Assessment

Assessment

How we find out how someone works with AI today, what comes out of the assessment, and why the result is not a grade or a personality box.

The assessment is the entry point into the methodology. Its job is to understand how someone uses AI in their real work today: whether they occasionally try it, use it regularly, build their own workflows with it, or already change a whole working system because of it.

We do not mainly ask which tools someone knows. What matters more is what they actually do with AI: how often they use it, for which tasks, what outputs are created, and where the way of working already changes.

#What comes out of the assessment

The result has four parts. Each answers a different question.

Part of the resultIn plain languageWhat it is for
LevelWhere you are today in your work with AISets the difficulty of the next recommendations
Superpower ProfileHow you most often create value with AIHelps choose a natural development style
Growth EdgeWhat will move you forward nowDefines the closest practical next step
SP ScoreA baseline numeric measureUseful mainly for repeated measurement and tracking progress

Level is the main result. Profile and Growth Edge add context. We do not use the score as a grade, but as a reference point you can return to later.

#Two assessment formats

Spark - quick questionnaire

About 5 minutes. It gives a quick orientation: how often someone uses AI, in which situations, and what type of AI work appears in their practice.

Deep - interview with Aimee

About 20 minutes. It goes deeper: concrete examples from work, outputs, habits, and decisions. Deep carries more weight in evaluation than Spark.

Spark is a good start. Deep is more precise because it is based on concrete behavior and examples from work.

#Why the result is not a permanent label

Work with AI changes quickly. A new tool, a new project, or one useful experience can change how someone uses AI within a few weeks. That is why an assessment result does not say "this is who you are forever."

It says something closer to: this is how you work with AI today, and this is the next step that makes sense now.

That matters especially for repeated measurement. A new result is not a correction of the original one. It captures movement: what changed in work, habits, tools, and impact.

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