An AI implementation roadmap turns "we should use AI" into a sequence of small, measurable steps. The goal of the first 90 days isn't transformation — it's one proven win and a repeatable way to get the next one.
Before day 1: choose the first use case
Score candidate tasks on two axes: value (time saved, revenue, customer experience) and effort (data quality, process clarity, integration work). Your first project should be high value and low effort. Typical good starters:
- Drafting first versions of proposals, emails, or reports
- Summarizing calls and meetings into CRM notes
- Answering repetitive customer questions from an approved knowledge base
- Qualifying and routing inbound leads
Days 1–30: pilot
- Name one owner and two or three pilot users.
- Write down the current process and how long it takes today — this is your baseline.
- Set up the tool with real (but low-risk) work and a simple human review step.
- Hold a 15-minute check-in each week to capture what's working and what isn't.
Days 31–60: measure and refine
- Compare results against the baseline: time per task, error rate, user satisfaction.
- Turn the best prompts and steps into a short written playbook.
- Decide your rules: what data can go into the tool, and what always needs human sign-off.
Days 61–90: scale or stop
- If the pilot hit its target, roll it out to the full team with the playbook and a short training.
- If it didn't, document why and move to the next use case on your list. A stopped pilot is a cheap lesson, not a failure.
- Pick the next use case and repeat.
What makes roadmaps fail
- Starting with the hardest, most visible project.
- No baseline, so nobody can prove it worked.
- No owner — "everyone" owns it, so nobody does.
- Ignoring training and expecting adoption to happen on its own.
Get a personalized roadmap
Our AI Readiness Scorecard can generate a personalized improvement plan with a 30/60/90-day roadmap based on your answers. For hands-on help, see the Strategy Sprint on our services page.