In 2020, I watched a perfectly good automation fail. The algorithm was correct. The engineering was solid. The automation worked. The single-click "Optymize" button could plan a full day of delivery routes in seconds — work that took expert planners hours. And yet — only 18% of users ever tried it. Most never came back.
The organization's hypothesis was "awareness." More demo sessions, tutorial walkthroughs, explicit tutorials showing exactly how the algorithm worked. Still 18% (nobody watched, duh!). We claimed an 80% increase in operational efficiency — achieved only when users actually used this star feature. The product manager wanted more training. The engineering team wanted attribution data.
I pitched UX research as the path to an answer — not because I knew what the answer was, but because the existing hypotheses were downstream of the wrong question.
"The question was never 'why won't they use it?' The question was 'how can we build enough trust in this feature that makes users feel competent, efficient and supported at their job?'"
Planners in the P&D industry had spent years building expertise in a specific cognitive process: grouping shipments geographically, applying constraints from memory, drawing on pattern recognition built over years of operations. The Optymize button asked them to invert that entirely — input constraints, then wait for the algorithm to produce routes. Not a small change. A complete behavioral inversion.
The interface never explained this trade. It never named what it was replacing or why the replacement was trustworthy. It asked people to delegate expertise to a system without first building a relationship with that system. Nobody designed the bridge between the old workflow and this new transition.
