Before wider rollout, an AI pilot should demonstrate useful performance against the current workflow on representative inputs, including missing information and exceptions. Evaluate the finished output after human review, total operating effort and cost, and whether intended users can operate it. Name owners and agree conditions for expansion, revision, or stopping.
A pilot should help leadership make a decision. It should show whether an AI-supported workflow is useful under the conditions the business expects to encounter, and what would need to change before wider use.
A polished demonstration answers a narrower question: can this approach produce a convincing result on the examples shown? That is a useful beginning. A rollout decision needs evidence about quality, effort, exceptions and the people who will own the service.
Agree on the decision before running the trial
Write down what the pilot is intended to establish. Be specific about its users, information sources, permitted actions and operating conditions. An internal drafting assistant and a system that sends customer responses need different evaluation plans, even if both produce similar text.
Set decision criteria with the business owner and the people responsible for delivery. Describe acceptable performance, issues that would block expansion and uncertainties that need further investigation. Agree how evidence will be reviewed before the team becomes invested in a particular result.
Compare against the current workflow
Evaluate the work people need to complete. If the system drafts a response, consider the finished response after checking and correction. If it retrieves information, examine whether the person can verify and use that information.
Compare the proposed process with the current one using similar work. Include the time spent preparing inputs, resolving errors and dealing with cases the system cannot handle. Capture the quality of the existing process as well. The comparison should reflect the actual alternative available to the business.
Use examples that reveal the limits
Build a test set from representative work, using information approved for that purpose. Include routine examples and cases that experienced employees find difficult. Keep some examples separate from those used to refine the prompts or configuration, so the final check includes work the team has not repeatedly tuned against.
A practical evaluation should investigate:
- Ordinary work: Does the system produce a useful result on the requests expected most often?
- Missing information: Does it identify what is unavailable, or present an unsupported answer?
- Conflicting sources: Does it expose the disagreement and direct the user to an appropriate next step?
- Unusual requests: Does the workflow recognise when the task falls outside its intended scope?
- Service interruption: Can people continue working when an integration or information source is unavailable?
- Repeated runs: Are results sufficiently consistent for this particular job?
Review both the output and what happened in connected systems. A message saying an action succeeded is insufficient evidence that the intended record was updated correctly.
An illustrative pilot review
Imagine a fictional services company testing an assistant that prepares project handover notes. The routine examples look clear and well structured. Reviewers then test records containing an unresolved client request and conflicting delivery dates.
The important question becomes whether the assistant preserves those uncertainties and makes them visible. A fluent handover that quietly chooses a date could create more operational difficulty than an incomplete draft that asks for clarification.
The team might decide to continue with mandatory review and a narrower set of source documents. It might instead pause to improve the underlying records. Either decision could be a useful pilot outcome. The trial has exposed a dependency that a presentation alone would not resolve.
Test whether people can operate it
Ask intended users to complete the workflow with the guidance they would receive after rollout. Observe where they hesitate, what they trust and which checks they skip. A process that depends on the pilot’s designer standing beside every user needs more work before expansion.
Confirm who maintains the reference information, handles exceptions and reviews proposed changes. Include recurring service costs, support and review effort in the operating picture. Passing a quality check does not establish that the business is ready to run the service.
Make the conclusion specific
A useful pilot readout explains what was tested, what passed, what failed and what remains unknown. Recommend a defined next step, with an owner and conditions for proceeding. Expand the proven scope gradually and revisit the evidence when the workflow, model or source information changes.
For a technical treatment of task design, grading and observed outcomes, see Anthropic’s guide to evaluating AI agents. Its developer-focused methods provide useful context; the evaluation plan still needs to reflect your business workflow.
Authentia provides implementation advisory to help client teams and delivery partners define these checks and turn pilot evidence into a clear rollout decision.