About Activero

Tomas Lund

Founder · researcher

I research what makes people — and language models — act, or fail to. And I turn it into systems companies can actually use: e-learning that sticks, workflows people do not abandon, and AI that says no instead of guessing.

Activero is the frame around it. The research is publicly published, so you do not have to take my word for anything — you can examine the method before you buy.

The research → LinkedIn → ORCID → tomas@activero.net →

What stops people is rarely the will. It is the resistance on the way.

The background is e-learning. For years I built training for companies and kept finding the same thing: the content rarely failed. The route to it did. People dropped off at the login, at an unclear form, at a rule nobody could remember.

That turned into a research question: what actually stops an action? The answer became Behavioral Friction Theory — a model of how competing options decide whether something gets done. It turned out to hold for both people and language models, which is what makes it useful for the AI work today.

Research and practice now run the same way: what I measure in the lab ends up as tools for clients, and what trips clients up becomes new measurements.

What I do

Five tracks, one method.

They belong together: the same model of what makes people act sits under the advisory, the products and the research.

AI & compliance

The EU AI Act in practice: how reliable is the system, when should it decline, and what do you show an auditor?

Behavioural design

Systems, texts and workflows that make the right thing the easy thing.

Learning

E-learning and explainer video — plus courses and workshops in building it yourselves.

Software

We build it, and measure how reliable it is before you put it to work.

Research

Published theory and data behind all of it — freely available.

Products

Activate LMS, ThinkAloud, TextThatWorks, Onboarding and Friktionskompasset.

How I work

No claims without numbers behind them. I measure before I recommend, and you get both the measurement and the method.

The method is open. The measurement protocol is public, so you can run it again on your own material. The value is not in keeping the procedure secret; it is in running it properly and handing over the evidence.

I do not promise faultlessness. Nobody can guarantee that an AI system never errs. What you can do is make sure the decisions that really matter always pass a human — and measure and disclose how often the system gets it wrong. That is also the version that holds up in front of an auditor.

See the method and the articles → (in Danish)