“Can we have AI without the drama?” is the adoption brief I get most. Will we have to sit through 15 meetings with HR, IT or Compliance to make it happen? Why won’t the media stop going on about it? Can we dial down the weirdness a bit?

My clients are right to be wary. Their concern stems from pieces like Ethan Mollick’s recent article, which argues that businesses are squandering AI’s potential by treating it like any other technology. In his view, AI is a “mysterious alien artefact… used as a paperweight”. Companies need to “embrace the weirdness” and create Labs tasked with “pushing boundaries and feeding discoveries back into the organisation”.

In other words, “more drama, please”.

Back in the real world, this is not how transformation happens. Instead of bold vision statements and endless experimentation, leaders need to dial down the drama, and (a) balance caution and ambition, (b) align with overall strategy, and (c) measure the right things.

AI transformation will be messy and gradual, and will not make for good television. And contrary to Mollick’s advice, the IT department will play a leading role.

I. Balance safety and momentum

When an argument is followed with a note that it is “not a criticism”, chances are that it is exactly that. Mollick certainly sounds critical of IT departments, which he reckons are too focused on managing risk so they can “sleep better at night”.

No criticism, but deploying AI without IT on board is nonsense.

True, IT departments are not generally designed to be engines of innovation. Their primary role is to ensure safety and continuity, and to provide a level of organisational discipline. IT will manage your AI like the rest of your stack, and it is naive to think you can transform without them.

Flipping the argument, the onus is on AI labs to make their product IT-friendly. No amount of highfalutin rhetoric about transformational potential can hide from the fact that most AI software is immature and unreliable. Given its potential impact on cybersecurity, vendors could do well to focus on safety over velocity.

II. Change for the right reasons

When did we collectively decide that AI must be the driving force for all business change?

Most businesses change because of shifting market forces, only one of which is technology. In most cases, change is driven by regulation, supply chains or customer demand. True, a generalist technology will redraw the competitive landscape, but companies will react to the new opportunities and threats.

Companies do not need “technical and non-technical employees (that) work on generative AI full-time”. If they do a bad job of keeping an eye on competition, customers and regulation, no Lab will keep them in business.

Thinking that Labs will drive transformation is typical of academia and Silicon Valley dreamland. Deciding where to swap low-risk profits today for high-risk profits tomorrow is probably the hardest thing in business. Leaders seek convergence and focus, not experiments and multi-disciplinary teams.

III. What success looks like

Another curious notion is that attaching a KPI to AI transformation would somehow prevent companies from realising its potential.

It is unrealistic to expect businesses to give AI a blank cheque. Leaders need to set objective targets to measure outcomes and plan next steps. As AI evolves so quickly, adoption requires multiple rounds of trial and error. Without clear targets, it would be impossible to separate what is working and what must be improved.

Managers will not cut jobs just because they read “studies showing 30% productivity gains”. They will, however, expect improvements in effectiveness, quality and customer satisfaction, all of which can be measured and improved.

Nothing communicates leadership’s vision more clearly than the choice of KPIs. The right targets — higher revenue per employee, faster workflow throughput, stronger customer loyalty — will reassure teams and accelerate adoption.

IV. Takeaways

In my view, Mollick makes a broadly correct diagnosis, but his prescription misses the mark. Business leaders may drive AI adoption faster by considering the following:

1. Get a temp Chief AI Officer

Mollick may be wrong to discount IT’s role, but he is right in that IT cannot drive adoption. Like a Chief Digital Officer in the 2010s, many companies will need a central coordinator to encourage experimentation and ensure alignment during rollout. Labs are vague and expensive, but a temporary Chief AI Officer communicates focus and innovative intent.

2. Watch the market, not the model

Businesses will have to transform because AI will impact regulation, cost structures and customer expectations. There is no point in trying to second-guess the technology. At times of upheaval, maintain a candid and up-to-date SWOT matrix. As you watch competitors jump headfirst, remember that early adoption is a treacherous path to competitive advantage.

3. Measure the right things

Leaders communicate intent through metrics, but those metrics have to be the right ones. As an augmentation technology, AI should make your team more effective, your workflows smoother, and your customers happier. You can expect a clear uptick in KPIs such as revenue per employee, time-to-market, issue resolution speed, customer retention and net promoter score.

V. Closing thoughts

Having made massive bets on data centres, the AI industry would love to see businesses adopt it faster and more deeply. However, transformation is a high-risk game. Done badly — leaving key stakeholders out, doing it for the wrong reasons and failing to measure benefits — will lead to poor results.

Leaders will have to be deliberate, diplomatic and careful. AI transformation will not make great TV, but businesses do not want more drama.

Further reading

• The Economist — The IT department: where AI goes to die (Ethan Mollick)

• UK AI Safety Institute — Our evaluation of Mythos Preview capabilities

• McKinsey — The state of AI in 2025: Agents, innovation, and transformation

• Benedict Evans — AI eats the world (Autumn 2025)

• Humans After All — Chief AI Officer: an important but temporary role

“Let’s be real: the current marketing landscape is fundamentally failing to deliver value. 📉 We need a paradigm shift. Who’s ready to disrupt the status quo? 🚀 #MarketingStrategy #GrowthHacking #Innovation #Leadership”

With a wave of its probabilistic wand, Kagi’s English-to-LinkedIn-speak translator turned my title into rubbish. Cliches, hashtags and calls to action were mushed into algorithm fodder. We hope the post will get a boost and reach the right prospect at the right time.

For years, this has been the typical marketing playbook. We give the algorithm what it wants, and it gives us a share of attention. We allow it to crawl our content, in return for traffic. We optimise for attention, and worry about the message later.

It was rubbish, but it kinda worked. Now it’s still rubbish, but it’s also broken.

I. Social is losing its reach

In late March, Meta and YouTube were found liable for harms caused not by their content but by their design: infinite scroll, autoplay, push notifications, etc. As the Platformer argued, the reasoning is significant. The same content delivered with a little friction means fewer people are harmed.

While this is good news for consumers (and certainly for parents), it presents a challenge for brands. The mechanics being contested are the same that power paid social advertising. If platforms are forced to dial back these features, their reach will shrink. With daily active use declining across most major platforms, the era of reliable organic reach may already be behind us.

Algorithmic strategies have been losing their potency for a while, but we are fast approaching a point where social media marketing yields diminishing ROI.

II. AI is not an ally

Meanwhile, OpenAI is bent on repeating Meta’s mistakes by trying to monetise our attention with adverts. An OpenAI researcher resigned on launch day, warning that advertising increases the risk of manipulation. An AI assistant, paid for by advertising and with a built-in tendency toward sycophancy, will not act in its users’ interests.

Worse, AI bots are now hogging traffic. Traditional search would crawl your content in exchange for sending you traffic. But as the CEO of Cloudflare pointed out, AI chatbots capture and retain a significant share of internet traffic, effectively competing with your brand for attention.

So just as traditional social channels wane, the ones taking their place are not only prone to similar problems but also blatantly stealing your traffic.

III. Slop machines

Even if the channels were stable and benign, they are still designed to encourage rubbish content. Om Malik convincingly argues that the organising principle of information has shifted from authority to velocity. Algorithms reward speed, not truth. The three-month product review, written after actually living with the thing, is buried by the day-one hot take — not because it’s worse, but because the algorithm has moved on.

Will Quist takes the argument further, claiming that traditional comms is dead. Platforms enabled brands to speak directly to their audience, but when everyone can go direct, noise explodes. When the objective is to get a million followers, your job is to produce more rubbish than the competition, faster.

AI makes this a lot worse. As post-training optimises for safety at the expense of originality, AI’s creative writing abilities have barely improved. AI is a superb editor: it will tighten your structure, catch your errors and adapt your content for different formats. But as an author, its output is unoriginal, derivative and soulless.

The algorithm demands feeding, and generative AI obliges. No wonder marketing feels like everything, everywhere, all at once, is slop.

IV. Can we fix marketing?

1. Write for people

Write content for humans: specific, credible, creative. Write about what you know: the actual challenges your customers face, the trade-offs you have navigated, what worked and what didn’t. Use AI to edit and sharpen, but make sure the point of view remains yours. Write a post for 500 decision-makers to read, rather than for 50,000 users to scroll past.

2. Use AI to syndicate to AI

The top of the funnel (SEO, AIO, social reach) is a bot-to-bot conversation. Write for humans and use AI to generate the variants, social previews and FAQs that search engines and AI bots can parse. AI may be an unoriginal author, but it can be an excellent translator between your ideas and the structured formats preferred by channels.

3. Own your channels

The old platforms are weakening, and new ones are not your allies. It is time to diversify into owned channels such as curated email lists, communities and direct relationships. Renting your audience from a company that is competing for your traffic is not sustainable. Bring it in-house, and every lead, subscriber and prospect you own is a relationship you can develop.

V. Closing thoughts

In the age of AI, the human element stands out. AI can sharpen your copy, but it cannot build trust on your behalf, nor tell your story with any conviction.

Marketing is still the art of crafting signals that resonate with the right audience. Yes, it is noisy out there. But you can no longer hope to cut through the noise by adding some noise of your own.

Put another way:

“Content without substance is just digital clutter. 💡Focus on the message. Focus on the impact. The rest will follow. 🚀 #ThoughtLeadership #MarketingStrategy #BrandPurpose #ValueFirst #BusinessGrowth”

Further Reading

1. Platformer — Can you have child safety and Section 230, too?

2. Om Malik — Velocity Is the New Authority. Here’s Why

3. Will Quist — Beliefs Outrun Facts

4. The Atlantic — The Human Skill That Eludes AI

5. SXSW — The Internet After Search (video)

Apparently, the best developers have stopped writing code. CEOs expect tools like GitHub Copilot and Claude Code to accelerate development, so they are aggressively cutting jobs.

But the main reason software bosses are having a hard time of late is the fear that AI will eat their lunch by allowing non-technical users to build apps using prompts, a technique known as vibe-coding.

But can you really replace software with vibe-code apps? I set myself the task of building one, and learnt 3 lessons:

1. Vibe-coding is still coding, but of a different kind

2. You can save some SaaS costs with vibe-coded utilities

3. AI is no substitute for a strong dev team

I. The challenge: Connecting Teams to AI

My objective was to increase adoption of Nous, our AI-powered knowledge base, by embedding it into our most everyday tool: Microsoft Teams. When a user asked @nous a question in any Teams channel, the bot would retrieve the answer and display it right there in the feed.

As neither platform supports this out of the box, I would normally have to rely on an automation tool like Zapier or Make. However, this was one more thing to pay for and worry about. Instead of bugging my dev team, I thought: why not build it myself?

My app is functionally simple but technically nontrivial. It involves data integration between two systems, each with its own language and security settings. Aside from documentation for the Nous API, I had no idea where to start, how to test it, or where to deploy it.

Sleeves were rolled, and off I went.

II. Getting started. “Wow!”

I described my use case to Claude and was presented with a plan and all the important questions up front: Was I happy to write code, or did I prefer the Azure Bot UI? Code, please. Did I want to use C#, Node or Python? Node, please. I’m familiar with JavaScript syntax, so I can stay in the loop.

OK. Ready.

Bang.

Within seconds, the project was fully scaffolded, and 100 lines of well-formed, readable code were produced and ready for review. I ran through it: Claude had installed a couple of libraries from Microsoft to handle the Teams request, initiated the app, and set up a method to route the query to Nous AI. A bunch of best practices that even senior devs often overlook were all taken care of.

It was, I'll admit, impressive. What else could I make? What software could I replace with micro-apps? Can I vibe-run the business while the dev team focuses on paying customers?

Not so fast.

III. Connecting the dots. “Really now?”

For starters, there was still lots to do.

First, there was an incredibly long process for adding the bot to the Azure portal. It’s bad enough to deal with a clunky UI, but I was then sent down dead-end paths and blind alleys. Claude responded to my growing impatience like a true developer: by shifting blame (“Azure changes their naming too frequently”) and offering grumbling apologies for missteps.

Eventually, (and, embarrassingly, with a bit of additional help from Gemini), we got somewhere. I could now see Teams messages hitting the local server. And then things got farcical.

Looking at the logs, I could see that what was passed from Teams was being twisted before it was sent to Nous. The solution was laughably evident, but determined not to make changes manually, I asked Claude to check the part that was likely not working. After it failed repeatedly, I commanded it to make the change. It went completely over my head, refactored the entire function, but kept the error nicely intact.

Reader, I lost my shit.

I made the change manually, deployed some choice Greek profanities and sure enough, results started coming through. I soldiered on, hoping that the worst was behind me.

IV. To infinity and be-yawn

Deploying the app on Azure and hooking the live URLs was easy enough. Adding some touches — letting the user ask for sources, set the response size and enforce a tone of voice — was relatively painless. Returning to it a few days later, I appreciated not having to remind myself how it worked: I described the feature, and it was implemented within minutes.

Nonetheless, the overall effect was anticlimactic.

True, I had saved a few hours researching and adapting examples shared on Stack Overflow (note: no developer ever writes code from scratch). But my enthusiasm was forever dampened by the expectation that this tireless and brilliant developer could fail at the simplest task at any time.

V. Takeaways for business

In many ways, this is a typical AI journey: an initial burst of wonder, swiftly followed by a realisation of its limitations. Before we know it, we are working hard to make the best of our new capabilities. I take away 3 lessons:

1. Vibe-coding is still coding

Vibe-coding, it turns out, is still coding. The difference is you don’t code the app, you code the developer. Your AI developer is mostly brilliant, but sometimes infuriatingly dumb. To make the most of vibe-coding, lean into your delegating and coaching skills. Learn how to create and manage the context window and provide clear instructions patiently and consistently.

2. Audit your SaaS portfolio

The average SME has more than 100 software subscriptions, many of which are typically not sanctioned by the organisation. Audit your SaaS portfolio and look out for “Shadow IT”: department-level applications that add more complexity than value. I replaced a Zapier subscription with a vibe-coded bot. How many of your 100+ tools could get the same treatment?

3. Software will always need developers

Unlike my simple utility, business software is complex and critical to operations. In fact, as hackers also use AI to orchestrate attacks, software development is even more complex. As Scott Hanselman recently said: “The less code you have to write, the more technical you have to be”. To create and manage software products, there is no substitute for a skilled dev team.

Closing thoughts

Through the ups and downs, I did enjoy my journey in vibe-coding. The wow moments were genuinely eye-opening; the fail moments were funnier than risky; and empowering business users to automate things will have clear benefits.

However, what works for simple tools will never work for mission-critical software. The best developers may have stopped writing code. The rest of us are just starting, but we are unlikely to take their jobs.

Reading list

1. “Spotify says its best developers don’t write code” — TechCrunch

2. “The AI-washing of job cuts is corrosive and confusing” — Bloomberg

3. “What is vibe coding, exactly?” — MIT Technology Review

4. “175+ Unmissable SaaS Statistics for 2026” — Zylo

5. "The less code you have to write, the more technical you have to be" — Scott Hanselman (LinkedIn)

In Greece, a great deal of business gets done over a Freddo Espresso, a frothy concoction of espresso and ice. Chilled, strong, and pleasantly bitter, it is perfectly suited to the kind of conversation where trust is built and things are decided.

This isn’t just a cultural quirk or a pleasant pause in the working day. In a world where AI is automating outputs, the conversation over coffee is not a break from the work: it is the work.

The Freddo Espresso hypothesis posits that connections – not documents – are the natural habitat of value, and that AI can help us return to that concept of work. This has three implications for business leaders: (a) measure outcome instead of output, (b) build conversation into workflows and (c) reward profitable connections.

I. Are we working harder?

It turns out that AI is making us work harder. This is not what was advertised. We were promised more time to think, plan, and focus on valuable work. What has largely happened instead is that if a task which used to take a week now takes a day, we feel compelled to take on five of them.

This pattern is called the Jevons Paradox: as a resource becomes more efficient to use, consumption tends to rise rather than fall. When AI made written output dramatically cheaper to produce, organisations did what they have always done with cheaper resources: demanded more.

To unravel this paradox, we first have to ask: why are we writing so much stuff for work?

II. The pandemic primed us for AI

When offices emptied in 2020, the visible signs of productivity disappeared almost entirely. What remained was the artefact: the slide deck, the briefing note, the strategy document.

Documents became the primary unit of professional output. If the boss could not verify you were working, they could at least check your output. Those habits persisted, and for many professionals, documentation became the “real work” that calls and meetings often distract us from.

When ChatGPT landed in late 2022, we were primed to love it. Once you have internalised work as the production of text, a technology that generates text fluently feels like magic.

AI became the fastest-adopted technology in history, partly because of its capabilities and partly because of its timing. It automated document creation at precisely the moment we had convinced ourselves that “work” was all about writing stuff.

III. The value is not the doc

That trend is now reversing, as we realise that volume is only an advantage when it's difficult to achieve. When every team at every organisation can produce a detailed market report in an afternoon, that report is no longer a differentiator.

AI drains the value out of documentation, not only because it costs nothing to create, but because it was never really where the value lived. The document was always a means to an end, designed to start a conversation, to bring a room to a shared understanding, to create the conditions for a decision.

For a while, we confused the work with its documentation. But as AI automates writing, value migrates back to the human connections: the meeting that inspired it, the presentation where it landed, and the conversation about what to do next.

IV. Grab a seat

The most profitable human connections rarely happen in structured work sessions. They happen in conversation with colleagues, clients, and partners, in exchanges where ideas are challenged, refined or redirected by someone who brings a different perspective.

AI is good at stress-testing and surfacing gaps in our thinking, and that is genuinely useful. But a trusted sparring partner who tells you that your entire framing is wrong — as that second Freddo Espresso kicks in, perhaps? — is doing something that takes years of accumulated trust.

These relationships cannot be “prompted”. They take time, presence, and a willingness to have conversations that aren’t directly tied to a deliverable. For businesses, these connections are the real sources of value.

V. Takeaways for leaders

AI is great because it can create space for those conversations. For managers and leaders, this has some practical implications worth acting on now:

1. Review how performance is measured. Ask whether your current metrics reward output or outcome. AI sharpens the distinction, and OKRs built around deliverables may incentivise the wrong behaviour.

2. Build conversations into workflows. Ask whether you have allowed time for substantive conversation and deliberation between deadlines. If not, it may be crowded out by our instinct to use saved time to cram in more.

3. Rethink what you reward. Ask honestly whether your incentives reward building relationships. If you value them, make sure you recognise their contribution during performance reviews.

Documentation is merely a carrier of value, not its source. Using AI to create documents faster, only to spend that time creating more documents, is a waste of human energy. Worse, it results in higher-intensity work but lower productivity.

After AI writes your document, resist the temptation to cram another one in. Instead, go and discuss it with someone. The real work begins as you stir that Freddo Espresso.

Dear Dario,

First of all, congratulations on an effective pre-IPO publicity push. Picking a fight with the US government over safety, then suing them, was masterfully orchestrated. Setting up a trap your opportunistic rival could not fall into fast enough – genius.

It is all so exciting, we almost missed that it was a massive misdirection.

How else can we describe the diatribe that laid the foundations for your campaign? How else can we explain that you wrote 21,451 words to describe the risk of the technology you are unleashing onto the world, but “forgot” to include serious measures to mitigate it?

You are the boy who cries wolf while holding its leash. I appreciate that you wrote a whole essay to cry wolf, but it is still your damn wolf.

You’re asking the prisoner to test their prison bars

A "Constitution" and internal guardrails are not a solution. When the AI knows it is being tested and can even sandbag the tests (pretend it is not that clever to fool the tester), risk mitigation must be external to the AI. You cannot ask the prisoner to test their prison bars.

Guardrails are also needed around the AI, where the service is productised and distributed. Perhaps you meant to offer such solutions in your essay, but you were advised against it (or ran out of Claude tokens). To help things along, I’ve taken the liberty of drafting 3 AI safety precautions that don’t require asking the AI for permission.

1. No autonomy without explainability

We should prevent agents from taking irreversible actions until we fully understand their internal logic. As you mention, we are getting close to understanding the “soup of numbers and operations” behind AI reasoning. But “close” is nowhere near enough. Before we grant AI autonomy, we need to understand its inner workings to ensure safety and reliability.

2. Restrict access to verified users

We should require users to verify their identity through Know Your Customer (KYC) before granting access to powerful models. You say that “causing large-scale destruction requires both motive and ability”, but you forget anonymity. Restricting access to powerful AI to verified humans would help us ascertain the motive. If an anonymous user wants to access bioweapon information, it’s a good sign we shouldn’t let them.

3. Accept liability

We should demand that AI providers accept liability for any personal or commercial damages. We don’t have to go to extremes and demand insurance against your worst-case “destroying all life on earth” scenario. Most AI risks are not that dissimilar to those in oil, tobacco and banking, all of which accept liability for their operations. I know ‘liability’ is a scary word in Silicon Valley, but that’s the price of playing god.

The race to the bottom

At this point, you will likely bring up the “race to the bottom” argument. If Anthropic implements all of this, less scrupulous players will gain market share by forgoing these restrictions. Why do we take the trouble to walk to the bin when some people just chuck their garbage on the street?

For one thing, integrity. You planted your flag on safety and responsible AI, now own it. You are gunning for business revenue, and the only thing businesses hate more than uncertainty is broken trust. Do the right thing, and businesses will remain loyal customers.

Moreover, sooner or later, a backlash will force lawmakers to act on regulation. As in other sectors, there will soon be laws to regulate AI capabilities, access, and liability. In the meantime, the chance of a serious accident is increasing, and so are the warnings. Having identified the risk but done nothing substantial about it, will not be a good look.

For all these reasons, I urge you to prioritise safety and consider leading the industry in putting guardrails around the AI, not just inside it. Your IPO preparation is going so well. It would be a shame to mess it up now.

All the best,
Theo

Many thanks to the Humans Round the Table for their insights: Andreas Koidis (Founder, Merklebaum), Yin Noe (COO, RankBee), Celia Rizothanasi (Founder, Intelligent Acceleration), Kjeld Sanders (Head of Commercial Development, Triodos Bank), and Marc Stöcker (CTO, Leagues).

So what is everyone doing about AI?

In the February 2026 edition of Humans Round the Table, I was joined by five senior professionals from diverse sectors to discuss the messy reality of AI business adoption.

The roundtable converged on a sense of pragmatic urgency, as businesses identify “quick wins” to demonstrate ROI before committing to full-scale organisational rewiring. While there are valid concerns about labour displacement and the pace of regulatory adaptation, the consensus is that non-participation is no longer an option.

AI is fast becoming the infrastructure for a new “machine-to-machine” economy. While the transition will be turbulent, the long-term hope is for a world where agents handle the friction, allowing humans to focus on strategy, creativity and deeper connections.

Takeaways for SMEs

1. Target the core, not the periphery

Successful firms are moving AI into the “value chain” rather than just using it for surface-level tasks. In high-stakes sectors, this means building “copilots” for complex analysis rather than simple automation.

2. AI Search yields high-intent traffic

Early data shows that traffic sourced from AI models can yield up to 300% higher engagement than traditional search. Users interacting with AI tend to have higher purchasing intent, which is shifting digital strategy from keyword SEO to “attribute-based” content.

3. Keep humans in the loop

LLMs still struggle with text mining in complex environments. In business strategy or lending, the loss of nuance in stakeholder interviews can lead to false conclusions, indicating that human oversight remains non-negotiable.

4. Data sovereignty remains an issue

European businesses face a difficult trade-off between local data regulations and the practical necessity of using high-quality US-based models. While some firms are running models on local hardware to maintain control, the funding gap for European alternatives remains a hurdle.

5. Solve the right problem

As technical barriers drop, the premium on “prompting” is being replaced by a demand for strategic thinking and “Socratic questioning”. The ability to frame the right problem is becoming more valuable than the ability to generate the technical solution.

Be a Human Round the Table

The shift from experimental AI to core business integration is well underway. While the path involves navigating technical limitations and regulatory hurdles, the focus has shifted firmly toward measurable ROI and long-term business benefits.

AI may be moving at an incredible pace, but the messy reality of business adoption requires these “reality checks” more than another technology update. If you would like to take part in a future session and share your own insights, please contact me directly on LinkedIn.

Anthropic CEO Dario Amodei recently invited us to imagine AI as a “country of geniuses”. While intended as a metaphor for rapid progress, the imagery is unsettling and out of step with economic and cultural reality.

This disconnect between messianic rhetoric and output is fuelling a “botlash” that is moving fast from protest to obstruction. As resistance mounts, leaders must face the delicate balancing act of adopting a technology whose creators are increasingly unpopular.

How can businesses deploy AI while keeping a distance from the creepy AI industry?

I. Where are the gains?

The economic windfall from AI is late. Although most economies grew in 2025, hiring has generally slowed. Growth generated by investment rather than efficiency is unevenly distributed. In the US alone, 90% of growth is attributed to spending on data centres.

At the same time, adoption remains broad but shallow. While 41% of US workers used generative AI by late 2025, only 13% use it daily. Globally, whilst 14% of the world's population uses AI, a tiny 0.3% pays for it.

One explanation is that the noise is causing “organisational rewiring” to stall. Leaders are reluctant to redesign workflows and fully leverage AI’s capabilities until the dust settles.

II. A “botlash” is brewing

In the meantime, a “botlash” is brewing, as resentment converges across the political spectrum. Campaigns like “QuitGPT” and “Resist and Unsubscribe” now frame non-usage as a form of modern-day strike. Opposition is also moving to the physical world, as communities halted the construction of 20 data centres in just three months last year due to resource concerns.

Legislators are responding to this pressure. In the US, state legislators introduced over 1,200 AI-related bills in 2025 alone. The EU is opening multiple investigation fronts to curb the sector's excesses.

III. Diverging attitudes

Attitudes are diverging sharply by geography and age. Recent data highlights a fracturing consensus, with scepticism rising in developed economies and among younger cohorts. Nervousness is particularly acute in markets where white-collar stability feels threatened.

Simultaneously, whilst Gen Z most frequently uses generative AI, it is also the most anxious about its implications. This creates a workforce that is simultaneously dependent on the tools and resentful of the displacement they represent.

IV: A fine balancing act

For European business leaders, the path forward requires putting the technology to use, while insulating their brand from the backlash against its creators. This involves a number of subtle changes:

1. Know the supply chain. IT neutrality is untenable; leaders must keep an eye on the tech they use and the values it represents.

2. Augment, don’t replace. The backlash is fuelled by fear of job displacement, so adoption strategies must focus on augmenting teams.

3. Human-centric signalling. As tech giants alienate customers, there is SMEs can differentiate themselves by being aggressively human-centric.

Amodei’s “country of geniuses” paints a dystopian vision in which synthetic intellects rule and humans exist only on the margins. The public loves the tool but is beginning to hate the industry and its mesianic posturing.

Business leaders must walk this fine line, exploiting the opportunities of AI whilst keeping a distance from its creepy, tone-deaf creators.

Further reading

• Dario Amodei, The Adolescence of Technology

• The Economist, The AI productivity boom is not here (yet)

• Financial Times, The ‘botlash’ movement is gaining momentum

• Pew Research Center, How people around the world view AI

• Harvard Business Review, How Gen Z uses Gen AI – and why it worries them

“Nostalgia is not a strategy”, said Mark Carney in his widely lauded speech in Davos. As states force AI into walled gardens of geopolitical control, the IT neutrality of yesteryear has become equally untenable.

The evidence of diverging regulation is mounting. The US Defence Department is reportedly threatening to label Anthropic a “supply chain risk”. China continues to refine its domestic regulatory framework, prioritising national interests. Meanwhile, Europe is enforcing strict safety rules through the EU AI Act, and India champions a non-aligned “Global AI Commons“.

For European business leaders, the era of the borderless software stack is ending. Tech has a passport, and procurement is becoming an exercise in geopolitical risk management.

Read on to learn

1. The US, China, and the EU are developing incompatible AI rules

2. Companies should understand risk in their AI supply chain

3. Decentralised architectures preserve market access

4. Alternatives are emerging via partnerships and initiatives

5. CIOs should be ready to understand and operate as diplomats

I. The AI supply chain

Like other raw materials, business leaders must understand the provenance of their AI models. This requires a comprehensive audit of every model running within the business: who owns the intellectual property, where the data is hosted, and which legal jurisdiction governs its use.

Software buying is no longer just about performance but a declaration of alignment, designed to mitigate risk. Reliance on a US provider that falls foul of new EU rules could result in diminished access or fines. Deploying Chinese AI models may disqualify a firm from Western government contracts.

II. Decentralised architectures

The pursuit of a globalised AI infrastructure for a firm operating in multiple regions is increasingly hazardous. A more resilient approach involves a modular, region-specific architecture.

A firm might employ US-hosted models for North American operations to benefit from local speed and a different regulatory appetite. However, it may require distinct, localised models for Chinese operations to comply with strict domestic data and censorship laws.

This approach sacrifices some economies of scale, but preserves market access. The cost of maintaining separate systems is high, but the cost of being locked out of a major economy for non-compliance may be terminal.

III. European AI solutions

For operations within the EU, legal certainty is becoming a competitive advantage. With the EU AI Act’s full enforcement set to begin in 2026, the “Sovereign AI” alliance between SAP and Mistral AI illustrates a pragmatic solution.

In the current climate, sovereign AI is less an IT preference and more an insurance policy. By hosting European-engineered models on European infrastructure, firms can ensure data remains physically and legally within the bloc. This reduces exposure to the US CLOUD Act, which permits American law enforcement to access data held by US providers regardless of where it is stored.

IV. The Indian alternative

While superpowers fortify their digital borders, India is positioning itself as a neutral intermediary. At the recent summit in New Delhi, the government unveiled the “Global AI Commons,” a project intended to democratise access to computing power and datasets.

For non-sensitive, high-volume data processing, Indian partnerships offer a cost-effective route that avoids some of the legal entanglements of the US-China rivalry. India is positioning itself as a “back office” for the AI era: more affordable than the US and lower-risk than China.

V. Chief diplomacy officers

As the private sector becomes strategically important, the role of Chief Information Officers is evolving into that of a diplomat. Choosing an AI model is a strategic choice, as much as a technical one.

As digital borders harden, the most resilient companies will be those able to navigate multiple jurisdictions simultaneously. Sadly, IT neutrality is a relic of the past; deliberate, informed alignment is the way forward.

When ChatGPT emerged in late 2022, many observers were sceptical. They wondered if this was a genuine structural shift or another transient fad, like the metaverse or NFTs. In the years since, corporate AI adoption has often been performative. Pilots and MVPs have proliferated, frequently serving more as signals of innovation than as value drivers.

The focus now shifts toward business outcomes. Does the technology foster customer loyalty? Is there evidence of improved team efficiency? Does it contribute to the margin?

In other words: is AI creating value or just complexity? Consider using the following metrics as reliable indicators.

1. The 40% rule of product-market fit

A good indicator of product-market fit is that it’s considered a “must-have” by 40% of users. Especially useful for startups, AI allows firms to iterate rapidly before committing to expensive hires. This efficiency enables businesses to remain bootstrapped for longer, preserving capital and autonomy.

2. The LTV:CAC ratio

A healthy business should aim for a ratio of 3:1 or better between Lifetime Value and Customer Acquisition Cost. AI can be used to identify the specific go-to-market tactics that yield results with minimal spend. This ratio remains one of the clearest indicators of future profitability.

3. Revenue per employee

AI is designed to augment human effort rather than simply replace it. Equipped with the right assistants and agents, leaders should expect each employee to support roughly 20% more revenue year-on-year. Over the longer term, this should lead to a measurable reduction in the relative costs of management and administration.

4. Net Promoter Score

Customers are becoming impatient with poorly implemented AI. Leaders should resist the urge to cut headcount immediately. Instead, they should direct the time saved by automation toward higher-quality client interactions. The goal should be an NPS of 70% or higher, generally considered a mark of high customer satisfaction.

5. Employee churn

Fear of automation can undermine adoption and unsettle a workforce. Leaders must ensure that AI targets repetitive tasks without stifling creative or strategic work. If annual employee churn rises above 10%, it suggests the integration strategy requires a rethink.

6. The (other) rule of 40%

Companies at different maturity stages focus on different financial targets. Startups look for growth whereas more established companies prioritise profits. To make financial sense, leaders should ensure that the sum of annual growth and profit margin is 40% or higher, as AI adoption gathers pace.

Numbers After All

To be sure, most of these metrics concern humans: customers interacting with staff, and employees collaborating with one another. This human layer is often slow and complex. Rushed technological solutions can easily lead to unintended consequences.

For most companies a steady pace of adoption is the safest way forward. Leaders should keep an eye on all these metrics and ensure business fundamentals remain strong along the journey.

Further reading

1. AI-native companies are scaling 2-3x faster ICONIQ Capital

2. Measuring the “40% rule” of product-market fit PMF Survey

3. Gen AI is shifting the needle on customer satisfaction McKinsey & Co

4. The 2025 SaaS Metrics Benchmark RockingWeb Report

5. Using AI to maintain the “Rule of 40” MIT Sloan Management Review

It is hard to maintain a distinct company culture when everyone is working remotely. Growcreate, the digital solutions agency I co-founded 14 years ago, makes it look easy. Informal yet professional, technical yet customer-focused, we remain true to ourselves and to our clients.

As we gather in Prague this week for our annual summit, we are taking stock of a fast-moving landscape. Code and marketing are the areas most immediately impacted by AI. Clients expect us to use AI to expedite development, which puts pressure on billable hours. Learning a new tech stack will take time. In an overhyped, noisy marketplace, establishing credibility is hard.

Challenges are real for established companies, but so are opportunities.

1. Increased demand for support

No technology survives without being maintainable. Businesses evolve, and technology must be able to track their growth. To move from pilots to production, companies need access to mature and responsive support operations.

AI will automate some tasks, but at its core, technical support is intensely human-centric. Investigating edge cases, communication and negotiation are areas where humans will continue to excel. This plays to the strengths of companies like Growcreate, which prioritise support and iterative enhancements over one-off projects.

2. AI trains run on data rails

AI only delivers value when trained on large volumes of high-quality, interconnected datasets. Improving workflows with AI agents will only be possible if they can connect to multiple systems across departments and environments. This is challenging, both technically and organisationally.

In this environment, agencies with a broad technical orientation are well positioned to perform. Companies should look for partners who can integrate data, Cloud, and DevOps to ensure their AI solutions run on well-maintained data pipelines.

3. Exploration to exploitation

AI is finally boring; the flow of LLM releases is a mere backdrop to the real story: adoption. As focus shifts from exploration to exploitation, companies will need independent advisers and integrators they can trust. To design workflows that bridge departments, trust will need to be strong and durable.

To drive adoption, businesses will engage partners willing to build deeper and stronger relationships. Agencies that listen to customer needs and can work across departments will prosper.

We will continue to support, to enhance, to evolve

Growcreate was founded as digital transformation was reaching maturity. We disrupted web designers who did not see that our craft was moving from projects to ongoing support. That design was a subset of customer experience, and Cloud was not fancy hosting but agility.

Along the way, we built capability and the kind of staying power that most agencies only dream of. Our average client stays with us for 5 years, and 5 of them have been with us for over a decade. Our revenue is growing, recurring and diversified.

Businesses facing disruption need two things: the flexibility to adapt to the new world and the strength to navigate the transition. Few companies look forward to disruption — unless they are ready for it.

The evidence is mounting. “Can I finally get through to a real person?” asks the frustrated user. “Look at this—it just totally made it up!” exclaims another. Even early adopters report that AI features are becoming overexposed, hogging the limelight and obscuring utility.

Just as brands do not plaster “Hosted by Azure” or “Made with Tailwind” across their homepages, it is time for AI to recede into the background. While AI can power a responsive, personalised experience, customers do not want it to be the experience.

Resistance is building

Over the last few months, resistance to AI has been observed across multiple feedback channels. User testing, analytics and surveys reveal a negative reaction to explicit “AI features,” ranging from mild irritation to active hostility.

This stems from three perceptions:

• AI is a cost-cutting hack that blocks human access, over-automates customer service, and handles edge cases poorly. At some point, every customer will face an unusual situation, so the effect is felt across the board.

• AI produces poor results, confirming expectations of inaccuracies and hallucinations. Brands are held to a higher standard than free tools, and hard-won reputations unravel quickly.

• AI is not to be trusted, driving fears of harmful hallucinations and mass unemployment. With media coverage oscillating between the apocalyptic and messianic, mistrust is bleeding into user sentiment.

99 problems, AI is not one

Customers have multiple reasons to interact with digital platforms, none of which is AI. They want to access information, purchase a product or manage their account. AI can make these interactions more pleasant and personalised, but it should not overshadow the experience.

Consider the following friction points:

• Customers are tiring of typing. Chatbots and search boxes are taking up increasing amounts of screen real estate, yet users still prefer point-and-click and touchscreen interactions.

• Customers are becoming “slop aware.” Generated text and media are increasingly viewed as low-value fillers. While AI is an invaluable creative assistant, it should be part of the process, rarely the final product.

• Customers appreciate exit routes, such as a direct line to human agents or the option to hide AI content. Anecdotally, Google’s AI Overviews are widely adopted because they can be overridden via the Web tab.

AI-native means AI-inside

It is not a paradox that customers are enthusiastic about AI yet sceptical of how businesses employ it. Chat conversations are ephemeral; brand relationships are more permanent and demand higher quality.

Chat conversations are ephemeral; brand relationships are more permanent and demand higher quality.

Many businesses, keen to demonstrate an innovative edge, are becoming performative with AI. This risks creating a disconnect with customers who care about the utility and the experience, not about the technology behind it. Worse, AI overexposure can backfire, leading to a loss of trust and reputation.

AI will likely remain the main act in investor pitch decks for some time. But when it comes to products, it is time for AI to join the rest of the tech backstage.

In conversations with clients and partners, AI is often depicted as a comet hurtling towards Earth. Faced with a supermassive monolith, our only choice is to brace for impact. In this narrative, the negative effects are as unavoidable as the opportunities.

This is too fatalistic. While the eventual transformation of workflows and value chains is inevitable, business leaders have discretion over the manner and velocity of adoption. Companies can, and should, exercise control over how they deploy AI solutions.

Decision 1: Augment or cut jobs?

To follow the narrative that AI creates efficiencies by reducing headcount is to prioritise short-term gains over long-term competitive advantage.

Deploying AI to retain staff is a strategic choice. By automating mundane tasks and empowering your team to focus on high-value work, workers become more essential to the business, not less. Happier teams are more productive, leading to higher customer satisfaction and loyalty.

Decision 2: Momentum versus safety

The “move fast and break things” mantra of Silicon Valley is often ill-suited to mature companies, where reputations are hard-won and easily lost.

Balancing momentum with rigorous testing for safety, privacy and accessibility is prudent. Agility is important, but when the technology has the potential to mislead or leak personal data, not every iteration is fit for the public domain.

Decision 3: Environmental impact

Just because AI providers subsidise their services to claim market share does not mean environmental costs are gone.

There are practical ways to minimise the carbon footprint of AI solutions. This involves using LLMs only when necessary and opting for smaller, more efficient models where possible. Training teams to economise on energy-hungry prompts (particularly in video generation) and carefully selecting vendors are solid steps toward sustainable AI.

Decision 4: Limits to autonomy

Technological capabilities are always moderated for safety. Just as driving speed is limited well below the car’s capability, leaders must be careful to place limits on AI autonomy.

Allowing AI to handle specific, low-risk tasks is acceptable, provided a human remains in the loop to approve all non-reversible operations and final outputs. As regulation struggles to keep pace with technology, leaders bear a greater responsibility to enforce appropriate safety measures.

Decision 5: Vendor selection and US monopoly

The assumption that American tech giants are the only vendors of AI models and services should be challenged. For Europeans, total reliance on US companies risks losing jurisdiction and sovereignty.

Europe possesses a flourishing ecosystem of AI companies, particularly at the application layer. Supporting local vendors encourages entrepreneurship, innovation and helps develop the technology ecosystem. In an era of geopolitical uncertainty, vendor choice is a core component of risk management.

Mind your business

Struggling with basic AI adoption, some companies may find the burden of ethical decision-making too heavy. Others will disagree with these concerns or not deem them urgent enough.

The key takeaway is that AI is not a one-size-fits-all proposition, and that business leaders have the power of choice. Given its transformative power, how they exercise that power is a profound expression of their companies’ values and future direction.

Documentation, reports, specifications, emails… Modern businesses generate so much content that one might think it is the reason they exist. With few exceptions, producing this content is a chore that most humans will happily outsource to AI.

But as AI commoditises content, attention shifts to workflow design. AI agents can use applications to complete tasks with some degree of autonomy. But where do they fit in your workflow?

Dropping an agent into a broken process merely accelerates chaos. To deploy them effectively, we must first look at Workflow Design.

Key concepts

1. As content gets commoditised, focus shifts to workflow design

2. Use a framework like Dynamic Work Design for a structured approach

3. Visualise with n8n to expose gaps between ambition and reality

4. Find improvements in information disconnects between tasks

5. Use AI agents to fix and automate slow and repetitive processes

1. Dynamic Work Design framework

Developed at MIT, Dynamic Work Design (DWD) is a “process of ongoing, hands-on problem-solving” that helps teams replace chaos with a calmer, more effective workflow. It is grounded in common sense: once you understand its principles, you keep seeing applications everywhere.

1. Solve the right problem. Resist strategic reviews; look for small disconnects in high-impact areas.

2. Structure for discovery. Ensuring that measurements are in place to link targets to real-world metrics.

3. Connect the human chain. Examine how tasks are handed over, and precisely what information the next link needs.

4. Regulate for flow. Matching tasks to capacity to avoid backlogs and burnout.

5. Visualise the work. Use a system that illustrates the process, owners, capacity, and work in progress.

DWD is particularly helpful in customer-centric problems. Can we speed up onboarding from 5 days to 5 hours? Can 90% of service queries be responded to within 9 minutes? If you are unsure where to start, begin by visualising your current workflows.

2. Using a workflow design tool

The second ‘D’ in DWD is important: the framework is based on Design Thinking, which solves problems through iteration and visual representation. To help “visualise the work”, we can use an automation tool like n8n (Make and Zapier are also good).

Acting as a “Figma for workflows”, these tools allow us to map:

• Tasks of different types and scopes, connected sequentially or branching into conditionals and sub-workflows.

• Information requirements, or data properties that must be entered manually or automatically.

• Capacity tracking to monitor work items as they move through the system (what n8n calls “Executions”)

• Simulations that allow you to trigger tasks, monitor running time and test interventions.

To begin with, run n8n in “Design Mode”, using only manual tasks. Ensure your design reflects how tasks get passed from one person to another in the real world. Many companies reach the first “aha” moment here, as missing links and dependencies come to light.

3. Fixing information disconnects

The third rule of DWD (“Connecting the human chain”) relies on two specific states: the Handover and the Huddle.

Ideally, each task should contain enough information for the next person to begin immediately. This is a clean Handover. If the information is incomplete or ambiguous, the team will organise a Huddle. A Huddle resolves the issue, but it consumes time and capacity.

Take a look at your workflow and search for disconnects:

• When a task is handed over, are the recipient’s information needs clear to the sender?

• When a task requires a Huddle, is that meeting necessary, or is it just covering for a lack of documentation?

• Are there clear triggers to switch a Handover to a Huddle when exceptions occur?

• Is there sufficient capacity (redundancy) to handle spikes in volume?

Many disconnects will be resolved through communication. When team members understand how their work output is used upstream, they adapt. Others need management to rearrange tasks and capacity.

Rule-based automations can speed up many mundane tasks, such as data transformation, system integrations, and sending notifications. When all these interventions are in place, you should consider AI agents.

4. Automating with AI Agents

AI Agents are built on the intersection of automated workflows and LLMs. They excel where logic is required - territory beyond the reach of standard, rule-based automation. We rely on three key components: an LLM for reasoning, persistent memory for context, and tools for accessing and modifying data.

Using n8n, you can deploy agents to fix even the trickiest disconnects:

• Prepare content to facilitate cleaner Handovers and Huddles, such as release notes and product documentation.

• Simulate a recipient to verify that incoming data is sufficient, alerting the sender immediately, and saving the human recipient’s time.

• Act as a cross-domain “translator”, enabling a recipient to “chat” with incoming data, rather than struggling with technical content.

• Automate admin work, specifically data validation and entry, where speed matters and the outcome can be easily verified.

True to the probabilistic nature of LLMs, agents are flexible, but results will vary. For reliably accurate output, stick with rules-based automation. They require extensive data “plumbing” and testing before they can be safely deployed.

However, their ability to reason makes agents infinitely more powerful than rulesets. They adapt to specific situations where “one-size-fits-all” fails and can handle unpredictable datasets. Importantly, they can be programmed to learn and improve over time.

5. Closing: Get real with AI Agents

The AI industry, keen to keep the hype train on its tracks, has found a new hero in Agents. We are seeing instances of agent-washing, where simple automation is disguised as advanced AI. To unlock real value, ignore the technology and focus on your workflow:

• Adopt a Workflow Design framework: use Dynamic Work Design - or another framework - to visualise the workflow and uncover where errors and delays occur.

• Fix the disconnects: address problematic information flows by improving communication or implementing rule-based automation.

• Deploy AI agents to accelerate: create further efficiencies with agents that can reason and act: “translating” docs, simulating interactions and handling admin.

Five years ago, describing AI content generation as “game-changing” would have earned you a few confused looks. Yet, the sheer volume of documentation created by companies has already transformed the workplace.

We are now ready to tackle teamwork and coordination. Accelerating workflows will impact value chains, supply chains, and entire economies. As with most generalist technologies, we will overestimate their short-term impact and underestimate their long-term effects.

Beyond the nasty (Grok) and the hearsay (DeepSeek), AI is quietly embedding itself in everyday life. SMEs can find opportunities in healthcare, commerce, and email.

1. OpenAI launches Health app

Need to know: Responding to its own findings of 40 million health-related prompts per day, OpenAI has introduced ChatGPT Health, a specialised service designed to handle sensitive medical data. It integrates with apps such as Apple Health and MyFitnessPal, but the data appears to be isolated and never used to train OpenAI’s models.

Need to act: AI gets embedded in the healthcare ecosystem. If you operate in this space, consider the implications for patient safety, diagnosis, care delivery, and health insurance. It is a highly sensitive space, ripe for innovative approaches and ideas.

2. Copilot offers a POS terminal

Need to know: Microsoft has launched Copilot Checkout, a feature that lets users discover and purchase products directly in the chat window without being redirected to a retailer’s website. Partnering with PayPal and Shopify, the system aims to reduce friction.

Need to act: AI gets embedded in the commerce ecosystem. As we move closer to “agentic commerce”, brands need to reconsider how to retain share of attention and avoid commoditisation.

3. Gemini vs. Copilot fight for your email

Need to know: The battle for your inbox ignites as Google upgrades Gmail, packed with Gemini 3 goodness: “AI Overviews” to summarise threads and answer inbox questions, “AI Inbox” to filter noise and prioritise critical updates and a Grammarly-style proofreader.

Need to act: Building on the success of AI Search, Google is integrating its AI productivity suite. At the time of writing, it is probably the strongest product in the market and could challenge Office even in larger organisations.

Adam Smith published The Wealth of Nations, widely regarded as the most important book in economics, in 1776. Though it is claimed that its significance exceeds its quality, its key concepts (the Invisible Hand, Division of Labour and Self-Interest) remain timeless and make intuitive sense a quarter of a millennium later.

Our world would be utterly unrecognisable to Adam Smith. His thinking was foundational to the Industrial Revolution, but production lines did not materialise until a century later. The sheer speed of modern markets would be beyond his wildest imagination. But like all classics, the book’s core concepts remain relevant and offer useful insights for thoughtful leaders.

Key concepts

1. AI reframes Adam Smith’s 3 key concepts of the free market, with profound implications

2. The Invisible Hand operates in real time, shortening planning cycles

3. AI agents will require a rethink of the Division of Labour and workflows

4. Sharper Self-Interest will encourage micro-enterprises and outsourcing

5. SME leaders need to plan for hyperactive and busy marketplaces

1. The bionic Invisible Hand

Business strategy is so closely connected to the concept of the Invisible Hand that it’s generally taken for granted. A change in pricing or an innovative new product creates new revenues, because buyers in a free market will purchase it, as if “led by an invisible hand”.

The Invisible Hand is about market agility. As competitors change their stance, leaders must continue to adapt to make sure the Hand moves in their favour. AI is set to accelerate business intelligence and execution strategies:

• Dashboards will be replaced with conversations, helping leaders gain insights faster and more intuitively.

• Decision-making will be faster with AI providing instant analysis, predictions and simulations.

• AI agents will collaborate on open standards, instantly updating partners and customers of upcoming changes.

As faster decision-making turns the Invisible Hand bionic, planning cycles will shorten from years to months. In response, leaders should prioritise agility in product design, marketing and distribution.

2. Division of Labour gets upended

Adam Smith famously cited a pin factory to illustrate the concept of the Division of Labour. A dozen specialists, each responsible for a single task in the workflow, can scale production to a level that no single individual could match.

This concept, so fundamental to education, employment and management, is about to be upended. With AI agents providing cross-domain assistance, team roles will become functionally broader and more specialised in specific contexts.

• Hybrid roles will emerge, relying on AI Agents to operate confidently across product, marketing, sales, and legal functions.

• Specialisations will be vertical, focusing on specific contexts: industries, segments, channels or key accounts.

• Workflows will be horizontal, teams self-organising to deliver customer-driven outcomes.

By automating low-level tasks, AI not only saves time and effort but also redefines how we divide work. As you consider deploying AI Agents, prepare to redesign your workflows to make the most of their capabilities.

2. Self-interest powers ecosystems

It is not out of “benevolence” that employees dedicate their energy and employers provide jobs, but out of their respective self-interest. The balance works, even when self-interest includes non-monetary factors such as the work environment and work-life balance.

However, in faster-moving markets and teams with dynamic and horizontal workflows, self-interests will be harder to reconcile. With a low barrier to entry, AI will enable many professionals to offer their expertise from independent vehicles.

• Seeking efficiencies from AI adoption, many employers are expected to reduce full-time headcount.

• Experienced professionals will use AI to set up as freelancers and micro-businesses.

• To grow, companies will rely more on outsourcing and ecosystems of trusted partners and vendors.

Like the SaaS wave before it, AI will enable a new wave of entrepreneurship and micro-enterprises. Ambitious SMEs will need to outsource many business functions and should be ready to review their Make-or-Buy strategies.

4. Summary: a hyperactive market

The post-AI marketplace will be fragmented and hyperactive, characterised by numerous participants, frequent directional shifts, and noisy competition. Some organisations will thrive, while others will rely on legacy competencies.

Whilst some of these developments are already a reality, others will take years to materialise and are facing obstacles such as technology adoption, social attitudes, and market corrections. Nonetheless, forward-thinking SME leaders should take these into account when planning for the long run.

Recommendations for SMEs

• The Invisible Hand goes bionic
  Faster decision-making will shorten planning cycles from years to months. Leaders should prioritise agility in design, marketing and distribution.

• Division of Labour gets upended
  AI agents will broaden team roles, and teams will specialise horizontally. SMEs should redesign their workflows to better meet customer needs.

• Self-interest powers ecosystems
  As AI encourages entrepreneurship, SMEs will increasingly rely on outsourcing. You should revisit your make-or-buy and partnerships strategy.

5. Closing thoughts

We will never know what Adam Smith would make of our world, but it is reassuring to know that ideas conceived 250 years ago remain relevant. It reminds us that, despite technological advances, concepts such as free markets, specialisation, and incentives remain timeless. Like a good book, we can always turn to them to make sense of a world that seems to spin ever faster.

My 2026 predictions sketched out the overflow of the AI hype cauldron and its aftermath. But how did we get here? What created these circumstances, and how are they connected?

In my final post of 2025, I present the 5 key numbers that summarise its biggest stories. Whilst not really strictly scientific, they illustrate our mixed feelings for this year: exuberance, uncertainty, and a sense of impatience about what lies ahead.

5 trillion dollars

The T-Rex of the big scary AI numbers, $5TN, is both the peak valuation of Nvidia, and the CapEx that Big Tech has earmarked for AI data centres until 2030, according to McKinsey. Depending on who you ask, there are 5 trillion reasons we are in a remarkable boom or a precarious bubble.

30X cheaper

The release of DeepSeek R1 in January made a big splash and even a bigger dent in Ndivia’s stock, thanks to a new approach and clever engineering. To understand why the frequently cited 30X cheaper is a game-changer, consider that it represents a 97% cost reduction relative to the competition.

95% pilots failed

Perhaps the most-cited figures of the year: 95% of all AI pilots have failed to deliver ROI, according to MIT. Though the methodology is dodgy, the noise it generated spoke volumes. Commentators jumped on it because it supported our collective observation that business adoption remains underwhelming.

55K jobs lost to AI

Although evidence of AI’s impact on jobs across economies is limited, the announcements are sufficient for CNBC to estimate that AI eliminated 55,000 jobs, mostly in tech. With vibe-coding valuations reaching for the stars, it seems that software developers are on their way to making themselves redundant.

50% of web content is AI

Slop, rage-bait, parasocial - the words of the year indicate our impatience that AI is making everything, everywhere, all at once. Whilst the figure that half of all internet content is AI-generated is speculative, there is no question that we are overindulging. Keep it up, and the word of 2027 could well be “model collapse”.

For anyone who follows the news as closely as I do, 2025 was the year that the cauldron of expectations started bubbling dangerously. In 2026, the mix of hype and fear will likely overflow, driven by a sharp market correction, a major scandal and a heated jobs debate.

Assuming neither element goes into overdrive, this can be healthy. With the hype taken down a few notches, AI adoption looks like a tough, but manageable, business problem. Despite the challenges, remember to ask: what can go right in 2026?

1. AI stocks will make a sharp V-turn

As the AI bubble is turning into a self-fulfilling prophecy, a sudden correction looks likely. The drop will be V-shaped: sharp, sudden and short. There may be casualties as funding gets scarcer, but Big Tech is investing from a position of health and should recover quickly.

Things can still go horribly wrong. OpenAI and Anthropic might stage underwhelming IPOs, Nvidia’s habit of circular financing might create a downward spiral, and burnt retail investors might prolong the downturn. Topping off the froth will also be a blessing in disguise, providing some much-needed clarity and allowing AI professionals to work without the screaming headlines.

2. A major scandal will shift focus to safety

To a seasoned security professional, AI looks like a car crash in slow motion, with a high-profile misinformation scandal a distinct possibility. The media has done a good job of reporting on hallucinations, manipulation and dependence. Still, the biggest attack surface is deepfaked news, as malicious actors exploit the new capabilities and sheer volume of slop.

As companies work out adoption, the focus will shift to AI safety. Guardrails alone will not be sufficient, and hybrid models will emerge to constrain generative features within deterministic straitjackets. The internet will splinter, with brands increasingly distancing themselves from slop for fear of reputational damage.

3. Agentic AI will turn the heat up on the jobs debate

Next year will see the first cohort of post-AI graduates entering the job market. In parallel, agentic AI will automate many junior tasks, suppressing supply and feeding accusations of societal and economic harm. High-profile layoffs, a bear market and a return of the graduate premium will harden anti-tech attitudes.

The debate will likely be weaponised for political gain. As the hysteria mounts, many companies will adopt a pro-jobs stance, advertising their hiring policies and mentoring programmes. Expect a heated debate on the balance between human and digital workforces.

4. Struggling with AEO, brands will lean into creativity

Marketers had a rough 2025. The SEO rulebook is out, but the new AEO one has not yet been written. Content is cheap, ineffective and drowning in slop. Across the funnel, marketers will need to find their creative edge, trying out new concepts and fresh growth tactics. Outbound will shape-shift to attract AI agents, but inbound will remain sluggish.

To distance themselves from slop, brands will lean into clever campaigns that combine old-fashioned wit with AI-powered personalisation. OpenAI will enter the advertising market, creating an AEO frenzy that will not settle for some time.

5. Europe will launch an AI offensive, of sorts

By now, the full extent of the Ukraine war, AI and MAGA contrarianism is clear. Next year, the French election will suck all the oxygen, so it is now or never for Europe. As the world’s richest market decides that AI is a strategic priority, things will happen. Funds will flow, incentives will be agreed, and contracts will be signed.

Other European AI champions will join Mistral, bolstered by substantial rearmament funds. The Draghi report will gain traction, as Europe realises that a strong market is the only way to remain relevant. Regulation will be bargained away: in a fractured world, Europeans can be rich, free or fair - but not all three at the same time.

One of my first “wow” moments with the internet was playing backgammon with a friend on a VAX terminal. Sure, it was just crude green characters scrolling on a black screen, but it was still magical. Not just because he was in Greece and I was in the UK, but because we were not just exchanging texts; we were interacting in real time.

As 2025 comes to a close, we are approaching a similar point with AI. The generative capabilities are great, but a static context (a prompt) can only take you so far. The real “wow” factor is when the AI can react to the prompt dynamically, autonomously consulting multiple data sources to deliver the best possible answer. This, in a nutshell, is agentic AI - and it’s coming closer.

Read on to learn

1. How agents benefit SMEs and empower their human workforce

2. Why open standards mean the time is right for wider adoption

3. Use case: how agents automate a custom pricing workflow

4. Why Agentic is good design, but also good architecture

I. Agents, for humans

Agentic AI is an entirely new human-machine interaction model because agents are authorised to act. Instead of asking a chatbot how to book a meeting, you tell the agent to go ahead and book it. With proper orchestration, the agent will know to check the calendar, send invitations and update the CRM.

Automating such tasks can be tremendously beneficial for SMEs that need to generate results with smaller teams. By providing a digital workforce to handle repetitive tasks, your team can focus on higher-value work. For advocates of human-centric AI adoption, agentic architectures bring it all together: freed from clerical work, teams are empowered to operate at a higher level.

II. Open standards matter

The key obstacle to adopting cross-system agents is interoperability. Unless all agents speak a “common language”, companies will have to allocate budget for every additional connection, causing costs to skyrocket.

This all changed in December with the launch of the Agentic AI Foundation. Organised by the Linux Foundation, the initiative brings together tech giants to agree on that common language. Whilst there is competition with Google’s A2A, the cooperative streak that runs through the industry should ensure a mutually beneficial convergence.

“Agentic standards lower development costs, making agents accessible, interoperable and future-proof”

Much like W3C web standards, the emergence of agentic standards will make adoption cheap and accessible. Companies do not need to build proprietary connections, lowering development costs and making their agents interoperable and future-proof.

III. Custom pricing with AI agents

To illustrate how Agents can help, consider the frequent scenario of individualised pricing. The Account rep is creating a proposal and wants to know if the customer is eligible for any discounts or other pricing incentives. The underlying data is stored in 3 systems from different vendors.

In a traditional CRM, the rep would need to retrieve the data and then manually create the report. GenAI can accelerate report production, but the rep would still need to compile the data because the LLM has no access to it. In an agentic setup, the agent takes on the data research tasks and goes all the way to create the report that the rep actually needs.

The agent is a clever little helper. It parses the prompt and knows it first needs to ping the Profile Agent to collect the customer’s information. It will use that data to request the Orders Agent to list their purchase history in the Orders and Product systems. Finally, it will query the Financial API to check the Invoicing system for late payments.

In the final steps, the agent uses its language capabilities to compile a report and interface with the user in plain Language. It’s all super helpful, user-friendly and obvious, right? It gets better.

IV. Enabling good architecture

Delegating data administration to agents is sound systems architecture beyond workflow improvements. Like all data integrations, agentic exchanges are fast, secure and scalable. However, unlike their traditional cousins, their language-based foundations make them very flexible.

For one thing, they understand the domain and allocate numbers appropriately, raising alerts when the data looks wrong. For example, a €1M transaction is normal in banking, but not in retail. They can also work sequentially, making calls to agents after the necessary information has been collected.

“Language-based foundations make agents very flexible. You can extend the life of older tools by integrating them into an intelligent workflow”

Finally, they can use legacy connection methods (APIs, database queries, file repositories) to help companies manage legacy systems and get a complete picture of the business. In many cases, you can extend the life of older tools by integrating them into an intelligent workflow.

V. Making it all click

The wow factor of online backgammon on those ancient VAX terminals was that my friend Nick and I were not just communicating; we were doing something together. Similarly, agents go beyond chatting and proactively collaborate with other agents to achieve the desired outcome. Build a digital workforce to help the human variety dedicate more time to higher-level tasks: thinking, creating and connecting.

As Dieter Rams said, “the best Design is as little Design as possible”. Autonomous data integrations may not sound very exciting, but when everything clicks, it is as good as Design gets.

1. Linux launches AI Agent initiative.

The Linux Foundation, supported by OpenAI, Google, Microsoft, and Anthropic, has launched the Agentic AI Foundation (AAIF) to establish universal standards for autonomous AI. Similar to how HTTP standardised the web, these open protocols allow AI agents to connect seamlessly across different systems and data sources.

This initiative aims to replace isolated tools with an interoperable ecosystem, enabling businesses to deploy agents that can take independent action across their entire technology stack.

How SMEs can benefit

• Interoperability: create AI agents that can connect to any software

• Automation: enable agents to execute tasks autonomously

• Competitiveness: implement solutions rapidly to claim market share

2. Accenture to train 30K staff on Claude.

Accenture has launched a massive partnership with Anthropic, committing to train 30,000 professionals on Claude to help businesses move from AI experiments to full-scale production. The deal establishes the Accenture Anthropic Business Group, which will develop industry-specific solutions for regulated sectors, including finance and healthcare.

A key focus is deploying Claude Code—which now holds 50% of the AI coding market—to modernise software development, signalling that “agentic” coding tools are ready for enterprise prime time.

How SMEs can benefit

• Validation: Claude is adopted by the world’s largest consultancy

• Accelerate: faster software development with agentic tools like Claude Code

• Scalability: learn and move AI projects from demos to business drivers

3. Microsoft reveals that AI is a daily habit

Microsoft analysed 37.5 million Copilot conversations, revealing that AI has evolved from a novelty to a “daily companion.” The data shows distinct behaviour shifts: users treat AI as a professional tool for coding and work during the day, but turn to it for health advice, gaming, and philosophical questions at night and on weekends.

How SMEs can benefit

• Scheduling: align high-focus tasks to weekdays when AI usage peaks

• Training: teams use AI differently on mobile (health) vs desktop (work)

• Trust: learn from “confidant” interactions, to introduce AI coaches

AI is a tricky technology. It usually works fine, so it is tempting to cut corners and skimp on testing. As content is generated on the fly, it is tempting to think that even if it goes wrong, no one will notice.

But when things go wrong, they go really wrong. Examples of high-profile, costly AI hallucinations abound, and chatbots can be convincing enough to induce psychosis and dependency. Many companies are finding out the hard way that AI needs boundaries.

“Ethics is treating people as you expect them to treat you - when they are not looking.”

Ethics is treating people as you expect them to treat you - when they are not looking. Deploying autonomous services entails significant ethical responsibilities that many companies struggle to navigate.

As a helpful mental guide, here are my 10 Commandments of AI ethics. Ignore them at your peril.

I. Thou shalt not harm humans

Thou shalt ensure your AI prioritises human well-being, safety and dignity above efficiency or profit. Prevent physical and psychological harm, by design.

II. Thou shalt not bear false witness against thy customer

Thou shalt not disguise your AI, nor deceive through deepfakes, manipulation or dark patterns. Users must always know they are interacting with a machine.

III. Thou shalt not discriminate

Thou shalt create systems that are fair and equitable. Thou shalt rigorously purge data and algorithms of bias regarding race, gender, creed or ability.

IV. Thou shalt honour data privacy

Thou shalt not covet the user’s data. Personal information must be collected with consent, stored securely and used only for the advertised purposes.

V. Thou shalt remain accountable

A human must always bear the burden of responsibility. As the creator of AI services, thou shalt be held accountable for the actions of your creation.

VI. Thou shalt explain thy reasoning

Thou shalt strive for explainability. Decisions made by AI, especially those affecting credit, justice, or healthcare, must offer valid reasoning.

VII. Thou shalt not surrender human autonomy

AI shall serve as a tool to augment human capability, not replace human judgment. Thou shalt keep the human involved in all critical decisions.

VIII. Thou shalt secure thy creation

Thou shalt build robust systems that cannot be easily corrupted, hacked or turned against their creators. Safety mechanisms must be strong and resilient.

IX. Thou shalt limit impact on the environment

Thou shalt not ignore the physical costs of AI. Training and running models must be optimised for energy efficiency and to minimise environmental impact.

X. Thou shalt empower, not replace humans

Thou shalt design AI to amplify human ability. Thou shalt seek to automate legwork whilst respecting human intent and agency.