A quick review checklist for recurring AI workflows

Review one workflow at a time:

Field	Example answer
Workflow	Draft first-pass vendor-risk summaries
Input	Security questionnaire, vendor website, contract notes
Output	Internal risk summary for procurement
Human checkpoint	Security owner approves before procurement uses it
Failure mode	The model misses a contractual data-retention issue

If the owner cannot write answers this specific, the workflow is still an experiment.

An AI workflow becomes business process when people repeat it, trust it, and stop checking every output from first principles. Review it before that happens.

This review is intentionally practical. It asks whether the workflow has a clear owner, acceptable inputs, understandable outputs, and a human checkpoint before important decisions are made.

ISO/IEC 42001 focuses on management systems for AI. That reinforces the main point: adoption is not only about model choice; it is also about responsibilities, processes, controls, and improvement loops ISO/IEC 42001.

Use this when

Use this for recurring AI-assisted workflows in operations, support, marketing, product, research, internal enablement, and engineering support.

It is most useful when the workflow is not regulated enough for a formal risk process but still important enough that mistakes would waste time, expose data, mislead a reviewer, or affect decisions.

Skip it when

Do not use this checklist as the only review for regulated, safety-critical, legal, medical, employment, or high-impact financial workflows.

Do not use it after the workflow is already embedded in business process without also reviewing actual usage logs, outputs, exceptions, and user behavior. A pre-launch checklist cannot tell you how people adapted the workflow once deadlines and shortcuts appeared.

What to check

1. Define the workflow in one sentence.
2. List the input data and mark anything sensitive, confidential, personal, or regulated.
3. Describe the expected output and who will use it.
4. Name the owner responsible for maintenance and escalation.
5. Name the human reviewer and when review is required.
6. Write down likely failure modes, including missing context, stale source material, hallucinated detail, biased framing, and overconfident summaries.
7. Decide whether the decision can be reversed if the AI output is wrong.
8. Define what evidence is needed before approval: sample inputs, sample outputs, reviewer notes, rejected outputs, and known limits.
9. Approve, approve with limits, or reject the workflow.

Approval with limits is often the right answer. A workflow may be fine for internal drafts, but not for customer-facing language. It may be fine with public data, but not with personal data. It may be fine for suggestions, but not for final decisions.

Watch the data movement

Pay attention to invisible data movement. A workflow may look low-risk because the output is harmless, while the input contains customer data, employee information, credentials, unreleased strategy, or proprietary source material.

Also check whether people can distinguish AI output from reviewed work. If the output can be copied into customer-facing, legal, financial, or executive materials, require a review checkpoint before it leaves the team.

The review should name the systems involved. “Uses AI to summarize tickets” is not enough. “Exports Zendesk ticket text into a hosted assistant, then pastes a summary into Salesforce” is reviewable.

Other ways to handle it

For low-risk personal productivity, use a simple usage policy instead of a workflow review.

For high-risk work, use a formal review path with security, privacy, legal, procurement, and domain experts.

For workflows that are already running, do a retrospective review instead. Pull examples from real usage, look for exceptions, and ask reviewers what they stopped checking because the workflow felt familiar.

Try this next

Pick one recurring AI-assisted workflow and write down the input, output, owner, reviewer, failure mode, reversibility, and approval limits. If any field is unclear, do not scale the workflow yet.