Choose an Enterprise AI Pilot With an Evidence Scorecard

An AI pilot should resolve an investment decision. If the team cannot explain what evidence would justify expansion, it is building a demonstration with an open-ended budget. Start with a workflow whose owner can describe the current work, the cost of an error, and what will change if the pilot succeeds.

Consider a hypothetical internal support team choosing between drafting responses and autonomously resolving requests. Both sound useful. Response drafting offers an easier boundary: an employee checks a suggestion before sending it. Autonomous resolution also needs permission checks, reliable actions, recovery, and exception handling. The second option may eventually create more value, but it asks the pilot to prove several things at once.

Score the evidence, not the enthusiasm

Use a short scorecard with five dimensions. For each, record the evidence, the uncertainty, and a named owner. A numeric score can help compare candidates, but do not allow a high aggregate score to hide an unacceptable access or safety issue.

• Workflow value: Which recurring task consumes time or causes rework? Establish volume and a baseline from observed work.
• Data readiness: Can the team access representative, approved inputs? Identify missing, stale, or restricted information.
• Review effort: Who can judge a correct answer, and how long will review take? Include corrections in the cost model.
• Integration: Where will the result enter the existing process? Identify authentication, handoffs, and operational dependencies.
• Reversibility: Can staff return to the existing method quickly? Assign a person who can pause the experiment.

Use evidence labels such as measured, sampled, estimated, and unknown. An estimate with an owner is more actionable than a confident score with no provenance. Before comparing products, reject candidates that depend on unavailable data or a reviewer who has no allocated capacity.

Define the acceptance test before implementation

For the support example, assemble a set of representative requests, including ambiguous questions, outdated policies, and requests outside the team's remit. Keep some cases separate from development so the team cannot simply tune to the examples it already knows.

Judge whether each draft is correct, supported by approved information, and usable after review. Measure end-to-end handling time and reopened requests alongside suggestion acceptance. Acceptance alone can reward plausible answers that employees do not check carefully.

Set thresholds with the workflow owner based on business consequences. A draft that suggests the wrong internal form and a draft that exposes another customer's information are different failure classes. Report them separately; an average quality score should not blur that distinction.

Make the next funding decision explicit

Run the pilot with a bounded cohort, defined spend, and a review date. At that review, choose among expanding, revising, or stopping. Expansion requires evidence that the complete workflow improves, the error profile is acceptable, and someone can operate it after the pilot team leaves.

Stopping is useful when it prevents an expensive rollout of a weak use case. Record which assumption failed so the next candidate starts with better information. The deliverable is a defensible decision, not a slide saying that users liked the demo.

Connect this scorecard to the broader discussion of measurable AI business outcomes. For program design, the same boundaries belong in the delivery and implementation scope.