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AI exposure report

Will AI replace Museum Technicians and Conservators?

Some of your tasks will be automated. People who use AI will have the edge.

Task exposure to today's AI

30/100

Moderate exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Museum Technicians and Conservators?

Not entirely. Today's AI tools can take on about 30% of the typical work in this occupation, mostly: enter information about museum collections into computer databases. The parts that stay human include: Repair, restore, and reassemble artifacts, designing and fabricating missing or broken parts, to restore them to their original appearance and prevent deterioration.; Install, arrange, assemble, and prepare artifacts for exhibition, ensuring the artifacts' safety, reporting their status and condition, and identifying and correcting any problems with the set up.; Clean objects, such as paper, textiles, wood, metal, glass, rock, pottery, and furniture, using cleansers, solvents, soap solutions, and polishes..

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable
Enter information about museum collections into computer databases. 68%
Coordinate exhibit installations, assisting with design, constructing displays, dioramas, display cases, and models, and ensuring the availability of necessary materials. 38%
Install, arrange, assemble, and prepare artifacts for exhibition, ensuring the artifacts' safety, reporting their status and condition, and identifying and correcting any problems with the set up. 15%
Prepare artifacts for storage and shipping. 12%
Repair, restore, and reassemble artifacts, designing and fabricating missing or broken parts, to restore them to their original appearance and prevent deterioration. 10%
Clean objects, such as paper, textiles, wood, metal, glass, rock, pottery, and furniture, using cleansers, solvents, soap solutions, and polishes. 10%

+ 18 more tasks (46% of the week) in the full test.

Your human edge

  • Repair, restore, and reassemble artifacts, designing and fabricating missing or broken parts, to restore them to their original appearance and prevent deterioration.
  • Install, arrange, assemble, and prepare artifacts for exhibition, ensuring the artifacts' safety, reporting their status and condition, and identifying and correcting any problems with the set up.
  • Clean objects, such as paper, textiles, wood, metal, glass, rock, pottery, and furniture, using cleansers, solvents, soap solutions, and polishes.

AI tools to try

ChatGPT / Claude for lesson materialsAI quiz and feedback toolsPresentation and image generators

Related roles

A 3-phase AI learning plan

Phase 1

Materials and preparation

  • Lesson plans, tasks and differentiated materials with AI.
  • Quizzes and answer keys.
  • Check facts and age-appropriateness yourself.

Project

Use AI to draft a gallery-talk outline, condition-report template and a 10-question visitor quiz with answer key for one upcoming exhibit, then verify every date and material claim against collection files.

Phase 2

Feedback and assessment

  • Rubrics and feedback drafts with AI.
  • Spotting common mistakes across student work.
  • Rules for student AI use and academic honesty.

Project

Build an AI-assisted scoring rubric for condition reports, run it over 10 of your own past reports to spot recurring gaps, and publish a one-page rule on AI use with no object or donor data entered.

Phase 3

Teaching with AI

  • Teach students to use AI critically.
  • Assignments that work with AI, not against it.
  • Share materials and practice with colleagues.

Project

Run a 90-minute workshop teaching two volunteers or interns to use AI critically for cataloguing an object batch, then compare their AI-drafted database entries against your own and log error rates.
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