AI Adoption Strategy

Who this is for

Businesses looking to unlock the potential of their AI adoption and create cultural alignment with the new direction of technology in the business. Usually 50–200 headcount, where leadership has committed to AI but the team is somewhere between quietly using ChatGPT on personal accounts and openly anxious about being replaced.

The problem

AI adoption inside technology teams is now a people problem more than a technology problem. The tools work. What’s missing is the operating model around them: which roles change, what “good output” looks like when an LLM wrote the first draft, how to measure productivity without punishing the people who haven’t onboarded yet, what gets escalated and what doesn’t, and how to talk about all of it without triggering a quiet wave of attrition.

Most scale-ups respond in one of three ways — top-down mandate with no enablement, bottom-up free-for-all with no guardrails, or a procurement-led tooling decision that solves nothing about how work actually changes. None of these survive contact with the team.

What you get

• Staff sentiment survey — current usage, capability gaps, anxieties, and where the cultural friction sits
• Strategic plan for adoption — sequenced rollout with owners, milestones, and decision points
• AI readiness diagnostic across leadership, managers, and ICs — usage, sentiment, capability gaps, and policy exposure
• Role-impact map for the roles most exposed to change (engineering, support, content, ops), with a “what’s changing, what isn’t” view per role
• Usage policy and acceptable-use guidelines — practical, plain-language, with examples
• Manager enablement on having the conversation: productivity expectations, performance signals, career path changes, redundancy risk where real
• Communications plan for leadership rollout — internal narrative, FAQs, and the awkward questions answered before they’re asked
• Skills uplift pathway — what every IC and manager needs to learn, with curated resources (we don’t sell training)
• Measurement framework for adoption and impact — leading indicators, not vanity metrics

How it works

8 weeks
Weeks 1–2	staff sentiment survey, leadership alignment, role-impact mapping
Weeks 3–4	strategic adoption plan, policy, manager enablement design
Weeks 5–6	rollout, manager training, skills pathway publication
Weeks 7–8	measurement framework, first review cycle, handover

Optional technical co-engagement

For teams that also need help with the technical side — tool selection, workflow integration, evaluation harnesses, or building internal AI tooling — this program can run alongside a technical track led by Chris Fowles. Scoped separately based on the engineering questions you’re trying to answer.