Why 95% of GenAI pilots fail — and the measurement fix nobody applies
The problem is rarely the model. It is that nobody designed a way to know whether the pilot worked. Here is the experiment-first framework we use.
The uncomfortable truth about AI pilots
Most generative AI pilots do not fail loudly. They fade. The demo impressed everyone, the tool got licenses, and six months later nobody can say what changed in revenue. The root cause is almost never model quality — it is that no measurement design existed before launch.
What a measurement design looks like
Before any AI initiative goes live, answer three questions in writing:
- Counterfactual: what would have happened without it? (holdout group, pre-period baseline, or geo split)
- Primary metric: one number, decided upfront, that finance already trusts.
- Decision rule: at what result do we scale, iterate, or kill?
A 4-week incrementality test anyone can run
Split your audience or regions, deploy the AI workflow to one half, hold the other. Compare the delta in your primary metric against the pre-period. It is not a peer-reviewed RCT — it is 80% of the certainty for 5% of the effort, and it is infinitely better than “the team feels faster.”
The takeaway
AI does not need more believers. It needs more evidence. Design the proof before you build the pilot.
Frequently asked questions
Why do most generative AI pilots fail?
Most GenAI pilots fail not because of model quality but because no measurement design existed before launch — there is no counterfactual, no agreed primary metric, and no decision rule for scaling or killing the initiative.
How do you measure the ROI of an AI initiative?
Define a counterfactual (holdout group, baseline, or geo split), pick one primary metric finance trusts, and set a decision rule before launch. Then run a 4-week incrementality test comparing treated vs. held-out groups.