After-sales and repair workshops
ISCO-08 7311, 7313After-sales watchmaker, Repair technician, Leather goods repairer, Jewellery repair craftsperson, After-sales service manager, After-sales advisor
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Repairing older and discontinued references
- Diagnosis on complex movements
- Explaining repairs and quotes to clients
- Restoration for the resale market
Losing value
- Writing repair quotes by hand
- Looking up parts in technical manuals
- Manual tracking of repair status
How the job will change
Opening a case, servicing a movement or restitching a bag stays bench work. AI changes the steps around it: photo-based pre-diagnosis at the counter, quotes generated from the repair history, assisted search in technical documentation and parts catalogues, and automatic updates to clients on the progress of their repair.
The job grows with the resale and restoration markets and with older references nobody else can service. A good repair craftsperson tomorrow will master old as well as current calibres or constructions, use diagnostic tools to save time without trusting them blindly, and explain a repair clearly to a client.
- 2026-2027
- Assisted quotes and documentation search in after-sales centres.
- 2028-2030
- Photo pre-diagnosis at the counter becomes common.
- 2031+
- Bench work unchanged; demand rises with resale and restoration.
Skills on discontinued calibres and old constructions sit with a few senior repairers close to retirement. Without planned knowledge transfer, some references will become unserviceable. 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