Testing, laboratory and certification
ISCO-08 3113, 3119Test engineer, Test technician, Laboratory technician, Certification engineer, Approvals manager, Test laboratory manager
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Test planning against IEC and UL standards
- Failure analysis after destructive tests
- Pre-compliance testing during development
- Relations with certification bodies
- Test bench automation
Losing value
- Writing standard test reports
- Manual recording of measurements
- Compiling certification files
How the job will change
Most of a test technician's work stays physical: preparing samples, wiring benches, running short-circuit, temperature-rise or endurance tests, and observing what happens. AI changes the work around the bench. It extracts measurements, flags anomalies in curves, drafts test reports and assembles certification files from the results. These tasks take real time today, but they sit at the edge of the job.
The job moves towards failure analysis and earlier pre-compliance tests during development, when fixing a design is still cheap. A good test professional tomorrow will know the standards in depth, explain a failure clearly to a designer, and keep the trust of certification bodies, who look closely at any AI-produced document in a certification file.
- 2026-2027
- Automatic extraction of measurements and first drafts of test reports.
- 2028-2030
- Certification files assembled automatically, test benches more automated.
- 2031+
- Stable lab teams, shifted towards failure analysis and pre-compliance.
Many experienced test technicians are close to retirement, and their knowledge of standards and bench behaviour is rarely written down. AI will not replace that tacit knowledge, so its transfer must be organised before they leave. 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