AI adoption & learning lead
Help teams change their work and measure the result.
The outcome this role owns
People can use AI appropriately in recurring work, explain when to stop and demonstrate a useful change in the workflow.
Four levels of maturity
Proposed framework · Evidence-based discussion · Not a validated assessment scale
Foundation
Identifies a recurring task and demonstrates appropriate AI use with human review.
Evidence to look for
A before-and-after workflow example
Pilot
Runs a small learning pilot with practice tasks, baseline measures and manager involvement.
Evidence to look for
Practice results with a baseline and review criteria
Production
Maintains role-specific learning and support, measures sustained use and checks work quality.
Evidence to look for
Follow-up evidence of sustained use and work quality
Organisational
Coordinates adoption across functions and revises the programme when work patterns or outcomes fail to improve.
Evidence to look for
A programme change prompted by observed results
A practical work sample
A team has attended training but rarely uses AI. Ask the candidate to diagnose the workflow, design one practice task and choose what to measure after four weeks.
Use synthetic or public data, explain the evaluation criteria in advance and allow reasonable adjustments. Discuss the reasoning as well as the finished artifact.
What to question
Training attendance or licence activation is reported as proof that work improved.
Related market signal: ai workflow automation
AI workflow automation is a signal of use in business processes. It is not a measure of change-management capability or successful adoption.
Highest observed shares among covered markets, 2025. These are context, not recommended hiring locations.
Eurostat source and definitions ↗
Framework sources
This guide is an original synthesis drawing on the sources below. The sources do not publish or endorse these role levels.