What will AI change in your workforce?

Your company's stance on AI(applies to every family: sets the adoption speed)

Craftsmanship (leather goods, watchmaking, jewellery)

ISCO-08 7318, 7311, 7313

Leather goods craftsperson, Watchmaker, Jeweller setter, Polisher, Prototype maker, Workshop team leader

AI exposure17%

Headcount need, in FTE

Exposure range
FTE
Need in 203099 FTE-1 FTE (-1%)
Need in 203598 FTE-2 FTE (-2%)
10080604020262028203020322035
How AI is used on these tasks todayNot enough observed usage on this job's tasks to publish a figure.Anthropic Economic Index, April and May 2026. Occupations: Shoe and Leather Workers and Repairers, Jewelers and Precious Stone and Metal Workers. AEI
Average automation potential of the job's tasks. Range for this family: 10 to 29%.ILO occupations used: Handicraft Workers in Textile, Leather and Related Materials (13%), Precision-instrument Makers and Repairers (21%), Jewellery and Precious Metal Workers (18%). ILO data
Your call: how much of this potential do you want to capture? Nobody can set it for you.
3. Adoption speed midpoint 2030
Inherits your company stance, adjustable for this family.
Default 10%: AI touches only the paperwork around the bench; output is capped by hand skills and demand for craft hours keeps rising.

Target AI skills

level 1 to 4
Foundation
Use AI assistants every day
Frame and phrase a request
Check and challenge AI outputs
Protect data and respect the rules

Job skills

Growing in value

  • Passing on gestures to apprentices
  • Quality control on complex references
  • Feedback to designers on manufacturability
  • Versatility across several product families

Losing value

  • Filling in paper production logs
  • Searching technical files by hand
  • Visual defect sorting on standard parts

How the job will change

Cutting, stitching, assembling a movement or setting a stone remain manual and AI does not change them. What changes is around the bench: digital technical sheets, assisted search in assembly instructions, vision systems that flag a defect on a skin or a setting, and automatic traceability of each piece.

The job stays defined by the hand and the eye. A good craftsperson tomorrow will still be judged on precision and consistency, but also on the ability to train newcomers quickly, to use the visual inspection tools without depending on them, and to give designers precise feedback on what can be made.

2026-2027
Digital technical sheets and assisted search in workshop documentation.
2028-2030
Vision-based quality checks on leather and settings become common.
2031+
Gestures unchanged; documentation and traceability fully digital.
Watch out

The main risk is scarcity: training a craftsperson takes years and many experienced workers will retire. Workshop capacity depends on apprenticeship schools and tutor time. See the seniority outlook below.

What if you hired fewer juniors?

Your 2036 seniors are the juniors you hire today.

Seniors available in 2036-2%1 FTE short
Gap above 5% fromnone
Your seniority mix today
Mid-level (4 to 12 yrs in the profession, the remainder)45%
For
Advanced settings
If every company makes the same bet, senior profiles will be scarce and expensive.
80601002026203020342040Senior need (held stable)Seniors available
Seniority mix, % of today's headcount
Juniors19%Mid-level42%Seniors34%

2026 2036

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  • What AI actually changes in jobs and skills
  • Why the junior pipeline matters more than most companies realise
  • How to integrate AI into workforce planning
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How the numbers are built

Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.

Public researchExposureAverage automation potential of the job's tasks, from the ILO's 2025 task-level scores.
Your decisionStrategic ceilingHow much of that potential you choose to capture. Your decision, not ours.
Your companyAdoption speedHow fast your company moves, set once for the whole company.
Job family defaultConversion to headcountHow much of the productivity gain becomes fewer people rather than more output.
Headcount effect

My simulation

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