Gini Loh System Tools

AI Automation Index

Will AI Replace Motion Picture Projectionists?

Task-level automation exposure for Motion Picture Projectionists: 17 O*NET tasks scored against their work activities, plus the simulator.

E-E-A-T Verified
Developed & audited by Gini Loh

Automation Exposure

26% weighted task index

17 of 17 tasks scored

Median Salary

$38,270 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

Low

1,480 people hold this job — size, not growth

Projected 2025–35

-6.2% employment change

300 openings a year · BLS line item

Pay by state

BLS OEWS publishes 12 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$22,750 – $82,260

Highest medians

New York $82,260
Illinois $78,990
California $74,840
Washington $39,070
Michigan $37,590

Lowest medians

Tennessee $22,750
Iowa $23,210
Missouri $29,140
Pennsylvania $29,850
Texas $31,160

State figures come from the 39-3021 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 26%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 69%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 7%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 4%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 9/100 Low

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

17 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 12%

2 tasks

Medium exposure (≥ 35%) 8%

1 tasks

Low exposure (below 35%) 80%

14 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Emerging & rewritten tasks

Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.

  • New 08/2020 · Incumbent

    Download digital keys to unlock digital movie files.

  • New 08/2020 · Incumbent

    Ingest digital content, such as films, advertisements, or trailers, into servers or projectors.

Constituent Tasks

#8030 · Core · 45% automatable

Monitor operations to ensure that standards for sound and image projection quality are met.

decision support — AI flags quality/safety issues; human owns physical verification

Monitor operational quality or safety.
#8029 · Core · 20% automatable

Start projectors and open shutters to project images onto screens.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#8040 · Core · 72% automatable

Open and close facilities according to rules and schedules.

data analysis — AI optimizes schedules; human handles exceptions

Arrange facility schedules.
#8031 · Core · 20% automatable

Operate equipment to show films in a number of theaters simultaneously.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#21201 · Core · 15% automatable

Perform regular maintenance tasks, such as rotating or replacing xenon bulbs, cleaning projectors and lenses, lubricating machinery, and keeping electrical contacts clean and tight.

physical manipulation — Basic equipment maintenance requires hands-on work

Perform basic equipment maintenance.
#8034 · Core · 20% automatable

Set up and adjust picture projectors and screens to achieve proper size, illumination, and focus of images, and proper volume and tone of sound.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#8035 · Core · 20% automatable

Inspect projection equipment prior to operation to ensure proper working order.

visual inspection — Hands-on verification of equipment functioning

Inspect equipment to ensure proper functioning.
#8039 · Core · 15% automatable

Perform minor repairs, such as replacing worn sprockets, or notify maintenance personnel of the need for major repairs.

physical manipulation — Basic equipment maintenance requires hands-on work

Perform basic equipment maintenance.
#8042 · Core · 20% automatable

Set up and inspect curtain and screen controls.

visual inspection — Hands-on verification of equipment functioning

Inspect equipment to ensure proper functioning. Operate audio-visual equipment.
#8046 · Core · 20% automatable

Coordinate equipment operation with presentation of supplemental material, such as music, oral commentaries, or sound effects.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#21202 · Core · 5% automatable

Clean the projection booth.

physical manipulation — Hands-on cleaning of work areas

Clean work areas.
#8033 · Supplemental · 20% automatable

Inspect movie films to ensure that they are complete and in good condition.

visual inspection — Hands-on verification of equipment functioning

Inspect equipment to ensure proper functioning.
#8044 · Supplemental · 15% automatable

Remove full take-up reels and run film through rewinding machines to rewind projected films so they may be shown again.

physical manipulation — AV equipment physically operated and adjusted

Prepare film for distribution or use. Operate audio-visual equipment.
#8047 · Supplemental · 20% automatable

Install and connect auxiliary equipment, such as microphones, amplifiers, disc playback machines, and lights.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#8041 · Supplemental · 20% automatable

Observe projector operation to anticipate need to transfer operations from one projector to another.

physical manipulation — AV equipment physically operated and adjusted

Operate audio-visual equipment.
#8048 · Supplemental · 80% automatable

Prepare film inspection reports, attendance sheets, and log books.

text generation — Prepare operational reports from data

Prepare operational reports or records.
#8032 · Supplemental · 10% automatable

Splice separate film reels, advertisements, and movie trailers together to form a feature-length presentation on one continuous reel.

physical manipulation — Handling and preparing film physically

Prepare film for distribution or use.
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Community Sentiment

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Method

How this was scored

  1. 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
  2. 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
  3. 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
  4. 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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