Driving AI Adoption.

Leaders diagnose why their team is not adopting and run the conversation that unblocks it.

Section 01

Human Layer

Surface and resolve the real reasons people resist.

Resistance is rarely about the tool. It is about identity, status, fear of obsolescence, and the cost of relearning. The human move is to name what people are actually protecting and to make the choice to adapt safer than the choice to wait.

The moves

H.1Name the cost of non-adoption explicitly, in terms each function understands
H.2Make experimentation low-stakes and visible at the top
H.3Replace blanket mandates with role-specific decisions about what AI changes
H.4Identify the small set of people whose adoption signals the rest

Section 02

AI Layer

Understand what is actually worth pushing the team toward.

Generic enthusiasm produces generic results. The AI move is to know — at a working level — what the technology can do well today, what it cannot, and where it is improving fastest. Without this, leaders end up driving adoption of the wrong things.

The moves

A.1Develop a working taxonomy of high-leverage use cases for the function
A.2Distinguish capability frontiers that are stable from those changing monthly
A.3Maintain a personal practice with the tools — not delegated to a champion
A.4Calibrate enthusiasm to what specific models actually do well

Section 03

Anti-patterns

What gets in the way.

◆All-hands enthusiasm with no specific use-case ownership
◆Tool rollouts framed as productivity gains with no accountability for outcomes
◆Champions selected for energy rather than relevance