Craftsmanship (leather goods, watchmaking, jewellery)
ISCO-08 7318, 7311, 7313Leather goods craftsperson, Watchmaker, Jeweller setter, Polisher, Prototype maker, Workshop team leader
Headcount need, in FTE
Target AI skills
level 1 to 4Job 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.
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.
Advanced settings modified
2026 2036
The AI Cookbook 2026
albert's guide to cut through the noise around generative AI and turn it into workforce decisions.
- 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

Run these scenarios on your actual workforce
AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.
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.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI