Why post-launch review matters
Most AI workflow risk appears after the first real users touch it: edge cases, missing source data, unclear ownership, staff workarounds, customer-facing drafts that need more editing than expected, and tools that save time only when volume is high. A short review loop prevents quiet workflow drift.
STOP AUTOMATION: do not let AI declare a workflow successful, expand access, change customer-facing wording, reduce human review, or invent ROI. A named owner must verify real examples, incidents, review effort, customer impact, and rollback triggers first.
Post-launch review card
| Field | What to capture | Owner review rule |
|---|---|---|
| Workflow and launch date | Name, use case, connected tools, launch date, reviewer, users, and allowed data. | Review one workflow at a time; do not blend results across unrelated prompts or tools. |
| Sampled outputs | At least 10 recent outputs or all outputs if volume is low, with links/screenshots/source notes. | Use real examples. Do not let AI summarize examples that were never inspected. |
| Accuracy and source control | Wrong facts, missing context, unsupported claims, stale data, tone misses, or edits needed before use. | Any invented customer fact, price, policy, deadline, approval, or guarantee requires a fix/pause decision. |
| Review effort | Average minutes to review/edit each output, who reviews it, and bottlenecks created. | If review effort is higher than the old workflow, mark it fix/pause until scoped down. |
| Incidents and complaints | Customer confusion, staff workaround, wrong routing, privacy concern, missed handoff, or correction sent. | Incidents need owner notes and a prevention step before expansion. |
| Keep/fix/pause/expand decision | Decision, evidence, next owner, due date, rollback trigger, and next review date. | No expansion without a named owner, sampled-output proof, and a rollback path. |
Copy/paste review snippets
Post-launch review note: We sampled [number] AI workflow outputs from [date range]. Keep/fix/pause decision: [decision]. Main issue: [issue]. Owner: [person]. Next review date: [date]. Expansion allowed? [yes/no].Fix request: Update [prompt/workflow/tool setting] because reviewed outputs showed [problem]. Do not expand this workflow until [owner] verifies [sample size] new outputs and confirms the rollback trigger is documented.Pause notice: We are pausing [workflow] because [incident/review effort/privacy/source issue]. Return to [manual fallback] until [owner] completes root-cause review and approves restart criteria.AI review prompt
Act as a cautious small-business AI workflow reviewer. Use only the verified post-launch facts below. Do not invent ROI, time savings, customer outcomes, incident history, approval status, legal advice, or performance metrics. Return: 1) missing evidence, 2) source/accuracy risks, 3) review-effort risks, 4) customer/privacy risks, 5) recommended keep/fix/pause/expand decision, and 6) STOP AUTOMATION triggers that require owner review before expansion.
Verified facts:
- Workflow name and launch date:
- Intended use case:
- Users/reviewers:
- Allowed source data:
- Sampled outputs and notes:
- Review/edit time observed:
- Incidents/complaints/corrections:
- Customer-facing impact:
- Manual fallback:
- Proposed next decision:Fast QA before expanding the workflow
- Sample real outputs from the live workflow, not demo prompts.
- Check whether human review effort is lower, equal, or higher than expected.
- Separate one-time setup mistakes from repeatable workflow risk.
- Block expansion if the workflow invented facts, changed policy wording, mishandled customer data, or created missed handoffs.
- Write the rollback trigger before adding more users, channels, or automations.
Related free assets: AI Workflow Test Case Checklist, AI Workflow Weekly Review Agenda, and AI Output Acceptance Checklist.
Disclosure: Horizon Flow is Andrew Burton's digital product catalog. This worksheet is useful without purchase; product links are labeled and UTM-tagged.