An annual AI writing workflow review should determine whether the workflow still serves a defined use case, protects its inputs, supports human verification, and produces evidence that claims and disclosures are accurate. Review sooner after a model, vendor, data source, policy, audience, or risk level changes. The goal is controlled use, not a ceremonial approval of every output.
Map the real workflow
Document where a request begins, which people and systems can add instructions, what source material is provided, which model or service is used, and where drafts are stored or published. Include browser extensions, integrations, shared prompt libraries, retrieval tools, and manual copy-and-paste steps. Name the owner for inputs, factual review, final approval, incidents, and vendor management.
Classify each use case. A brainstorming draft, a customer-facing instruction, and a regulated filing do not have the same review needs. NIST's AI Risk Management Framework is a voluntary framework for managing AI risks, while its AI RMF Playbook organizes suggested actions under Govern, Map, Measure, and Manage. Use those functions to structure questions, not to claim that a checklist creates compliance or certifies a tool.
Review inputs, sources, and confidentiality
List what users are allowed to submit and what must stay out of the system. Check contractual, privacy, security, copyright, and retention requirements before uploading customer records, unpublished material, credentials, or other restricted information. Review the vendor's current data-use, retention, deletion, training, and access terms, and record anything that could not be verified.
The Federal Trade Commission's privacy and confidentiality reminder for AI companies emphasizes that companies must honor their privacy and confidentiality commitments. A workflow owner should therefore compare actual handling with promises made to users and customers rather than relying on a generic statement that data is safe.
Sample outputs and verify claims
Select representative outputs, including an ordinary case, a difficult case, a sensitive case, and a known failure. Trace factual statements to cited sources. Check whether quotations, dates, calculations, links, and product claims survive independent verification. Review for unsupported certainty, missing limitations, misleading attribution, hidden promotion, discriminatory effects, and instructions that could cause harm if wrong.
Record the sample, expected standard, reviewer, findings, and correction. Do not score only fluency. A polished paragraph can still be fabricated, outdated, or unsuitable for its audience. Require a named human approval before publishing material whose error could materially affect another person.
Compare the current model and settings with the version last approved. Re-run a small fixed set of test prompts so changes in accuracy, refusal behavior, formatting, or source use become visible. Preserve the test inputs, expected characteristics, actual outputs, and reviewer notes. This is a change detector, not a claim that the sample predicts every future result.
Test controls, incidents, and continuity
Verify account access, role changes, logging, deletion requests, suppression rules, and escalation paths. Review any incident, near miss, complaint, retraction, or confidentiality concern from the year. Confirm that the team can identify affected outputs and stop publication when a source or model behaves unexpectedly.
Test the exit plan. Export essential templates and workflow records in a usable format, preserve source provenance, and define a manual fallback. Compare costs, latency, error rates, editing time, acceptance rates, and corrections against the original purpose. If the workflow creates more verification work than value, narrow or retire it.
Close the review with approved use cases, prohibited inputs, evidence, unresolved risks, owners, and due dates. The result should let a new reviewer understand what the system does, where judgment remains human, how errors are detected, and what happens when the service changes.
Sources
- AI Risk Management Framework — National Institute of Standards and Technology
- AI RMF Playbook — NIST AI Resource Center
- AI Companies: Uphold Your Privacy and Confidentiality Commitments — Federal Trade Commission, Jan. 9, 2024