AI products die when they ask for a new habit. Enterprise users do not want another destination. They want the thing they already do to take less time. My adoption programs start by mapping existing workflows, not by designing a standalone AI experience.
When I moved Atlassian Intelligence adoption, the inflection was not a marketing push. It was meeting engineers and PMs inside Slack and Jira, the surfaces they already lived in. The program work was sequencing three team backlogs so the integration shipped before the standalone polish. That order feels wrong to product teams and it is usually right for adoption.
I ask four questions before I green-light a new surface: where does the user start today, what is the smallest action that proves value, what telemetry proves that value, and what happens when the model is wrong. If the last answer is unclear, I do not expand traffic.
Integration programs also fail at the handoff. Support docs, admin controls, and trust copy are not launch polish. They are part of the feature. I put them in the same milestone as the API, not the week after GA.
If adoption is flat, I assume the workflow is wrong before I assume the model is weak. Change the entry point, tighten the prompt or action, measure again. Most enterprise AI programs quit one iteration too early.