The Quiet Retreat

Organisations are rationing AI for the people who do the work while betting their headcount on it.

On Monday 26 October, staff at Sweden’s Government Offices opened their browsers and found that ChatGPT and Claude no longer loaded. From now on, they were told, it would be Microsoft Copilot: the one tool the ministry had vetted. Nobody had done anything wrong. The decision was careful, defensible and entirely typical of what is now happening in large organisations across Europe and beyond.

Two years ago every large organisation wanted its people using AI. Now many of them are quietly taking it away. The tools have got better, and also more expensive and more visible. AI inside the enterprise is meeting three forces that have nothing to do with model quality: rationing from above, resistance from below, and a stigma on work that a machine has touched. Each is understandable, but together they suppress what AI’s value depends on: broad, open, everyday use.

The rationing

The Swedish block is the visible version of a quieter process. In large companies, compliance and governance rarely ban AI outright. They strip it: features switched off, web access disabled, the stronger models removed, the choice narrowed to whichever vendor procurement already trusts. Step by step, that often leaves a tool that no longer does what people needed.

I have seen this from the inside, and I think it is among the most damaging things an organisation can do to its own AI programme. People try the stripped-down tool, conclude that “AI isn’t that useful”, and stop. The organisation then reads the low usage as proof that the technology was overhyped.

Cost is the second lever. In 2026, AI vendors moved from flat-rate licences towards paying per use, and budgets that had been experiments became line items finance noticed. In June, Walmart, which had given staff unlimited use of its in-house AI agent, put every employee on a fixed allowance. Controlling spend is reasonable. But a flat cap measures only cost. It hits the employee who has rebuilt a whole workflow around AI exactly as hard as the one generating slides nobody reads, and in practice it lands first on the heaviest users, who are often the ones getting the most out of it.

Rationing also drives use out of sight. Germany’s Institut der deutschen Wirtschaft found that 29% of employees who use AI at work use tools their employer didn’t provide. The figure rose to 45% where employers actively encouraged AI skills. Organisations are teaching people to use AI and then not handing them the tools, so people bring their own. The organisation saves on licences and gains a shadow AI problem it can’t see, govern or learn from.

The resistance

Use is still rising. A European Central Bank survey of some 20,000 people found that the share of euro area workers using AI on the job doubled in two years, to 52% in 2026. Enthusiasm is not keeping pace. The share who view AI positively slipped from 43% to 41%, and 41% say they are simply not interested.

It doesn’t help that AI has become the stated reason for cuts. In the same week Sweden blocked its chatbots, the Dutch government announced it would shed an estimated 25,000 central government jobs over the coming years, leaning on AI to do the work. When employees hear “AI strategy” and think “redundancy plan”, resistance is the rational response. In an April 2026 survey of 2,400 knowledge workers in Europe, the UK and the US by Writer and Workplace Intelligence, 29% admitted to actively undermining their company’s AI strategy, rising to 44% among Gen Z.

The stigma

The subtlest force is social: using AI makes you look worse. In a 2025 study published in PNAS, researchers at Duke University asked some 4,400 participants to evaluate colleagues. Those who used AI were rated lazier and less competent than those who got the same help from a person, and participants anticipated this and hid their own AI use. Everyone has by now received AI “workslop”, output that looks like work but leaves the recipient to do the thinking, so the prejudice has some basis. But it falls on careful users as much as careless ones.

Regulation is about to make that stigma detectable. Since 2 August 2026, the EU AI Act has required AI providers to mark generated text in a machine-readable way. Anthropic now watermarks Claude’s output worldwide; OpenAI began doing the same for ChatGPT in the EU in October. Detection is not yet public, but it is coming, and the watermarks work at the level of the model, which can’t tell a full draft from a corrected comma. Anthropic says a detected mark means a text “may have been processed by Claude”, and that translating or summarising your own writing can carry it. Lawmakers explicitly exempted assistive editing. The watermark doesn’t. Worse, heavy rewriting erodes the mark: in reported tests of OpenAI’s scheme, swapping a quarter of the words cut detection to 17%. The watermark will catch the honest professional who asked AI to check their grammar, and miss the one who had it write everything and paraphrased the result.

Authorship has never been binary. Editors, ghostwriters, translators and speechwriters have shaped “personal” texts for centuries, and we judged the result by its quality and by who put their name to it. A watermark answers a narrower question: did a machine touch this? In a culture that already penalises AI use, that is enough to make a careful professional think twice before letting AI review their work, which is exactly the low-risk, high-value use organisations should want most.

Who answers for it

The same bias runs through how we judge AI decisions. In compliance-critical work, human judgement is known to be fallible, which is why we built four-eyes principles and layered reviews. Even those miss things, and we accept that as the cost of doing business. An AI system that errs once is declared unfit. Holding AI to a standard no human process meets is stigma in a risk officer’s language. The fair test is to measure both against the same standard: accuracy, consistency and whether the reasoning can be audited afterwards.

Where AI and human review really differ is accountability. A layered human review works because several people look and each of them answers for what they signed. When a reviewer misses something, there is a name, a conversation and, if needed, a consequence. An AI can’t be held to account, and employees sense the gap: in a Bitkom survey this spring, 62% of German workers said it was unclear who is responsible when AI gets something wrong. If nobody owns the machine’s decision, the safest move is to distrust it. The fix is to make ownership explicit: a named person accountable for how the AI review is designed, tested and monitored, just as a head of compliance answers for the review process rather than for every individual file. Accountability can move up a level, but someone still has to hold it.

What it costs

Put these forces together and you get a predictable pattern. Access is rationed, so experience stays with a small group. Resistance and stigma push the rest underground, so the organisation doesn’t learn from it. What remains visible is the central programme: a handful of sanctioned use cases, a dashboard of licence counts, and a growing suspicion that AI “isn’t delivering”.

The ECB’s numbers show why that matters. The median AI user reports saving about three hours a week, nearly 8% of working time. But because only half of all workers both use AI and report any saving, the gain across the whole workforce shrinks to under 4%. The value of AI in an organisation depends less on how good the tool is than on how many people use it well. Every measure that narrows that base, whether a block, a cap or a reason to hide, cuts directly into the return.

Two objections

“We can’t afford unlimited access.” True, and nobody should offer it. But a cap is a design choice. Tie budgets to demonstrated outcomes rather than flat allowances, and keep basic access universal, because the long tail of occasional users is where most of the learning happens.

“Compliance comes first.” It should. But meeting compliance shouldn’t mean shipping a tool that no longer works. Govern the data and the use; don’t amputate the capability.

Make it normal

The way forward is to remove the reasons people hide. Leaders should use AI visibly themselves and say so. They should judge work on its quality and on who stands behind it. They should write disclosure norms that distinguish assistance from authorship, and give every AI process a named owner. And when they need to control cost or risk, they should govern access rather than withdraw it, because every employee pushed onto a personal account is a lesson the organisation will never learn.

The tools work. Most enterprises have yet to let their people be seen using them.