AI strategy: why it fails, and what works

21 % of AI projects generate value. 85 % never reach production. Those numbers do not describe a technical problem — they describe decisions made too early, on the wrong criteria.

This cluster takes apart the mechanics of failure before it gets expensive: the 6 mistakes made before launch even happens, the 4 sentences that give away a doomed project (“we have the data”, “the POC worked”), the cognitive biases that make a demo look like a capability, and the AI-intern trap — automating a dysfunction only makes it faster.

It also holds the antidote to FOMO: you are not late, you are just in time. And the question of sequence almost nobody asks — optimise before you transform, because three to six months of optimisation beat a transformation built on already broken processes.