AI Success Metrics: Writing Success Criteria for a Pilot Before It Starts

A pilot with no agreed measure of success turns into a debate. Enthusiasts point to the answers that impressed them, skeptics point to the ones that did not, and the decision drifts. The fix is to decide what "worked" means before the first question is asked — a short list of AI success metrics everyone signs up to in advance.

What are the four kinds of AI success metrics?

• Answer quality. For example, a set share of sampled answers scored as correct and sourced. The scoring sheet in how to score a RAG answer gives you a consistent way to measure it.
• Trust. The system says so when the answer is not in the documents, instead of guessing.
• Adoption. Users from each role say they would use it again — the simplest and most honest adoption signal in a short pilot.
• Governance. Reviewers confirm that access, personal data and logging meet your rules.

Set the actual thresholds with your own team. They depend on your risk and your use case, so there is no universal number. A compliance-heavy use case might require a very high share of sourced answers and zero guessing; an internal operations use case might accept more imperfection in exchange for speed.

What goes into a one-page pilot brief?

Put the criteria on a single page that the sponsor, the champion and the reviewers all sign off before the pilot starts:

• The question this pilot answers. "Will a RAG work on our documents for our people?"
• The sample. Which documents, from which area — see what to share in a pilot.
• The users. Which roles, and how many from each.
• The criteria. The four kinds above, with your thresholds.
• The decision it leads to. Build, extend, or stop.
• The date the decision is made. Fixed in advance.

A day-by-day version of the pilot itself is in the two-week AI pilot plan.

How do you measure each metric in practice?

For the governance line, the audit record matters most: every question, answer and source should be logged and available to your reviewers — see what an AI audit trail should prove.

Why do sponsors like this approach?

A short, well-defined pilot is a low-commitment way to learn before committing to a build. A team we worked with took exactly this approach — a small sample of their own documents and real users asking their own questions — and came away with evidence about their own content before any build decision. That is a far stronger basis for an investment case than a vendor's demonstration, and it protects the sponsor from the most common failure in enterprise AI: a pilot that succeeds and still never reaches production.

Approve a pilot, then approve a build with evidence.

Frequently asked questions