What will AI change in your workforce?

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

Testing, laboratory and certification

ISCO-08 3113, 3119

Test engineer, Test technician, Laboratory technician, Certification engineer, Approvals manager, Test laboratory manager

AI exposure26%

Headcount need, in FTE

Exposure range
FTE
Need in 203095 FTE-5 FTE (-5%)
Need in 203590 FTE-10 FTE (-10%)
10080604020262028203020322035
How AI is used on these tasks todayIn AI conversations about this job's tasks: the AI does the task itself (automation) or helps the person do it (augmentation).Anthropic Economic Index, April and May 2026. Occupations: Electrical and Electronic Engineering Technologists and Technicians, Inspectors, Testers, Sorters, Samplers, and Weighers. AEI
Average automation potential of the job's tasks. Range for this family: 21 to 32%.ILO occupations used: Electrical Engineering Technicians (27%), Physical and Engineering Science Technicians Not Elsewhere Classified (26%). 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 40%: Test campaigns are set by standards and product launches; time saved on reporting is partly reinvested in pre-compliance testing during development.

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
Applied
Rethink one's process with AI
Analyse data with AI
Produce content with AI

Job 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.
Watch out

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.

Seniors available in 2036-5%2 FTE short
Gap above 5% from2036
Your seniority mix today
Mid-level (3 to 10 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
Juniors25%Mid-level42%Seniors28%

2026 2036

Free guide

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
Download the AI Cookbook
The AI Cookbook 2026, albert

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.

Book a call

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

Add the job families you want to compare. Your PDF report covers every family in your simulation, with the assumptions you saved.

albert

Get your AI impact report

A PDF built from the families and assumptions you set. It covers every family you adjusted.

Your settings are saved with your request, so whoever calls you starts from your own assumptions.