How Executives Can Trust the AI Tools Their Workforce Uses

Giving employees access to generative AI creates a new leadership question: how do we know the answers are accurate enough for people to act on?

A convincing demonstration can show what is possible. Approving a wider rollout requires evidence about everyday performance, the consequences of mistakes, and how the organization will catch them. Executive confidence should come from a process the business can inspect and repeat.

Define accuracy for the job

Start with a specific workflow. An assistant answering questions about company policy needs to find the current policy, interpret it correctly, and recognize when an exception requires human judgment. A meeting summary needs to preserve decisions, owners, and deadlines without inventing commitments.

These tasks need different evaluations. Agree on what a correct answer contains, which mistakes are unacceptable, and when the tool should decline to answer. An overall accuracy score can hide a serious weakness in a small but important category.

Test the work employees actually do

Build an evaluation set from representative employee questions, with expected answers reviewed by people who know the subject. Include ambiguous requests, outdated documents, conflicting sources, and questions the available information cannot answer. Keep a separate set of cases that was not used to tune the tool.

Evaluate the complete experience employees receive, including the documents it retrieves and the instructions it follows. Ask reviewers to check whether answers are correct, complete, and supported by the cited source. A citation is useful only if it actually supports the claim.

Report the number and types of cases tested alongside the results. Compare the AI-assisted workflow with the current process, including time spent correcting mistakes. A small pilot gives preliminary evidence; it does not establish that the tool will perform equally well across the company.

Give leadership a clear decision

A useful executive scorecard answers four questions:

• Where does it work? Which tasks and employee groups were evaluated?
• How often does it fail? What are the error rates by task and severity, including unsupported answers and failures to escalate?
• What happens when it is wrong? Which outputs require review before anyone acts, and who owns that review?
• When do we pause? What results would trigger a restricted rollout, investigation, or rollback?

Set acceptance criteria before reviewing pilot results. Expand access when the evidence supports it, and keep uses that have not been evaluated outside the approved scope.

Keep earning confidence after launch

Assign an owner to sample real outputs, investigate employee reports, and rerun evaluations when models, instructions, or source documents change. Train employees to recognize the tool’s limits and give them a simple way to flag questionable answers. This approach aligns with NIST’s evaluation guidance, which recommends testing under realistic conditions and monitoring performance after deployment.

Executives need a defensible basis for deciding where AI can help their workforce. That basis is specific: measured performance on defined tasks, visible limitations, and accountable people who can intervene when results fall short.