Film and video editors and camera operators: AI exposure and career outlook
Film and video editors and camera operators (SOC 27-4030) sit at the 67th percentile for measured AI exposure among the 342 occupations tracked here, measured from a composite of Microsoft Research and Anthropic Economic Index telemetry. An estimated 39% of tasks are already automated and 63% are being reshaped rather than replaced — both modelled figures, not direct measurements. Of the Claude usage observed in this occupation, 42% follows an automation pattern and 58% an augmentation pattern, which places it in the Orchestrator opportunity group: AI reaches deep into this work and amplifies it. The leverage goes to whoever directs it, and that can be you. In Canada the role maps to NOC 52110 (Film and video camera operators), and ESDC's COPS 2024–2033 outlook for that unit group is balance.
Figures last updated 2026-06. Every number on this page is labelled measured or modelled; where a source has no coverage for this occupation, it says so rather than showing a zero.
Key facts
- SOC code
- 27-4030
- Canada NOC 2021
- 52110
- TEER level
- 2 — College / Apprenticeship 2+ yrs
- COPS outlook
- Balance
What the measurements actually say
Three independent studies measure AI exposure for this occupation directly. The two percentages below them are ours, modelled from those measurements.
| Metric | Value | Provenance |
|---|---|---|
| AI applicability | 16% | Measured — Microsoft Research, from 200,000 Copilot conversations classified against O*NET work activities. Occupation-group average of 2 detailed roles. |
| Observed AI usage | 19% | Measured — Anthropic Economic Index, share of tasks observed being performed with Claude (CC-BY 4.0). Occupation-group average of 2 detailed roles. |
| Academic AI exposure | 58th percentile | Measured — Felten-Raj-Seamans AIOE index across 774 occupations; the index underlying Statistics Canada's Canadian estimates. Occupation-group average of 2 detailed roles. |
| Automation-pattern usage | 42% | Measured — the share of observed Claude usage in this occupation where the task is handed over rather than iterated on (Anthropic Economic Index). Occupation-group average of 2 detailed roles. |
| Augmentation-pattern usage | 58% | Measured — the complement of the row above; the two sum to 100% by construction. This is the pattern where the person stays in the loop. |
| Estimated task automation (modelled) | 39% | Modelled from this occupation's measured telemetry composite, mapped through anchors calibrated to Anthropic's published aggregate findings. Our estimate, not per-occupation telemetry. |
| Estimated task reshaping (modelled) | 63% | Share of tasks where AI acts as co-pilot rather than replacement. Modelled from the measured telemetry composite, calibrated to BCG's published aggregate reshaping rates. Our estimate. |
AI is transforming how this work is done. Professionals who adapt their workflows will thrive; those who don't face increasing competition.
In Canada: NOC 52110
Film and video editors and camera operators map to NOC 52110 — Film and video camera operators at TEER 2 (College / Apprenticeship 2+ yrs). ESDC's COPS 2024–2033 projection for this unit group is Balance.
Mapped via Statistics Canada's official SOC 2018 → NOC 2016 → NOC 2021 correspondence tables (closest match of several correspondence candidates). Several candidates were available, and this one matched the occupation title on every word — no other did — so the TEER level read off its second digit is reported.
What this job actually involves
These are the O*NET task statements for Film and video editors and camera operators— the work itself, as reported by people doing the job — grouped by the work activity each task belongs to. Showing 12 of 43, core tasks first.
The exposure figures above are for the occupation as a whole. We do not have a per-task measurement: no published dataset tells us which of these specific tasks AI is doing. Read this as what the job is made of, not as a ranking of what is at risk.
5 of these 12statements do carry one measured signal: Anthropic publishes, per O*NET task, how much of the observed Claude usage on it looked like automation rather than iteration. Those are marked below. It still says how people use AI on that task, not how much of it AI can do — and the unmarked statements carry nothing rather than inheriting the occupation's figure.
Microsoft's applicability score was itself produced by classifying Copilot conversations against the O*NET work-activity catalogue — the same catalogue these groups come from. The published file gives one number per occupation, not one per activity, so we can tell you that 16% of this occupation's work activities are covered by observed AI usage, but not which ones.
SOC 27-4030 is a BLS occupation group. These statements are pooled across the 2 detailed occupations in it, so a given task may apply to only some of them.
Making Decisions and Solving Problems · 7 tasks
Compose and frame each shot, applying the technical aspects of light, lenses, film, filters, and camera settings to achieve the effects sought by directors.
Core task · Importance 4.8/5 (O*NET) · reported by 1 of 2 occupations in this group
Organize and string together raw footage into a continuous whole according to scripts or the instructions of directors and producers.
Core task · Importance 4.7/5 (O*NET) · 65% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Edit films and videotapes to insert music, dialogue, and sound effects, to arrange films into sequences, and to correct errors, using editing equipment.
Core task · Importance 4.6/5 (O*NET) · 60% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Select and combine the most effective shots of each scene to form a logical and smoothly running story.
Core task · Importance 4.6/5 (O*NET) · 39% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Set up and operate computer editing systems, electronic titling systems, video switching equipment, and digital video effects units to produce a final product.
Core task · Importance 4.5/5 (O*NET) · 64% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Trim film segments to specified lengths and reassemble segments in sequences that present stories with maximum effect.
Core task · Importance 4.4/5 (O*NET) · 58% of observed AI use on this task is automation-pattern · reported by 1 of 2 occupations in this group
Cut shot sequences to different angles at specific points in scenes, making each individual cut as fluid and seamless as possible.
Core task · Importance 4.4/5 (O*NET) · reported by 1 of 2 occupations in this group
Controlling Machines and Processes · 2 tasks
Operate television or motion picture cameras to record scenes for television broadcasts, advertising, or motion pictures.
Core task · Importance 4.7/5 (O*NET) · reported by 1 of 2 occupations in this group
Operate zoom lenses, changing images according to specifications and rehearsal instructions.
Core task · Importance 4.4/5 (O*NET) · reported by 1 of 2 occupations in this group
Handling and Moving Objects · 1 task
Adjust positions and controls of cameras, printers, and related equipment to change focus, exposure, and lighting.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 2 occupations in this group
Communicating with Supervisors, Peers, or Subordinates · 1 task
Confer with directors, sound and lighting technicians, electricians, and other crew members to discuss assignments and determine filming sequences, desired effects, camera movements, and lighting requirements.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 2 occupations in this group
Coordinating the Work and Activities of Others · 1 task
Review footage sequence by sequence to become familiar with it before assembling it into a final product.
Core task · Importance 4.5/5 (O*NET) · reported by 1 of 2 occupations in this group
Task statements and work activities from the O*NET Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA), used under CC BY 4.0. O*NET® is a trademark of USDOL/ETA. USDOL/ETA has not reviewed or approved this material.
That's the average. Is this you?
Everything above describes Film and video editors and camera operators as a whole. Three questions, and nothing is stored or sent.
Where are you in your career?
What does your week mostly look like?
Is your employer deploying AI?
Where the leverage is
Upskill in AI-driven automation (e.g., Make, Zapier) and generative tools in your domain. The opportunity is to pivot from execution to directing AI-augmented workflows.
AI is transforming tasks rather than replacing entire jobs. Routine scheduling and communication automate, but human decision-making remains critical.
Master workflow automation platforms (e.g., Make, Zapier, CRM integrations) to double your output and position for fractional leadership roles.
This guidance is written per exposure band — three texts across all 342 occupations, framed on the ILO's transformation-versus-displacement distinction. It is editorial guidance for the reshaping band, not an occupation-specific research finding about film and video editors and camera operators.
Sources for this occupation
Only the sources that hold data for film and video editors and camera operators are listed. A study that does not cover this occupation is not cited here.
- Microsoft Research — Working with AI (Tomlinson et al., arXiv:2507.07935): Measured AI applicability, from 200,000 anonymised Copilot conversations classified against O*NET work activities.
- Anthropic Economic Index: Measured observed AI usage per occupation, from Anthropic's open per-SOC dataset (CC-BY 4.0).
- Felten, Raj & Seamans — AI Occupational Exposure index: Measured academic exposure percentile; the index underlying Statistics Canada's Canadian AI-exposure estimates (Mehdi & Morissette, 2024).
- O*NET Database 31.0 (USDOL/ETA), CC BY 4.0: The occupation's task statements and work activities, verbatim. Joined on SOC 2018 with no crosswalk — O*NET-SOC is built on SOC — and carrying no claim about which tasks AI touches.
- Anthropic Economic Index — collaboration split (release 2026-06-26, CC-BY 4.0): Measured share of observed Claude usage following an automation rather than an augmentation pattern. This is the second axis of the grouping above.
- Statistics Canada NOC 2021 concordance: Official SOC 2018 → NOC 2016 V1.3 → NOC 2021 V1.0 correspondence tables. TEER is the second digit of the resolved code, so we report it only where the concordance gave a single candidate.
- ESDC COPS 2024–2033: Projected labour-market assessment per NOC 2021 unit group, from the Canadian Occupational Projection System open dataset.
- BCG — AI Will Reshape More Jobs Than It Replaces (April 3, 2026): Published aggregate reshaping rates, used to calibrate our modelled percentages. The per-occupation figures here are our estimates, not BCG's data.
Related occupations
Other roles in Creative & Media.
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