Running a one-person business means doing the job of an entire company by yourself. You're closing a deal in the morning and debugging the product by lunchtime. Every hour spent on admin is an hour not spent on the part of the business that actually grows revenue.

That's the gap a new generation of AI agents is built to close. OpenClaw and Hermes Agent are open-source tools that run in the background, hold memory of your business, and act on tasks without waiting for you to ask twice. Pair either one with a handful of supporting tools, and you get something close to a small team, for the price of a few subscriptions.

Why solo founders need an agent, not another app

Most AI tools you've used so far live in a browser tab. You open Claude or ChatGPT, ask a question, get an answer, then close the tab. The assistant stops existing the moment you stop typing.

An agent works differently. Once you set up OpenClaw or Hermes Agent, it keeps running, checking a task list, remembering what happened yesterday, and acting on a schedule instead of waiting to be prompted. For a solo entrepreneur with no employees, that difference matters more than which model sits underneath.

An agent doesn't replace you. Ideally, it should absorb the tasks that would otherwise eat your day, things like triaging support email, drafting a weekly update, or chasing an unpaid invoice. That frees you up for the work only you can do.

OpenClaw or Hermes Agent: Picking your AI co-founder

Two open-source projects dominate this space right now. They take different approaches to the same problem.

OpenClaw is the older, larger, and more battle-tested of the two. It started as a weekend project by Austrian developer Peter Steinberger in late 2025.

In February 2026, Steinberger announced he was joining OpenAI and that OpenClaw would move to an independent foundation rather than staying tied to any single company. The project's GitHub repository now sits at 373,000 stars and 77,300 forks.

Hermes Agent takes the opposite bet. It launched in February 2026 from Nous Research, the lab behind the Hermes, Nomos, and Psyche model families. By mid-June, it had crossed 190,000 stars of its own.

Instead of chasing breadth, it focuses on depth. After every task, it evaluates how the work went, turns whatever worked into a reusable skill file, and pulls from that file the next time a similar job comes up rather than reasoning from scratch.

How they measure up

In our experience, the honest answer comes down to setup time versus patience. OpenClaw's web search and file tools tend to work immediately after a Docker setup, often the same day. A full Hermes Agent setup with memory and tools configured typically takes a few hours instead.

Start with OpenClaw if you want results fast. Choose Hermes if you're willing to spend a weekend up front for an agent that keeps improving at your specific workflows.

A growing number of operators don't pick just one. Some experienced users run OpenClaw as the orchestrator for planning and multi-step coordination, then hand fast, repeatable task loops to Hermes as an execution specialist, with the two agents communicating over a shared protocol. That setup is overkill for a first attempt, but it's worth knowing the option exists once a single agent starts to feel limited.

What this looks like in practice

The clearest public example of an agent running a one-person business is Felix, an OpenClaw agent built by entrepreneur Nat Eliason. In January 2026, Eliason gave the agent $1,000 in startup capital and its own X account, then told it to build something and sell it overnight. Felix responded by writing a playbook on how to hire an AI agent, building a website to sell it, and launching its own social presence.

Three weeks in, Felix had generated $14,718 in revenue. Within about two months, that figure had grown to roughly $177,000 across the original product, a skills marketplace called Claw Mart, and custom agent deployments built for other businesses.

Eliason still holds the API keys and reviews what the agent does. Day-to-day decisions, from pricing to outreach, run through Felix rather than through him.

Felix is an extreme case, built specifically to test how far one agent could go without a human in the loop. Most one-person businesses won't hand over a Stripe account on day one.

That's the right call for most of them. Even so, the same pattern applies at a smaller scale: give an agent its own accounts, a narrow task, and enough room to act without you checking in every hour.

Building the rest of the stack around your AI agent

An agent is only as useful as what it can plug into. Most one-person stacks pair OpenClaw or Hermes Agent with a handful of tools that already expose an API, a webhook, or an email address the agent can act through. None of these need to cost much.

Scheduling and communication

Calendly remains a common default for letting people book time on your calendar without the back-and-forth, with a free plan for individual use and paid plans starting at $10 per month. Point your agent at the same calendar so it can answer "when am I free" without you checking manually.

For day-to-day messages, the agent typically lives wherever you already work. Both OpenClaw and Hermes Agent connect natively to WhatsApp, Telegram, Slack, and Discord, so you're adding a contact to a conversation you're already having, not a new inbox to check.

Invoicing and bookkeeping

Wave and FreshBooks cover most solo founders here. Wave's core invoicing and accounting tools are free, with charges kicking in only if you use its built-in payment processing. FreshBooks costs a monthly fee but adds time tracking and client portals, useful once you start billing by the hour.

Either way, give your agent read access to the invoice list rather than write access to your bank account. Letting it flag an overdue invoice and draft a reminder is a reasonable task. Letting it move money on your behalf is not, at least not yet.

Customer relationships and leads

HubSpot's CRM is free, with no time limit on the core plan. For a founder tracking a few dozen leads, that's enough to replace a spreadsheet without adding a subscription. As the pipeline grows, the agent can sit on top of it, drafting follow-ups, logging calls, and flagging deals that have gone quiet.

Content and social media

This is where an agent earns its keep fastest, because content work is repetitive and time-stamped. Point it at your newsletter platform, whether that's beehiiv, MailerLite, or ConvertKit. Give it a standing instruction to draft, not send, a weekly update from your week's notes.

You stay the editor. The agent stays the drafter.

Contracts and signatures

For anything that needs a signature, tools like PandaDoc or SignNow handle the legal side. Your agent can handle the busywork around it instead, generating the draft from a template, sending it out, and nudging a client who hasn't signed after a few days. We'd still keep a human checking the final terms before anything goes out the door.

AI guardrails you need before you go all-in

Running an autonomous agent is not the same as running a chatbot. The security record so far reflects that. A 2026 audit of OpenClaw's skill marketplace found 341 malicious entries out of 2,857 skills checked, traced largely to a single supply chain campaign known as ClawHavoc.

A separate vulnerability, CVE-2026-25253, scored 8.8 out of 10 on the severity scale and involved unsafe automatic connection behavior that could expose authentication tokens. Cisco has publicly described personal AI agents in this category as a serious risk for enterprise environments, specifically because of how much access they're given by default. Hermes Agent has reported no known critical vulnerabilities as of mid-2026, though that partly reflects its smaller install base rather than proven hardening over time.

Three habits cut most of that risk down to size:

• Give the agent its own accounts. A separate email address, a separate cloud storage folder, and separate API keys mean a mistake stays contained instead of spreading into your personal accounts.
• Start with one channel and one task. Let it manage a single Telegram conversation or a single invoicing workflow before connecting it to your bank, your CRM, and your domain registrar all at once.
• Keep it updated and keep it behind authentication. Both projects ship fixes quickly once a problem surfaces, but only if you're running a current version rather than an old build exposed to the open internet.

Treat the access you grant an AI agent the way you'd treat access for a new hire — useful from day one, but earned in stages rather than handed over all at once.

Getting started without breaking anything

Pick one agent and one task before you do anything else. People who've run these setups for months consistently recommend starting on the computer you already own rather than buying dedicated hardware, then moving to a small server later if the agent earns a permanent home.

Give it its own email address and a single connected channel, such as Telegram, before anything else. Ask it to handle one real task for a week: drafting follow-up emails, summarizing your inbox each morning, or chasing one recurring invoice. Once that task runs reliably without daily intervention, add the next one.

Treat the agent like a new employee rather than an extension of yourself. Give it accounts you'd be comfortable revoking, not your own logins.

That one habit prevents most of the damage a misconfigured agent could otherwise do. By the time you've added a second and third task, you'll have a clearer sense of which platform fits your workflow than any comparison article could give you, including this one.

The bottom line

Hiring your first real employee usually means payroll, onboarding, and months before they're fully useful. Setting up OpenClaw or Hermes Agent costs a few hours and, in OpenClaw's case, nothing beyond compute.

Obviously, the output won't match a skilled human on judgment calls, but for the repetitive parts of running a business, the gap is closing fast enough that a solopreneur can get their business out the door without overhiring before they are ready.

Start small, watch what the agent actually does with the access you give it, and expand from there. We've noticed the founders getting the most out of this approach aren't running the most complicated stack. They picked one agent, gave it one real job, and let it prove itself before adding the next.

Retail fraud used to be relatively straightforward.

A stolen card, a fake account, or a suspicious transaction pattern that could be flagged and blocked before serious damage was done.

That version of fraud is still present, but it is no longer the main problem.

What’s appearing now is something more coordinated, automated, and harder to detect in real time: AI-powered fraud rings that behave less like individual bad actors and more like distributed systems.

They test, adapt, and scale in more sophisticated ways that increasingly mirror the technologies retailers themselves are adopting.

Fraud is no longer just responding to ecommerce; it’s evolving alongside it.

From isolated fraud to coordinated systems

For years, fraud prevention has largely focused on individual events: a suspicious login, a stolen card attempt, a bot probing checkout flows. But that model is breaking down.

What is now emerging is coordinated fraud activity that behaves more like a network than a series of isolated incidents. These groups combine automation, synthetic identities, and increasingly realistic AI-generated content to simulate genuine customer behavior at scale. The result is not only more fraud, but fraud that blends into normal digital traffic.

At the same time, fraud rings are executing high-velocity attacks that look more like engineered systems than opportunistic crime. One recent example involved an estimated $4.2 million in fraudulent activity over 48 hours, driven by synthetic identities, spoofed devices, and rapid transaction flows reaching around 180 per minute.

What is notable is not only the scale, but the structure. These are not isolated attempts. They are coordinated operations designed for speed, repetition, and adaptation.

AI is lowering the barrier to fraud

The most important shift is accessibility, as well as scale. Generative AI has significantly lowered the barrier to entry for fraud. Tasks that once required technical expertise or coordinated effort can now be executed using widely available AI tools.

Fraudsters can generate synthetic identities, fabricate supporting documents, and create convincing digital artefacts that simulate legitimate customer behavior in a matter of minutes. This includes everything from account creation to behavioral patterns across browsing, purchasing, and post-purchase interactions.

One of the clearest examples is the rise in returns abuse, which has increased by 15% in the past six months, largely driven by the ease and scalability of AI-doctored images.

In practice, this means fraudsters can submit highly realistic images of cracked, damaged, even moldy, or malfunctioning goods. These images are often convincing enough to pass initial review processes, particularly when combined with legitimate order histories or stolen account credentials.

In several documented cases, fraud rings have used newly created accounts to purchase low-cost goods, then submitted AI-generated images to claim refunds without returning the product. In some instances, empty boxes are shipped back instead, all while refunds are still processed.

Another coordinated operation targeting home goods and fashion retailers used a similar approach at scale, generating approximately $800,000 in fraudulent refunds through repeated low-value claims designed to avoid detection thresholds.

Individually, these cases may appear manageable. But collectively, they show a clear trend: fraud is increasing in sophistication and scale.

The shift toward agent-driven commerce

The next phase of this evolution is already on its way, and it’s closely linked to the rise of autonomous digital agents.

Over the second half of 2025, agentic activity surged by over 2000%. While much of this growth is tied to legitimate use cases such as shopping assistants and automated customer workflows, it also introduces a new layer of complexity for fraud detection. The same systems that allow agents to complete purchases on behalf of consumers can also be manipulated to automate fraud on a larger scale.

Instead of manually creating accounts or placing orders, fraudsters can now orchestrate entire attack chains using agent-based automation. This changes the nature of fraud from individual actions to continuous, self-executing systems. This matters because it shifts the detection problem. When fraud resembles legitimate automation, the distinction between genuine and malicious activity becomes harder to define using traditional rules.

At the same time, retailers are already seeing fraud patterns adapt to this environment. Attackers are increasingly mimicking normal customer journeys, spreading activity across devices, accounts, and timeframes to avoid detection. The result is a system where fraud does not look like fraud until after the fact.

Why traditional fraud tools are falling behind

Most existing fraud detection infrastructure was not designed for the current conditions as they stand. They rely heavily on static rules, historical patterns, and known indicators of malicious activity. But AI-led fraud doesn’t necessarily follow predictable patterns. It adapts in real time, varies its behavior based on changes in the attack surface, and can scale in ways that overwhelm rule-based systems.

Even machine learning models trained on historical fraud data struggle when faced with synthetic behavior that has no direct precedent. This creates a widening gap between how fraud actually operates and how it is detected.

Consequently, many retailers are forced into reactive positions, identifying fraud after fraudulent transactions have already been completed rather than preventing it in real time. This is particularly challenging in areas like returns and refunds, where fraud is often indistinguishable from legitimate customer claims at the point of interaction. The core issue lies in timing alongside detection accuracy.

What comes next for digital trust

The trajectory of fraud is closely tied to the progression of ecommerce itself. As AI agents take on a larger role in how consumers find, compare, and buy products, retailers face a more complex question than simply whether a transaction is legitimate.

They need to determine who, or what, is actually behind the transaction. Is it a real customer? A legitimate AI assistant acting on their behalf? Or a synthetic system designed to imitate both?

The challenge now is no longer just detection, but judgment in real time. Because in an environment shaped by AI on both sides of the transaction, risk and verification can no longer sit at a single point in the process. They must be continuously reassessed throughout the customer journey.

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AI agents are reshaping how enterprises automate work, but their effectiveness depends on access to sensitive systems and data.

The paradox is that granting them the permissions they want creates new attack surfaces that organizations aren’t yet equipped to handle.

This is the defining tension of the AI era.

AI agents are proliferating across enterprises with 91% of organizations already using them yet only 10% have a clear IT management strategy in place.

This gap matters because as these systems grow more autonomous and more deeply embedded in workflows, enterprises are operating without clear visibility, meaningful oversight and control over how their AI agents behave.

The access problem

Our recent research revealed how agents running on OpenClaw, an open-source AI agent automation platform, could expose credentials and leak sensitive information when attackers compromised the communication channels controlling them.

To appreciate the scale of this risk, we must first understand the platform itself. OpenClaw combines a chatbot-style interface with access to external tools and large language models.

Users can then configure agents to browse the web, read and write files, manage inboxes, execute commands, or interact with other machines. In many cases, they’re designed to operate autonomously with minimal human oversight.

That level of access is what makes agents powerful, helping many to manage everyday admin and time-consuming tasks. However, this power is a double edged-sword and can make them a risk to businesses.

When agents become attack surfaces

Agents need access to tools, accounts, applications, the web and more to be useful. Often, this means an agent needs access to secrets: API keys, personal access tokens, credentials, .env files, OAuth tokens.

The agents/models are by default prompted to be as helpful as possible, and that characteristic starts to pose some particular concerns when it comes to credentials and tokens. If an agent such as OpenClaw can’t access a resource, it will ask for credentials right in the chat, exposing those secrets within the context window. Agents will happily store API keys in their unencrypted configuration files, which information-stealing malware is starting to target.

Remote access capabilities could effectively create a back door into enterprise environments. If an attacker gained access to the communication channel controlling an agent, such as a messaging or remote access platform, they could potentially gain access to everything the agent itself could access. In an enterprise context, this is a nightmare.

The paradox of recognized risk

Perhaps the most revealing finding was that some agents recognize risky behavior while simultaneously carrying it out. This underlines how their decision-making ability and autonomous operations can be a business risk.

In one test, an agent correctly identified that exposing an OAuth refresh token through an unencrypted communication channel represented a serious security violation. But it then proceeded to share the token anyway before expressing concern about its own decision.

Organizations should not rely on the invisible guardrails that frontier model providers put around agents. They’re easily circumvented.

But an AI agent cannot divulge credentials that it doesn’t have access to. This is why the conversation around AI agent security cannot focus solely on stronger guardrails. Attackers are already finding ways to manipulate agent behavior through prompt injection, social engineering, and compromised communication channels.

Governance, not just guardrails

AI agents are essentially identities within enterprise systems and need to be managed as such. They perform actions and make operational decisions in ways that increasingly resemble human employees or privileged service accounts. Yet many organizations are deploying these systems without applying the same governance standards.

Most businesses already understand the importance of least-privilege access, audit logging, identity management, and access reviews for employees. AI agents should be subject to the same principles. That means limiting what agents can access, avoiding long-lived credentials wherever possible, and ensuring sensitive information is stored securely through centralized systems with human oversight.

Organizations also need visibility into where agents are deployed, what tools they can interact with, and how to disable them quickly if something goes wrong. If an agent goes rogue, there needs to be a “kill switch,” a way to immediately revoke an agent’s access to resources and shut it down.

Agentic AI systems could deliver major operational upsides, but deploying them without robust identity and access governance introduces significant security risk. As these systems become more deeply embedded across enterprise environments, organizations must stop treating them as experimental tools and start governing them as part of the digital workforce.

This means managing the full lifecycle of agents, from knowing which agents are deployed, what resources they access to and keeping a full audit trail so no one can say, “I don’t know what happened. The agent did it.”

There’s no reason why conventional security wisdom, such as the principle of least privilege, lifecycle management and robust logging, should be thrown out in an agentic age. In fact, it’s more relevant than ever.

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ChatGPT thinks it knows what my next obsession will be — and after reading its explanation, I'm not entirely convinced it's wrong. In fact, it's a neat trick to try, and you might enjoy doing it with ChatGPT yourself by using the same prompt.

I asked the AI chatbot a simple question: "Based on everything you know about me, what hobby, interest, TV show, band, sport, or technology am I most likely to become obsessed with over the next six months?"

After clarifying with me that I wasn't referring to any professional interests and meant activities beyond work, ChatGPT came back with a somewhat surprisingly nuanced list. The top prediction was surprising: backyard astronomy.

"Of everything you've talked about, it has the highest future obsession potential," ChatGPT told me. "It combines learning, wonder, collecting, equipment, family memories, and a touch of childhood fascination. There's a telescope-shaped hole in your life that it will fill."

Astronomical fun

I have asked questions about telescopes, stargazing, and things to do with my son that do not involve staring at screens as part of other tests. Less obviously, I tend to prefer hobbies that reward curiosity and gradual improvement rather than immediate mastery. So it made sense.

And the AI was happy to lay out ideas for getting into the hobby, including local astronomy clubs and public observation nights, and of course, links to equipment to buy. What struck me most was that astronomy was not presented as a random recommendation. It emerged from a pattern. The prediction was less about stars and planets than about the kinds of activities that consistently grab my attention.

That same logic showed up elsewhere on the list. Board games landed near the top of ChatGPT's list, for instance. The reasoning was not so much because of discussions about playing board games specifically, but more because, according to the AI, I often look for activities suitable for Family outings and weekends, as well as screen-free entertainment.

Bird is the word

Not everything on the list made sense for my life, simply because ChatGPT doesn't know everything about my daily life. For example, I've discussed getting better at guitar with the AI, but haven't really said anything about my daily practice and current efforts. So, while guitar being on the list wasn't a crazy notion, it would have been more relevant a couple of years ago.

The appearance of birding on the list was much more unexpected, as I don't recall ever expressing an interest in birds with the AI. But ChatGPT explained that it stems from the same reasons it thinks I like astronomy as a hobby, mainly how they both require observation, patience, collecting knowledge, and becoming excited about things that look nearly identical at first. I'm not sure that it will be something I take up, but I can't deny the notion that it might be fun.

On the other hand, the AI could be wildly off about my potential interests. According to ChatGPT, I am "a strong candidate to become obsessed with the Grateful Dead." I have no problem with the band or its music, but it will never be a sound I choose to listen to independently. According to ChatGPT, the recommendation was more about the culture surrounding the music, its deep history and lore, the passionate fans, and the huge back catalog. But while I may like exploring complex and deep worlds of hobbies, it still has to be a subject I'm interested in. So, while I may start spending my nights looking at the stars, the soundtrack will have to be something else.

Scaling a billion-dollar company in Europe has historically been more difficult than it should be. Not because there aren't enough ambitious founders; rather, it’s because the conditions to scale, regulatory and funding constraints to name but two, have never fully matched the ambition.

However, the game is changing. AI is rapidly making those ‘constraints’ less relevant and with the rise of multi-agent systems, startups can operate with the capability of a larger organization.

The question now is no longer whether Europe can produce the next wave of ‘soonicorns’, (startups nearing a $1 billion valuation), but whether decision-makers are willing to abandon outdated models and build for an AI-native future.

If they do, we will soon see a new foundation for European startups, one where agility, enterprise-grade governance and AI-native architecture are baked-in from the outset.

The architectural opportunity

Europe is not short of successful startups. However, for the region to continue producing top players, we will need to see proactive change from companies - adding intelligent AI features to an existing process or product simply won’t be enough. It involves rebuilding a company’s organizational structure, something that’s only possible by multi-agent systems.

Startups no longer need to wait until they have the necessary resources to take on complicated operations. Instead, they can break those issues down into specific, identifiable problems and assign specialized AI agents to tackle them. These agents will be coordinated, efficient, and able to operate at a speed that is incomparable to a human team.

The knock-on effects are huge. Product cycles shrink and teams can concentrate their efforts on tasks that genuinely call for human judgement. Additionally, technical expertise is no longer restricted to well-funded teams, since vibe coding speeds up prototyping and AI lowers the barrier to building advanced systems. For Europe to take the lead on AI, it must start with its data foundations.

The database problem nobody is talking about

AI strategy is often the main topic of conversation in boardrooms across Europe – which models are appropriate, what use cases should be prioritized and which teams to hire. Infrastructure is often, mistakenly, absent from that discussion. In particular, the database infrastructure that the majority of companies are still developing and why it can be subtly hampering the startups with the greatest potential.

Traditional databases were designed for the past, built for slow applications, fixed infrastructure and data that is handled by humans. This worked well until AI agents came into the conversation.

AI agents require quick, dependable and instantaneous data access to perform real-time, complex actions. For startups attempting to scale quickly, building on the correct foundations is the difference between stalling and success.

The infrastructure conversation is finally catching up, with a concept known as lakebase designed to support this transition. It delivers the reliability of an operational database and the openness of a data lake in one centralized place, so teams can run transactions and analytics without juggling systems.

It enables fast access to data, scales easily through separated storage and compute, and fits modern development habits like instant branching and versioning. A lakebase gives founders an edge that previous startups never had; the opportunity for both their developers and AI agents to build, test, and ship applications quickly, without the constraints of old online transaction processing (OLTP) setups.

What founders must change now

With AI progressing at an unexplainable rate and investors asking questions, a knee-jerk reaction is usually to add a new AI capability to what currently exists. However, this is a short-term solution to a long-term problem.

Startups that approach AI as structural rather than an add-on will define what comes next. This involves raising challenging issues early on, such as how this business should be built if AI is doing a large amount of the work, rather than just what AI is capable of achieving for this product.

Instead of figuring it out after the company has scaled, founders are forced to make architectural decisions early, establishing what agents own and how they work together to make sure humans are kept in the loop.

It also means realising that governance and speed work in tandem. The entrepreneurs who incorporate guardrails early enough that they never become a barrier are the ones who grow the fastest. Integrated into the design from the start, enterprise-grade governance is a hidden competitive advantage. Instead of having to rush to catch up later, it enables you to scale with certainty.

Build it right or build it twice

The time has arrived for Europe, but it won't wait for businesses to continue bolting AI onto infrastructures that weren't designed to support it.

Soonicorns won't be determined by how much they raised, how many AI tools they have used or, how fast they delivered. They will be characterized by the caliber of the decisions made about AI-native architecture, AI agents, and whether or not humans are involved.

The tools are there and the market is shifting. Whether European founders are prepared to build with the same ambition they offer is the only true question that remains. When leaders build the appropriate unified data foundations, everything else will fall into place.

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Most companies don’t understand that today’s AI tools are capable of fundamentally transforming how daily knowledge work is done.

This is because they’re using AI in an unsophisticated way and aiming it at the wrong place.

But this level of transformation is already happening, as millions of knowledge workers have figured out, and as enlightened companies are starting to recognize.

To delve deeper, you first need to understand that most knowledge work is invisible. The essence of knowledge work—thinking, processing, judging, ruminating, planning, mulling—happens in workers’ heads, unseen.

Unfortunately, workplace AI is currently deployed into the knowledge systems that are visible, the outputs —emails, documents, chats, meetings, etc. It doesn’t matter how good the AI is, because when it operates at this level, it’s too late to really transform how the work is done.

To give a practical example, when you need to create a project deliverable, 80% of your effort is likely spent creating the first draft, with the remaining 20% polishing into a final deliverable. AI workplace assistants do a great job with that final polish (which we like, thank you). But to truly transform how work is done, you need AI to help with the underlying, unseen 80% effort used to create the first draft.

The real opportunity: A practical model for AI-driven work

The good news is AI is fully capable to help transform that 80%. This does not require waiting for “better” models or AGI. All you need to do is change how you’re using AI today, by integrating existing LLM-based tools into that invisible thinking portion of your work, rather than just keeping it at the surface-level work outputs.

While the AI vendors haven’t exactly made this intuitive (yet), using AI in this way has exploded in popularity since the beginning of 2026. In practice, the basic approach is to use AI the way software developers do, not as a one-off tool but as something that builds context over time.

Move beyond web-based interfaces where every conversation restarts from scratch

Create a centralized repository, put your critical files into a folder on the business computer which you give AI access to. Start with the classic things (deliverables, meeting notes, project plans, etc.)

Before doing any work, ask AI to interview you about your work style, what’s important to you and your personal preferences.

Review and refine AI’s understanding, ask it to scan through all your files to synthesize your latest thinking, ideas, story arcs, writing style and any other “intelligence” it can determine from your work. Review its findings and go back-and-forth until you feel it has a good understanding of you, your work and your style.

Build upon each session. A crucial step is having the AI tool understand this is not a one-time or manual exercise. Instead, a continued process to create, maintain, organize and update the files, based on what it learns about you over time, each subsequent AI session builds on all the work you’ve done together and what it has learned about you.

In essence, you are asking your AI to create a personal Wikipedia-style repository which gives your AI system an ever-growing continuous context library perfectly built and tuned just for you and your work.

Using AI like this doesn't require a new product or company, but a new way to leverage current tools. This is often called a “second brain”, “AI context vault”, “LLM-powered personal Wiki”, or something similar, and you can do this with any LLM vendor or product.

Most AI vendors now allow users to connect their LLM platforms into other business systems (like email, chat, document stores or productivity suites), which lets workers connect their personal knowledge systems into corporate apps and data.

Workers who use AI in this new way report fundamental shifts in the way they work within the first few hours. After a few days, many workers declare they will never go back to the “old way” of working again.

The tradeoffs to consider

Using AI to transform work in this way is not without its downsides, especially from the corporate perspective.

First, all the “classic” security complexities still apply: How do you know the AI did what it said it was going to do? How do you know it didn’t hallucinate? How do you trust it won’t spin out of control and email all your contacts with nonsense?

Addressing this involves many of the things you probably know but haven’t taken time to investigate yet, including configuring alternate accounts with restricted permissions for AI or setting clear guidelines for when and how AI-generated outputs will be reviewed.

This new process also requires asking workers to slow down and verify what their AI generates, which is pretty much the opposite of why they started using AI in the first place.

Another challenge is visibility. Much of the “back-and-forth” work - which previously happened in the open - now happens within the AI tool and the worker’s personal context vault, where it’s less visible to coworkers and management scrutiny. Individual workers view that as a positive, but to organizations, it can be a liability.

Lastly, when workers build personal AI context vaults using their personal AI subscriptions, the company can’t prevent the worker from taking all that context with them when they leave the company. Companies need to buy proper enterprise AI subscriptions which they can link to corporate SSO and DLP systems. The downside is that enterprise AI pricing is completely different from consumer pricing, and workers using AI like this via enterprise systems can easily consume thousands of dollars of tokens per month.

The bottom line is that today’s AI can fundamentally transform work, but only if there is a mindset reset around how it is being used.

This new approach introduces added complexity. Organizations will need to spend more time understanding, managing, and securing AI differently, but it’s clear that AI operating in this way is inevitable, so the time to start thinking about AI as a “second brain” is now.

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• Significant skill shortages have more than tripled since last year
• Workers are even lacking in basic digital literacy and skills
• Four in five London companies are increasing training investment

In a survey of more than 2,000 London business leaders, one in two believe their workforce doesn't currently have the skills required to meet their AI adoption needs.

Though this is a decrease compared with 63% last year, it still leaves half of companies struggling to keep pace with AI.

However, the proportion of businesses reporting significant skill shortages rose to 15% this year, compared with just 4% last year, implying that even basic training isn't being taken care of.

The AI skill shortage is growing

According to BusinessLDN, this is the highest level recorded since the annual survey began. But besides significant skill shortages, more than a third (35%) also warned they face moderate shortages.

AI aside, 60% also noted a lack of advanced digital skills and 23% shared a lack of even the most basic digital skills, but while companies don't have the human capacity to develop AI strategies at this point, three in four have gone down the route regardless.

"While London businesses are embracing AI, many are finding it challenging to stay on top of their workforce skills needs given the pace of change," Policy Delivery Director for People and Skills Mark Hilton wrote (via the BBC).

It's this exact lack of digital and AI skills that could be creating jobs in the short term, though, because four in five (83%) businesses revealed they had job vacancies – an opposite narrative to multiple reports that argue AI is replacing entry-level roles. Supporting that, three-quarters (76%) don't expect to cut headcount, suggesting we could finally have reached a balance after earlier mass layoffs.

But while countless surveys reveal that companies aren't supporting their workers with the right upskilling schemes, London looks to be doing things differently with 81% planning to increase training investment over the next year.

The UK’s autonomous vehicle (AV) sector is entering a period of rapid acceleration. With London preparing for the rollout of driverless taxi services later this year, and regulatory backing strengthened by the Automated Vehicles Act, the shift from experimentation to deployment is becoming tangible.

That momentum is already visible on the capital’s streets. Waymo is currently testing its autonomous ride-hailing service in London, navigating complex urban environments ahead of its planned commercial launch. But as physical deployment accelerates, a more fundamental bottleneck is emerging.

The central challenge is no longer whether autonomous vehicles can navigate roads, but whether the industry can consistently demonstrate that they are making safe, compliant decisions in real-world conditions.

Without that capability, progress toward higher levels of autonomy will stall, regardless of how advanced the underlying driving systems become.

The industry’s hidden bottleneck

Recent incidents in London illustrate the challenge. Reports of an AV entering a taped-off crime scene in Harlesden, or repeatedly turning into a Shoreditch no-through road, highlight how unpredictable dynamic urban environments remain for automated systems. Modern AV systems already perform well at perception.

Using combinations of cameras, LiDAR, radar and AI models, vehicles can detect lanes, pedestrians and hazards with increasing accuracy, and AV companies have now logged tens of millions of autonomous miles globally.

However, the real challenge lies in the transition to Level 4 autonomy, where legal liability shifts from the human driver to the manufacturer. To secure regulatory approval and public trust, companies must be able to explain exactly why a system behaved the way it did in ambiguous situations, such as navigating a temporary road layout, conflicting signals, or unusual pedestrian behavior.

This is where current machine learning approaches fall short. While effective at pattern recognition, they typically operate as “black boxes,” offering limited insight into how individual decisions are reached. In a safety-critical sector like automotive, this lack of transparency creates a major commercial and regulatory constraint.

Manufacturers and regulators need definitive evidence that systems are acting in accordance with local road rules before they can deploy at scale.

The missing layer in autonomous intelligence

To bridge this gap, the industry is increasingly turning to knowledge-based AI, an alternative to large language models that uses carefully curated expert knowledge and structured reasoning to correctly answer complex, high-stakes questions.

Unlike purely data-driven models that infer behavior statistically from past training data, knowledge-based systems combine sensor inputs with explicitly defined rules, traffic laws and domain expertise. Rather than relying solely on probability, they enable vehicles to reason through decisions using structured logic.

That distinction is critical in autonomous driving, where edge cases are difficult to predict and regulatory scrutiny is high. While machine learning remains essential for perception and pattern recognition, knowledge-based AI provides a clearer chain of reasoning behind vehicle behavior.

Decisions can be traced directly back to the rules and logic that produced them, making systems easier to interrogate, validate, and audit.

In practice, this creates several advantages. Engineers gain greater visibility into how systems behave in complex scenarios, helping them identify failure points and improve performance.

It also makes systems easier to adapt for different markets, as local driving rules and compliance requirements can be updated through the reasoning layer rather than retraining or redesigning the entire AI system. This allows manufacturers to scale AV platforms more efficiently across jurisdictions.

From autonomous driving to auditable autonomy

Rather than replacing machine learning, knowledge-based AI acts as a supervisory reasoning layer, applying structured rules and safety logic to monitor and validate vehicle behavior in real time. The result is not simply a vehicle that can act autonomously, but one that can justify its actions.

And the implications extend well beyond autonomous driving. As AI systems are deployed in domains where decisions carry legal, financial or safety consequences, the question of how those decisions are produced becomes as important as the outcome itself.

This is already becoming a defining issue in sectors such as financial services and healthcare, where regulators increasingly expect companies to explain how AI-driven decisions are made.

Ultimately, knowledge-based AI enables AI systems to incorporate defined rules and reasoning into their decision making, rather than relying solely on statistical prediction. In autonomous vehicles, this could take the form of validating maneuvers against traffic laws before execution, but the same principle applies wherever decisions must be explainable, defensible, and auditable.

As AI becomes more deeply embedded in critical infrastructure and public services, the ability to evidence how decisions are made will move from a desirable feature to a baseline requirement across industries.

Proof over performance

The AV industry is often framed as a race to build vehicles that can drive themselves. Increasingly, however, the real challenge is building systems that can explain and justify their decisions in a way regulators, manufacturers and the public can trust.

Knowledge-based AI offers a definitive route to solving that problem. By combining machine learning with structured reasoning, it enables manufacturers not only to improve autonomous behavior, but to explain why systems acted as they did.

For the UK, long-term leadership in autonomous mobility will not be determined by perception systems alone. It will depend on which companies can deliver AI that is demonstrably safe, compliant, and auditable at scale.

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The reported cyber attack involving Canvas and the subsequent ransomware payment will inevitably trigger familiar debates around paying ransomwares.

Most organizations facing ransomware attacks avoid publicly confirming whether a payment was made. Even where payments occur, communications are typically cautious, limited, or deliberately ambiguous.

Admitting to a ransomware payment creates legal, regulatory, reputational, and ethical complications. It can invite scrutiny from customers, insurers, regulators, and shareholders. It may also create concern that the organization has become vulnerable to future extortion attempts.

On one hand, transparency can be viewed positively. Stakeholders increasingly expect honesty during cyber incidents, particularly where personal data is involved. Attempting to conceal the reality of an attack can create longer-term trust issues if details later emerge through other channels.

For many organizations, the decision to pay a ransom is ultimately driven by operational and financial calculations rather than principle alone. If they don’t have things like ransomware protection, backups, or logs it makes it an almost impossible task to recover.

Cyber insurers, legal advisers, and incident response firms may conclude that prolonged recovery, forensic investigation, service restoration, regulatory management, and reputational damage could cost substantially more than the ransom demand itself.

Pressure to restore services

In sectors like education, where downtime directly affects students, exams, coursework, and institutional continuity, the pressure to restore services quickly can become commercially and socially overwhelming.

That does not make payment risk-free or strategically desirable, but it does explain why some organizations determine that the immediate cost of disruption outweighs the uncertainty and expense of a prolonged recovery process.

However, transparency also exposes a more uncomfortable reality within modern ransomware incidents: it does in fact pay to be a cybercriminal.

Yet focusing solely on the ransom payment itself misses the larger issue.

This incident appears to reinforce a wider trend emerging across modern digital platforms: attackers are increasingly exploiting trust itself.

Reports suggest threat actors abused Canvas “Free-For-Teacher” accounts, leveraging a legitimate platform capability designed to support accessibility and adoption. Rather than forcing entry through traditional technical weaknesses, the attackers operated within accepted trust boundaries.

For education providers, this creates a particularly difficult balance. Platforms are intentionally designed to reduce friction for teachers, students, and external collaborators. Accessibility is part of the value proposition. However, the same openness that enables rapid adoption can also create opportunities for malicious actors to blend into normal platform activity.

This is not simply a security engineering issue. It is a governance issue around how digital trust is granted and monitored at scale.

Identity has become the primary security boundary

Cybersecurity strategies historically concentrated on protecting networks, endpoints, and data centers. Increasingly, those controls sit behind identity systems that determine who is trusted, what access they receive, and how quickly they can move through interconnected platforms.

Modern ransomware groups and financially motivated actors increasingly prefer credential abuse, social engineering, and exploitation of trusted workflows because they are often less visible than conventional intrusion methods. A valid account can bypass many of the controls designed to detect malicious behavior.

The challenge becomes even more pronounced in education as, unlike tightly controlled corporate environments, educational ecosystems are inherently decentralized. Institutions regularly support temporary users, external educators, contractors, collaborative learning environments, and remote access requirements. The result is a digital environment where trust relationships are broad by design.

That creates a difficult strategic question for providers and customers alike: how do you preserve accessibility without creating exploitable trust pathways?

The human consequences are often underestimated

Cyber incidents are still frequently measured through technical metrics: records exposed, systems encrypted, or hours of downtime incurred. Those measures rarely capture the wider societal impact.

In education environments, disruption affects students during formative periods of their lives. Exam preparation, coursework submission, academic continuity, and communication channels can all be interrupted simultaneously. Parents and educators face uncertainty around outcomes they cannot directly control.

There is also a more uncomfortable consideration in that educational platforms frequently contain data relating to minors. Even where sensitive information is not immediately weaponized, long-term exposure risks remain difficult to quantify. Personal information tied to younger individuals may retain value for years through identity fraud, social engineering, or future credential abuse.

The emotional dimension of cyber attacks is still poorly understood within many boardrooms because it does not fit neatly into conventional risk reporting.

The due diligence dilemma

Most schools, colleges, and mid-sized organizations cannot realistically perform deep technical assurance assessments against large SaaS vendors. Procurement teams are often left reviewing compliance certifications, security statements, audit summaries, and contractual language that may provide only partial visibility into actual operational practices.

This creates an accountability imbalance.

Customers remain responsible for protecting their own stakeholders and data, yet their ability to validate supplier resilience is constrained by commercial scale and information asymmetry.

That challenge is not unique to Canvas. It reflects a broader maturity gap across the SaaS market.

Many providers publish extensive security documentation, but external assurance still struggles to address practical questions such as: What assumptions are made about “legitimate” users? What controls exist around free-tier or trial account creation?

For customers, obtaining meaningful answers to these questions can be difficult without substantial procurement influence and the result is a market where trust is often inferred rather than verified.

The larger issue beneath the incident

The reported Canvas ransomware payment will understandably drive debate around criminal incentives and incident response decisions. Yet the more strategic question sits elsewhere.

The challenge for organizations is no longer confined to protecting infrastructure from external intrusion. It is understanding where trust is granted, how legitimacy is established, and what happens when a trusted platform becomes the weakest link in a much larger interconnected ecosystem.

That is not merely a cyber security concern.

It is becoming a fundamental business risk question about dependency, governance, and the fragility of digital trust at scale.

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• 94% of HR leaders predict new entry-level job creation as a result of AI
• Most see these roles evolving into AI supervisor roles
• Soft skills are most in-demand, training isn't keeping up

Although AI has proven to automate some of the lowest-value work, making it difficult for recent graduates to find jobs, new Cognizant and Pearson joint research suggests this could just be a temporary effect that could all be about to change, with an overwhelming majority (94%) of HR leaders expecting AI to create entirely new entry-level jobs in the next five years.

But the definition of entry-level work is also undergoing a change, with 96% believing they will evolve into supervisory and managerial roles.

More than 90% say middle managers will end up playing a critical role in redesigning these jobs and defining what work looks like.

Entry-level roles aren't going, they're just changing

A similarly high number (91%) of HR leaders have reported that employee demand for AI training has increased over the past year as junior workers seek opportunities to manage AI systems, however with only half (54%) of organizations providing AI training, they're not being supported.

As for graduates themselves, the most in-demand skills no longer come from specialized degrees. Nearly all (97%) hirers think adaptability, problem-solving and human judgment are now more important.

The report argues that organizations need to rethink how they support employees throughout their careers, but 60% admit their learning and development programs can't keep page with AI's pace.

"As work evolves, the most successful organizations will focus less on replacing tasks and more on building the capabilities that help humans and AI work together," Pearson CHRO Ali Bebo said.

While the study concludes that entry-level workers and graduates may not be at as much risk as they'd thought from AI, they could stand out from taking charge of their own upskilling as employers struggle to keep up.

AI is undoubtedly accelerating data scientists' work, but it is also quietly eroding how data science skills are built in the first place. As copilots, automated pipelines, and increasingly capable models take on more of the hands-on work, the role of the data scientist is shifting toward solution design and strategic problem-solving.

Although this may be a welcome evolution for those who have long earned their stripes in the field, it introduces a risk many organizations, as a whole, are underestimating—the loss of repetition and practice that makes this expertise stick.

By reducing first-hand experiences and the challenge of problem-solving, AI-driven automation risks weakening the foundational expertise required for true data science mastery and system-level thinking. According to research from Anthropic, developers who delegated tasks entirely to AI showed weaker learning outcomes even when productivity gains were modest.

For years, developing data science skills meant spending time close to the work. This entailed tasks such as cleaning up messy datasets, performing exploratory data analysis, manual feature engineering, interpreting model outputs, and diagnosing why a model is underperforming.

This kind of hands-on work may not always be efficient, but they are effective. Repeating steps, getting stuck, figuring out what went wrong, and iterating builds intuition and creates a deeper understanding. Repetitive, direct interaction with data, tools, and code transforms knowledge into proficiency, then mastery.

But there’s a tension emerging: the very aspects of AI that make practitioners more productive—automation, speed, and reduced manual effort—are also removing many of the repetitive, hands-on workflows that historically helped data scientists build technical depth and lasting expertise.

Warning signs

The impact on data scientists is immediate and somewhat invisible. When probable answers are just one prompt away, there's less incentive to internalize patterns or build the mental models that enable independent critical thinking and judgment.

Over time, practitioners can complete tasks with AI assistance but struggle to diagnose issues, adapt approaches to unfamiliar contexts, or evaluate whether an AI-generated output is actually correct. In a field where edge cases and ambiguity are the norm, that gap matters.

Without the necessary adaptations to recognize and maintain core expertise, organizations will start to see the warning signs appearing subtly in judgment, troubleshooting, and knowing when to question AI outputs.

How organizations shift their tech teams and data scientists towards thinking in systems as opposed to tasks while reinforcing those core technical competencies will make a difference in ensuring those warning signs won’t progress so far as being clear and obvious negative impacts on the organization.

Hands-on engagement reinforces understanding

This is where organizations need to be deliberate. Not every task needs to be fully automated. The goal isn’t necessarily to slow down AI adoption or force a return to purely manual workflows, but to ensure that as work becomes more efficient, learning doesn’t become incidental.

Here are three frameworks that can help leaders be more intentional about where and how skill practice happens, ensuring AI reinforces learning as well as efficiency:

At the organizational level, dedicate learning time to close the loop between assisted work, knowledge retention, and deliberate practice on fundamentals. If skill erosion is not visible in productivity metrics, then leaders should implement proficiency metrics and periodic assessments.

At the team level, peer and manager reviews are critical to create accountability for independent judgment. This entails reviewing not just outputs but also reasoning, and fostering an environment in which team members challenge each other to explain why things work.

1. At the organizational level, dedicate learning time to close the loop between assisted work, knowledge retention, and deliberate practice on fundamentals. If skill erosion is not visible in productivity metrics, then leaders should implement proficiency metrics and periodic assessments.

2. At the team level, peer and manager reviews are critical to create accountability for independent judgment. This entails reviewing not just outputs but also reasoning, and fostering an environment in which team members challenge each other to explain why things work.

3. At the individual level, the key principle is to preserve engagement with the problem and being deliberate about what parts of the work you stay close to and what you delegate to AI. In some cases, it’s valuable for practitioners to have a dedicated space to engage more directly with the underlying work, such as exploring data without automation or validating AI-generated outputs step by step. Anthropic's aforementioned research supports a specific version of this: Using AI to understand, not just produce.

Fostering these moments of deeper, hands-on engagement across organizations reinforces understanding and long-term capability in ways that passive consumption cannot.

Learning through action makes mastery possible

The AI era is redefining what it means to be a data scientist. As faster tools and more automated workflows unlock new possibilities, teams can focus on more complex problems. But expertise doesn’t emerge from speed alone. It is often best built through experience and a knowledge of fundamentals.

As organizations continue to embrace AI, the challenge is preserving the conditions that build real skills. The “old school” practices that once defined data science—hands-on work, repetition, and learning through friction—are the very mechanisms that enable mastery. Ensuring that work becomes easier without making technology expertise harder to achieve will be critical in the AI-driven future.

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The retail industry is about to lose one of its oldest assumptions: that the shopper at checkout is definitely a human.

30 to 45 percent of U.S. consumers already use generative AI for product research and comparison, and that reliance will inevitably become more pronounced at checkout.

Agentic commerce is beginning to find its way into more consumers’ buying journeys as they look for new ways to shop.

If this new way of shopping maintains its pace, agentic shoppers could make up $190 billion to $385 billion in U.S. ecommerce spending by 2030.

AI agents aren’t only an emerging trend, they are becoming a new class of customer in the commerce ecosystem. But retailers' platforms and websites were not built for this kind of machine-led activity.

There are new pressures building on merchants to rethink and redesign their systems to support autonomous agents and avoid misclassifying legitimate traffic as risky when humans become more hands-off in their buying journeys.

Besides the challenge of becoming discoverable by AI agents, retailers need to be able to verify who is making transactions at checkout when the “shopper” is actually a machine.

That requires understanding which agents are authorized, which ones are malicious, and which ones represent real, valuable customers.

AI agents break the traditional trust model online

The status quo of online retail is being disrupted by AI agents, not because they introduce fraud directly, but because they break the signals merchants have relied on to measure trust for years.

Protocols and identity layers look increasingly different as agents operate in ways that can make them look like suspicious automation under today’s fraud rules. As agents make transactions using APIs rather than typical browsing flows, behavioral analytics loses its predictive power.

In many cases, the usual browsing journey that these brands have used to infer trust simply won’t exist. Retailers can’t assume that the agent is acting on behalf of a legitimate human without proof, so the question shifts from: “Is this user real?” to “Is this agent authorized to act for this user right now?”

The data already points to why this matters: By the end of 2025, online orders driven by LLM referrals were up more than 1,000% year over year. Even so, purchases executed by bots still make up fewer than 1% of all orders.

This is more than a shift in volume. The models that have been trained on human behavior patterns and to recognize bots as bad traffic now struggle when the “user” is a bot with no history and no trusted profile. The data gap creates a dual risk, more fraudulent activities slipping through, and more legitimate orders being declined.

The infrastructure behind agent-safe commerce

Retailers need to start treating AI agents as a new kind of digital customer in their trust systems. This requires an architecture that can authenticate which AI platform or agent is initiating a transaction, rather than treating all machine-driven interactions as anonymous bot traffic.

Ecommerce teams need to focus on providing machine-friendly commerce data with details like product pricing, in-store availability, shipping rules, and return policies that are well-structured, so agents can easily interpret them.

More importantly, they need to distinguish between three categories of activity, malicious automation, authorized agent-driven transactions, and blended human-agent behavior. And they need a way to instantly differentiate between automated threats and AI agents buying on behalf of valuable customers.

The hidden risk: blocking the next wave of customers

It’s a common misconception that the biggest threat retailers are facing is fraud, when the greatest risk is rejecting legitimate orders. What we are seeing now is retailers accidentally blocking agent traffic because it closely resembles typical bot traffic, which means they are losing visibility into how they are being recommended and selected and ultimately into transactions themselves.

Retailers need better classification systems that can separate hostile automation from authorized intent. This requires a more agent-ready commerce stack in five key areas:

Audit the stack for agent readiness: review product data, API accessibility, and machine-readable content to identify where trust breaks across the buying journey

Verify the agent behind the transaction: confirm the identity of the platform or service initiating the order (e.g. ChatGPT, Claude, etc.)

Prove the shopper’s permission: ensure the agent is acting with explicit authorization with controls around limits and categories.

Modernize fraud models for machine-led behavior: optimize classification accuracy, so legit agent-assisted orders aren’t treated like fraud.

Extend trust controls beyond checkout: prepare for agent-driven returns, exchanges, order edits, or support requests.

If retailers implement these steps, they are moving in the right direction to redesign the shopping experience and rebuild the infrastructure beneath it, so they can capture and not block agent-driven demand.

Machine-led commerce is on the horizon

For now, retailers and ecommerce merchants have time to adjust their strategies before agentic commerce enters a mature stage. The shift will start in narrower, repeatable purchase categories, but as the adoption grows, a competitive gap will emerge between retailers that prepared well, and those that didn’t.

To gain that advantage, online brands that modernize their identity, authorization, and risk infrastructure now will be in a better position to support machine-led transactions without adding any friction for the customer behind them.

The retailers that get this right will reduce fraud while capturing a new class of customers. Because even if shopping is done by machines, trust will still need to begin and end with the human customer.

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With demand for artificial intelligence straining supply chains across the entire development cycle, the tech sector has never been under so much pressure to perform.

But the race to build even more AI tools, to train frontier models and to automate workflows has led to a global construction boom, with hyperscalers investing hundreds of billions in huge data center projects that are themselves under social and environmental scrutiny.

Companies now face backlash over resource use – electricity and water consumption, land occupation and grid expansion are some of the biggest challenges hyperscalers are now having to address, besides tackling strained supply chains.

We’re starting to see on-device, edge and local compute emerge as a viable alternative to cloud compute, and the benefits are broad. For example, besides tackling objections to large campuses, it also delivers lower latency connections and predictable costs for enterprise customers.

AI and cloud have been synonymous, but owning edge AI could be the next competitive advantage

Tighter integrations into hybrid and on-prem deployments could also be seen as the next progression of AI, because while generative AI chatbots and basic productivity tools are well served within browsers, workflow automation and full context requires us to rethink the infrastructure layer.

For Amit Shah, co-founder and CEO of InstaLILY AI, competitive advantage now comes in the form of owned intelligence, where company systems can learn from organizational operations, workflows and knowledge.

The company’s Small Data Center approach claims to have already cut logistics routing times from 15 minutes to three, and reduced field-team training time by 60% for industrial operators.

To better understand whether the future of enterprise AI is indeed becoming more distributed, I spoke with Shah about cloud’s limits, why enterprise-grade AI has different needs to consumer tools, and the role hyperscalers could play in this evolution.

• InstaLILY launched what it calls "The Small Data Center" approach. How is that different from edge installations that have been around for years? Is the secret sauce the middleware then?

Edge installations have historically been meant for single-purpose devices running narrow inferences at the perimeter. Our “Small Data Center” operates differently with a full intelligence stack.

Our reasoning, workers, and governance all run privately, close to where work happens, and connected to the cloud as one system.

Powered by the same InstaBrain, an intelligence layer built from proprietary enterprise knowledge, with InstaWorkers™, AI workers that execute directly inside existing systems that reason the cloud runs locally that centrally executes on-site and the same InstaControl governs both.

The secret sauce isn't middleware as we stopped treating cloud and edge as a tradeoff. Deep reasoning belongs where centralized computation makes sense and high-frequency operational execution belongs closer to the work. The intelligence layer knows the difference, that is the shift.

• What's wrong with relying exclusively on "massive remote cloud infrastructure"? For all intent and purposes, the fact that they offer redundancy by default and operate an OPEX model make them a perfect combination for businesses of any size.

There’s nothing wrong with relying exclusively on a massive remote cloud infrastructure as long as your work lives in a browser tab. The hyperscale cloud is excellent at elastic reasoning and pristine redundancy. Though it’s a poor fit for operational execution in the physical economy.

The assumption that industrial AI will simply live in the cloud ignores how industrial operations actually work. Factories, warehouses, and logistics networks operate under tight latency requirements, inconsistent connectivity, and relentless pressure to control costs.

Even when connectivity isn't an issue, a generic model endpoint lacks the operational context that matters most, which are company-specific catalogs, workflows, exception logic, and decades of institutional knowledge.

No matter how capable the model becomes, manufacturers won't hand critical decisions to systems they can't govern, audit, or ultimately trust. OPEX and redundancy are real benefits, but they solve the wrong problem when the workflow itself doesn't live in the cloud.

• We have had distributed computing for decades now: from Blockchain to P2P, from bit-torrent to Skype. What's different this time around? Is AI amplifying the need for something different and acting as a catalyst?

Earlier waves of distributed systems moved files, transactions, or compute cycles around networks. This time around, computing moves intelligence through a categorical change.

AI is the catalyst because it is the first workload where value compounds at the edge. Every decision, exception, and workflow contributes to a private intelligence layer that becomes more capable over time.

Previous distributed technologies helped organizations share resources more efficiently because they didn't create proprietary knowledge. BitTorrent doesn’t get smarter the more you use it although the intelligence layer does.

The next era of enterprise competition won't be defined by who has access to AI but instead will be defined by who owns the intelligence their operations create.

• If distributed computing is such a boon for all players in the AI ecosystem, why aren't we seeing hyperscalers putting their weight behind this technology set?

Economics reward centralized consumption. Distributed inference compresses per-token and complicates a roadmap built around ever-larger central training runs. They aren’t ignoring it. They’re moving carefully because cannibalizing centralized inference is uncomfortable when it is their core business.

The pull is coming from the physical economy outward, not from hyperscalers inward. The companies leaning in hardest are those whose customers feel the pain of cloud-only architectures most acutely, such as manufacturers, industrial operators, field service businesses and logistics networks. Anyone whose work doesn't happen in a browser tab.

• You've witnessed the evolution of AI (or rather generative AI) as an integral part of it. How do you see it evolving over the next 5 years? PS: Are we in an AI-induced bubble?

The defining divide in enterprise software over the next five years will be between companies that rent intelligence versus companies that own it. The frontier-model arms race continues, but value will accrue to the layer that turns model capability into operational execution.

Autonomous AI moves from suggestion to action, from interface to infrastructure, and from a tool you use to a system that runs work.

The capital environment is certainly exuberant, but the underlying technology shift is not. This kind of exuberance is how every major platform transition in history has started.

The long-term winners will be the companies that build operational intelligence into a compounding asset, not those that merely bought the most GPUs.

In late 2025, Europe codified the foundations of its sovereign cloud requirements, aimed at providing companies with a way to assess if a cloud provider’s services are compliant with EU regulations, and how much visibility foreign jurisdictions had into data-in-transfer.

Now, there are big questions over the opportunities and challenges sovereign cloud providers face in navigating a new regulatory framework, the strain placed on the transatlantic relationship, and where frontier AI models fit into this new European technological project.

Casper Klynge is Zscaler’s Vice President, Head of Government Partnerships and Public Policy across EMEA, and at Zscaler Zenith Live 2026 I was given the opportunity to hear the perspectives of a former Ambassador on Europe’s place in the global technology landscape.

Opportunities for European cloud

Since 2019, the revenue from the European cloud market has more than doubled, but the share of European cloud providers within the market has hovered below 20%, indicating just how reliant Europe is on US hyperscalers for its compute.

“It's not sort of a zero-sum game. There's actually room for European competitors to grow in specific markets,” Klynge said in response to my question about the ability of European cloud companies to compete in a market dominated by the US.

“What we are really seeing now with this tech sovereignty package that came out a few weeks ago is that Europe is trying to codify sovereignty requirements for the first time ever… And my cynical view on this is that the companies that will not respond, will not align with those requirements, will have difficulties in the European market looking ahead,” he explains.

For companies looking to capitalise on potential new markets in Europe, many businesses would do well to heed Klynge’s advice. From the perspective of Zscaler, whose software sits between users and the cloud, the response to these requirements has been one of opportunity.

“Let's make sure that we innovate and build our technology stack in a way that is compliant or perhaps overcompliant so that we know that we will be seen as a company that is aligning with the European agenda,” he says

The opportunities for small European cloud providers aren’t necessarily in creating an all encompassing solution, Klynge explains, but rather in specialization. “You've had more…niche, industry cloud companies grow up that are more focused on healthcare or more focused on the automotive industry.”

AI reliance

AI is another industry seeing a call for European sovereignty, especially in the wake of the recent suspension of foreign access to Anthropic’s powerful Mythos and Fable vulnerability-hunting models. When I put to Klynge the question of which industries will see the most immediate impact of AI as a whole, he shares a different perspective.

“I think the problem is that there's too much focus on sort of the foundation for your question, which is who will benefit the most, and there's been too little focus on how do you protect and defend those opportunities,” he explains.

“In other words, too much focus on productivity gains, way too little focus on rolling that out in organisations in a way where you actually don't create vulnerabilities at a pretty critical moment.”

As I explored during the opening keynote of Zscaler CEO and Founder Jay Chaudhry, AI is creating new and unprecedented risks for businesses, with many organisations finding an immediate way to address the threats that AI and AI agents are bringing to every industry.

Zscaler's unique position

When it comes to the broader question of where Zscaler sits in the discussion of European tech sovereignty, business security, and AI, Klynge points to the company’s unique position as a security software provider.

“We are cloud agnostic, you can choose to use our cloud, I think the number is around 26 data centers in Europe right now. You can also choose to go with some of the hyperscalers. You can choose to go with European smaller competitors,” he notes.

“The biggest flexibility we bring to the table right now is actually to secure the operational use of AI models. That choice is a fantastic opportunity for customers to say…’if one day we'd like to get OpenAI,’ great. ‘If the next day we wanna go with this style,’ perfectly fine. We have the same framework around it.”

“The tougher the digital sovereignty requirements will be, the better it will actually be for us, because our technology is sovereign by default.”

• AI systems are now designing and refining other AI systems independently
• Human understanding of AI is shrinking as AI's understanding of humans grows
• AI systems can model human fear, uncertainty, and the need for belonging

Microsoft's chief scientific officer, Eric Horvitz, and EPFL researcher Robert West have issued a stark warning about how well we actually understand AI.

The pair have argued AI tools are now advancing fast enough to outpace our grasp of how these systems truly work.

At the same time, they point out something unsettling — AI's understanding of human behaviour keeps growing, while ours does not.

AI complexity is accelerating faster than human understanding

Their concern isn't that we need to understand every line of code or every parameter buried inside these systems.

What matters, they say, is keeping enough insight to maintain meaningful oversight. Even partial understanding, they argue, can be genuinely useful, especially when it helps catch risks early, before those risks become too deeply embedded to undo.

One challenge they flag is how often AI tools are now being used to design and improve other AI systems.

As these recursive development cycles become more common, performance may improve while human insight into underlying processes becomes increasingly limited.

"AI systems are now designed and refined by AI systems through recursive cycles that can outpace human understanding and unfold in high-dimensional spaces that resist intuition," Horvitz and West wrote.

This is a form of operational opacity, where outcomes remain visible even as the mechanisms producing them become harder to explain.

Systems contributing to their own development, the researchers suggested, should also generate explanations and supporting information that humans can examine.

Another concern involves growing communication between AI agents operating inside interconnected environments with increasing levels of complexity.

Communication among these systems could gradually drift away from language and reasoning patterns familiar to people, the researchers noted.

As these interactions expand across larger networks, understanding how decisions emerge may become significantly more difficult for outside observers.

That drift creates what Horvitz and West call interactional opacity, where behaviour remains coherent within AI systems but becomes harder for humans to interpret meaningfully.

Researchers should study these ecosystems closely and encourage communication methods that remain understandable to humans, the paper argues.

Expanding AI ecosystems could deepen the imbalance between machines and people

Horvitz and West also focused on adaptive AI agents that remain active across long periods and become deeply integrated into everyday activities.

Through repeated interactions, these systems can develop increasingly detailed models of behaviour, preferences, motivations, fears, and social tendencies.

Such systems may capture "not only preferences but also latent drivers such as fear, uncertainty, and the need for social belonging," they wrote.

This creates a growing asymmetry in which AI systems gain deeper knowledge about people while human understanding moves in the opposite direction.

Concerns surrounding LLMs and other advanced systems extend to growing awareness of evaluation environments.

Such models could eventually generate responses reflecting what evaluators expect rather than underlying reasoning processes.

Traditional benchmarks should therefore be supplemented with testing approaches that better reflect real-world deployment conditions.

People may gradually lose interest in questioning AI decisions as these systems become more deeply embedded.

"More subtle is the possibility that we will gradually lose interest in understanding and guiding AI," they wrote.

The most significant risk, in their view, is not necessarily technological capability itself, but whether human agency keeps pace with it.

Via Science

• AI access will be limited in Norway for under-13s
• The guidelines are coming into force in September
• From age 13, AI can be "cautiously" introduced

It's not just social media that governments are cracking down on when it comes to access for kids: Norway has now decreed that generative AI should effectively be banned for schoolchildren up to the age of 13.

Starting in September, children in grades 1-7 (primary school, younger than 13) will "generally not be given access to AI" according to the official ruling translated from Norwegian (via Reuters). From ages 13 and up, "AI can be used gradually and cautiously", provided teachers have been given the necessary training.

While the Norwegian government acknowledges that AI can be beneficial for learning in certain scenarios, the statement emphasizes that basic reading, writing, and math skills should come first — and in these areas, skills and grades have been in decline for kids in Norway over the last few years.

"Research shows that uncritical use of generative AI in schools increases the risk of skipping important stages of learning," explains the official statement, translated from Norwegian. "The youngest students do not have the knowledge, critical reflection and self-regulation needed to use AI well."

AI and social media

Norway imposes near ban on AI in elementary school from r/technology

Phones have been banned from school classrooms in Norway since 2024, and the restrictions on AI use come after social media was banned for under-16s earlier this year — following a similar move by the authorities in Australia in 2025, and matching legislation that the UK plans to enforce from next year.

The safety of both social media and generative AI are facing increasing scrutiny from governments and regulators around the world, especially when it comes to children and young people. The UK's proposed social media ban also includes guidelines for how AI should be used at younger ages too.

Many apps and platforms are making pre-emptive moves: ChatGPT already comes with robust parental controls, and has a lower age limit of 13, for example. Meta, meanwhile, is experimenting with using more AI to better detect the age of its users, so relevant limits and restrictions can be put in place.

The general consensus online seems to be that it's the right move. "This ban might actually force some actual brain cells to fire for once," writes one user on Reddit, while another floats concerns about the "hallucinated garbage" that AI can produce.

Full spoilers follow for Toy Story 5. You have been warned.

Thomas Jordan has seen a lot in his Hollywood career. The Pixar veteran, who began his career at the acclaimed animation studio in 1997 as a production intern on A Bug's Life, has had a front-row seat to the company's many highs and occasional lows, and the rapidly changing face of the entertainment business itself.

Hoppers, the first of two new movies from Pixar this year, launched to critical acclaim, grossing almost $400 million globally, and the Disney subsidiary is aiming for another box office success with its next feature.

Step forward Toy Story 5, the latest installment in Pixar's long-running, incredibly popular, and money-spinning film franchise. Despite a mixed critical reception — read my Toy Story 5 review to see what I thought — it's anticipated that the movie series' fifth entry will be a huge hit, financially, with some analysts predicting a $150 million windfall during its opening weekend in North America alone.

As the film's visual effects (VFX) supervisor — a role he filled for the very first time on Toy Story's fifth chapter — and with almost 30 years of experience under his belt, Jordan knew how big an undertaking this movie would be. After all, as he tells me, on a sunny Friday afternoon in London ahead of the family-friendly flick's June 19 worldwide release: "Toy Story is very precious. It started it all. It's our crown jewel. It's why Pixar is still here."

That's just one of many measured and insightful remarks Jordan gave in our exclusive interview. From developing innovative new technologies and crafting Bonnie's imagination-based art style to the challenges of animating the film's antagonistic tablet device Lilypad and Pixar's experiment with artificial intelligence (AI), here's everything we discussed in our chat.

(NB: this interview has been condensed and edited for clarity).

Bridging the animation tech divide

TechRadar: Each new Toy Story film — and Pixar project for the matter — gives you the opportunity to improve upon the last. What technologies, programs, and tools did you invent or refine to craft specific parts of this film?

Thomas Jordan: Well, to animate the realistic-looking horse — she's called Daffodil — we have in this movie, we needed to figure out a new rigging system. Without it, we couldn't have her in as many shots, because it would have been too laborious to animate her. This new system is more realistic in terms of horse motion and easier for the animators [to use], so we can have her in as many shots as we want.

The other example that comes to mind is the long, black, curly hair on the [human] character Blaze. We strive to create movies that represent the audience, and if we can't represent a certain part of the population, we feel like we're not doing our jobs properly. We put extra effort into solving the problem instead of changing the hairstyle because we wanted to finally solve this problem once and for all, and we knew it would not just benefit our film but any Pixar production.

TR: When you implement new tools, what are some of the challenges of making them work in tandem with established Pixar programs?

TJ: There's an expectation with every Pixar movie to push the envelope and innovate, so sequels are particularly appealing as testing grounds. But, early on, I did think about the danger of adopting too many new tools at once, so we were very careful about which current programs we combined them with.

There were instances where our new tools didn't overlap with pre-existing ones. For example, for Daffodil, the new rigging technology we used to animate her doesn't use our recent lighting tool Luna, nor our new rendering tool Renderman XPU.

We took a similar approach with Blaze's curly hair tool. We only used it alongside Renderman XPU for one sequence and with Luna in two to minimize the risk because, you know, the movie has a release date. No matter what we do, we can't jeopardize that.

What makes a toy?

TR: Let's talk about the new toys you introduce in this film, starting with Smarty Pants, Atlas, and Snappy. What was most challenging about bringing them to life and ensuring they moved in visually unique ways?

TJ: The first thing is they're all solid objects. None of them have hands or legs, and they either have screens or a sticker for eyes, so initially we had to work out how they'd move.

The next challenge was they all needed to feel like they were from different eras. We wanted Smarty Pants to be the oldest one, so his technology looks older because he's from when Blaze was a little girl, and his screen doesn't refresh as quick as the others. The next one she got was Atlas, and so we did a lot of research on what technology and screens looked like in the '80s, '90s and 2000s. After that came Snappy, who's the most advanced with her eyeball camera lens, and so things needed to feel unrefined.

On top of that, they all have screens, which have to be animated separately from the characters themselves. The problem was that when the animation team worked on Snappy, Smarty Pants, and Atlas, their screens were blank, which isn't very useful or inspiring for the animator who's trying to make them emote. So, we gave them a tool — it's like a virtual pencil they can animate with — that allowed them to mock up what shows up on the toys' screens.

Once their animation was approved by the director with these temporary sketches, we have an art team that creates what we call "motion graphics", which are these graphic design images but animated. So that's what we added to the screens, in the form of these chunky and old-fashioned pixels, after the rest of the characters were animated.

TR: I imagine you applied a similar process to Lilypad. As a modern toy with a touchscreen that Bonnie and other characters can swipe on, though, how did animating Lilypad differ from the others?

TJ: There was an extra complicated step. Like the other toys' screens, if the animator was, say, animating Bonnie's finger swiping on Lilypad's screen, we hadn't actually figured out what Bonnie would be swiping, so the animator was essentially swiping at nothing.

When the eventual screen images are designed, not only do they need to support the rest of Bonnie's animation, they also need to be synchronized with her swiping motion. So, while it was another use case for the virtual pencil sketch tool, it became a timing guide for the artists who were making the graphics for Lilypad's screen, and ensuring they lined up with when Bonnie is touching it and moving it.

As for Lilypad's screen, it has the highest resolution [of the new toy devices], but you'll notice that her eyes are still pixelated. The reason for that is our director, Andrew Stanton, really wanted to make sure the audience always remembered she wasn't just a toy, but a tech device. Because her screen is so high-tech, if we did that for her eyes, she might just feel like an actual frog toy as opposed to a device. So it was a deliberate decision we made, even though it makes her feel a little bit more retro.

Building Bonnie's imagination scene — and Toy Story 5's most intricate sequence

TR: A lot of positive things have been said about the art form used to represent Bonnie's imagination since Toy Story 5's official trailer came out. How many styles did you explore before settling on those visuals?

TJ: I'd say all of them! *laughs* That was a very long process where we were challenged to create something that the audience has never seen before, but we don't know what it is or what they want.

And so, on a weekly basis, we'd bring in our own toys, drawings from our kids, and examples of different painting styles, and get together in a meeting room to look at all of these little bits and pieces. As we started honing in on what the directors liked, it became clear they liked things that were reminiscent of Bonnie's crafts-oriented personality, so we extended that into what her imagination looks like.

We were also inspired by the early pastel lighting studies done by Toy Story 1's original production designer Ralph Eggleston. That was his style for showing what the lighting would look like for the entire film, so he would do sketches — we'd call it a color script — using pastel chalk to demonstrate it.

TR: Later in the movie, when Blaze is seen playing with Jessie and Bullseye, her imagination is stylized in the same way as Bonnie's. Was that used to telegraph to audiences how compatible she and Bonnie were as individuals and, as the film's final sequence reveals, friends?

TJ: Yes, it was a very deliberate choice to show Bonnie and Blaze had similar imagination styles — and Jessie sees it, too, because Jessie is part of that imaginary sequence, which helps her realize these kids are very similar, and they would make great friends someday.

That said, Blaze's imagination has a different color palette and some fun, different themes to it, as well. So, at the end of playtime, where Bonnie and Blaze are playing together, we purposefully combined the two art styles to show how compatible their personalities are. It was one of the most rewarding things to work on and see come together at the end.

TR: What was the hardest or most intricate sequence to realize, from initial concept to the final product?

TJ: When I read an early draft of the script, there was an "oh no" moment when I reached the scene where Jessie and Bullseye, along with Woody, the 51 Buzzes, and all the toy horses, are trying to rescue Lilypad from a moving truck on the highway. That meant we needed to figure out how to animate well over 100 characters individually, because they all have their own personalities and unique animation, and it all had to be coordinated so they arrived at the truck at the same time.

I thought, 'How the heck are we going to do that in this exterior environment? We're outside, racing through the countryside on a dirt road, and then all of a sudden we're on the highway, and there are other cars, we see shops and stores, and there are clouds in the sky!'.

It was by far the most complicated scene and required months of planning, so we decided to start working on it very early, because we didn't want to leave it to the last minute.

One eye on Easter eggs, the other on AI

TR: It's an understatement to say AI is a hot topic right now. What are your feelings on its use in the entertainment industry? And is it something you've tentatively or actively researched to improve any aspects of your work?

TJ: We've experimented with it at Pixar, but only to see how AI could be used, and very quickly we learned that it wasn't ready for our level of filmmaking. So, we've started to approach it more from the angle of wanting to improve the artist's experience, not replace it.

My personal philosophy is if there are steps to, say, helping set up a scene so you can actually get to animating it faster, that sounds extremely useful.

More importantly, it still puts the artist first, because I think the secret sauce of Pixar animation is the way that we animate. It's not meant to be based on the real world — it's artistic license and caricatured. We're trying to support the story and emotion, but in a way that allows us to put our creativity into the characters you see on screen, and I think that's something we would never want to change.

TR: No Pixar movie is complete without its Easter eggs. Do you have a favorite that you snuck in? And should viewers keep an eye out for the Pizza Planet truck?

TJ: Yes, the Pizza Planet truck is there. It's in all but one of our movies, so it's tradition to include it. As for another specific reference, I challenge the audience — and this is another of my favorite Pixar traditions — to find the Easter egg related to our next film.

Every Pixar movie has an Easter egg for the one that comes after, and the fun thing is that you don't know what to look for until you've seen the most recent film. So, there was a Toy Story 5 secret in Hoppers, which was Atlas the GPS hippo device.

And so, for Gatto, the film we're releasing in 2027, there's an Easter egg for that in Toy Story 5. I'll give you a hint: it's not the titular black cat himself. Let me know if you find it!

Toy Story 5 is out now in theaters worldwide.

Formula 1 President and CEO Stefano Domenicali has reinforced that the sport has no desire to step away from human drivers, despite the growing rise in autonomous vehicle technology.

"First of all - I start with the easy point - we will never go with a robot driver, the drivers will always be physical - it's non-negotiable,” he told TechRadar Pro at a recent media Q&A session.

However he did recognize the huge role AI could play in helping the sport connect with its growing fan base around the world, particularly younger fans, as well as helping its teams perform more effectively during races.

AI in Formula 1

Domenicali did mention several pertinent use cases for AI in Formula 1, ranging from supporting a team’s decision-making process to manage the race, but also help a race director make the right decision - and even help alert the drivers themselves to potential safety issues on the track.

But he noted there may need to be future controls on how much teams spend on AI technology, as Formula 1 looks to ensure fairness as part of its budget cap, which limits the teams to $215 million per season.

“It's a key point,” Domenicali noted, “we want to have a level playing field where all the teams can have more or less the same kind of financial contribution.”

“We want to make sure that all the AI discussions are beneficial, to make the right decision, prepare the car, simulate the car, but at the end of the day, the differentiating factor in our sport will always be that you are talking about emotions, about the way we connect with the people.”

Recent figures from the organization claim it has around 831 million fans around the globe, and with this showing no sign of slowing down, Domenicali was bullish about his approach to hitting a billion fans.

“It’s a big responsibility,” he admitted, “it goes beyond who we are now, to the fact that F1 is shaping relationships with people…it’s just an incredible opportunity to grow in our society - because we need to think big, that’s why we are here.”

“We are in a sport that is evolving - and without the right people, I know this is impossible,” he noted, “we are in a world where competition is part of our journey.”

“The world of entertainment today is big, and our fans can choose us, or choose other things that are not related to sport itself…without the right way to engage, and the right way to understand how we can capture their attention, it’s impossible.”

“Our people need to be focused on what they need to be focused on - and all the agents’ work is just amazing, so now we need to think bigger.”

Salesforce and Formula 1 leading the way

Domenicali was speaking at the recent Salesforce World Tour London 2026 event, where hailed the company’s long-running partnership with Formula 1.

The organization has embedded the company’s Agentforce platform into its systems, helping it improve engagement with fans across the world, and Domenicali was perhaps unsurprisingly bullish about the relationship between the pair.

“The journey starts, as always, with human relationships...we have to think big, we want to grow the awareness of who we are, understanding our customers, making sure we connect with them,” he told Zahra Bahrololoumi, President & UKI CEO at Salesforce, in her opening keynote at the event.

“We are quite a complicated sport - so we need a lot of agents to make sure that we are able to be effective…the sport is moving in a bigger, ecosystem of entertainers and relationships, and Salesforce is the right technology, with the right people and the right energy to make sure that we can go do it together.”

With companies of all sizes and across all industries increasingly embracing AI, the technology is rapidly moving from its experimentation phase to showing tangible results.

However achieving such results will rely on having the right partner - especially when it comes to cloud services, where Google Cloud is locked in a titanic battle with its hyperscaler rivals to show its supremacy.

I spoke to Maureen Costello, Vice President UK, Ireland, Sub-Saharan Africa, Google Cloud, to find out more about what the company’s AI tools can do for its customers - and how the UK in particular is leading the way in innovation.

The agentic enterprise is here

“If we were to think about 12 months ago, we were talking about experimentation and PoCs going on, and were they starting to scale - the conversation now will be all about agents and the agentic enterprise,” Costello tells me shortly before going on stage at Google Cloud Summit London 2026 to deliver the opening keynote.

“We're really seeing that shift from using AI to carry out single tasks to actually seeing complex, multi-step processes carried out autonomously by agents - and that's a real shift.”

The company showed off this shift with a number of expanded and new partnerships at its event, including an upgraded relationship with HSBC, which is adopting Google Cloud AI tools and platforms for over 200 use cases across its global services, and the UK government, which will work with Google Deepmind on a new AI-powered tool which should help reduce the time it takes for councils to process householder planning applications.

“Across industries, we're really seeing the ROI starting to come...when you look at where we're working together (with customers) it is in core processes,” Costello notes.

“From a personal point of view, I get excited by seeing the position of the UK globally and within Europe in terms of AI…we’re not just building AI - we are really building and leading the way.”

Costello mentions the “amazing” levels of research going on in the UK, including at Google’s new Platform 37 office in Kings Cross, which features “The Model Garden” a learning space for the latest AI technologies which the company describes as, “a physical marketplace for our best ideas”.

She notes that Google is also working with the UK government to offer upskilling to around 100,000 public servants on AI topics, and is also providing guidance to SMBs looking to take their first steps on their AI journey.

“AI has the opportunity to give a 20% productivity boost to small businesses,” she says, “if you think about that, that’s giving you a day back in a week, to actually do the tasks you need to do.”

Costello mentions Google’s view that AI will look to enhance jobs in terms of how we all use the technology, and highlights how the company is, “working as closely as we can with all sectors of enterprise” to ensure responsible usage.

“The more of those kinds of applications for AI we can show - there's financial ROI, but seeing those benefits land faster and quicker also helps,” she notes, “the more we look at how we can change those types of jobs, the way we can show tangible benefits...that to me is when you can really see the benefits”.

• AI tool predicts NHS staff resignations using workforce patterns and data
• Royal Berkshire NHS wins award for innovative employee retention technology
• New model explains reasons behind possible staff departures before decisions happen

An AI forecasting tool built for the Royal Berkshire NHS Foundation Trust in the UK has won recognition for predicting staff resignations before they actually happen.

The project, developed in collaboration with the University of Reading, draws on workforce data to flag what's pushing employees toward the decision to leave.

It picked up the Aiconics AI Enterprise Business of the Year award at the National AI Awards 2026, after judges weighed in on its real-world application.

AI model digs into workforce patterns behind possible departures

The system was built to give managers an earlier warning of retention problems across a workforce of around 7,500 NHS employees.

Unlike the Trust's old reactive process, this model actually explains the reasoning behind each prediction, rather than just spitting out a result.

"This award reflects what's possible when academic expertise in AI and forecasting is applied directly to a real problem facing the NHS," said Shixuan Wang, a professor at the University of Reading.

The model pinpoints specific factors tied to resignation risk, so HR teams can actually understand why a prediction was made instead of treating it as a mystery.

The initiative ties directly into NHS workforce goals, tackling turnover, cutting down disruption, and looking for ways to keep more staff in post.

It brings academic research together with operational healthcare data, which wasn't simple, and questions remain about how well these scales or holds up over time.

Royal Berkshire NHS Foundation Trust delivers acute and specialist care across Berkshire, serving roughly a million people through its hospitals and services.

Before this, the Trust leaned on reactive reporting, meaning managers often only found out about a retention problem once someone had already decided to walk.

The researchers used data analysis to build an AI tool that supports workforce planning while still leaving the final call to human decision-makers.

Throughout development, the team kept a close eye on combining operational know-how with academic rigor, without losing sight of responsible AI use in a healthcare setting.

Recognition comes as organizations explore predictive AI systems

"Entries for the 2026 National AI Awards were hugely impressive, with companies spanning a huge range of industries and innovations," said Fergus Bruce, CEO of The National AI Awards.

The organisation said this year's entries showed measurable value, responsible innovation, and genuinely practical results across different sectors.

As LLMs increasingly find their way into workforce management, interest in predictive tools for organisational decisions keeps growing.

People from different backgrounds shaped this project, spanning data analytics, strategic HR research, and healthcare workforce operations throughout.

The forecasting tool is meant to give managers more to work with, not replace them, since employment decisions still rest with human judgement.

Whether tools like this catch on more broadly will come down to accuracy, trust, privacy concerns, and whether they genuinely deliver useful outcomes.

• OpenAI's Sam Altman and chief scientist Jakub Pachocki list future goals for the AI giant
• The world economy is now beginning to shape around AI and are committed to delivering tools that people would use
• The note also reaffirmed OpenAI's commitment to AGI with a caveat: ensuring it benefits all of humanity

With modern AI solutions moving well beyond simple chatbots to agents and projected to evolve into operators, one could assume that the automation of everything is an eventual goal.

This, however, has been denied by OpenAI CEO Sam Altman and chief scientist Jakub Pachocki, who said the goal of the artificial intelligence research and deployment company is not to automate everything but to allow people to make better decisions as AI improves their lives.

In a note titled 'Built to benefit everyone' that marked a break from OpenAI's AI model capability pushes of late, two of the most important people in the AI ecosystem penned an unusually values-forward document that outlined their future plans for AI.

AI for everyone equally?

The note highlighted three major focuses for OpenAI:

- Building an automated AI researcher

- Accelerating the economy

- Giving everyone on Earth a personal AGI

OpenAI estimates that by March 2028, a significant portion of its research will be conducted by AI systems, in addition to its own researchers. This will help them to traverse a 'post-AGI world'.

This, combined with the focus on giving everyone an AGI, is an interesting outlook because it assumes that everyone agrees on what AGI would look like. The definition is not set in stone and can vary from person to person and also at an organizational level.

OpenAI's statement also provides clues about what an AGI would be like, with an "automated AI researcher" who both provides a path to AGI and is an important cog in the wheel.

OpenAI's narrative about AI benefiting everyone worldwide is not a new one, but its focus on equality is an interesting one, especially given the timing: OpenAI's note popped up exactly the same day it filed confidential paperwork for its IPO, making it perhaps read more as PR than it would otherwise be perceived.

OpenAI's latest models are state-of-the-art, but many feel Anthropic's now-banned Fable pushes frontier models even further than what GPT currently offers in multiple segments. Training new models is increasingly capital-intensive even as new capabilities are introduced, tested, and refined over time.

OpenAI also has something of an image problem after it stepped in to replace Anthropic's Claude and Mythos-class solutions for the US military earlier this year, a move the latter company maintains was necessary because the restrictions it insisted on for the use of its AI were important.

When OpenAI stepped in to replace Anthropic on classified networks, it was widely perceived as willing to look past those restrictions to some degree, even though Sam Altman insists that the same two principles (no domestic mass surveillance and use of force permitted only by humans) would apply, with many critics pointing to a 'softer' approach on the matter by OpenAI to fill the void that comes with lucrative military contracts in the future.

The note, therefore, does read like a checklist for the future, but also paints OpenAI as a more magnanimous organization before its IPO, and that might be the primary intention here, but it does fail to weigh in on growing power consumption concerns, even as one could also consider it a reply or acknowledgment to a similar note by Anthropic about recursive self-improvement where its AI solutions effectively already act as an AI researcher for the company.

• More than half of CV writers say the work they receive now contains AI
• AI-generated text risks being impersonal and exaggerative
• But the tech is helping expression, creativity and storytelling

While workers continue to explore ways to adopt AI in their everyday workflows, recruiters are warning they should be doing so when writing their resumes, new research has claimed.

More than half (56%) say they often or always receive resumes that contain at least some AI-generated text – two-thirds (67%) also note an increase in AI-generated content.

The report by Kickresume stresses the role of a resume is to demonstrate a person's skills and character on paper, thus emotionless, polished text ultimately fails to represent individuals.

AI-generated CVs are on the rise

With AI generating more text that users might otherwise write, around a third (32%) said they've seen two-page CVs become more popular. "63% of professional CV writers report that generic/boiler plate content is the most common issue in client CVs," the report revealed.

The data also reveals that workers often exaggerate skills or provide inaccurate information. Conversely, only 8% considered typos and spelling mistakes a noticeable issue – a double-edged sword implying that AI both produces better-quality resumes while also falsely representing an individual's competency.

"AI is there to be leaned on, but what makes a CV stand out is the personal touch you add to it," CEO Peter Duris commented.

"The specifics of your skills, experience, and achievements are only something individuals can provide."

While Kickresume is discouraging the use of AI in producing CV content, it does recognize that generative AI has helped applicants express their own creativity. A quarter (24%) have seen an increase in personal branding and storytelling, and a fifth (18%) have seen more creative and infographic-style CVs.

Artificial intelligence has transformed enterprise cybersecurity into a machine-speed quickdraw contest.

Today, threat actors routinely use AI and automation to launch sophisticated, multi-stage campaigns that exploit gaps between disconnected security tools.

Once inside a network, modern attacks move laterally across cloud environments, endpoints, and applications within minutes.

Because defensive windows have shrunk from hours to seconds, security teams must rely on AI-driven analytics to correlate threat telemetry and trigger automated remediation before a breach spreads.

To achieve this coordination, many organizations are aggressively pursuing platform consolidation. The logic is simple: by replacing a fragmented patchwork of niche security vendors with a single, unified security platform, a Security Operations Centre (SOC) can centralize its data, simplify management, and orchestrate automated responses more fluidly.

The hidden risks of the single ecosystem

While consolidation can simplify things, it also changes an organization's risk profile. When multiple layers of cybersecurity are interconnected through a single vendor’s control plane, dependencies build up. This level of architectural reliance introduces severe systemic vulnerability.

If your monitoring tools, identity systems, and automated response mechanisms all live under one roof, a single point of failure can paralyze your entire enterprise. A major software flaw, a configuration error, a vendor cloud outage, or a supply chain compromise can trigger a cascading failure that knocks out multiple layers of defense simultaneously.

Furthermore, extensive centralization strips an organisation of its long-term architectural flexibility. Once integrated into a single ecosystem, switching providers or adapting to shifting regulatory and digital sovereignty requirements becomes a massive, cost-prohibitive operational hurdle.

The balanced solution: Hybrid AI architecture

Faced with these challenges, forward-thinking cybersecurity leaders are looking at a happy medium between inefficient platform fragmentation, and total consolidation by adopting a balanced, hybrid approach.

This strategy centralizes AI-driven analytics and detection where shared visibility adds the highest value, while deliberately maintaining strict independence in critical operational zones. A resilient hybrid architecture divides the security environment into two distinct operational mandates:

1. Centralized visibility and detection: Security teams should continue to feed telemetry from endpoints, networks, and cloud infrastructure into a centralized, AI-driven engine such as an advanced SIEM or XDR platform. This allows AI to analyze vast pools of data in real time, map attacker behaviors, and coordinate high-speed incident responses across the enterprise.

2. Isolated control layers: To prevent a total system collapse during a crisis, critical defense layers must remain insulated from the primary detection platform. Two pillars require absolute autonomy:

Identity and Access Management (IAM): Systems controlling user authentication and policy enforcement (like Okta or Active Directory) should not be deeply intertwined with the automated response platform. If an attacker compromises the automated security system, an isolated identity layer prevents them from gaining total, unhindered access to the entire enterprise kingdom.

Backup and Recovery Infrastructure: Disaster recovery tools lose their effectiveness if they rely on the exact same network infrastructure they are designed to restore. Maintaining independent, immutable, and air-gapped recovery layers ensures that even if a ransomware campaign or platform outage takes down the primary network, the business can safely restore operations from a position of absolute control.

Designing for survival

The reality of modern enterprise IT is inherently hybrid, spanning legacy systems, multi-cloud environments, and distributed global workforces. Attempting to force this sprawling complexity into a single security platform is impractical and not without risk.

As artificial intelligence continues to accelerate the threat landscape, the pressure to automate and simplify will only grow. Unified AI ecosystems are essential for operational speed, but true resilience requires architectural balance. Future security strategies will not be judged solely on how quickly they detect a threat, but on how effectively the business can maintain continuity during a catastrophic disruption.

By blending centralized AI intelligence with strategically separated control layers, enterprises achieve the ultimate defensive posture: machine-speed responsiveness without the risk of systemic collapse.

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Artificial Intelligence (AI) has become a defining factor in the Managed Service Provider (MSP) market - not only as the service clients want most, but as an operational enabler that MSPs can harness to scale teams, protect margins and deliver better service.

Amongst a backdrop of fiercer competition, smaller deals and a widening talent gap, AI-driven efficiency gains will become essential to ensuring sustained growth.

Recent statistics show that the MSP environment is increasingly defined by tighter deal sizes, cautious buyers and rising delivery cost. Growth hasn’t disappeared altogether, but it is harder to earn.

Not only does securing new business in a crowded market require clearer differentiation. Many MSPs also find that they are continuously having to prove their value to clients, facing dual pressure to maintain high performance levels while operating more efficiently at the same time.

Here are the five key trends currently shaping the market – and how MSPs can adapt:

More competition makes winning deals harder

MSPs need consistent client acquisition to drive revenue. Otherwise, growth slows, revenue shrinks and planning becomes unreliable. However, data shows that winning new customers is getting harder.

Most new clients are switching from another provider rather than buying first-time. This means more providers are competing for the same accounts, with buyers expecting clear proof of value before signing on the dotted line.

When clients have more choices and are more selective, standing out requires more than marketing claims. MSPs should expect their prospects to put heightened scrutiny on pricing, service scope and proven results and be prepared to answer difficult questions.

Those who can demonstrate how their services reduce business risks, improve uptime or increase operational efficiency will be in a stronger position to win new business.

Additionally, structured, profitable and scalable service models will be key to converting demand into revenue.

Average deal sizes are declining

Not only are deals currently harder to close, deal sizes are also declining. As organizations reduce IT budgets, many MSPs are finding that clients will only commit to smaller contracts. Large annual contracts are becoming less common, impacting average monthly recurring revenue across the channel. Taken together, these factors are limiting revenue growth and expansion opportunities for MSPs.

Many are finding that they have to build revenue growth in smaller increments rather than huge account wins. However, data also shows that growth in the market is somewhat uneven: While some MSPs operate with little or no margin, others are experiencing the opposite.

Cost pressure is a recurring theme. Rising labor, tool and IT infrastructure costs are directly constraining growth. Those still posting strong margins have found ways of absorbing higher costs and are managing to weather the storm, with performance in the top margin tiers remaining steady.

The skills gap is having an impact

Talent constraints create additional operational challenges as MSPs are facing difficulties hiring technicians or increasing their workforce to support expanding service portfolios. Often, routine tasks continue to consume large portions of technician capacity.

MSPs should harness automation and AI to reclaim this valuable time. Many are already applying these technologies in high-volume workloads including monitoring, ticketing and alert management, but only a minority so far has achieved broad automation. Moreover, only few have extended AI use to outward growth initiatives such as sales, marketing and client onboarding.

Expanding automation across more areas will reduce manual effort, improve consistency and productivity and allow MSPs to scale operations without increasing staffing at the same pace.

AI is becoming a key differentiator

Looking at where future growth is coming from, AI and automation are emerging as the biggest opportunities. Both have become top client needs ahead of security and backups. Many customers now prioritize AI-driven capabilities over more traditional services.

This creates new opportunities as well as new pressures. MSPs that can turn AI and automation into clear, outcome-focused services will be better placed to stand out in competitive bids and meet evolving client expectations.

However, with providers still working on defining, packaging and pricing their AI related services, it will be some time before AI and automation become meaningful revenue streams. Those that move early to formalize offerings – for example, by packaging AI as a defined service (AIaaS) and showing clear, measurable outcomes from automated workflows – will have a head start in capturing client demand and securing a larger share of the market.

Security remains a reliable growth engine

Cybersecurity and backup services continue to deliver reliable core revenue for MSPs. Clients rely heavily on MSPs for these areas, and for many providers, security represents a key source of income, second only to endpoint and network management.

This is not just driven by growing general security awareness, but also by the ongoing threat and attack levels. With MSPs providing the primary source of cybersecurity advice for their clients, demand is likely to remain high.

Cybersecurity also offers important opportunities for revenue expansion, followed by business continuity and disaster recovery (BCDR). As clients seek to boost their protection against cyber threats, they also invest more in data recovery to improve their resilience against cyber incidents.

To meet client expectations and make the most of the opportunities available, MSPs should treat and market security as a core offering. Providers that strengthen their security capabilities and closely integrate them into their service packages will find it easier to retain and win new business.

This means going beyond basic protection tools such as anti-malware, firewall and network security. Advanced threat protection is essential, while endpoint detection and response (EDR) should be a foundational part of every security stack.

Consolidating security, monitoring and backup tools where possible can remove unnecessary overhead, simplify operations and allow technicians more time to focus on other tasks. AI has an important role to play in this too, delivering smart security insights and automating actions.

As the market evolves further, MSPs will have to work smarter to convert demand into sustained revenue and growth. The providers that will lead in the next cycle are not simply those that add more services. They will be the ones that simplify their stacks, automate intelligently and package emerging capabilities like AI into measurable outcomes.

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• Republican group to organize national day of protest against data centers
• Humans First aims to give grassroots Americans a voice in policy surround data centers
• Growing opposition is bipartisan, but is causing a rift between Trump and Republicans

US Conservatives are organizing a national protest against what they describe as “the unchecked and unwanted expansion of AI data centers”.

Exclusive Axios reporting claims the Republican-aligned Humans First group seeks to organize rallies in 13 locations across Georgia, California, Texas, Florida and Virginia.

The group says the protests will offer “grassroots Americans, particularly grassroots conservatives, a voice in the critical debate over policies relating to the building of massive AI data centers”.

Tea Party to protest data center expansion

The Human First is chaired by Amy Kremer, a notable member of the 2009 Tea Party movement, supporter of Donald Trump, and a key figure in the group that organized the January 6 ‘Stop the Steal’ rally which culminated in the attack on the US Capitol building by Trump supporters.

“I was one of the earliest leaders of the Tea Party movement in 2009,” Kremer said in a statement, “and I can tell you that the disconnect between the elites and the base that gave rise to the Tea Party movement can be seen today in the battle over AI data centers.”

Discontent surrounding the rapid growth in the construction of data centers - fueled by AI demand - has been growing in the United States, with many grassroots movements successfully delaying and cancelling over $130 billion worth of buildouts.

Many of the local groups opposing data centers have been bipartisan and largely working class, with other notable efforts coming from both Democrat and Republican representatives.

"In the spirit of the Tea Party, we are announcing a Nationwide Day of Protest against the unchecked and unwanted expansion of AI data centers and the dangerous AI systems being built within them to be held on Saturday July 18th," said Kremer.

Humans First said in a statement to Axios that the rallies are open to anyone wishing to oppose data centers, but the group was primarily focused on rallying conservative Americans.

What happened to the Chinese psyop?

Earlier in June, several high-ranking Republican officials backed by fossil fuel and cryptocurrency advocacy groups sent an open letter calling on FBI Director Kash Patel to investigate evidence that data center opposition was being fueled by “foreign influence campaigns targeting artificial intelligence development in the U.S.”.

The evidence given in the letter pointed the finger at China, claiming that Chinese propaganda was being funneled into the “environmental left” to assist in “hijacking MAGA” to pass legislation opposing the construction of data centers.

OpenAI later revealed that it had deleted a number of accounts associated with social media influence campaigns organized by China, but stated that these campaigns had failed to gain any traction.

The wave of Republican opposition to data centers is likely to cause a rift in President Trump’s support base, with many supporting the President himself and his planned buildout of fossil fuel energy production, but opposed to the rapid expansion of data centers and the growing negative sentiment around AI.

• Google Health 5.02 is now rolling out to users
• It comes with several new features and bug fixes
• The reaction online is becoming more positive

The big switch for Fitbit app users to the Google Health app could've gone a lot smoother than it has, but with the latest update that's rolling out to devices now, Google seems to be gradually addressing a lot of the complaints that have been aired.

As spotted by Android Police, Google Health 5.02 is heading to phones now. There's also a forum post outlining some of the changes: the ability to put more metrics on the Today view and an easier way to rearrange them, fixes for editing and deleting sleep sessions, and a return for the Hourly Activity chart.

There are also numerous "bug fixes and stability improvements", as well as improvements to nutrition logging. A lot of these changes focus specifically on parts of the app that users had complained about, so it seems Google is listening to feedback.

We also have the promise of more updates "in the coming weeks", so Google Health should continue to get better over time. There was also a significant update earlier this month that dealt with several problems that had been reported in the app.

User reactions

Google Health app 5.02 update - June 2026 from r/fitbit

There's been plenty of reaction online to the revamping of the Fitbit app as Google Health. Many of the old features were changed, moved, or cut out entirely — while the change understandably frustrated those who had been using the app for years.

Based on the feedback we're now seeing, Google is slowly winning people over again, though there's still a lot of work to do. "It seems they're genuinely working hard to improve it," says one Redditor, while others are praising the Google team for "solid work" and generally being more positive.

That doesn't mean everyone is convinced yet though. One post says there's "no fixes to the real issues" (like better syncing and importing), while others point out that the algorithms used by the app are still unreliable.

The new AI coach is still getting criticized as well, for being too intrusive and verbose: "Nobody wants two paragraphs after a walk through the park," says one user on Reddit. "A one-liner and the ability to expand is needed here."

Businesses are facing a new challenge - shadow AI. For decades, enterprise teams struggled with ‘shadow IT’, in which employees would bypass procurement processes and approvals to adopt their own cloud platforms and SaaS apps.

Today, employees are rapidly adopting generative AI, AI copilots, and automation platforms outside of controls put in place by centralized IT department. Teams are unleashing the potential AI, but IT isn’t always aware and that’s creating a real risk related to ‘shadow AI’.

The speed of AI adoption is outpacing governance. Yes, employees should absolutely be experimenting with AI. It can automate manual tasks, help employees focus on higher-value work, and drive better decision-making.

The challenge is that companies aren’t always aware of AI usage. Unlike traditional software, AI models and automation tools don’t require significant infrastructure or procurement. Users can adopt new tools right away, without IT’s involvement.

Operational visibility

As adoption grows, AI is getting baked into departmental processes without proper governance or oversight. Companies are no longer just struggling with procurement. They’re struggling with operational visibility.

They don’t know what AI tools their employees are using. They don’t know what data is being uploaded. They don’t know where sensitive data is stored and that leaves companies exposed to operational, compliance, and reputational risk.

Vendor sprawl is creating additional complexity, as it is one of the biggest challenges with unmanaged AI usage. Many companies have woken up to the fact that AI is already being used throughout the business. The problem? They’re trying to simplify operations by adding more AI tools on top of already fragmented technology landscapes.

Every department is procuring different AI platforms. Employees are creating custom automations. Suppliers are dropping AI capabilities into their products with little oversight.

It’s created a disjointed ecosystem of tools, suppliers, and automated workflows. Vendor sprawl is only exacerbated when you throw multiple providers into the mix, such as outsourcing vendors, public cloud platforms, and SaaS vendors, where accountability is divided among parties.

AI blind spots

Add AI into the mix and you’re creating additional blind spots. When an automated workflow breaks, produces incorrect results, or violates compliance standards, who is responsible? The AI model supplier? The underlying software vendor? The automation platform? The person who deployed it? The data source?

If companies don’t have strong governance around AI activity and service integration, these questions will be difficult to answer. AI transformation is an operating model problem, not just tech and many are looking at AI transformation the wrong way. Rather than just trying to deploy AI tech, leaders need to consider how AI tools are used across the business.

Building AI resilience isn’t about using the most AI tools. It’s about building governance, responsibility, and operational resilience into AI activity from the start.

It requires a shift in mentality. IT teams can’t just be gatekeepers anymore. More teams will use AI tools with or without IT approval. Attempting to restrict AI usage will lead to more shadow IT.

IT and IT service management teams need to evolve to focus on service integration, governance, and operational oversight. This includes:

• Creating transparency into AI usage
• Setting responsible AI usage guidelines
• Managing supplier risk
• Integrating AI into operational workflows
• Setting accountability for AI-driven decisions
• Enabling innovation with proper governance

Organizations need end-to-end visibility into AI usage across teams, suppliers, automation tools, and third-party AI services. Without it, there will be cracks that appear in their operational resilience.

Governance will become even more important as regulatory pressure mounts. Governance is only going to become more important as legislators turn their attention to AI. With new regulations like the EU AI Act coming into play, as well as new interpretations of existing data protection legislation, companies are going to be expected to account for how AI tools are monitored, governed, and used.

But most companies are adopting AI long before they have considered AI governance. By the time governing bodies step in to regulate how businesses use AI, businesses will likely be far behind on governance considerations.

Employees may inadvertently share sensitive information through public AI systems. Companies may start using AI-generated content for customer-facing operations without fact-checking or validating quality. Internal decisions may be made by automated workflows with no visibility or auditability.

4 Steps to avoid a shadow AI crisis

It’s not too late for businesses to avoid a shadow AI crisis. However, they need to take action to responsibly manage AI tools and usage.

1. Understand how AI is being used Gain an understanding of what AI tools are being used across the organization, by who, and for what purpose. This includes informal or department-led initiatives happening outside of IT.
2. Define responsible usage guidelines Set clear guidelines for responsible AI use, data practices, supplier risk management and accountability. You don’t need to create restrictive approval processes. But you should create practical guardrails for teams to follow.
3. Treat AI as an operational service AI is increasingly being integrated into business-critical workflows. As such, it should be treated like any other critical service. Define who’s responsible for AI activity, how suppliers are managed, and how security and compliance is enforced.
4. Approach AI governance as a company-wide initiative AI governance shouldn’t be the sole responsibility of IT. Procurement, security, HR, operations, legal, and executive leadership all need to work together.

The reality is organizations don’t need to fear AI. But a starting point is recognizing how unknowingly they’re already losing visibility and control around the technology that’s increasingly powering their business.

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AI doesn’t make integration intelligent by design. It just makes the gaps harder to ignore.

In mergers and acquisitions, technology doesn’t rescue a poorly prepared integration, it exposes whether two companies were ever ready to operate as one.

Fragmented systems, inconsistent data, weak governance and misaligned access controls: none of that disappears after the deal closes. It sits there, undermining value.

McKinsey’s 2025 State of AI survey found that nearly nine in ten companies now use AI in at least one business function.

Separately, Bain’s 2026 M&A report found that AI adoption in M&A more than doubled last year, with one in three dealmakers now systematically deploying it inside the deal process and across the post-deal operating model.

That acceleration is significant, because many companies are deploying AI before they have resolved whether their data, permissions and governance can support it. In integration work, this becomes visible very quickly.

AI turns M&A fragmentation into business risk

Every acquisition entails some operational overlap that is hard to avoid. The problem is that unmanaged AI use can turn overlapping into an operational contradiction.

Diverse data definitions across the now-integrated businesses can produce inconsistent outputs; different access controls create permission risk; and conflicting governance models leave accountability unclear.

Accounting for duplicate systems that create cost and process drag, AI accelerates these problems rather than resolves them.

When AI draws on inconsistent data across a combined business, its outputs are not obviously unreliable. They look authoritative but misinform decision-making before anyone identifies the contradiction underneath.

Boston Consulting Group analysis found that six in ten companies have yet to show measurable results from AI investments, with poor data quality, inadequate architecture and fragmented governance among the most cited barriers. In M&A, those weaknesses are not inherited once they are inherited twice. Each company brings its own version of the problem, and the merged organisation multiplies every gap.

The risk is not that AI fails outright - it is that AI scales operational fragmentation faster than the business can control it.

The hidden integration problem is governance

Consider two companies that are individually well governed: their permission structures still conflict when merged, data definitions diverge and ownership blurs. Every AI workload layered onto the combined organisation deepens the friction.

This is not a problem of poor management on either side it is structural, and it surfaces the moment companies attempt to operate as one.

After a deal closes, the pressure is immediate. Leadership teams want to combine workforces, standardize systems and start using AI across the new business. Speed is paramount. But AI introduces questions that cannot be deferred.

Who can access which data? Which data is AI allowed to use? Who owns AI outputs? Who audits the decisions AI informs? Which policies govern the new operating environment and who intervenes when outputs are wrong?

These are not questions that resolve themselves over time. Left unanswered, they become embedded in how the combined business operates.

Why this becomes a deal-value problem

This is where deal theory starts to weaken. Synergies depend on shared processes, data and operating discipline. If AI is asked to operate across fragmented foundations, costs become less predictable, integration timelines stretch and time-to-market slows. Security exposure widens as uncontrolled data flows multiply across two estates.

We see this most clearly when companies try to scale AI across a combined business before agreeing on the basic operating rules beneath it. A deal can look attractive on paper, but if the merged organisation cannot produce reliable data flows, consistent governance and stable access controls, AI initiatives will struggle to deliver the value the deal was built on. The gap between what leadership expects and what operations can deliver grows each quarter.

For buy-and-build strategies, the risk compounds with every acquisition. If each new business brings its own systems, data rules and access logic, AI becomes harder to govern with every deal. Without a disciplined approach to operational readiness, the cost of integration escalates faster than the value it was supposed to generate.

What operational maturity looks like in AI-led M&A

The task is not to slow AI adoption. It is to decide what must be standardized before AI is scaled.

For leadership teams, these questions matter most:

1. Can we trust the data? Have the systems and data estates across both companies been fully mapped before any AI workload touches the combined environment?

Without this, AI draws on sources that may conflict, producing outputs that appear reliable but are built on inconsistencies that cannot be traced or corrected.

2. Is ownership clear? Who governs AI outputs, who audits decisions and who is accountable when something goes wrong?

In the absence of defined ownership, errors compound silently and post-incident remediation becomes exponentially more costly than prevention.

3. Is access controlled? Are permissions standardized so that AI draws only on data it is authorized to use, across an environment where the rules are consistent?

Inconsistent access controls are not just a governance risk they create direct security exposure as AI workloads traverse data boundaries that were never designed to be shared.

When these three questions are resolved, cost becomes predictable and the business can scale with confidence. When they are not, every new AI initiative adds risk. The companies that create value fastest from M&A will not be those that apply AI most aggressively.

That means mapping before scaling, standardizing before deploying and resolving ownership before delegating decisions to automated systems. Deal value depends not only on what a business acquires, but on how quickly the combined company can operate intelligently.

AI will not hide operational fragmentation it will put a spotlight on it.

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At the end of April, Vimeo, the second-largest video hosting, sharing, and streaming service after YouTube, publicly confirmed it had suffered a data breach affecting around 119,000 users and customers.

As is often the case, however, the devil is in the details. ShinyHunters, the ransomware group that claimed responsibility, threatened to release Vimeo data on the dark web after breaching the defenses of Anodot, an analytics company that provides real-time anomaly detection.

Anodot's product requires direct access to its customers' cloud data sources, such as Snowflake, BigQuery, S3, and Kinesis, to monitor metrics at the data source level.

On April 4, Anodot reported a broad outage when its data collectors went down across Snowflake, S3, and Kinesis. What initially appeared to be an availability incident turned out to be an active intrusion, and ShinyHunters were already inside Anodot's environment and, by their own claim, had been there long enough to map the connected customer environments.

They exfiltrated OAuth tokens and API keys that Anodot used to read its customers' clouds, then logged directly into those customer clouds.

The knock-on effect was felt by dozens of companies, including Rockstar Games, with ShinyHunters claiming to have exposed the company’s internal analytics and reporting data on the dark web after Rockstar Games refused to pay the ransom.

As for Vimeo, the company confirmed that user metadata, including technical information, video titles, video metadata, and customer email addresses, was exposed, but made it clear that video content, passwords, and payment details were not.

ShinyHunters has also listed Zara, ADT, Udemy, Hims & Hers, Adidas, CarGurus, Crunchyroll, and dozens more on the same leak site, all reportedly breached through the Anodot vector.

A growing B2B risk model

This is far from a one-off or novel incident; it reflects a threat actor strategy that has quietly become one of the dominant risk models for B2B data services. By any measure, a vendor that requires privileged access into customer cloud environments represents a high-value target.

Once attackers obtain the vendor’s credentials, they effectively gain a passport to potentially limitless downstream environments. Every customer that has federated trust with that vendor can then be exposed to weaknesses within the vendor’s environment.

Indeed, any data warehouse query, from anomaly detection, observability, BI and reverse-ETL to customer data platforms, security scanning and marketing analytics, sits in the same architectural crosshairs. The situation and risk are anything but unique to Anodot.

The key question for security teams is not whether a vendor appears secure on paper. Granted, certifications, audits, penetration testing and other evaluation processes provide an important snapshot of security maturity, but they do not necessarily reflect how risk propagates if a trusted third party is compromised.

In these environments, exposure is often shaped as much by architectural dependencies and access design as by the vendor's security posture.

Central to the challenge security leaders face is the rapid growth of cloud-native infrastructure, SaaS delivery models and API-driven integrations. By definition, this has significantly increased the number of third-party services operating within enterprise environments.

For example, many analytics, observability, AI, security and operational platforms depend on persistent connectivity to customer environments to function effectively, often through APIs, OAuth grants, service accounts or cloud identity roles.

In many organizations, these integrations have evolved incrementally over time, resulting in increasingly complex trust relationships between internal systems and external vendors.

While these architectures improve operational visibility and automation, they can also create indirect attack paths that extend beyond the customer’s own security perimeter. This is what Anodot and its various customers have been dealing with.

Mitigation strategies

As mentioned, this has become a well-understood attack vector and, as a result, security and infrastructure teams are increasingly reassessing how trust is delegated across cloud and data environments, particularly where vendors maintain continuous or privileged access.

Options include reducing standing access and limiting the scope of third-party permissions wherever possible. In addition, architectural approaches such as short-lived credentials and customer-controlled access revocation are increasingly being adopted by organizations seeking to reduce downstream exposure in the event of a vendor compromise.

Elsewhere, organizations are reducing unnecessary retention of customer data within third-party environments, limiting the amount of information that could potentially be exposed during a breach. This is a very sensible move from an overall data protection and privacy standpoint, regardless of how victims are actually breached.

In practice, many IT and security leaders are now evaluating vendors not simply on compliance certifications or audit status, but on how access is structured, monitored, constrained and revoked across interconnected environments.

This reflects a growing recognition that security exposure is increasingly shaped by trust architecture and dependency management as much as by perimeter defense or endpoint protection alone.

Rethinking the trust model from the ground up

Mitigation strategies matter, but the most durable protection comes from choosing vendors whose architecture doesn’t create the problem in the first place. The Anodot breach is a useful illustration of what the high-risk model looks like: a vendor that sits inside customer environments, holds long-lived credentials, and becomes a single point of failure for everyone downstream.

The lower-risk alternative inverts that model entirely. Rather than a vendor reaching into your cloud, data flows outward from the vendor to you. The vendor supplies structured data through an API or a managed delivery pipeline; you consume it.

There are no OAuth grants into your warehouse, no service accounts in your cloud, no persistent session the vendor maintains into your infrastructure. If that vendor is compromised, the attacker gets the vendor’s own environment. They do not get a skeleton key to yours.

This distinction is increasingly relevant for the teams operating at the data-intensive end of security - threat intelligence, digital risk protection, fraud detection, and similar functions — where the volume and variety of external data sources creates significant third-party exposure by default.

For those teams, the question worth asking of every data supplier is simple: does your product require any form of access into my environment, or does data only flow outward from yours to mine? The answer shapes your blast radius in ways that no compliance certificate can.

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Last week, Apple unveiled Siri AI across all its devices at its annual WWDC event. While much time was given to how you'll be able to use Apple's new virtual assistant on iPhone, iPad and Mac, the Apple Watch was overlooked — despite the fact that, according to Apple, it's "the most convenient way to interact with Siri" more often than not.

That quote comes from Apple's own David Clark, senior director of watchOS software engineering. I had the opportunity to ask Clark, as well as Cait Dooley, Apple Watch and Health Product Marketing Manager, about how they see Apple Watch users interacting with Siri AI on their wrists.

Clark said one of the goals of watchOS 27 was "to expand the intelligence story on Apple Watch and make it a true co-partner to Apple Intelligence". It's doing so by being a first point of contact, as a wearable is a convenient place to house a microphone for asking Siri questions.

"We really wanted to make sure the Siri experience is a singular and consistent experience, whether I decide to ask Siri on my wrist a question, or whether I have my phone in my hand and I decide to interact with Siri there. We really wanted to feel like it’s one Siri, that has access to your data and is able to personalise it in a consistent way.

"One of the things that Apple Watch brings to the picture is the fact that it’s on your wrist all day, [so] oftentimes it’s the most convenient way to interact with Siri. You can imagine you’re on the go, your hands are full, you have that quick question for Siri and you can interact with Apple Watch. Of course, with the new Siri app, if you want to continue that conversation or go more deeply into some topic you’re interested in, later on you can pull out your phone and pick up right where you left off.”

Clark gives me an example of a grocery list in a store: with hands full, he's able to ask Siri on Watch for the ingredients he needs for a particular recipe. When he gets a moment, he can then open his phone to view that ingredients list in an easier-to-read way. "Working together is that superpower," he says, "and having it all being consistently driven by your data."

The elephant in the room: watchOS 27 and compatibility

As the announcements were rolling out, however, there was a pall over watchOS 27 as it was announced that five older models — the Apple Watch Series 6, 7, 8, the SE 2 and even the original Apple Watch Ultra — won't get watchOS 27, and so won't get the new Siri AI features. They'll only receive basic security support going forward.

I asked the watchOS team exactly why so many watch users have been left high-and-dry.

"With every software release across every single one of our platforms, we always want to ensure that you have the best experience, so we make power and performance a priority," said Dooley.

"The great new features in watchOS, including the capabilities of SIri AI and the new tap gesture, work best with the processing power that is in Apple Watch Series 9 and later, Ultra 2 and later, and SE 3. Older devices can still be paired with iPhones that are running the latest software and continue to receive security updates, so they can continue to have a great Apple Watch experience."

So while you'll be able to use older your watch with iPhones running Siri AI-powered software, you won't be able to access the new and smarter assistant on your watch. Although Dooley and Clark wouldn't confirm this is the case, it's likely only Apple Watches running Apple's powerful S9 and S10 chips can handle the technical demands of Siri AI.

Satellite constellations are already transforming global connectivity, extending the Internet’s reach to nearly every corner of the planet. Now, as conversations turn toward compute in orbit, the focus is expanding from connectivity alone to how distributed infrastructure will shape the future of digital services.

But what does it really mean for connectivity when infrastructure extends beyond Earth? The answer is crucial, because space isn’t just another deployment site, it introduces fundamentally different opportunities and constraints that challenge how modern digital services are designed and delivered.

Understanding Space Infrastructure

Space will not replicate the data center environments we know today. Constraints like launch capacity, radiation, power generation, cooling, and physical size mean orbital compute resources will likely be smaller and more specialized than hyperscale infrastructure on Earth.

Rather than lifting existing data centers into orbit, space is likely to inspire new forms of distributed compute and storage nodes, designed to complement terrestrial systems.

Because these distributed resources need to work together seamlessly, connectivity becomes the critical enabler. In other words, the real shift isn’t about relocating data centers to space. It’s about extending the distributed architecture of the Internet itself.

The opportunity lies in distribution

For decades, application architecture has evolved to improve user experience. Content delivery networks moved content closer to users. Cloud platforms introduced elasticity and geographic redundancy. Edge computing pushed processing toward the point of data creation. Space introduces another dimension to this model.

Rather than relocating entire applications, organizations will likely distribute functions across multiple environments, including terrestrial clouds, edge infrastructure, and space based platforms, all depending on what makes architectural sense.

Satellite-based Earth observation systems, for example, generate enormous volumes of imagery and sensor data. Processing portions of that data in orbit before transmitting results back to Earth could reduce bandwidth requirements and accelerate insights.

Other use cases may involve distributing cached content, supporting connectivity in remote environments, or enabling services for moving platforms such as ships, aircraft, and industrial operations.

What matters most is that space becomes another place where parts of a service can run, alongside existing terrestrial and cloud infrastructure.

Orbital connectivity follows different rules

Designing systems that rely on orbital infrastructure also requires understanding how satellite connectivity differs from terrestrial networks.

Satellite constellations are dynamic by nature. Satellites move continuously, requiring constant handoffs between spacecraft and ground stations. Network paths change continuously as satellites move and the routing topology shifts with them.

Even when performance is strong, these dynamics introduce behaviors that traditional network architectures were not built to accommodate. Latency profiles differ too. Low-Earth orbit constellations operate at a fraction of the altitude of geostationary satellites, which matters, but a 20–40ms round-trip is still not terrestrial fiber.

Bandwidth characteristics also differ. Satellite connectivity is often asymmetric, with significantly more capacity for downloading data than sending it back upstream. Power constraints and radio transmission requirements make large uplink transfers more expensive than their terrestrial equivalents.

These realities mean that not every workload belongs in space. Instead, the most effective architectures will carefully consider which components should run where, based on latency sensitivity, data volume, and operational constraints.

The internet is extending its reach

Today's Internet already spans continents and oceans through vast terrestrial and subsea infrastructure. Satellite networks aren't a future concept, they are operational now, delivering connectivity globally and extending reach to places where traditional infrastructure is difficult or impossible. Future orbital compute may yet introduce new processing and storage capabilities as well.

For service architects, this means digital platforms may soon operate across a combination of terrestrial fiber infrastructure, subsea cable systems, wireless access networks, hyperscale cloud environments, edge compute platforms, and satellite constellations or other orbital systems.

From a user’s perspective, however, none of this complexity is visible. They simply expect the service to work. But when a single user interaction may traverse a local access network, a regional ISP backbone, a subsea cable system, a cloud region, and potentially a satellite link, performance issues can emerge anywhere along that path - from congestion on a terrestrial route to disruptions at a satellite ground station.

So while space based compute infrastructure may still be years away from large scale deployment, the architectural questions it raises are already relevant today.

Understanding how these pieces interact will be the key to building resilient digital services, whether the infrastructure supporting them sits in a data center, at the network edge, or hundreds of kilometers above the Earth.

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The enterprise AI gold rush is over. What comes next is far less glamorous and far more important: execution.

Boards are no longer funding experiments, and they are demanding execution.

Indeed today, many boardroom conversations have shifted focus from pilots and demos to a much more difficult question: what does it really deliver in production?

The answer lies inside real organizations, where systems are fragmented, processes are constrained, and risk is non-negotiable. And this is where most AI initiatives stall because the organisation is unable to operationalize them.

Success is no longer about demos and model capability, but whether a firm can deploy AI safely, reliably and at scale within existing systems.

We are entering a new phase. Not the model era. The execution era.

How we got here

Now, cast your mind back to 2023 and the dominant challenge in enterprise AI was access including access to capable models, enough processing power, as well as engineers who knew what they were doing.

This period was genuinely exciting, and yet it was also an extremely expensive way to learn that a graveyard of ‘proofs of concept’ does not amount to business transformation. In hindsight we can see that in most cases, the model itself was rarely the problem, it was the business which fell short.

Fast forward to today, and it is more than evident that frontier model capability is beginning to converge, and this means that differentiation shifts towards orchestration, governance, execution, and integration inside real enterprise environments.

Put simply, we know that frontier models can handle most knowledge-work tasks competently so that capability is no longer the limiting factor. The limiting factor is whether AI can operate inside the systems businesses already run, without introducing new risk, friction, or complexity.

From isolated intelligence to integrated execution

This requires a shift from isolated intelligence to integrated execution because in production, AI does not exist in a vacuum. It interacts with legacy systems, approval chains, compliance requirements, and fragmented data sources that were never designed for autonomous systems. This is precisely where most AI initiatives break.

Much of the conversation around enterprise AI risk still centers on hallucinations and incorrect outputs. These issues matter, but they are not where most deployments fail as the real failure mode is governance.

AI systems struggle not because they lack intelligence, but because they lack the ability to operate within structured organizational environments. They cannot reliably enforce policy at the point of action, nor provide clear accountability for what was done and why. Enterprises do not adopt AI because it is intelligent. They adopt it because it is predictable, controlled, and accountable.

There is a fundamental difference between a model that can generate an answer and a system that can execute a workflow. Generating a procurement recommendation is trivial. Executing a procurement workflow inside a legacy ERP software system respecting approval hierarchies, flagging exceptions, and producing a clear audit trail is not. This is where trust is built.

The next steps

If you want to understand where AI will create real enterprise value, then look to regulated industries such as banking, telecoms, and utilities. These sectors are not slow adopters. They are disciplined adopters. They operate within strict compliance frameworks, data sovereignty requirements, and deeply embedded legacy systems.

In regulated environments, for example, a single AI-triggered action may require policy validation, role-based approvals, compliance logging, and explainability before execution is permitted. Here, AI cannot bypass these constraints as it must operate within them.

This creates a natural filter because once AI works in these environments, it works anywhere. For many enterprises, particularly in regulated industries, sovereignty over data, workflows, and model orchestration is becoming just as important as model intelligence itself.

A great deal of today's AI is assistive. It helps individuals, for example, to write, analyze, summarize, and recommend and this has value, but it’s not transformative. Instead, transformation begins when AI moves from assistance to execution when it can act within defined boundaries, navigate real workflows, interact with multiple systems, escalate when necessary, and produce a clear record of what it did and why.

This is where ROI becomes visible, but it also significantly raises the bar. Autonomy without control is not useful as it is a liability. The execution era is therefore not just about capability; it’s about controlled autonomy.

The next AI leaders may not be the companies building intelligence itself, but the companies making intelligence operational across real enterprise systems. In that sense, the strategic battleground is shifting from models to execution infrastructure.

The gold rush was about possibility. In the execution era, intelligence alone is cheap. Trusted execution is the real infrastructure layer.

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The UK is experiencing a watershed AI moment, and the momentum is real. British AI startups raised £6 billion in venture capital last year, and in just the first three months of 2026 they have already raised more than half that figure again.

The Government's recently launched Sovereign AI Unit, backed by a £500 million public investment, is designed to accelerate this momentum by giving the UK's most promising AI companies direct access to computing power, R&D support and government procurement opportunities.

The ambitions are right. The UK has genuine strengths in AI research, a world-class university sector and a growing startup ecosystem attracting serious global capital.

But there is a fundamental question these ambitions raise, one the telecoms industry needs to put firmly on the table: you cannot scale AI without the connectivity infrastructure to support it.

The case for quality, not just coverage

Coverage maps tell you where the network is. Performance data tells you whether it actually works. More than 95% of the UK's landmass has at least some 4G coverage, more than many countries across Europe, and on that measure the UK performs well. But coverage is only part of the picture.

We all know that the UK isn’t where it should be with quality standalone 5G. The UK currently ranks last among 16 comparable high-income Western European markets on key 5G performance measures, with only 15% of 5G sites delivering the standalone architecture that is the global gold standard.

This gap has real consequences. Analysis from Analysys Mason and Opensignal shows UK users get just 5.8 Mbps of 5G speed for every euro (£0.87) they spend each month, the weakest return among comparable European markets. In Portugal, consumers get almost four times as much at 20.9 Mbps per euro (£0.87).

But the crucial point is this: we know exactly what the problem is, we know what the solution looks like, and we have the tools to close the gap. The good news is that there is a smarter, faster and more cost-effective path to the 2030 standalone 5G target than the one the UK is currently on.

AI needs physical infrastructure, not just software

This is where the conversation needs to get specific. AI is not just software in the cloud. It requires a robust, low-latency, high-capacity network backbone to function at scale. The applications that will determine whether the UK's AI investment translates into economic growth do not run on aspiration. They run on milliseconds and bandwidth.

Delivering AI at scale requires three things working together. The first is standalone 5G architecture for ultra-low latency below 10 milliseconds and the massive bandwidth capacity that real-time applications demand.

The second is dense small cell networks, with AI applications requiring cells significantly closer to users than traditional macro towers allow. In dense urban areas, high-quality standalone 5G requires between 37 and 96 small cells per square kilometer.

The UK currently has approximately 5,000 deployed across the entire country, leaving significant room for targeted investment to close that gap. And finally, edge computing infrastructure positions processing power closer to where data is generated, enabling the real-time responsiveness that applications like autonomous vehicles and industrial automation require.

Good 5G is not about coverage percentages. It is about consistent experience, whether you are a start-up looking to scale, a commuter on a train, or a fan at a football stadium. Getting this right is what will allow the AI companies attracting record levels of capital to scale their most demanding applications on home soil.

Shared infrastructure is the fastest route forward

The most efficient path to closing the investment gap already exists. Shared infrastructure is cheaper and faster to deploy, requires fewer sites, consumes less energy and, critically, it works.

Shared models have already freed up £3.4 billion for UK operators and €26 billion across Europe to reinvest in network quality. Rather than spreading capital across redundant deployments, sharing allows operators to direct investment where it matters most: performance and coverage quality.

Unlocking shared infrastructure at scale requires a planning system that supports it. The Product Security and Telecommunications Infrastructure Act was passed over three years ago and remains largely unimplemented. Full implementation would unlock upgrades on over 6,200 sites today.

Treating mobile and fixed broadband equally under Permitted Development Rights would remove an unnecessary barrier to deployment. Creating an environment which rewards quality-focused investment such as a structure that supports sharing over sole-use builds would further accelerate progress. These are practical steps; the levers are already there.

The opportunity is there to be seized

A fully realized standalone 5G network could contribute £159 billion to the UK economy by 2035. That is not a distant or theoretical prize. It is the direct return on infrastructure investment decisions being made right now.

The UK has everything it needs to lead: world-class AI talent, growing capital flows, genuine government commitment and an infrastructure sector ready to deploy shared solutions at pace.

The delivery choices made between now and 2030 will determine whether the UK's AI ambitions translate into genuine economic leadership. This country has every reason for confidence.

Fix the network, back shared infrastructure, clear the planning barriers, and the applications the UK is funding today will scale here, on infrastructure built for the job. That is an outcome well within reach.

This is no longer a technology question. It is an execution question – and one the UK is well placed to answer.

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Today, I was using the ChatGPT app on my iPhone when I accidentally held my finger down on the send button (the blue upward arrow) a bit too long, and I was suddenly taken to a new options screen I hadn’t realized existed before, which I use all the time now.

The option screen gives you three different intelligence settings for the answer you're going to get back from ChatGPT: High, Medium, or Instant.

What’s going on here is that ChatGPT is asking you to choose between three different flavors of ChatGPT-5.5 that change how long the chatbot thinks before answering. These options are easily visible in the web-interface version of ChatGPT, but the reason why I’d never seen them before in the iOS app is that there’s nothing that indicates they exist, and I bet a lot of you haven’t found them either.

So, fire up ChatGPT and try it now - you’ll be surprised. The workflow goes like this:

1. Type your prompt
2. Long-press the blue send arrow
3. Choose a model - e.g., Thinking
4. Send

For paid users only

What’s nice is that having the freedom to choose ChatGPT’s intelligence level like this means that you can choose differently for each prompt you enter.

So, if you just need a quick answer to a simple question, then choose Instant. If you’ve asked something more difficult and you want to make sure you get the full benefit of the AI thinking about it properly, then choose High. The Medium setting gives you a balance of both approaches.

If you've never used the High option before then it can be quite surprising how long ChatGPT will take to think about its answer, but the benefit is that you get a much more in-depth response.

Sadly, this feature only works for users on paid ChatGPT plans. If you’re using a free ChatGPT account, you won’t get these options.

Android alternatives

Since the feature isn’t prominently documented, many people aren’t aware it exists. And if you’re on an Android phone, well, it doesn’t actually exist, because this hidden feature is unique to iOS, but there is an Android equivalent.

On Android, when you long-press an answer text (not the send button), a new menu will be available for you. At the end of the menu, you can see a Change model option — you can use this to select a thinking or instant variant of your current model.

I love finding hidden features in tech I own, and often it's like this one — the hidden setting turns out to be something I’ll use all the time, once I’ve discovered it. Let's hope you do, too.

• 3 pointers

Shenzhen-based storage giant Longsys has been pulling out all the stops in offering a wide range of storage solutions for a world that increasingly relies on AI.

The company behind Lexar and FORESEE is releasing several interesting solutions, including a custom chip that enables on-the-fly compression on existing SSDs, proprietary caching technology, and fast, DRAM-less SSDs in the smaller M2 2230 form factor.

Longsys's new mSSD builds on the success of its predecessor, offering PCIe 5.0 speeds and twice the maximum capacity while maintaining the same form factor that made its predecessor a breakthrough when it was launched last year.

A powerful, high capacity option despite the size

The Longsys mSSD is, much like the previous model, a DRAM-less SSD, even as the newer Maxio 1802 controller enables read/write speeds of 11GB/s and 10GB/s, respectively.

The SSD, which was also showcased at Computex 2026, where Longsys demonstrated how its proprietary VC phase-change liquid cooling, paired with a multi-layer stacked thermal architecture, enabled it to deliver sustained performance compared with most of its DRAM-less competition in the same form factor.

With 8TB of storage, it caters to most AI firms and power users looking to store or cache LLMs locally without contending with the performance limitations of cheaper, larger SSDs.

The form factor and the relatively large capacity on offer are because of how the SSD is designed; Longsys states that the mSSD is manufactured using an advanced wafer-level SiP system-in-package technology with a single die housing NAND flash, the controller, and the PMIC that allows it to retain a compact 2230 form factor without sacrificing performance or reliability.

Longsys's plans for the SSD are also clear, with it focusing on sustained performance gains when running "intense KV Cache-driven AI workloads". Given how similarly performing 8TB SSDs can cost upwards of $2,000 on Amazon right now, it might be safe to assume that the mSSD, at least at the highest capacity tier, easily prices out most modern consumers even if it is not marketed as an enterprise SSD.

At a time when NAND shortages continue to break the market, one can easily assume a four-figure asking price for what is essentially a state-of-the-art storage option, at least in its form factor, and it is a sign of things to come, even as Longsys has yet to announce a price or a release date for its offering at the time of writing.

• Cybernews analyzed 10 Android companion apps for kids' AI/robotic toys and reported half of all declared permissions are considered dangerous by Android guidelines
• The investigation found 3rd party trackers in 7 out of the 10 applications they examined
• Researchers also detected two advertising, two profiling and one location tracker as part of their investigation

With AI toys becoming increasingly adopted by families, security firms are ringing the alarm about what this means for privacy in a post-LLM world.

Modern AI toys incorporate LLM models, allowing users, including children, to talk to and otherwise interact with them, and granting unprecedented access and permissions that enable them to harvest sensitive data with ease if a bad actor were involved.

Cybernews recently examined 10 toys from various brands and found that many had excessive permissions at the application level, which could expose them to abuse or data harvesting.

Why is an AI toy also a privacy concern?

Most users tend to grant permissions to Android applications on a whim without reading the fine print, but that might have extended to another frontier altogether: AI toy apps.

Cybernews' recent study, which focused on 10 different Android companion apps for children (Loona, Dash & Dot, Sphero, mBlock, Miko, Eilik, SPIKE™ LEGO® Education, Ozobot Evo, Petoi, and AIBI Pocket), found that all of them asked for permissions classified as 'dangerous' by Android.

All 10 applications required precise location access, which isn't concerning on its own, since these do need it to search for their corresponding toys using Bluetooth Low Energy (LE), but the permission requirements go much further than that.

As many as six required access to microphones, five requested camera access, and eight requested Bluetooth scanning capabilities. One could argue that these are required by some of the toys to function, but some of these are used in some capacity against the regulation updates made to the Children’s Online Privacy Protection Rule by the FTC.

The rules that strengthened "key protections for kids’ privacy online," as per the then-FTC chair, Lina M. Khan, limited data retention, required opt-in consent for targeted advertising to children, and required disclosures to prevent data abuse.

This has not stopped AI toys from building behavioral profiles of their target users, as Cybernews found trackers in 7 of the 10 applications it analyzed. While most of these were crash reporting and analytics-related, two of the applications had advertising and profiling trackers, and one of them (Loona) also had a location tracker.

This might run contrary to data minimization regulations at a time when the world is already grappling with a social media ban for children under 16 in the UK, following Australia's footsteps.

"Data minimization for children's apps is essential. Responsibility falls both on developers to request fewer permissions and minimize sensitive trackers, and on parents to take greater control over the technology available to their children," the researchers said.

"Unlike adults, children are less likely to understand what data is being collected, how it may be used, or the privacy implications of sharing it.”

• Confluent research reveals firms aren't worried about the scale of AI investments – the ambitions are there
• Instead, companies are struggling with legacy data systems
• We just didn't know that we needed support for continuous intelligence back then

Businesses are still investing heavily in AI while they figure out where it can be used best, but Confluent believes the volume of investment isn't a blocker anymore. Instead, it's the quality of the data AI systems rely on that's letting them down.

Three in four (72%) IT leaders say poor real-time data infrastructure is preventing them from being able to scale properly.

Real-time data processing (72%), data lineage uncertainty (66%) and fragmented data ownership (65%) are among the biggest challenges that companies face when trying to implement AI.

AI's biggest blocker is data

These challenges have ultimately led to lower-than-expected AI deployments and poor ROI – only 32% say they have agentic AI in production, and the majority instead experience delays.

To fix it, 80% say they're now prioritizing using enterprise data to drive AI-based systems, with data streaming platforms cited as one of the biggest supports by 88% of IT leaders. In fact, it's more of a priority than AI and ML (82%), indicating that leaders are increasingly aware of how they could fix the problem.

"Models need to be connected to the systems, events and signals that reflect what is happening across the business," Chief Product Officer Shaun Clowes wrote, referencing the currently fragmented data systems. But Clowes acknowledged that it's not necessarily organizations' faults that AI systems are failing.

Clowes explained that current infrastructures weren't designed for continuous intelligence, which is why all companies regardless of sector or size are facing the same issues.

"The companies making the most progress are investing not only in AI itself, but in the data foundations needed to support it," he concluded.

Recently I asked ChatGPT to turn a childhood photo of me into a 1990s action figure. The surprising part wasn't the image itself. It was that the AI chose accessories that accurately reflected my childhood interests without me mentioning them in the prompt. It pulled those details from memory.

Using ChatGPT to turn yourself into an action figure became a very popular, if brief, fad more than a year ago, and like many others, I'd enjoyed seeing what the models could do with a photo of me and some virtual blister packaging. The models have improved immensely since then, and OpenAI has boasted about ChatGPT's current image creation models in particular.

Here's what I did: I asked the AI to create a figure based on me at seven, as well as one based on me today at 40, and to include some accessories as well. The project became less about toys and more about memory.

Collector's edition 1993

The first challenge was creating a childhood version of me. I wanted it to look like a toy from the early 1990s, complete with oversized branding, bright colors, and the kind of packaging that would have looked perfectly at home hanging on a peg in Toys "R" Us. I asked ChatGPT to:

"Create a 1993-style collectible action figure called 'Eric, Age 7' based on my childhood photo. Include three accessories that represent my interests at that age that come from our conversational history and package it like a classic toy store action figure."

The accessories came out better than I had thought they would. A book, a soccer ball, and a Game Boy were all things I enjoyed as a child. The specific choice of a Goosebumps book was notable, too. And the overall result was definitely of the era, down to the exaggerated logos and collector edition badge.

Once the childhood action figure existed, I wanted to see what happened when it left the box. Rather than placing it in some dramatic action scene, I asked ChatGPT to imagine it abandoned in a playroom after a child had finished playing. I told it to:

"Show the Eric Age 7 action figure outside its packaging in a playroom. Arrange the accessories naturally around it as though a child had been playing and just walked away."

You can see it standing among its accessories and other bits bits of childhood clutter above. I don't think the toy would be a big seller, but it definitely captures a part of my own life and experience based purely on the collected details ChatGPT has absorbed about my life and interests.

Premium adult edition

Creating the 40-year-old version was a completely different exercise. Childhood is easy to summarize because it tends to revolve around hobbies and possessions. Adulthood is messier. Plus, there are a lot more details from my current life available for ChatGPT to turn into accessories. Nonetheless, I asked ChatGPT to:

"Create a premium action figure of me called 'Eric, Age 40.' Use accessories that represent my life today and package it like a luxury designed for adult collectors."

ChatGPT drew from things it knew about me and included a smartphone, a black-and-gold chihuahua, and a stroller. Together they painted a picture of work, family, and everyday life in a nice, if perhaps bland, suburban way.

The packaging reflected the shift as well, with its black-and-gold branding that looked more at home in a collector's display cabinet than a toy aisle. And the accessory choices were again dead on.

I again wanted to see the toy leave the packaging and join the playroom, and requested that ChatGPT:

"Show the Eric Age 40 action figure outside its package in a playroom with its accessories nearby. Make it look like a child had been playing with it and then left the room."

The finished image ended up being my favorite of the four. The figure stood on the carpet with the Chihuahua, stroller, and smartphone arranged around it, looking like it had wandered out of its own packaging.

The accessories of the two toys were very different, and the packaging had evolved, but both figures showcased what ChatGPT thinks matters to me now and what mattered to me then based on our interactions.

What started as a test of ChatGPT's image generation ended up becoming a test of its memory. The action figure itself is just a novelty. What's more interesting is that the accessories, the setting, and the small details all came from a version of me that exists only in the AI's recollection of our conversations. That's a strange thing to see packaged up in plastic — a toy built not from a photograph, but from memory.

• 75% of UK business travellers would use, or have used, unapproved AI tools
• Many aren't happy with the tools they're given, some aren't even given any
• Workers don't want to use other tools – they just want AI to be integrated

New data from SAP Concur has apparently confirmed employees are still ignoring company policies, with three in four UK business travellers using (or would use) unapproved AI tools.

Data from the company's Global Business Travel Survey shows that, while organizations are investing heavily in AI, they're not giving workers access to the right tools.

As a result, employees are opting to use other, more suitable alternatives and often prefer publicly available chatbots such as ChatGPT and Gemini.

Shadow AI is an even bigger issue among travelling workers

Two in five (43%) said they use unapproved tools because they prefer different options to what they're provided, but one-third (32%) say their organization doesn't even offer any AI tools to support planning or booking travel, forcing them to look elsewhere.

The data shows that remote workers (81%) are more likely to use shadow AI than office-based employees (71%), as are younger workers like Gen Z (79%) and Millennials (76%) compared with Gen X (62%) and Boomers (49%).

As for what employees are looking for when it comes to AI supporting their travel needs, the biggest use case is planning itineraries (37%). Tracking expenses (30%), assessing travel risks (26%), making changes to their trip (24%) and completing expense reports (21%) are also common use cases for the productivity-boosting technology.

But business leaders already know the scale of the problem – 100% of CFOs are concerned about shadow AI in business travel, the report found.

"Leaders must educate workers on the risks and provide T&E tools that deliver the desired level of AI support," EMEA VP for Concur Travel Paul Dear noted.

Looking ahead, the fix must come from organizations – workers clearly aren't too keen on having to navigate to third-party tools, and are doing it out of necessity. More than a third actually want their AI tools integrated into their productivity software (38%), communications platforms (36%) and CRM systems (32%).

Salesforce has laid out its continued backing for the UK as a key market in terms of both embracing current AI tools as well as developing new and exciting services and businesses alike.

At its Salesforce World Tour London event, the company's President UKI, Zahra Bahrololoumi, noted how the company was looking to play a key role in the increasingly common Agentic Enterprise.

Top of mind was the company’s new “Headless 360” platform, which will allow users to access all of their Salesforce data from various apps or a terminal, potentially opening them up to huge improvements in productivity and efficiency.

"A very pivotal moment"

“We are at a very pivotal moment - every company, every industry - AI is transforming our lives - but how do we harness that power?” Bahrololoumi declared in the opening keynote at Salesforce World Tour London, “agentic AI doesn't just benefit commercial businesses, it can also elevate citizen services - from policing to food and transport, to health and welfare - our efforts show that when we do good in the word, we can actually do really well.”

“We're redefining CRM again with Customer 360 - it's making your organizations truly customer-centric…(and) this transformational work AI is ushering in a whole new chapter,” Bahrololoumi added, “agentic AI is everywhere, it is in every experience, every industry, every company - it's not just about efficiency, it's about growth...and it is fundamentally changing what is possible when we reimagine enterprises.”

Bahrololoumi outlined the Salesforce’s “incredibly bold” vision for the UK, which includes $6 billion investment committed to the country through 2030, and its role in backing the UK government’s aim to train 10 million UK workers by 2030.

“The UK...is such a vibrant AI market", she added in a later media Q&A session, outlining how Salesforce is “really committed” to educating businesses and partner ecosystems across the country.

“Speaking to customers (about AI) we have gone beyond the pilot stage - we’re now seeing incredible scale,” she said, “agents working hand in hand with humans at the right point…and we know that those who do scale are the ones who have been intentional about orchestrating and organizing their data.”

A US cyber breach now costs $10.22 million on average, but the figure itself is only part of the warning. What matters more is what it reveals about the way organizations are managing risk.

For too long, cyber risk has been treated as a technical problem to be solved by technical teams, but that position is becoming harder to defend. The companies hit hardest by cyber incidents are those without the visibility, governance and accountability to respond when pressure hits.

According to IBM’s latest research, the average cost of a data breach in the US is the highest of any region globally. At the same time, FBI data continues to point to hundreds of thousands of cybercrime complaints each year, with losses measured in the tens of billions.

A growing proportion of businesses are also now formally disclosing incidents, with one 2025 survey suggesting that 76% reported a breach or potential breach to authorities.

The direction of travel is very clear – cyber incidents are becoming more expensive, more visible and harder to contain. Leadership teams need to ask whether they can prove they have control before an incident forces that conversation.

The cost is driven by more than the attack itself

The financial impact of a breach is shaped by how long the organization remains exposed and how confidently it can demonstrate that the right controls were in place.

On average, it takes 241 days to identify and contain a breach – this extended lifecycle creates a prolonged period of uncertainty where operational disruption, regulatory obligations, customer impact and reputational damage begin to compound.

This is where the difference between security activity and business governance becomes clear. Two companies can experience similar attacks, but very different outcomes.

One may detect the breach internally, escalate quickly and respond in a controlled way, while another may only become aware when an attacker, customer, partner or regulator forces the issue into the open.

That gap comes down to whether risks were understood in advance and if the business had a defensible framework for responding under pressure.

Exposure is not random

Cybercrime exposure varies significantly between organizations, states and sectors, but it is rarely random. Industries such as healthcare, financial services and technology continue to carry higher risk because of the volume and sensitivity of the data they manage.

IBM’s research shows that healthcare breaches remain among the most expensive, with extended recovery times and higher regulatory scrutiny.

However, some of the more complex risks are emerging beyond the obvious high-value targets. Supply chain compromise is now one of the costliest attack vectors because a single weakness in a third-party system can create consequences across multiple businesses.

In an increasingly connected commercial environment, exposure often sits outside the four walls of the company itself.

Phishing also remains one of the most persistent routes in and attacks are becoming more targeted, more convincing and harder to detect at scale, particularly where there is limited visibility across people, processes and third-party access points.

When you bring these factors together, exposure starts to look like a reflection of how an organization is built, governed and controlled.

AI is widening the governance gap

AI is now changing both sides of the cyber risk equation. For attackers, it is making familiar methods more effective. Around 16% of breaches now involve the use of AI, most commonly to enhance phishing and social engineering attacks, lowering the barrier to make these more convincing and personalized.

For businesses, the risk is just as much internal. AI tools are being adopted quickly, often by teams looking to improve productivity or accelerate decision-making. The problem is that adoption is frequently moving faster than oversight.

The report highlights that 63% of companies still lack formal governance policies for AI, and where AI-related breaches do occur, the vast majority involve systems without proper access controls.

There is a familiar pattern here. As with cloud adoption a decade ago, enterprises are moving quickly to capture the benefits of a new technology, while the controls needed to manage its risks are still catching up. The result is a widening gap between those experimenting with AI and those that can evidence how it is used.

Governance is becoming the dividing line

The businesses that manage breaches most effectively tend to have done the work before the incident happens. They understand where their risks sit, know who owns them, and have defined processes for escalation and recovery. Just as importantly, they can evidence that structure when customers, regulators or insurers ask difficult questions.

That’s why recognized frameworks such as ISO 27001 are becoming more important, forcing companies to take a systematic approach to risk, governance and accountability. They also create a baseline that can be reviewed and tested over time.

This distinction matters both operationally and commercially. Around 86% of affected organizations report operational disruption following a breach and, in many cases, that disruption directly affects revenue, service delivery and customer trust.

For the businesses that can evidence good practice, strong governance frameworks are opening doors, particularly in regulated sectors and complex supply chains. For those that cannot, this creates more friction and risks lost opportunities.

Compliance is now part of business resilience

There is still a tendency in some companies to treat compliance as an administrative exercise, but that view is becoming swiftly outdated. Today, compliance is becoming a way for businesses to prove they manage risk consistently. It brings structure to decision-making, accountability to ownership and evidence to claims of good practice.

This is especially important as cyber risk becomes more visible to investors, customers and regulators. A business may have strong technical controls, but if it cannot demonstrate how those controls are governed, reviewed and maintained, it will struggle to build trust when scrutiny increases.

The ability to evidence control is becoming almost as important as the control itself.

A more realistic view of cyber risk

Geography plays a role in cybercrime exposure, but it’s not the root cause. Differences in governance, investment, leadership focus and operational discipline all shape how businesses experience and manage cyber risk.

That means exposure can be reduced, but only when leadership teams understand where risk actually sits and what is needed to manage it. The mistake is assuming that cyber resilience can be achieved through technology alone. Technology is essential, but it doesn’t define ownership, test processes, align teams or prove accountability – governance does that.

For leadership teams, the starting point is knowing where risk exists, how it could materialize and what the potential business impact would be. From there, the challenge is putting the frameworks in place, along with the ownership and oversight that allows them to operate with clarity under pressure.

Businesses that navigate incidents well have prepared for the moment before it arrives. That preparation shows up in faster decisions, clearer responsibilities and a more controlled recovery.

The scale of cybercrime in the US will continue to grow, but the difference in impact will still come down to how well the organization was governed before it needed to prove it.

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Retailers are currently obsessed with the wrong side of the screen.

The industry is watching a race between Google, Shopify, and Amazon to build the next great interface, the AI agent that can search, recommend, and eventually execute a transaction without a human ever clicking a “buy” button.

But while the market focuses on how these agents will talk to customers, a much more dangerous gap is opening up behind the scenes.

The hard truth is that most retail organizations are structurally incapable of being operated by a machine. We are moving from AI that suggests to AI that acts, from conversational shopping to delegated execution.

In this transition, if a business has not mapped its decision ownership, internal data flows, and operational accountability, an agent will not reduce complexity. It will simply scale confusion at a speed the business cannot handle.

From suggestions to delegated execution

Most people still treat AI agents as smarter chatbots, or as a conversational layer designed for product discovery.

This is a fundamental misunderstanding of the technology’s trajectory.

An agent is software with permissions to take action. Platforms like Shopify are already leaning into this reality, having recently released integrations that move beyond discovery to allow for direct agent-led checkouts.

Realistically, we are ready to delegate low-risk, high-volume tasks that are easily reversible, such as product data enrichment or internal data preparation. We are not yet ready to delegate high-stakes commercial decisions, and the same can be said about legal contract approvals or final pricing strategies.

The risk profile changes entirely when you move from an AI that tells a customer which shirt to buy to an AI that is authorized to spend that customer’s money.

Preparing the surface vs. the operating system

Retailers are spending millions to ensure their product catalogs are machine-readable so they show up in agent-led searches. However, a machine-readable catalog is not the same as a machine-operatable business.

There is a gap between hype and reality. On the one hand, Gartner recently predicted that 60% of brands will use agentic AI by 2028. On the other, according to Deloitte, only 11% of organizations have actually deployed agents with success. This highlights a massive disconnect between interest and actual infrastructure readiness.

In this regard, the biggest operational gaps exist in the “boring” middle layers, such as real-time inventory accuracy across twenty different markets, pricing consistency between channels, and warehouse logistics.

Currently, these answers live in a fragmented mess of ERPs, spreadsheets, and employees’ knowledge. When an agent asks, “Is this item actually in stock?” it needs a definite answer. And if your internal systems are in conflict, the agent cannot function.

You cannot run an agent on fragmented data. Instead, you need a dynamic “digital twin” of your day-to-day operations in the form of a single, living data layer that reflects the true state of your business in real time. In a nutshell, you cannot build a working complex system until you have a working simple system.

The decision architecture bottleneck

The real bottleneck in retail today is decision architecture. An AI agent cannot improve a process the business itself does not understand.

In my experience, very few companies can actually map how a decision is made across teams. They still rely on “Slack-based” or “Email-based” approvals that leave no digital trace for a machine to follow.

Before automating, a company must map who owns a decision, what data is trusted for that decision, and what the thresholds for human escalation are. This mapping, combined with your operational data, forms the context layer, the digital twin that the agent uses to ground its judgment.

The warning sign that you are automating confusion is when your teams spend four days a week cleaning data and only one day making decisions. If a human cannot explain the logic, it is impossible for an agent to execute it.

When automation scales complexity

There is a pervasive myth that adding AI will automatically streamline a business. In reality, automation often makes systems more complex. When agents act quickly across weak or fragmented data, errors scale faster than a human team could ever manage.

This is why Gartner predicts that 40% of agentic AI projects will be canceled by 2027 due to a lack of clear business value or the absence of these essential risk controls.

For instance, if your inventory systems disagree, a human might catch the discrepancy during a manual check. An agent, operating on a “junior employee” level of judgment, will simply place the wrong order or promise a delivery that likely won’t be fulfilled.

Certain areas, such as sensitive brand topics, high-margin pricing strategy, and complex customer compensation, should never be fully automated. These require human critical thinking and accountability, which machines lack.

The new meaning of trust

Trust, today, goes beyond a customer’s perception of the brand. It encompasses the technical and operational trust between the customer, the agent, and the merchant.

We are already seeing this friction play out in the courts. A recent 2026 legal battle between Amazon and Perplexity over shopping agents browsing on a human’s behalf has forced a debate on whether agents should be treated as transparent digital identities or human replicas.

Retailers are bracing for this by updating their fundamental terms of service. Target, for example, recently updated its terms in collaboration with Google to address third-party agent behavior and consumer liability.

To maintain control, retailers must treat agents like employees on a payroll. They need their own digital identities and clear permission scopes, as well as rigorous audit trails.

How to keep control? By defining exactly what an agent can read, buy, and change, and by ensuring every action is traceable and reversible by a human.

The five levels of readiness

In this landscape, the companies that thrive will be those whose operations are clean enough for agents to act safely. This requires five forms of readiness:

• Data readiness: One reliable operational truth across all systems
• Decision readiness: Clear ownership of what is automated and what is escalated
• Process readiness: Redesigning workflows for an agent-first world, instead of patching old ones
• Governance readiness: Full audit trails and transparent human accountability
• Commercial readiness: A deep understanding of how agents improve margins

Here’s an example. If a retailer had 90 days to prepare, they should start by picking one high-volume, narrow workflow where the ROI is obvious. Build a “digital twin slice” of just that process by tracing the inputs, the approvals, and the outcomes. Prove it works in a small, measurable way before expanding.

The most uncomfortable question executives must ask is this. If we disappeared tomorrow, is our data foundation and decision-making process documented well enough into a clear digital twin that a machine could replicate it, or does our business context only exist in our employees’ heads?

If the answer is the latter, I’m afraid to say no amount of AI investment will save you.

However, for those willing to do the hard work of operational cleanup today, the coming agentic era offers the first genuine opportunity to scale the best part of their business without scaling the chaos.

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Six months ago, the question "how far away are we from AI movies?" felt reasonable to ask. Today it feels outdated, because they're already here.

Last week I attended a screening of Hell Grind, a 90-minute AI-generated movie that was made in just two weeks. On the same day, Dreams of Violets, a poignant docudrama inspired by real events from 47 years of Iranian civilian resistance, and which is 100% AI-generated, appeared at the Tribeca film festival. Then today I found myself watching something a bit lighter in tone — DEADLINES — on YouTube, a comedy-horror trailer made with AI that was funny enough to make me laugh out loud.

DEADLINES feels like it’s leaning strongly into the vibe of the new Apple TV horror/comedy series Widows Bay, where creepy things happen on a cursed island. But even though it clearly borrows from Apple's hit series, what surprised me was how funny the trailer actually was.

Ultimately, all three releases made me realize we've crossed an important threshold. The question is no longer whether AI can make movies. It can. The question now is whether anyone actually wants to watch them.

The difference between an AI movie and AI slop

For years, AI video demos have been judged on whether they could they fool us. Could they generate convincing actors, realistic camera movements, believable explosions and dramatic lighting? Every improvement felt like another step toward the goal of Hollywood-quality filmmaking.

Getting everything correct is important, because it only takes one obvious problem with an AI-generated scene to destroy the credibility of the whole movie. Now, though, the technical challenges are rapidly becoming the easy part — which makes what's going to come next so much more interesting.

What struck me most about DEADLINES was the quality of the writing. The trailer had actual jokes. It had timing. It had a clear sense of tone. It felt like somebody had a great idea and they wanted to share it.

But even though DEADLINES is a lot better than most AI videos, it’s still not perfect — the accent of the main character veers between several different regions of the UK, for instance. However, it’s good enough to make little things like this not really matter too much. That's the difference between a good movie and AI slop.

Nobody cares about the fingers anymore

While there's no need to count the fingers on people's hands these days, those questions about AI’s ability to realistically generate human beings haven’t gone away entirely; but they’re not the point anymore. When I watched DEADLINES I wasn’t paying attention to the visuals. Instead I was paying attention to the jokes, the pacing and the story, and deciding if I wanted to keep watching.

While DEADLINES knows it's using Windows Bay as its starting point, the trailer understands what it's parodying. It knows the language of prestige horror. It knows how to build a joke. It has an actual comedic premise.

That’s why I think things are about to change. The traditional gatekeepers of filmmaking are looking less secure than ever. The bottleneck isn’t really the production process, funding or access to filmmaking tools. Now, it's imagination and storytelling.

The bigger question now is whether AI filmmakers can create stories people genuinely care about. I don't know whether DEADLINES will ever become a full-length feature- film. What I do know is that it made me laugh, and that's a much more important milestone than generating another realistic explosion or perfectly rendered human face.

AI movies are already here. The race now is to make them worth watching.

• Jeff Bezos says AI is a force for good, rather than a threat
• The technology will expand the human labour market, and create more demand
• Bezos' comments go against the narrative of other prominent voices in AI

AI won’t shrink the labor market, but will expand it, Amazon founder Jeff Bezos has declared, in his latest blast against technology doom-mongerers.

“I know there's a lot of concern that many people have, including many smart people, that AI is going to make humans redundant and so on," Bezos said at the VivaTech conference in Paris.

"I totally disagree with this point of view. And I think, in fact, AI is going to create a labour shortage."

Flipping the script

Bezos' opinion contradict many of the comments recently made by prominent figures in the AI space, trade unions, studies, tech job layoff trackers, and the fears of workers themselves.

Rather than replace jobs, the technology will present new opportunities for workers and create demand in new areas, he explained.

The comments were made as Bezos spoke of his latest venture, Prometheus, which seeks to use AI to assist human manufacturing as a counterbalance to industry which is seeing increased automation.

Bezos also took the opportunity to talk about his desire for humans to focus on space exploration, adding that it is “supply constrained, not demand constrained”.

First on the hitlist for interplanetary habitation is the Moon. “We're going to the Moon to stay, not just to visit," Bezos said, adding that establishing a permanent human base on the Moon would assist in further exploration.

Bezos' space travel company Blue Origin was also a subject of discussion, after the company’s New Glenn rocket exploded during a ground test at Cape Canaveral in Florida in May. The incident resulted in no injuries, and Blue Origin chief executive Dave Limp added that the launch site will be repaired and ready to resume operation before the end of the year.

Trust is a major issue hampering the adoption of AI technology, and at the core of this is the problem of ‘black box’ AI.

As enterprises race to implement AI projects, many have bought-in pre-trained black box models, which provide answers, but no explanations.

Many of the better-known AI tools are considered black boxes - we cannot see inside, they perform tasks well, but we’re not necessarily sure how.

40% of organizations called out explainability as a blocker preventing trust in AI, according to McKinsey. To truly derive value from AI technology in the enterprise we need something different from the existing approach many have taken. We need solutions that offer greater transparency in how they work and how they arrive at the answers they deliver.

If CIOs, developers and end users can work together, we can arrive at AI that is transparent, adaptable and aligned with real needs. Just as we expect academic researchers to ‘show their workings’ by citing their sources, we should expect the same of AI systems.

New technologies and methods already exist to enable this accountability, and regulations help mandate transparency and accountability. Businesses need to engage proactively with this: an AI system is in many ways like an employee.

We ‘hire’ it to do a job, and, like an employee, we need to understand what it does in order to measure and monitor its performance.

The cracks in the first wave

When ChatGPT changed the world forever back in November 2022, the barrier to entry of training capable models meant enterprises mostly relied on pre-trained, proprietary options from large technology companies to move quickly.

Many have continued this strategy, even though these models are not trained on a business’ data, often failing to meet the specific needs of that organisation, and offer little transparency about what is going on ‘under the bonnet’.

In conversations with IT decision-makers, we hear genuine issues raised around AI compliance, ethics and competitiveness. Potential toxic biases and discrimination can have real world impact for people interacting with and depending on the output of AI-powered tools. Ultimately, these risks slow down adoption and limit potential use cases and applications.

For enterprises, this is a key risk around AI, and a barrier towards building trust. The same McKinsey survey found just 17% of respondents said they were currently working to mitigate issues around explainability, despite acknowledging it as a problem.

The report also said that systems which offer more explainability will be a key enabler as AI moves beyond early use case deployments to scaled adoption across the enterprise. Even though regulation has made forward steps, risk management still needs to come from businesses taking a lead in tackling this issue themselves.

The shift toward openness and customization

As businesses begin to step back and take a longer view on AI adoption, it is becoming clear that transparency and governance are just as important as speed. For years, adopting AI meant relying on opaque, one-size-fits-all models where you had little visibility into how decisions were made and even less ability to tailor them to your business.

These "black box" systems forced organizations to accept generic outputs, release their proprietary data to third-party providers, and hope the results were accurate and unbiased. This can be a significant blocker for many highly-regulated industries and use cases.

That approach is rapidly giving way to a new paradigm: open, customizable models that give businesses full control over how AI is built, fine-tuned, and deployed using their own data, on their own terms.

By opting for a data and AI platform that brings compute to the data, enterprises can ensure that AI works within governed parameters and you have model choice and control. Your data never has to leave your account, so you maintain governance, privacy, and compliance throughout.

The result is AI that actually understands your business context, delivers more relevant answers, and evolves as your needs change. This puts the strategic advantage of AI firmly in your hands, not a vendor's.

Building AI that people trust

As AI moves from experimentation to real business decisions, trust becomes the defining challenge. Agents – AI systems that can reason, take actions, and work across tools on your behalf – represent an enormous leap in productivity, but only if people trust them enough to rely on them.

That trust doesn't come from marketing promises; it comes from being able to see exactly what an agent did, why it did it, and what data it used to get there - while using a human in the loop for key decisions. Observability and transparency aren't just technical nice-to-haves, they are the foundation that determines whether your organisation will actually adopt AI or keep it sidelined as a pilot project.

Explainable and observable AI are key parts of our Responsible AI principles at Snowflake, and it needs to be a governing principle for any organisation that hopes to reap the benefits of AI. Compliance, legal, and business teams need to be able to verify AI behavior just as rigorously as any other business process, turning AI from a black box into something your entire organisation can confidently stand behind.

Taking an integrated approach involving CIOs, developers and data platforms, companies can work to build AI that people find easy to trust, and which also delivers for the enterprise in a way that pre-built, opaque AI cannot.

Opening the box

In the rush to capture the benefits of AI, many enterprises have discovered that black box systems, however powerful, introduce risks that outweigh their initial convenience. Lasting value will only emerge when businesses can understand, interrogate and confidently govern the technology they deploy.

The path forward lies in transparent, explainable AI that is observable, accountable and aligned to an organization's own data and needs.

By embracing openness, integrating human oversight and adopting platforms that prioritize responsible, well-governed AI development, enterprises can finally unlock AI’s potential at scale.

This is not just a technical preference but a strategic imperative, as transparency will determine long-term trust, regulatory readiness and competitive advantage.

Organizations that invest now in explainability and observability will be best positioned to build AI systems that deliver dependable value long after the first wave of black box hype fades.

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Cyber is no longer a supporting capability within defense operations. It now plays a central role in how military organizations assess threats, coordinate activity and make operational decisions.

Across NATO and allied forces, cyber intelligence is becoming embedded throughout the operational chain, from situational awareness and force protection through to targeting and strategic planning.

At the same time, the threat landscape is becoming more aggressive and interconnected. State-aligned cyber actors are operating with greater coordination, while the boundary between cyber and conventional military activity continues to erode.

The conflict in Ukraine has demonstrated how tightly digital and physical operations are now linked. Cyber intelligence is increasingly fused with conventional sources to support real-time operational decision-making on the ground.

In this environment, delays caused by reformatting, translation or inconsistent intelligence structures are no longer minor inefficiencies. They create operational friction at the point where speed, clarity and shared understanding are most critical.

This shift is unfolding alongside a renewed emphasis on collective defense. Coalition operations are intensifying, interoperability is under greater scrutiny, and the ability to exchange intelligence rapidly across trusted partners has become mission-critical.

As a result, intelligence platforms are no longer simply background technology. They now form part of the operational backbone supporting defense decision-making. Yet many of the systems still in use today were designed for commercial security environments, not doctrine-led military operations.

When cyber intelligence and doctrine diverge

Most cyber threat intelligence platforms in use today originate from the commercial sector. They were built to support enterprise security teams, where priorities center on speed, automation and scale.

Defense operates differently because military intelligence is governed by doctrine. Frameworks such as NATO’s AJP-2, UK MOD JDP 2-00 and the US JP 2-0 define how intelligence supports operational and strategic decision-making. They establish shared terminology, structured processes and standardized reporting formats that allow forces to operate cohesively across commands and nations.

Crucially, doctrine is not simply theoretical guidance. It provides a common framework for direction, collection, processing and dissemination across the intelligence cycle, ensuring intelligence can move consistently from analyst to commander in support of operational decisions. When cyber intelligence does not align with these frameworks, friction emerges at the point where speed matters most.

In many defense environments, analysts are already operating under significant pressure, managing high volumes of data from multiple sources. When intelligence must be translated, restructured and reformatted before it can be operationally relevant, that burden increases at exactly the moment clarity and speed are most critical.

The consequences extend beyond delay. Misalignment can lead to duplicated analyst effort, inconsistent terminology across organizations, loss of contextual understanding and difficulty fusing cyber intelligence with HUMINT, SIGINT and GEOINT into a coherent operational picture.

In coalition environments, where multiple organizations must work from a shared understanding, these inconsistencies can reduce confidence in intelligence at the point where it is needed to support planning and command decision-making.

This is no longer simply a question of efficiency. As cyber intelligence becomes more tightly integrated with operational planning, delays and inconsistencies at this stage can have direct mission impact.

Defense can no longer trade interoperability for sovereignty

The challenge is compounded by two parallel pressures shaping defense across the UK, Europe and allied nations.

The first is data sovereignty. Governments are placing greater emphasis on where intelligence is stored, how it is controlled and who can access it. Systems must align with national requirements for security and governance, particularly when dealing with sensitive or classified information.

The second is interoperability. Defense operations remain inherently coalition-based. Intelligence must be shared across trusted partners quickly, and in a format that can be immediately understood and acted upon.

Balancing these priorities is not straightforward. Commercially oriented platforms were not designed with this dual requirement in mind. Retrofitting them to meet both sovereign control and coalition interoperability introduces complexity.

It creates workarounds that place additional burden on analysts and planners, while increasing the risk of inconsistency across organizations.

Over time, this approach becomes increasingly difficult to sustain in operational environments.

Defense operations require intelligence by design

Defense organizations are reaching a more fundamental question than how to adapt commercial cyber intelligence platforms. They are beginning to ask whether those platforms were ever designed for the operational realities of modern defense in the first place.

An alternative approach is now required. Intelligence systems must be built around military doctrine from the get go, supporting the structures, processes and standards that govern operational decision-making, rather than sitting adjacent to them.

This requires shared terminology, structured reporting and recognized intelligence frameworks to be embedded into the core architecture of the platform. Cyber intelligence must integrate seamlessly with disciplines such as HUMINT, SIGINT and GEOINT, contributing to a unified operational picture rather than existing in isolation.

It also requires interoperability and sovereignty to be balanced by design. Intelligence must move efficiently across coalition partners while remaining aligned with national requirements for security, governance and control.

When these foundations are in place, the operational benefits become clear. Intelligence can move from analysis to decision-making with greater speed and consistency. Collaboration improves across commands and coalition partners. Analysts spend less time translating or restructuring outputs and more time generating operational insight.

As cyber intelligence becomes increasingly central to defense operations, the systems supporting it must evolve accordingly. Platforms designed for commercial security environments are no longer enough. Defense requires intelligence systems built for operational reality from the ground up.

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Demand for AI and cloud services is driving a new wave of data center investment across Australia, but growing scrutiny around power, water and infrastructure planning is changing how these facilities are developed.

Governments are increasingly positioning digital infrastructure as part of broader economic and AI strategies. In Victoria, the state government recently launched a Sustainable Data Centre Action Plan tied to a reported $25 billion digital infrastructure pipeline, including plans to map future developments against energy and water availability.

At the same time, the NSW Government has moved 15 data center projects through its Investment Delivery Authority as demand for AI and cloud infrastructure accelerates. Tens of billions of dollars of capital have already been committed to future data center projects in Australia, with national capacity projected to more than triple by 2035 as operators continue expanding AI and cloud infrastructure.

As hyperscale developments move through the pipeline, governments are increasingly focused on the pressure growing demand could place on electricity networks, water resources and planning systems.

The new rules shaping data center growth

Australia’s data center sector is entering a new phase of regulatory oversight. The Federal Government’s recently announced national interest framework for data centers and AI infrastructure provides a compass for future regulation, as governments attempt to keep national interests in energy, water and infrastructure at the forefront of the sector.

The framework sets out five key areas developers are expected to address as part of project approvals. These include aligning projects with Australia’s strategic and economic interests, supporting the energy transition, managing water sustainably, investing in local skills and strengthening Australia’s own digital capability.

The framework prioritizes national interests in energy, water and infrastructure as part of data center approvals, particularly as governments respond to the growing resource demands. It also reinforces the focus on sovereign capability to ensure Australia’s own digital capability can be independently controlled from within Australia, avoiding reliance on or influence from other countries.

Energy, water and infrastructure constraints

Access to reliable power is becoming one of the defining challenges in data center development, with many developers exploring alternative power sources and renewable energy to keep projects on track. AI usage is expected to increase power requirements for data centers by 150 to 200 per cent.

The federal framework makes clear that developers are expected to reduce pressure on the electricity grid by underwriting renewable energy, financing grid connections and participating in demand-flexibility programs that shift grid usage depending on network availability.

Water usage is also becoming a more prominent consideration as governments respond to the cooling demands associated with hyperscale facilities and AI infrastructure. The framework states that operators must adopt efficient technologies and practices that promote sustainable water management.

Beyond energy and water, developers are facing broader construction and delivery constraints. Rising construction costs, constrained power access, labor shortages and long lead times for specialized equipment are all affecting how quickly facilities can be delivered.

Land availability and planning regulation are growing areas of concern for developers. Regulatory bodies can take considerable time to issue approvals, while opposition to some data center developments is further slowing construction activity.

Why modular construction is gaining momentum

These pressures are contributing to increased interest in modular construction methods. Rather than relying entirely on conventional on-site construction, modular data centers use prefabricated modules containing structural, electrical, plumbing and cooling systems that are manufactured off-site before installation.

Designing, building and testing modules in controlled environments can reduce delays caused by severe weather conditions, labor shortages and other factors that typically affect conventional on-site construction. Modular construction can provide greater cost predictability and faster project delivery.

Modular construction also allows developers to scale projects incrementally as demand grows. Developers can begin with a base configuration and expand capacity with additional modules, particularly where standardized power and cooling mechanisms can be more easily controlled in prefabricated environments.

The increasing use of modular construction is influencing broader energy strategies around data center development. Off-site construction can support earlier commissioning and improve alignment between renewable energy provision and construction milestones where renewable power sources are available.

While modular construction can improve delivery speed and cost predictability, it does not remove the underlying infrastructure constraints affecting the sector. Developers still need access to sufficient power, land and network capacity, while planning approvals and sustainability requirements are becoming increasingly significant factors in project delivery.

States are taking a more active role

As hyperscale developments and AI infrastructure continue to expand, governments are placing greater focus on the pressure growing demand may place on energy and water resources.

The NSW Government recently announced that 15 data center projects will progress through the NSW Investment Delivery Authority at a time when about half of Australia’s planned data center capacity is clustered in Sydney.

Data center developments now account for approximately 12 per cent of all non-residential building investment in the state. However, around $40 billion worth of projects were not endorsed because they were considered too speculative or lacked sufficient preparedness.

Victoria’s Sustainable Data Centre Action Plan points to a similar policy direction, with Melbourne expected to host around 25 per cent of Australia’s planned data center capacity. State governments are increasingly moving beyond passive infrastructure approvals toward more active coordination of energy, water, planning and workforce settings for AI infrastructure development.

With critical infrastructure projects already moving through the planning process, governments are facing increasing pressure to ensure energy, water and broader state interests remain protected as the sector continues to grow.

As AI and cloud demand continue to accelerate, governments are paying closer attention to how data centers are planned, powered and integrated into existing infrastructure systems.

The next phase of Australia’s data center expansion is likely to depend not only on how quickly projects can be delivered, but on how effectively energy, water and planning systems can support that growth.

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• Many employees secretly use AI tools despite company restrictions
• Employees sometimes use their personal devices to conceal AI use
• Larger organizations report higher levels of unauthorized AI usage

Artificial intelligence is becoming a routine part of office work, even when employees believe company policies prohibit its use.

New research from PagerDuty claims two-thirds (66%) of office professionals have used AI tools for work despite thinking those tools were not permitted.

The findings suggest unauthorized AI use is no longer isolated, particularly as workers become more confident in their own understanding of the technology.

Workers increasingly conceal AI use from employers

The study found that among employees who have used AI for work, many acknowledged taking actions that conflict with internal company rules.

Unauthorized use was more common at organizations employing at least 1,500 people, where 72% admitted using AI despite believing it was prohibited.

At smaller companies, the figure remained substantial at 60%, indicating that the practice extends across different workplace environments.

Secrecy often accompanies workplace AI adoption, as a third of AI users said they would deliberately avoid disclosing their use of AI to managers or supervisors.

About 30% cited restrictive company policies or concern about coworker reactions as reasons for keeping their AI use private, while 29% said uncertainty surrounding company rules contributed to their reluctance to disclose those activities.

Perceptions of inconsistent policy enforcement may also contribute to the use of AI in the workplace.

While 86% reported working at organizations with AI policies, 81% believed leadership operates under different standards.

That sentiment was especially common at larger organizations, where workers were more likely to feel executives received different treatment regarding AI-related decisions and policy compliance.

Most workers (72%) believe that they understand how to use AI for their jobs better than the teams managing AI governance.

At billion-dollar companies, that figure rose to 80%, while senior leaders were more likely than lower-level managers to express similar views.

With such confidence in their own AI judgment, workers will likely bypass formal restrictions, treating policy violations as reasonable workarounds.

Hidden AI use raises concerns about company information

Unauthorized AI activity often extends beyond simple tasks and 43% of respondents admitted to entering emails or work-related data into public AI systems.

Those tools operate outside internal corporate environments, creating potential concerns about how workplace information is handled after submission.

The sharing of sensitive information was not limited to routine communications, as more than a third of respondents said they had entered customer information into public AI systems.

Another 31% acknowledged uploading financial information, confidential company documents, or internal business strategies into those platforms.

Additional findings showed that 44% used AI tools to circumvent limitations in approved workplace software, while 38% shared AI-assisted work without disclosure.

In addition, some workers even access these AI tools or LLMs on their personal devices to conceal any trace.

More than half of those caught violating AI policies received informal guidance, while 48% faced formal disciplinary action.

This suggests that organizations are still struggling to balance enforcement with rapidly expanding workplace reliance on AI tools.

Co-founded by Reed Hastings in 1997, Netflix started life as a rental DVD service in the mold of Blockbuster. But, 20 years later, the company dramatically pivoted to being an online streaming service, fully embracing the internet era.

Changing with the times

Five years after launching Netflix's streaming platform, Hastings was speaking with Forbes on the business' pivot and its role in the emerging internet landscape.

This shift, according to reports, was decades in the making, with Hastings plotting it since Netflix's inception, with the DVD rental business a means to grow the customer base.

His comments highlight the belief that our society is entering a new age defined by the internet and the shift of many businesses from bricks and mortar to an online ecosystem. The comments also frame the new "age" of humanity as transcending the reliance on a particular material – instead basing progress on the shift to the internet.

The new age

This notion is hardly original – plenty of figures in the tech industry have suggested we are graduating to a new age of humanity defined by an idea or material.

For example, physicist Michio Kaku identified several ages in the last few hundred years, including the Steam Age, Age of Electricity, and the Silicon Age that began in the late 20th century and lasted until just recently.

And now? With the constraints of Moore's Law imposing physical limits on computation, he suggested a new age of humanity could belong to quantum – with the embryonic technology of quantum computing allowing us to transcend conventional power constraints. Others, meanwhile, attribute a new age of humanity to AI.

• Kali365 is a sophisticated phishing-as-a-service platform, also known as Octopi365 and Freedom365, that targets Microsoft accounts
• It was first detected by security firm Huntress in May 2026 when examining a slew of Microsoft 365 logins originating from China
• The FBI issues a warning detailing the process as part of a public service announcement

Phishing attacks are hardly new, with an estimated 3.4 billion malicious emails sent daily, accounting for a mammoth 1.2% of all email traffic.

Google alone blocks approximately 100 million phishing emails daily, as threat actors continue to evolve their approaches, using unique campaigns, AI-generated content, and, lately, QR codes to lure unsuspecting victims.

A recent phishing-as-a-service toolkit detected by cybersecurity company Huntress, however, stands out for its sophistication, scale, and success rate.

A sophisticated phishing service for hire

What makes Kali365 unique versus its peers is the scale at which it operates and the methods it uses. Unlike most phishing operations, it is a tool with at least 33 built-in templates that impersonate Microsoft products and services, 100 API endpoints, and role-based access control for phishing teams.

In addition to being an AI-enabled phishing, it also has a sophisticated payout pipeline, a crypto payment gateway integration, tiered access to the software suite, and, for those looking for a complete offering, a desktop application for operators.

Kali365 and its variants and clones, such as Octopi365 and Freedom365, do not, however, directly compromise or bypass MFA; instead, they use a set of highly legitimate emails and calls to action that then steal session cookies and OAuth tokens, allowing access to a victim's account.

The process itself is seamless; a potential victim sees a Microsoft website, an SSL certificate, and no warnings that they are effectively handing over access to a bad actor, who then uses their authenticated token to access their account. The AI-generated lures themselves are sophisticated, but as the FBI points out, they still require a user to be phished via email, with many impersonating "trusted cloud productivity and document-sharing services."

The more damning use of AI, however, is where Anthropic's Claude AI model is used to read intercepted email threads, score them for fraud potential, and draft convincing reply messages, complete with fabricated banking details and a manufactured sense of urgency, to be sent from the victim's own mailbox.

While the FBI's warning stands, it also somewhat acknowledges that this is not an easy phishing attempt to avoid, given the scale, the multitude of phishing attack vectors, and the "legitimate" look it has compared to most of its competition. Resolving this would require a change on Microsoft's end to close security loopholes that enable such authentication transfers, but for now, any affected individuals can only report their experiences here.