5 Questions to Ask Before Starting an Enterprise AI Pilot

Enterprise AI pilots have a well-documented failure mode: they demo well, generate excitement, and then quietly stall before reaching production. The cause is rarely the model. It's usually that a handful of foundational questions were never answered before the project started.

1. What decision will this actually change?

A pilot needs a specific decision or workflow it's meant to improve — not a general direction like "explore AI in operations." If you can't name the person who will act differently based on the model's output, the pilot has no clear owner and no clear path to production.

2. What data do we actually have, and how good is it?

Most delays come from discovering, mid-pilot, that the data needed doesn't exist in usable form. A short, honest data readiness assessment before kickoff — not during it — saves months later.

3. What does "good enough" look like, numerically?

"More accurate than before" isn't a success criterion. Define the specific metric (precision, recall, forecast error, time saved) and the threshold that would justify moving to production, before the first model is trained.

4. Who reviews the model's mistakes, and how often?

Every model makes mistakes. Enterprises that plan for this — with a defined review process and escalation path — move to production faster than those that treat errors as a reason to distrust the whole initiative.

5. What does production actually require that the pilot doesn't?

Monitoring, retraining triggers, access controls, and incident response are not pilot-phase concerns — until the pilot succeeds and there's no plan for them. Scoping this work early avoids a second, unplanned project just to operationalize what already worked.

None of these questions require an AI specialist to answer — they require discipline. The pilots that make it to production are usually the ones where someone insisted on answering them first.