The first 90 days of AI adoption: a practical roadmap for leadership teams
By MMT Consulting · · 6 min read
Many leadership teams know they should be doing something with AI, but are unsure where to start. The risk is not moving too slowly. It is starting with tools instead of problems. This outline gives a calm, practical order of work for the first 90 days.
Days 1 to 30: understand and set rules
Begin by learning what current AI tools can and cannot do, using your own business questions rather than demos. Let leaders and heads of department try the tools directly, because opinions formed from hands-on use are better than those formed from headlines.
At the same time, write a short AI usage policy. It should say what data may never be entered into public AI tools, who approves new tools, and how staff should check AI output before using it. A one-page policy now prevents most problems later.
Days 31 to 60: choose a few use cases
Ask each department for its most repetitive or time-consuming tasks. Score the ideas on three things: how much time or money they could save, how easy they are to try, and how much risk they carry. Choose two or three that score well on all three, and avoid starting with the most ambitious idea.
- Good first candidates: drafting and summarising documents, analysing spreadsheets, answering common internal questions, preparing reports
- Poor first candidates: anything involving sensitive personal data, legal commitments or fully automated decisions about people
Days 61 to 90: pilot, measure and decide
Run small pilots with a named owner for each one. Agree beforehand how you will measure success, for example hours saved per week or errors reduced. At the end of the period, review honestly: keep what worked, stop what did not, and note what you learned.
Invest in people, not only tools
Tools change every few months, but the habits of good prompting, checking results and protecting data last. Short, practical training for the people who will use AI every day usually delivers more value than another software licence.
Keep a human responsible
AI can draft, summarise and suggest, but a named person remains responsible for anything that goes to a client, a regulator or a board. Make that clear from the start and the rest of the adoption becomes easier to manage.