The problem this solves
The pattern repeats across companies: an AI rollout generates two weeks of enthusiasm, then usage settles at a fraction of the team while everyone else quietly returns to old habits. Licenses and credits keep getting paid for, leadership assumes the transformation happened, and the gap between the AI-fluent few and everyone else widens. Tool updates make it worse: features change monthly, and last quarter's training is already stale.
How we work
We treat adoption as a measurable, improvable metric rather than a training checkbox. Each cycle starts with usage data: who uses which AI capabilities, how often, and for what. Interviews and short surveys fill in the why behind the numbers, because non-usage almost always has a specific reason, whether that is distrust after a bad output, unclear use cases, or workflows the tool does not fit.
Then we work the gaps: short role-specific sessions built around real tasks rather than feature tours, updated playbooks that show exactly where AI fits into each workflow, and office hours where people bring actual work. When vendors ship significant updates, we digest the changes and brief the team on what matters, so nobody has to follow release notes.
Adoption metrics get reported monthly, and the enablement plan adjusts to what the numbers show rather than to a fixed curriculum.
Deliverables
- Monthly usage and adoption reporting by team and capability
- Adoption barrier analysis combining data and interviews
- Role-specific enablement sessions each cycle
- Maintained AI playbooks and workflow guides
- Update briefings when tools change significantly