Service 02

AI Adoption & Enablement

Replaces tool licenses nobody opens with adoption you can evidence.

The problem this solves

You bought seats. A handful of enthusiasts use them well, most people tried once, and leadership has no view of what changed. Adoption fails on role clarity and governance, not on model quality.

Move an organization from scattered personal AI use to a governed, measurable capability people actually use in the work.

What's included

Governance, playbooks, and a number leadership can read

Adoption work is sequenced by role, starting where the evidence is easiest to collect.

  1. 01

    Entry — Readiness Assessment

    Role-level use-case inventory, data-sensitivity review, and a candid read on where AI will and won't help this year.

  2. 02

    Core — Enablement Program

    Usage policy, role-specific playbooks and prompt libraries, hands-on working sessions with real accounts and records, and an adoption scorecard.

  3. 03

    Ongoing — Capability Retainer

    Quarterly capability reviews, new-tool evaluation, internal champion coaching, and refresh of playbooks as models and workflows change.

Proof of work

What changes when adoption is owned

Patterns we see repeatedly once usage is governed rather than encouraged.

Ownership

Every AI-assisted step has a named owner and an escalation rule.

Pilots stall because no one is accountable for output quality. We define what the system may decide alone, what it must escalate, and who reviews the escalation rate each month.

Measurement

Usage reported by role, not by license count.

The adoption scorecard tracks which roles use which playbooks on real records, so leadership can see capability rather than seat activation.

Start with an assessment.

Every engagement begins with findings, not a proposal for software.