New technology pays off only when the organization changes around it.

Most AI and data programs don't fail on the technology. They fail because decision rights, budgets, roles and frontline work stay exactly as they were. I treat readiness as part of go-to-market strategy, so adoption is designed in from the start rather than hoped for after launch.

Signs you need this

The technology launched, but people still work around it.
Nobody is sure who decides what the new tools should change.
Budgets and incentives still reward the old way of working.
Usage numbers look fine, but business results haven't moved.

What I do

Map the decisions the new capability is supposed to change, and who owns each.
Redesign roles and decision rights where they block adoption.
Line up budgets and incentives with the outcomes you want.
Work with frontline teams to change the daily workflow, not just the software.
Set adoption metrics tied to business results, not logins.

What you get

An organizational readiness assessment
A decision-rights map for the new capability
An adoption plan with owners, milestones and metrics

Proof

Led adoption of a customer data platform and AI tools across 2,500 independently owned dealerships at FordDirect, where nothing could be mandated and every rollout had to earn its use.

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Building AI habits across a team starts with reps: read The AI Range →