Personas
Not everyone needs the same data and AI literacy. A customer service agent reading a dashboard, a marketing manager designing an AI workflow, a data engineer building the pipeline underneath and a director deciding where to invest all need something different. Our Data & AI Skills Framework splits the workforce into four personas, so a program can be aimed at real jobs instead of at everyone at once.
Behind the personas sits our Data & AI Skills Framework: 21 skill domains in four categories, with over 900 skills defined underneath them. That is what lets us assess where someone actually stands, aim a training track at the gap, and measure data and AI literacy growth instead of attendance.
Each of the four personas maps to its own skill domains and levels, which is why a track for a Data & AI User looks nothing like one for a Data & AI Specialist. Specialist tracks are built to order rather than taken from a catalog — talk to us about what your data team needs.
Three groups, three different starting points — and three different things that made it click.
At first I thought it was going to be very technical, but it became very easy to follow and actually made me realise I use AI more than I think.
Data & AI User
Learner, AI Literacy Fundamentals
The practical exercises were among the strongest parts of the programme. They turn the course from a learning experience into an enablement experience.
Data & AI Builder
Learner, Data & AI School
What sets Data Booster apart for us is that the trainings feel purpose-built for Jaarbeurs from day one, because they are. The in-depth discovery phase ensured the content connected directly to the daily reality of our people.
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Through a skill assessment at the start of the program. Job titles are a starting point, not the answer — a marketing manager in one organization is a User, in another a Builder. The assessment places people against the framework, and because it runs before day one you can group cohorts by level instead of by department.
People move. A User who starts building dashboards becomes a Builder, and Builders who move into leadership need the Leader skills. We aim a track at where someone actually works today, and reassess as the program runs.
No. Many organizations start with the User group, because it is the largest and the foundation for everything else. That scales both ways — we have built this for a 300-person company and for an academy serving a 6,000-person organization across several countries and languages. What we do advise is not to skip the leaders: teams read quickly whether data and AI are genuinely part of how decisions get made.
They are the fourth persona in our framework: the Data & AI Specialist. Their technical depth is rarely the gap — translating a business question into an analysis, and the result back into a decision, usually is. Specialist tracks are built to order rather than taken from a catalog, so it is a conversation rather than an off-the-shelf track.
The method is the same — cohort-based, hands-on, exercises on your own tools and data. The content is not. Each persona gets its own track, aimed at the skill domains that matter for that group.
Yes, and right now it is the question we get most. Agents move the work from doing a task to specifying, checking and owning it — that changes what Users and Builders need, but it is not a reason to skip either group. Some organizations already run thousands of agents while others have deployed none, so we set the level of the agent content per organization, and we help leaders think through what it means for roles and job architecture.
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