Across organizations, two ideas have become almost unquestionable:
“Everyone must upskill in tech, data, and AI.”
“Data scientists must become business partners.”
They sound reasonable. They are well intentioned.
And yet, they systematically miss the point.
What these narratives overlook is not a lack of skills or goodwill, but a confusion of cognitive regimes.
Business expertise is built through slow accumulation, situated judgment, and deep exposure to real constraints — financial, regulatory, operational.
Technology and AI expertise, by contrast, thrives on abstraction, fast learning cycles, and continuous recombination of models and concepts.
These are not two stages of the same learning curve.
They are orthogonal forms of cognition.
When organizations push for universal upskilling, they often dilute what makes business expertise robust.
When they expect data scientists to “think like the business,” they frequently suppress the very abstraction power that makes data science valuable.
The result is neither alignment nor synergy, but cognitive erosion on both sides.
At the age of AI, performance does not come from making everyone think alike.
It comes from composing heterogeneous cognitive spaces deliberately — and protecting them from mutual colonization.
This is not an argument against collaboration.
It is an argument against cognitive flattening.
I explore this more deeply in a framework on Cognitive Spaces and Guardrails — for those designing transformation systems, not slogans.