I ran a workshop series for a design organization — prompt engineering, Cursor demo, MCP walkthrough, VQA automation examples. Engagement in-session was high. Post-session adoption was low. I tried to figure out why.

My initial hypothesis was tool confusion. The tools were too complex, too removed from existing workflows, too much to integrate alongside delivery pressure. I added more structure, clearer examples, better documentation. Adoption stayed low.

What I eventually understood: the barrier wasn’t cognitive. It was identity.

Designers kept saying things that sounded like practical concerns but were actually existential ones. “This feels like engineering.” “If I can generate this in thirty seconds, what am I actually contributing?” “Is leadership going to expect twice the output now for the same headcount?” These weren’t questions about how to use the tools. They were questions about what design is for and whether these tools threatened the answer.

This matters because it means the standard AI adoption playbook — better tools, better training, more examples — doesn’t address the actual problem. You’re solving a skills gap that isn’t the primary bottleneck. The primary bottleneck is that people don’t have a clear picture of what their judgment is worth in a world where AI handles significant portions of execution.

I changed the approach. Less time on tools. More time on the question: what does design judgment become in AI contexts? Where does human evaluation become more important, not less? The argument isn’t “don’t worry, designers are safe.” It’s specific: here are the categories of decision that require exactly the kind of judgment experienced designers have developed, here’s why AI systems don’t handle them reliably, here’s where your expertise becomes more valuable as AI handles more execution.

That reframe reduced the identity friction enough to make the skill development conversations productive. The tool training could happen after people had an answer to the existential question, not before.

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