What Actually Matters When AI Projects Compete for Attention?
Mon 10 Aug 2026
AI projects are multiplying. The harder question is which ones deserve our time, money, and attention.
Across the UK, Germany and France, this month’s coverage focuses on what actually matters, beginning with the first week’s theme: defining value. We start with the business problem, consider AI as a portfolio, and examine what is credible enough to scale.
UK: Start With the Problem, Not the AI
The UK perspective challenges one of the most basic assumptions behind enterprise AI adoption: that having an AI strategy is necessarily the right starting point.
Ben Pearce, founder of the Tech World Human Skills podcast, said he had seen organisations begin with “we need an AI plan” rather than identifying a business challenge suited to the technology.
He pointed to an organisation that used employee adoption of Microsoft Copilot as evidence of AI success, but could not identify use cases that had saved time, enabled something new, or improved customer outcomes.
Pearce instead has framed potential AI opportunities around two questions: where can the organisation remove toil, and what can it now do that it could not do before?
That changes the starting point. AI becomes a means to an end, not the end in itself.
Germany: Treat AI Investment as a Portfolio
The German perspective asks what happens when several promising AI initiatives compete for the same time, funding, and attention.
At Big Data & AI World Frankfurt, Data & AI Strategy Consultant Tiankai Feng presented a figure suggesting that only 4% of companies are creating substantial value with AI. His argument was that organisations need to assess AI projects against existing business goals, potential value, and the effort required to deliver them.
“Based on how much effort it would take and how much value we believe it would give us, we can then build a portfolio,” he said.
For Feng, AI projects should not continue to draw investment simply because they have already started. They have to keep earning their place.
France: Value Has to Be Defensible
The French perspective added governance and accountability to the value question.
Through the analysis of past Tech Show sessions, it was argued that many organisations do not lack AI initiatives. What they lack are clear criteria for deciding which should move forward.
A use case therefore needs more than technical promise. It should connect to an identifiable business objective, a named owner, a measurable metric, an acceptable level of risk and a realistic route to deployment.
Once AI begins influencing decisions, explainability, auditability, and accountability also move from principles to deployment criteria.
That makes value a question of defensibility. Leaders need to explain why a use case is worth backing, what outcome it should improve, and who is accountable for that outcome.
Choosing What to Back
The three views approach the same issue from different directions.
The UK asks whether AI is solving something the business actually needs, Frankfurt queries whether the project deserves investment relative to everything else competing for attention, and Paris questions whether the use case is specific enough, owned enough, and well enough understood to justify moving ahead.
As AI projects multiply, the organisations making the most progress may not be those launching the most initiatives. They may be those able to say why a use case matters, what it should change, what it will cost and what would cause it to stop.
