Film and Video Editors: daily tasks & AI impact
This page breaks Film and Video Editors into the top 15 daily O*NET tasks and scores each for Generative AI automation probability. Those task scores roll up to an overall vulnerability index of 56/100 (Moderate automation risk). Struck-through rows are ≥80% automatable; highlighted safer rows are under 30% or primarily physical.
High-automation exposure
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Durable human work
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.
| Task | Importance | AI probability | Nature |
|---|---|---|---|
| Organize and string together raw footage into a continuous whole according to scripts or the instructions of directors and producers. Core | 4.66 | — | — |
| Edit films and videotapes to insert music, dialogue, and sound effects, to arrange films into sequences, and to correct errors, using editing equipment. Core | 4.6 | — | — |
| Select and combine the most effective shots of each scene to form a logical and smoothly running story. Core | 4.58 | — | — |
| Review footage sequence by sequence to become familiar with it before assembling it into a final product. Core | 4.55 | — | — |
| Set up and operate computer editing systems, electronic titling systems, video switching equipment, and digital video effects units to produce a final product. Core | 4.53 | — | — |
| Trim film segments to specified lengths and reassemble segments in sequences that present stories with maximum effect. Core | 4.39 | — | — |
| Cut shot sequences to different angles at specific points in scenes, making each individual cut as fluid and seamless as possible. Core | 4.37 | — | — |
| Review assembled films or edited videotapes on screens or monitors to determine if corrections are necessary. Core | 4.37 | — | — |
| Determine the specific audio and visual effects and music necessary to complete films. Supplemental | 4.36 | — | — |
| Mark frames where a particular shot or piece of sound is to begin or end. Supplemental | 4.31 | — | — |
| Verify key numbers and time codes on materials. Core | 4.16 | — | — |
| Manipulate plot, score, sound, and graphics to make the parts into a continuous whole, working closely with people in audio, visual, music, optical, or special effects departments. Core | 4.14 | — | — |
| Program computerized graphic effects. Core | 4.11 | — | — |
| Study scripts to become familiar with production concepts and requirements. Core | 3.96 | — | — |
| Supervise and coordinate activities of workers engaged in film editing, assembling, and recording activities. Core | 3.75 | — | — |
What remains human
No tasks currently fall under the safe threshold for this occupation.
FAQ
Which Film and Video Editors tasks are most exposed to AI?
No task in this set currently exceeds the 80% threshold, so risk is spread across moderate probabilities rather than a few catastrophic duties.
Which Film and Video Editors tasks are safest from Generative AI?
This profile has little “safe” task mass under our thresholds. Workers should treat AI fluency as mandatory and actively map transferable skills into lower-risk roles.
How should I read the Film and Video Editors task table?
Importance comes from O*NET incumbent ratings. Automation probability is model-generated for current Generative AI capability (not speculative AGI). Digital / physical / mixed labels describe the modality of the work, which strongly correlates with near-term automation potential.