AI-generated video needs client review even more than filmed footage - because generative cuts fail in frame-specific ways: a hand with six fingers at 0:12, text that melts at 0:31, a background that reshapes between two frames of the same shot. The good news is that the review workflow you already run - versions, frame-accurate notes, recorded approvals - is exactly the right instrument. This guide is how to point it at AI-generated material, plus what to check before any client ever sees a generated frame.
Why AI-generated cuts need a stricter review
Filmed footage fails in ways humans have trained eyes for: bad exposure, soft focus, poor sound. Generated footage adds failure modes nobody's eye defaults to catching - anatomical drift, object permanence errors, text rendering glitches, motion that stutters between coherence states. Two properties make these especially treacherous in client rounds:
- They are frame-local. An artifact can exist for 8 frames in the middle of an otherwise clean shot. Scrubbing past it at normal speed hides it; your client finds it on their third watch, at home, on a big screen.
- They are version-local. A regeneration fixes the artifact and changes three other things. Without a version stack, nobody can say which take is which.
Pre-client checks before any AI cut goes out
The artifact checklist
- Hands and faces - the classic generators' tell; scrub every close-up frame by frame.
- Text and logos - generated text degrades at scale; replace with real typography in the edit.
- Edge continuity - backgrounds reshaping between frames, objects appearing or vanishing at cut points.
- Motion coherence - limbs and cameras that momentarily stop obeying physics.
- Audio sync - generated voice against generated mouths is a compound risk.
- Provenance - note which shots are generated, for your records and your client's.
Frame-by-frame scrubbing is not optional in this workflow - it is the AI-generation equivalent of QC dailies. The review tool's frame stepping exists for exactly this pass.
The review workflow, step by step
- Generate and curate internally. Produce multiple takes; pick the survivors. Clients should see your curation, not your generation backlog.
- Upload the curated cut as a version. Regenerations become the next version of the same deliverable - never a new file with a new name.
- Send a watermarked review link. For unreleased generated material, the watermark layer matters doubly: it labels the preview and keeps the frame attributable if it travels.
- Collect notes at the exact frame. Artifact reports are inherently frame-local ("at 0:41, her hand is wrong") - pinned comments are the only format that captures them usefully.
- Fix and version. Regenerate the offending shots, upload the next version, and let the client verify their specific note against the new cut.
- Record the approval. Same rule as filmed work: a named person approves a named version at a recorded time.
| Filmed footage review | AI-generated review | |
|---|---|---|
| Typical failures | Pacing, coverage, grade | Frame-local artifacts, continuity errors |
| Note density | Clustered at scene level | Scattered at single frames |
| Fix path | Re-edit or reshoot | Regenerate the shot, version everything |
| Pre-review pass | Technical QC | Frame-by-frame artifact scrub |
| Constant | Frame-accurate notes, versions, recorded approval | The same |
What to tell clients about AI usage
Disclosure is becoming a professional norm, and it is simpler than it feels: "the concept b-roll is AI-generated and graded to match; the interview footage is camera-original." Clients who know what they are reviewing give better notes - a stakeholder who assumes everything was shot treats an artifact as sloppiness, while one who knows it is generated treats it as a take to replace. Watermarked previews help here too: they signal an in-progress cut that is expected to carry rough edges.
Doing it in one workspace
The efficiency case for running generation and review in the same place is version hygiene: when the generated cut, its artifact notes, and its regenerations live in one project stack, the question "which take fixed the 0:41 hand" has an answer. Frooty's AI generation studio (beta) sits next to the review workflow for exactly this reason, and the agent flows in the MCP guide stage B-roll candidates for the fix-it round. Related reading: why watermark reviews and review best practices. Product pages: AI creation and review and approvals.
FAQ
Do AI-generated videos still need client review?
More, not less. Generation removes shooting constraints but adds frame-local artifacts that only careful review catches. The review round is where generated material becomes client-ready material.
What should I check before sending AI video to a client?
The artifact checklist: hands and faces frame by frame, generated text and logos, edge continuity between frames, motion coherence, and audio sync. Your curation is part of the deliverable - never send a client your raw generation output.
How do clients give feedback on AI-generated video?
The same way as on filmed cuts - frame-accurate comments on a review link - with one difference: notes tend to be frame-local artifact reports, which pinned comments capture precisely and email cannot.
Should I disclose AI use to clients?
Yes. A one-line disclosure of which shots are generated produces better notes, manages expectations, and is quickly becoming the professional norm as content credentials spread.