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AI Strategy

From AI Hype to Real Value

Most AI programmes fail long before the model does. They fail at the moment someone confuses a demo for a decision.

March 2025 · 8 min read · By Disha Gupta

From AI Hype to Real Value

Every organisation I meet has a version of the same conversation happening in three different rooms, with three different vocabularies, and no agreed way to end it.

The failure is rarely technical. Models work. Vendors deliver. What breaks is the join between a capability and a decision: nobody wrote down what the thing was supposed to change, so nobody can tell whether it did.

The antidote is unglamorous. Before any build, write one paragraph describing the decision this work informs, who owns that decision, and what evidence would flip it. If the paragraph is hard to write, the project is not ready — and that is information, not a delay.

A demo proves the technology is possible. Only a decision proves it was worth building.

Once the decision is named, the rest gets easier. Scope narrows on its own. Success metrics stop being invented at the end. And the conversation moves from whether the output is impressive to whether it is useful — a much better argument to be having.

Where teams get stuck

Three patterns show up repeatedly: proving capability instead of value, optimising for breadth before anything works narrowly, and treating the pilot as the finish line instead of the first honest measurement.

None of these are fixed by better tooling. They are fixed by writing things down, agreeing them out loud, and being willing to stop.

What to do on Monday

Take your current list of initiatives. For each one, write the decision it informs in a single sentence. Whatever survives that exercise is your real roadmap, and it will be shorter than the one you had.

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Next step

Have a problem worth structuring?

I take on a small number of projects at a time — usually where the question is still fuzzy and the stakes are not.