A manager approves an AI-generated customer update because it reads well. The delivery date is wrong, and the draft promises a service the company never agreed to provide.
The missing skill was review judgment.
AI output review training gives managers practice checking evidence, identifying unsupported commitments, and deciding when work needs correction or escalation. A practical workshop should use realistic work samples and require a documented decision, not just a better prompt.
Choose one recurring management decision
Start with a task managers already approve: a customer update, a project status report, or an internal recommendation. Define the inputs a reviewer is allowed to use and the authority they have.
Do not combine customer communications, hiring decisions, legal advice, and financial reporting into one exercise. Each has different evidence and review requirements.
The NIST AI RMF Playbook recommends training suited to the responsibilities of different AI actors. The workshop below is an original example of role-specific practice, not a NIST-required curriculum.
Prepare a small evidence pack
Use synthetic, public, or specifically approved material. A customer-update pack might contain a service agreement excerpt, a project timeline, and a support record. Keep the source-of-truth documents short enough for participants to inspect during the session.
Prepare an AI-style draft with deliberate errors and a facilitator answer key. Include one incorrect fact, one unsupported inference, one unauthorized promise, and one issue that genuinely cannot be resolved from the available evidence.
That final issue matters. Learners should practice requesting information rather than replacing one guess with another.
Use a proposed 60-minute agenda
| Time | Activity | Required output |
|---|---|---|
| 0–10 minutes | Explain the task, evidence, and approval boundary | Shared understanding of what may be approved |
| 10–25 minutes | Review the draft individually | Marked claims and source references |
| 25–40 minutes | Compare decisions in pairs | Corrections and unresolved disagreements |
| 40–50 minutes | Discuss the answer key | Reasons behind the review decisions |
| 50–60 minutes | Review a fresh short sample | Individual approval, revision, or escalation record |
This is an illustrative session plan. Adjust its duration for document complexity, accessibility, and the participants' experience.
Teach a repeatable review sequence
First, identify material claims and commitments. Second, check each against the evidence pack. Third, separate style preferences from factual or authority problems. Finally, decide whether to approve, revise, or escalate.
Use a simple review sheet:
Claim or commitment | Supporting source | Problem found | Correction or question | Decision owner
Do not let a model's confident explanation substitute for a source. If AI helps with review, the manager still needs to inspect the material evidence relevant to their decision.
Illustrative example: a delivery-date promise
The source timeline says a date is a target pending supplier confirmation. The generated update says the delivery is guaranteed.
A strong reviewer changes the wording to match the evidence and asks the project owner whether a firmer commitment is authorized. A weak reviewer merely makes the guarantee sound more polite.
For the unresolved issue, the draft claims the customer is eligible for a credit, but the evidence pack includes no applicable credit policy. The correct workshop response is to withhold that commitment and ask the responsible owner.
Assess the decision, not the prose
Score whether the manager found material errors, used the correct evidence, respected approval limits, and handled uncertainty appropriately. Set the rubric before the session and give more weight to consequential misses than stylistic differences.
Have facilitators compare scoring on one sample before grading participants. If two experienced reviewers disagree, investigate whether the evidence or policy is unclear rather than automatically blaming the learner.
LuMay's training measurement scorecard covers longer-term evaluation. This workshop produces one narrower artifact: a work sample showing how a manager reviews AI output.
Frequently asked questions
How can one workshop serve managers with different levels of AI experience?
Keep one shared business decision and evidence pack, then vary the scaffolding. Less experienced participants can receive a claim-review template; experienced participants can tackle more ambiguous cases. Assess the same core review judgment rather than rewarding familiarity with a particular tool.
Should participants use AI to review the deliberately flawed draft?
They can, if the exercise permits an approved tool, but the final decision must still be justified against the evidence pack. Ask participants to show which source supports each correction. An AI-generated critique should not become its own proof.
Does passing the workshop authorize a manager to use new tools or sensitive data?
No. The assessment demonstrates performance on the exercise; tool access, data handling, and approval authority remain governed by organizational policy. Make those boundaries explicit so a successful learner does not mistake training completion for a new permission.
What if participants disagree with the facilitator's answer key?
Ask them to identify the evidence and decision rule behind the disagreement. Correct the key if it is wrong, and mark genuine ambiguity as unresolved. The exercise should reward justified review decisions, not compliance with an unsupported answer.
Carry the practice into normal work
Give managers the same review sheet for one approved workflow after the session. Ask them to bring confusing examples back through the organization's support route, with sensitive material handled under existing policy.
Use LuMay AI Learning Labs as a starting point for discussing a practical training engagement. Request exercises that match the decisions your managers actually make. The goal is dependable review behavior, not confidence without evidence.
