AI learning
AI learning and enablement
Role-based pathways that move people from knowing AI exists to using it safely and meaningfully in their actual work.
From AI literacy to AI capability.
Training shouldn't end when somebody finishes an e-learning module. AI learning works as a pathway that runs into people's actual work, their team and the organisation.
- Completion is not adoption.
- Knowledge is not behaviour.
- Tool access is not capability.
- AwareWhat is changing?
- UnderstandWhat can AI do, and what can't it do?
- DiscoverWhere could it help in my work?
- PractiseCan I use it safely on realistic tasks?
- ApplyHow does this become part of my workflow?
- AdaptWhat changes about my role, process or team?
- ScaleHow does the organisation learn from successful use?
One AI rollout does not create one adoption journey.
Research and UX methods show how differently people meet the same tool. A developer, an administrator, a product manager and a senior leader shouldn't get identical AI enablement.
Enthusiastic experimenter
Already using AI daily, sometimes outside approved tools. Needs clear boundaries and a way to share what works.
Cautious domain expert
High standards, deep knowledge, unconvinced AI can meet them. Needs evidence on their own tasks.
Overwhelmed administrator
Time-poor and process-heavy, promised that AI will save time. Needs concrete workflows, not another course.
Sceptical manager
Asked to champion something they haven't seen work. Needs a clear picture of what changes for the team.
AI-enabled developer
Fluent with the tools and often moving faster than governance. Needs shared practice and clear guardrails.
Governance stakeholder
Accountable for risk. Needs rules that translate into decisions people can actually make.
These are illustrative patterns, not universal personas. The personas that matter emerge from research inside your organisation.
Different people may need different learning, communication, support, interfaces, governance, incentives and workflows.