for several reasons.

For one, the scale of data associated with AI exceeds anything many organizations have previously encountered. According to IDC, annual global data creation is expected to more than triple over the next five years, reaching 718 zettabytes by 2030, representing a CAGR of 26.9%.

AI workloads also continuously generate additional data through logs, metadata, synthetic outputs, model updates and training datasets. Just as importantly, AI performance is heavily influenced by the quality of the underlying data infrastructure. The ability to access large, well-managed datasets efficiently has a direct impact on business outcomes.

When discussing return on AI investments with the C-suite, technical leaders should connect infrastructure decisions to financial performance. GPUs are typically among the most significant items within AI budgets, and any time spent waiting on storage input/output represents underutilized investment.

Storage architectures that are not designed for AI workloads can constrain performance and reduce overall returns from compute investments.

One example for an AI-optimized architecture approach is tiered storage. Not all data delivers the same value at every stage of the AI lifecycle, nor does it require the same level of performance. Frequently accessed datasets used for active model training and inference may benefit from high-performance storage, while historical data, archived outputs and compliance-related records can be moved to lower-cost capacity tiers.

By aligning storage performance and cost with the value and usage profile of different datasets, organizations can optimize infrastructure spending while maintaining access to the data needed to support AI innovation, governance and future model development.

Organizations that invest in cost-effective scalable, future-ready, AI workload optimized storage and data infrastructure are better positioned to unlock value from AI initiatives, while building a foundation that can support future growth.

AI data, compliance and regulatory risk

Legal and regulatory considerations should also form part of AI planning from the start.

As AI laws are established, organizations must continue to comply with existing data-related obligations, including regulations like UK GDPR and the Data (Use and Access) Act 2025.

Any organization with EU customers should also consider EU requirements around data governance, transparency and record-keeping. Retention periods for training datasets and model records can often be longer than anticipated, making long-term storage planning a decisive factor.

Addressing these needs early helps organizations avoid costly remediation efforts later and supports more effective governance as AI deployments scale.

Talking a proactive approach to protecting AI ROI

As AI adoption grows and data volumes continue to expand, demand for storage capacity is increasing rapidly. This is creating new supply chain and procurement challenges across the industry.

As a result, organizations can no longer assume that capacity will be readily available whenever it is needed, particularly for large-scale AI projects.

In practice, this means considering forecasting storage requirements alongside GPU and infrastructure investments, exploring longer-term capacity planning arrangements, and incorporating storage needs into AI business cases from the beginning.

A proactive storage strategy is becoming essential for businesses looking to support future AI workloads with confidence, minimize operational risks, and maximize long-term returns from their AI investments.

The last word

AI ROI depends not just on the quality of its algorithms and applications, but also on how effectively organizations manage, store and govern their large-scale data estates.

Even a small difference in cost per terabyte can become significant when applied across petabyte- and exabyte-scale environments.

Organizations that integrate forward storage planning into their AI strategy, treat data infrastructure as a strategic asset and prepare early for future capacity requirements will be ideally positioned to realize the full value of their AI investments.

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For years, the UK connectivity debate has focused on fiber availability. Coverage maps, rollout targets, premises passed and investment updates have dominated the conversation.

That focus made sense. The country needed better infrastructure, and there was real pressure on operators, government and the wider market to show progress. Better networks had to be built, and in many places, they have been.

Ofcom’s Spring Connected Nations update shows how far that conversation has moved on. Full fiber broadband is now available to 82% of UK homes, while gigabit-capable broadband reaches 89%. Coverage is not solved everywhere, and regional gaps still matter, but the center of gravity is shifting.

For many mid-market and enterprise organizations, the question is no longer only: can we get fiber? Increasingly, it is: when something goes wrong, who owns the problem?

The market has not always been able to answer that clearly. As connectivity becomes more tightly linked to business-critical operations, that is becoming an expensive weakness.

The problem with modern connectivity supply chains

The infrastructure build-out of the last decade has been a success in many ways. Fiber has reached new places. Wholesale models have grown. Alternative networks have created more choice. Access supply has become more competitive.

But that same progress has also created a more complex operating model. A typical enterprise connectivity arrangement can involve several parties: an access network operator, a wholesale aggregator, a reseller and a managed services provider. Each party may have a role to play, but each also has its own systems, escalation routes, commercial incentives and operational boundaries.

When everything is working, that complexity is easy to ignore. When there is a fault, it quickly becomes the problem.

For a multi-site organization running business-critical applications, a manufacturer managing connected logistics, or a professional services firm with regulated data dependencies, the cost of delay can be significant.

A business’s dependency on connectivity has changed in both character and volume. Organizations are moving quickly to support AI workloads, distributed teams, hybrid cloud environments and more automated operations.

Ofcom’s Connected Nations UK Report 2025 shows that between September 2024 and August 2025, providers reported 616 resilience incidents across fixed and mobile networks. Outages above the reporting thresholds affected 12.7 million customers and caused around 192 million customer hours of lost service.

Ofcom also noted that third-party failures, including street works causing cable breaks and failed backhaul circuits from wholesale providers, made up around 14% of reported incidents.

For enterprise buyers, that is the accountability problem in plain sight. The failure may sit in one part of the chain, but the operational impact is felt by the customer trying to keep sites, systems and services running.

Why mid-market organizations feel the gap the most

There is a particular pressure point here for mid-market organizations. They are running connectivity that supports business-critical operations, distributed sites and cloud-dependent workloads. But they are typically buying through channels designed for volume, not accountability.

They carry the operational dependency of an enterprise customer without the contract size that historically bought priority escalation, named account management or direct access to the people who own the network.

That gap does not appear in the SLA document. It appears on a Sunday night when something breaks.

A few years ago, a slow SLA response might have caused a few headaches. Today, it can stop production, delay customer service, interrupt access to data or leave a site unable to operate properly. Businesses are built on connectivity.

AI inference at the edge, real-time data processing and cloud-connected operations are not designed for fragmented fault resolution. They do not fit neatly with unclear escalation paths or supplier chains where responsibility moves from one party to another before anyone fixes the issue.

Yet many buying conversations have not caught up. Bandwidth, speed and price still dominate procurement. Headline SLAs are scrutinized, but service assurance, ownership of the underlying infrastructure and escalation clarity are often treated as secondary details.

That mismatch will become more costly as the operating environment becomes more demanding.

The infrastructure question buyers should be asking

Most organizations know the standard questions to ask when they buy connectivity. Is there coverage? What is the cost? What is the headline SLA? What capacity is available? Who else is using the provider? All are important questions, but they do not fully describe the service experience.

The questions that really matter are more practical. When a fault is raised, how many organizations are involved in resolving it? Does the provider own the physical layer the service runs on, or is operational control spread across several parties? What does the escalation route look like at 11pm on a Sunday? Is there one organization accountable for the outcome, or a contractual matrix that has to be navigated before anyone can act?

The market has spent years explaining what infrastructure investment looks like. The next conversation needs to be about what operational accountability looks like.

That does not mean every provider needs to own every asset in every location. The UK connectivity market will always involve partnerships, wholesale relationships and specialist delivery models. But buyers should be much more interested in where responsibility sits when service degrades.

A shorter, clearer operational chain matters. It gives providers better visibility of the network, fewer hand-offs during fault resolution and a stronger ability to act rather than simply escalate. It also gives customers something they increasingly need: confidence that someone is accountable for the service, not just the contract.

Ownership is becoming the real differentiator

The next phase of business connectivity will not be won on availability alone. It will be won by providers that can combine reach with operational control, service assurance and clear ownership.

For buyers, that means looking beyond headline speeds and asking harder questions about accountability. Who sees the fault first? Who has the authority to fix it? Who is responsible for the customer outcome?

Because when connectivity is simply a utility, ambiguity might be tolerated. When it is the foundation for AI, cloud, data and day-to-day operations, ambiguity becomes a business risk.

The fiber rollout has changed what is possible. The next battleground is making sure someone owns the problem when possibility turns into pressure.

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Open-source malware has changed shape.

What once focused on noisy cryptomining has moved toward something far more valuable: access.

Our recent data shows attackers are increasingly targeting credentials and secrets embedded in software dependencies, with UK organizations firmly in scope.

This shift marks a move away from opportunistic abuse toward deliberate supply-chain compromise. Instead of draining compute cycles, attackers are positioning themselves inside build pipelines and developer workflows.

The goal is persistence, not disruption.

For organizations that rely heavily on open source software, this fundamentally changes both the threat model and the potential impact.

This is what “shift left” actually means in 2026: controlling what enters the build, not just detecting what runs in production.

Why credential theft has overtaken cryptomining

More than half of malicious open-source packages now focus on stealing credentials and secrets, overtaking cryptomining as the dominant threat type. The reason is straightforward. Credentials offer lasting value. They provide persistent access, broader reach across environments, and a lower risk of detection than resource abuse. A stolen token or API key can unlock entire systems, not just a single machine.

Cryptomining, by contrast, is easy to spot and quick to shut down. It consumes resources and triggers alerts. Credential theft blends in and can be executed in seconds. It exploits the trust placed on developer workflows to operate in a safe environment.

For attackers looking to maximize return while minimizing exposure, this approach maximizes returns whilst doing away with the risk of being discovered.

The implication is clear: protecting runtime infrastructure is no longer enough. The security boundary now starts at dependency intake and at the developer environment.

Multi-stage malware becomes the norm

Modern open-source malware is rarely single-purpose. Our analysis shows dropper and loader behavior increasing by nearly 2,900 percent year over year in Q1 2025, signaling a shift toward engineered, multi-stage attacks.

Around 77 percent of malicious packages distributed through open source ecosystems now combine multiple threat types. Droppers appear in nearly all observed cases, while secret exfiltration features in close to two-thirds.

These packages are designed to evolve after installation, pulling in additional payloads or changing behavior over time. This reflects industrialized campaigns rather than opportunistic experimentation. Attackers are investing in resilience, stealth, and scale.

For defenders, this means signature-based thinking is outdated. If malware is modular and adaptive, controls must focus on provenance, behavior, and prevention before execution. Again, this is what “shift left” actually means: securing the build graph itself, not just the workloads it produces.

Supply chains under direct pressure

The widespread use of open source, particularly within the JavaScript ecosystem, creates systemic exposure. Modern applications routinely depend on hundreds of direct and transitive npm packages. That density of reuse creates efficiency, but also amplifies upstream risk.

Recent activity linked to the Lazarus group illustrates the threat. More than 200 malicious packages were identified, almost all concentrated in npm. When a single ecosystem underpins financial services platforms, government services, and critical national infrastructure, concentration risk becomes a strategic issue.

A compromised dependency does not stay isolated. It propagates through shared frameworks, internal libraries, and CI pipelines. In sectors built on speed and reuse, upstream compromise quickly becomes downstream impact. This is why dependency governance is no longer just a developer hygiene issue; it is a board-level supply-chain concern.

Automation turns one package into thousands of compromises

Today’s malware increasingly targets CI/CD pipelines and developer workflows optimized for automation. When a compromised dependency enters a build, it can quietly extract API keys, certificates, and access tokens without triggering runtime alerts. Automation does the rest.

What starts as a single poisoned package can spread across hundreds or thousands of builds. The very systems designed to accelerate delivery now accelerate compromise.

The practical takeaway is uncomfortable but necessary: if build systems are automated, security controls must be automated at the same level. Manual review cannot scale against automated distribution.

AI coding assistants and the hallucination problem

AI-assisted development introduces an additional layer of risk. Studies and testing have shown that large language models can, in a meaningful percentage of cases, suggest packages or functions that do not exist. Developers under time pressure may attempt to install or rely on these hallucinated dependencies, unknowingly expanding the attack surface.

Hallucinated package names, fabricated examples, and unsafe dependency suggestions can quietly undermine supply-chain integrity. Attackers are already exploiting naming conventions and trust models to seed packages that appear legitimate to both humans and machines.

Each hallucination creates rework, friction, and lost productivity. Much of this waste could be reduced if AI systems were grounded in authoritative, real-time package intelligence rather than pattern prediction alone.

Our recent research reinforces this point. The company found that smaller AI models augmented with live package intelligence significantly outperformed larger standalone models when handling dependency upgrades and package selection tasks. The findings suggest that real-time ecosystem context matters more than model size alone when developers are making security-sensitive decisions. It also helps smaller models are 70x cheaper compared to frontier models.

This has direct implications for software supply-chain defense. If AI coding assistants recommend dependencies without verifying package provenance, maintenance status, or ecosystem trust signals, they risk accelerating the spread of malicious or hallucinated packages into production environments.

In practice, secure AI-assisted development will depend less on increasingly large models and more on whether those models are connected to authoritative, continuously updated software intelligence.

Here too, the lesson is upstream control. Guardrails must sit at the point of dependency selection, not after the code ships.

Why defenders are falling behind

Many UK security controls remain focused on detecting threats after code is deployed. Attackers have moved upstream. They target the build process, the dependency graph, and the trust relationships developers rely on.

This mismatch leaves organizations well prepared for runtime incidents but exposed during development. As long as defenders assume malware announces itself loudly, supply-chain compromise will continue to slip through unnoticed.

“Shift left” is often treated as a slogan. In practice, it means enforcing policy before installation, validating provenance before execution, and blocking malicious packages before they enter the graph.

Stealing the keys, not the cycles

Open-source malware has evolved from stealing compute to stealing access. Credentials unlock ecosystems, not just machines. For UK organisations, this makes supply-chain security a strategic concern rather than a technical afterthought.

Preventing malicious code from entering the build is now more effective than responding after deployment. The quiet shift from coins to credentials has already happened. The question is whether defenses will adapt quickly enough to match it.

If it isn’t automated, it won’t scale.

What organizations should prioritize now

To respond effectively, UK organisations should focus on a small number of structural controls:

● Gate dependency intake with automated policy enforcement before packages enter CI/CD.

● Continuously monitor for secret exposure within build environments and revoke compromised credentials rapidly.

● Enforce provenance and integrity verification for open-source components, including transitive dependencies.

● Ground AI coding tools in authoritative package intelligence to prevent hallucinated or malicious dependency suggestions.

None of these measures eliminate risk. But together, they realign defenses with where attackers are actually operating: upstream, automated, and inside the supply chain.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit