- 01
Define the workflow learning job
Treat “ai transition” as a search job to investigate, not as proof that a SEELE feature exists. First turn the query into a repeatable sequence with explicit inputs, review points, and a reversible handoff. Write the intended audience, source owner, desired change, protected details, reviewer, and delivery condition before selecting any interface or model. That brief keeps the evaluation specific and makes an unsupported assumption visible early. For this checkpoint, preserve the rights memo, and ask the factual editor to record claim scope before the reversible handoff.
- 02
Prepare inputs for motion effects
For this topic, assemble the authorized shot, desired motion cue, protected scene elements, timing, and compositing requirements. Record where each source came from, who may use it, and what must remain unchanged. Use a small representative asset for the first pass, keep the original untouched, and define a fallback route so experimentation cannot silently become the production master. In the decision log, preserve the test fixture, and ask the delivery owner to record destination fit before the final sign-off.
- Confirm ownership, consent, and allowed reuse For a controlled test, preserve the brief version, and ask the delivery owner to record claim scope before the production checkpoint.
- Preserve an untouched source and version history For a controlled test, preserve the source ledger, and ask the workflow owner to record reversal cost before the editorial approval.
- Name the reviewer and acceptance condition For a controlled test, preserve the test fixture, and ask the source custodian to record input provenance before the production checkpoint.
- 03
Test observable controls for ai transition
A bounded evaluation should inspect motion direction, intensity, camera relationship, masks, edge behavior, temporal coherence, and reversibility. Change one meaningful variable at a time and record the date, workspace, account context, input, setting, result, and failure. Topic selection can prioritize the question, but it does not establish availability, quality, speed, licensing, or a supported SEELE workflow. For a controlled test, preserve the control log, and ask the workflow owner to record control availability before the production checkpoint.
- 04
Review evidence, safety, and policy boundaries
Treat effect names as evaluation topics, not proof of a one-click SEELE feature or guaranteed artifact-free result. Use only authorized media, separate observed behavior from marketing language, and check current first-party documentation for any product-specific claim. For third-party products, competitors, plans, models, and platform rules, attach a verification date and primary source; an absent statement is an evidence gap rather than proof of a limitation. In the decision log, preserve the authorization record, and ask the policy reviewer to record camera logic before the fallback decision.
- 05
Approve a reversible production handoff
Before advancing “ai transition”, inspect transitions and edges across frames, compare against the original shot, and retain a clean fallback plate. Document remaining manual work, unresolved evidence, destination requirements, and the person accepting the result. The handoff should preserve sources and test notes, allow correction, and avoid promises about output quality, turnaround, business performance, publishing, or access that the evidence does not support. Before moving on, preserve the authorization record, and ask the rights reviewer to record control availability before the dated decision.