What does an AI implementation roadmap look like?
It depends on where your firm is starting. If people are not yet using AI safely, the roadmap starts with rules, an approved tool and training, and custom builds come later. If most of the team already uses AI daily, the roadmap focuses on picking a few high-value workflows, piloting them and deciding what to build. A good roadmap for most firms covers six to twelve months in stages, with decisions at the end of each stage rather than a fixed plan for the whole year.
Who this is for
CEOs, managing partners and COOs who have been asked “what is our AI plan?” and want something more concrete than buying licenses, but lighter than a large consulting report.
The short version
- Work in stages: foundations, adoption, pilots, then build and scale.
- Each stage ends with a decision, not just a deliverable.
- Keep the roadmap short enough to fit on one page and review it every quarter.
Why most AI roadmaps go wrong
Two patterns are common. The first is no roadmap at all: the firm buys licenses and hopes usage follows. Our page on why giving your team ChatGPT licenses isn’t an AI strategy explains why that rarely works. The second is a roadmap that is too big: a long list of use cases, a multi-year timeline and no clear owner. It looks thorough and then stalls at the first project.
A useful roadmap sits in between. It names a small number of goals, sequences the work so each stage makes the next one easier, and leaves room to change course as you learn.
Stage 1: Foundations (first month)
Before anything else, people need a safe, approved way to use AI.
- Choose a standard tool. One business-grade assistant for general work, with data protections that keep company information out of model training. See ChatGPT vs Claude vs Copilot.
- Write short rules. What data can go into AI, what must be checked by a person, and who approves new tools. See how to write an AI acceptable use policy.
- Name an owner. One person accountable for the roadmap, even if it is part of their existing role.
Decision at the end: Is everyone who needs access able to use an approved tool without guessing what is allowed?
Stage 2: Adoption (months one to three)
The goal here is everyday use by most of the team, not advanced projects.
- Run hands-on training built around real tasks. See how to train non-technical employees to use AI.
- Follow up with short sessions over several weeks so the habit forms.
- Collect the tasks where people find AI useful, and where it falls short.
This stage also surfaces your best candidates for deeper work, because the people doing the job are the ones who notice where AI saves time.
Decision at the end: Which two or three workflows are worth a focused pilot?
Stage 3: Pilots (months two to five)
Pick a small number of workflows and test them properly. Good pilot candidates are frequent, time-consuming and easy to check, such as first drafts of recurring documents, summarizing long files, or preparing meeting materials. Our page on where your firm should start with AI covers how to choose.
For each pilot, set:
- A clear owner and a small group of users.
- A before-and-after measure, such as time per task.
- A fixed length, usually a few weeks.
See how to run an AI pilot that doesn’t stall for the full method.
Decision at the end: For each pilot, roll it out, change it, or stop it.
Stage 4: Build and scale (months four to twelve)
Some pilots work well with an off-the-shelf assistant and good prompts. Others need more: access to your CRM, email or document storage, or several steps run automatically. That is where custom work comes in.
- Decide what to build and what to buy. See build vs buy AI tools.
- Build the first custom tools around workflows that already proved their value in a pilot.
- Plan for upkeep from the start: who monitors the tools, fixes them and updates them. See who maintains AI systems after the consultant leaves.
Decision at the end: What to scale firm-wide, and what goes on the next roadmap.
What a one-page roadmap includes
Keep it simple enough that anyone in leadership can explain it:
- Goals. Two or three outcomes, such as “most of the team uses AI weekly” or “cut proposal drafting time.”
- Stages and dates. The four stages above, with rough timing for your firm.
- Owners. One name per stage or workflow.
- Measures. How you will know each stage worked.
- Budget. Licenses, training and any build work. See how to control AI costs across a team.
- Review date. A quarterly check to update the plan.
Common mistakes
- Starting with the build. Custom tools work best after people know how to use AI and you know which workflows matter.
- Too many pilots at once. Two or three focused pilots beat ten half-run ones.
- No owner. A roadmap shared by everyone is owned by no one.
- Ignoring people’s concerns. If staff fear for their roles, adoption slows. See what to do when employees are scared AI will replace them.
- Never revisiting the plan. AI tools change quickly. A roadmap that is not reviewed every quarter goes stale.
When you do not need us
If your firm is small, has a leader with time to own the work and already uses an approved AI tool, you can follow these stages in-house. Outside help is more useful when you want an experienced view on which workflows to prioritize, structured training for the whole team, or engineering for the tools that come out of your pilots.
How Ruby helps
- AI strategy: building a one-page roadmap, choosing pilots and setting the rules and measures for each stage.
- AI training: hands-on training and follow-up coaching that move the team through the adoption stage.
- AI engineering: building and maintaining the custom agents and integrations that come out of successful pilots.
If you want help turning “we should do something with AI” into a plan, book a call with Ruby.