Start an AI agent pilot with one bounded task, a named owner, a small test set and explicit approval rules. Measure the quality of the completed work, including human review and rework, before expanding access or automating more steps.
An AI agent is commonly understood as a system that can use tools to take steps towards a goal. Its actual autonomy varies by product and configuration. For a manager, the practical question is what it can read, change and send, and who remains accountable for those actions.
This original pilot checklist from GREEZA Academy & Consultancy, LLC supports learning in AI, leadership and operational improvement. It does not assume that every task needs an agent.
What is a suitable first pilot?
Choose work that is repeatable, easy to check and reversible. A useful example is drafting an internal weekly progress summary from a small set of approved project notes. The agent can prepare a draft while a project owner checks the facts and decides whether to distribute it.
Avoid combining several uncertain processes in the first experiment. If source notes are incomplete, responsibilities are unclear and the desired output keeps changing, automation may make the confusion harder to diagnose. Stabilise the basic workflow first.
How do you write a one-page pilot brief?
Use these seven fields: purpose; allowed inputs; expected output; actions requiring approval; accountable owner; success measures; stop conditions. Write each in operational language. Instead of “improve productivity,” say “produce a weekly draft that accurately identifies open actions, their owners and recorded due dates.”
Specify missing-data behaviour. For example: if a source does not state a due date, the draft must say “not recorded” rather than invent one. If two sources conflict, the system must flag the conflict for review. These rules turn a vague demonstration into a testable workflow.
What permissions should the pilot use?
Give the system only the access needed for the agreed task. Reading approved notes does not require permission to change the project plan, email customers or approve spending. Keep those capabilities separate and review them deliberately if the pilot later expands.
Agree how the team will handle sensitive information and check the organisation’s approved tools and data rules. Do not put confidential records into an unapproved service just to make the demonstration more realistic. Synthetic examples can be useful during early testing.
How do you create useful test cases?
Build a small test set that includes ordinary work and awkward cases. For a progress summary, include a normal week, missing due dates, contradictory status updates, duplicated actions and an overdue item with no owner. Write the expected handling before running the test.
Ask the reviewer to classify each output as accepted, corrected or rejected, with a brief reason. Keep the examples that expose failures so the team can check whether later changes actually improve the result. A polished demonstration using only easy cases does not establish reliability.
How should you measure the result?
Compare the same type of work before and during the pilot. Record total completion time, review time, corrections, missed items and whether the final output is usable. Include the effort spent preparing inputs and managing exceptions.
Illustrative example: a manual summary takes 30 minutes. An AI draft takes 4 minutes, but review takes 12 and corrections take 6. The assisted workflow totals 22 minutes, giving an 8-minute reduction for that example. If correction time rises to 20 minutes, the apparent benefit disappears. These are fictional numbers showing a calculation, not results achieved by GREEZA or a client.
Treat quality as a separate measure. A faster summary that misses an important action may be less useful. Set acceptance criteria in advance so enthusiasm for the technology does not change the definition of success halfway through.
When should you stop or expand?
Pause when the system takes an unauthorised action, repeatedly invents facts, cannot handle ordinary exceptions or creates more review effort than the task justifies. Investigate whether the issue comes from the process, inputs, instructions, tool configuration or the proposed use itself.
Expand only after repeated tests show acceptable performance and the accountable owner agrees on the next boundary. Add one capability at a time so failures remain understandable.
What framework can support the review?
NIST’s voluntary AI Risk Management Framework organises risk work around Govern, Map, Measure and Manage. It offers a broader foundation for discussing responsibilities, context, evaluation and risk responses: https://www.nist.gov/itl/ai-risk-management-framework
What questions do managers often ask?
Does an agent need permission to send messages? Only if sending is part of the approved task. Drafting and sending can remain separate.
Is a good prompt enough? No. Inputs, permissions, tests, monitoring and human ownership also affect the outcome.
Can one successful test justify a rollout? No. Test representative cases and exceptions, then monitor changes after deployment.
How can you develop the underlying skills?
Explore current AI, digital transformation and management learning options with GREEZA Academy & Consultancy, LLC: https://greezaacademycourses.learnworlds.com/courses
Connect the pilot to a broader improvement method using our DMAIC guide: https://greezaacademy.com/blog/dmaic
For diagnosing recurring process problems, read: https://greezaacademy.com/blog/root-cause-analysis-service-team-example