How do you introduce AI to experts already working at the edge of innovation?
Starting with researchers’ work, then creating practical opportunities to experiment, learn, and build trust in AI.
Start with the research
Footwear, apparel, and athlete-performance experts needed to see where AI could improve their existing work.
Early wins · Shared learning · Broader AI adoption
- 1Map the research work
- 2Find useful early wins
- 3Keep learning together
The challenge
Footwear, apparel, and athlete-performance researchers already worked in a sophisticated technical environment. The challenge was identifying where AI could make their work better.
Introducing new capabilities meant understanding the research itself: the tasks, friction points, and opportunities where better tools could create immediate value.
My approach
I partnered with researchers, product managers, data engineers, and change leaders to map how research happened using a jobs-to-be-done approach.
Together, we identified high-value opportunities for tangible early wins and created a recurring learning environment where researchers could experiment, exchange discoveries, and explore emerging capabilities with technical teams.
What changed
Those early wins helped rebuild trust, created momentum, and contributed to broader adoption of AI tools across the research organization.
The work also established one of the enterprise’s earliest dedicated environments for ongoing AI learning, giving researchers a place to keep exploring as the technology evolved.