Maintenance
ISCO-08 7233, 7412, 3115Maintenance technician, Electromechanical technician, Industrial electrician, Automation technician, Maintenance planner, Maintenance team leader
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Interpreting predictive maintenance alerts
- Automation and PLC programming
- Diagnosis on complex connected equipment
- Reliability analysis and failure root causes
Losing value
- Writing intervention reports by hand
- Searching technical manuals and schematics
- Fixed-interval preventive rounds
How the job will change
Repairing a gearbox, replacing a motor or rewiring a cabinet remains hands-on work that AI does not touch. The change is in the work around it. Assistants search manuals, schematics and past work orders to suggest likely causes of a failure; sensors and models flag drifting equipment before it breaks; intervention reports are dictated and entered automatically in the maintenance system. Planning becomes more condition-based.
The job moves towards diagnosis and reliability. Technicians intervene more often on alerts and less on fixed schedules, and they must judge whether a predictive signal is credible. Automation, electronics and connected equipment gain weight against pure mechanics. A good technician tomorrow combines hands-on mastery with the ability to read data and to challenge a diagnosis suggested by a tool.
- 2026-2027
- Troubleshooting assistants and dictated work orders in the maintenance system.
- 2028-2030
- Predictive maintenance widespread on critical equipment; fewer fixed-interval rounds.
- 2031+
- Technicians focus on complex diagnosis, automation and reliability engineering.
The main risk is shortage rather than surplus: skilled maintenance technicians are hard to recruit and many are nearing retirement. AI tools will not make up for missing qualified hands on site. 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