Nearly three-quarters of knowledge workers already use AI, often with tools their employer has not sanctioned (Accenture × Wharton, 2026).
Personal productivity gains can move the bottleneck rather than remove it — Deutsche Telekom saw invention disclosures more than double while assessment capacity stayed flat.
thyssenkrupp's IP team turns repeated prompts into written, tested and shared methods before automating them.
The model is rented and replaceable; the method around it is the asset a company can own.
Two statements are often treated as the same thing: "our people use AI" and "our company uses AI". The 2026 research suggests they describe very different situations — and that the gap between them is where most of the value is lost.
Individual adoption is already high
Accenture and Wharton report that nearly three-quarters of knowledge workers already use AI at work, frequently through personal or unsanctioned tools. Microsoft's AI Diffusion report for the first quarter of 2026 measures AI use across working-age populations: 31.3% in the United States, 31.1% in Germany and 26.5% in Vietnam, among others.
The differences between countries are modest. The difference between individuals and organisations is large. A salesperson who drafts proposals with AI in twenty minutes, a support lead with a private prompt for common tickets, an analyst who cleans a weekly report with a chatbot — each has redesigned a small process. None of it belongs to the company, and all of it leaves when that person does.
When people move faster than the process
The AI in IP report published by LexisNexis in September 2026 contains a clear example. At Deutsche Telekom, inventors began using AI tools to write invention disclosures on their own initiative; guidelines followed later. In the first half of 2026, disclosures more than doubled compared with a year earlier and became more detailed and better structured.
The report also notes the consequence: the productivity gains of inventors did not reduce the assessment workload at the same rate. The team that evaluates, prioritises and files each idea received twice the input with the same capacity. The bottleneck moved one step down the line.
This is a common pattern. Personal AI speeds up one role; if the surrounding process stays the same, the next role absorbs the extra volume.
From a chat to a company method
The same report describes how thyssenkrupp's IP team approaches the problem. It starts from the work rather than from tool features: which tasks take the most hours, where AI can sensibly help, and how difficult the change would be. A task then moves through five stages:
Explore — employees experiment freely, within clear rules for confidential data.
Structure — a repeated prompt becomes a written method with context, steps and expected output.
Test — the method is checked against known good examples.
Standardise — it is shared with the team instead of remaining in one person's chat history.
Automate — only when volume and stability justify it does software run the method through an API.
One observation from the chapter summarises the logic: the model often does not learn from the organisation's experience — the surrounding method does. Models are rented and interchangeable; documented, tested methods are an asset the organisation keeps.
Light rules, real usefulness
thyssenkrupp did not begin with an extensive AI policy. It started with two principles: people may experiment, and professional responsibility does not change. Adoption followed usefulness rather than usage targets — people used what saved them time, and the organisation captured what worked.
Questions worth asking
Who are the heaviest AI users in each team, and which tasks do they use it for every week?
What would leave with them if they left tomorrow?
Which of those tasks feeds another team? Speeding it up may create a queue downstream.
Who is responsible for turning a good prompt into a documented process? If nobody is, that is the organisational gap.
Which methods are tested against known good outputs before being shared?
Individual AI use is a habit. AI embedded in a documented, tested process is an asset — and the second depends on someone deliberately building the method around the model.
Sources
Accenture and Wharton, The Age of Co-Intelligence, 2026.