How to build an AI adoption strategy.
Six decisions, in order.
Most AI strategy advice produces a document. A document is not a strategy — it is a record of one. The strategy itself is a short list of decisions about what you are solving, who owns it, and what would count as proof. Make those six, in order, and the plan writes itself. Skip them and no framework will save you.
Steve Buckner
40 years in IT since 1986. Creator of the AI Capability Rollout Framework. More about the author →
What is an AI adoption strategy?
An AI adoption strategy is the set of decisions that determine what your organization will roll out, to whom, in what order, and what will count as success. It is distinct from the framework that carries those decisions out and from the roadmap that schedules them. A strategy answers six questions: which problem you are solving, who owns the rollout, what data is in bounds, where the first pilot runs, what evidence counts as proof, and what happens once that proof exists. None of the six requires a technical background.
Strategy, framework, roadmap: three different things.
These get used interchangeably, which is part of why AI planning stalls. They are not the same and they are not substitutes.
Strategy — the decisions
What problem, whose job, what is in bounds, what counts as done. Judgement calls about your own organization that only you can make. This page.
Framework — the system
The repeatable stages, tools, and checkpoints that carry the decisions out. See the AI adoption framework.
Roadmap — the calendar
The same work placed against dates, dependencies, and who is doing what in which week. See the AI implementation roadmap.
The order matters. A framework applied to undecided questions produces well-organized drift — real activity, real meetings, no accumulating result. Decide first.
The six decisions.
1. Which problem, not which tool
Name a specific workflow that is slow, repetitive, and low risk if it goes wrong. Not "improve productivity" — something like "the weekly supplier reconciliation takes one person two days." Tool-first strategies fail because you cannot measure the effect of a tool, only the effect on a workflow.
Decided when: you can state the workflow and roughly what it costs you today.
2. Who owns it
One named person, with the authority to decide what is in scope and to call a pilot finished. Not a committee. Not "IT will handle it" unless someone in IT has actually agreed to own the operational outcome.
Decided when: three colleagues asked separately would give the same name.
3. What data is in bounds
Three categories are enough: freely usable, use with caution, never. Until this is answered, careful people avoid AI for anything that matters and less careful people create the exposure. Both problems have the same cause.
Decided when: someone can answer it without asking you. See AI guardrails.
4. Where the first pilot runs
One workflow, one team, with a respected colleague running it. Choosing a team that is already sceptical to prove a point is a common and expensive mistake — the early majority copies people it trusts, and a reluctant pilot produces a reluctant result.
Decided when: you have named the workflow, the team, and the person. See the AI adoption curve on why the choice of person matters.
5. What evidence counts
Set the standard before you run, not after. Time saved against a measured baseline, error rate, throughput, or quality assessed by a defined reviewer. Decide the number that would make this worth continuing — and the one that would make it worth stopping.
Decided when: it is written down and someone else could check it. See measuring AI ROI.
6. What happens when it works
The decision everyone defers, and the reason first wins evaporate. Decide now who documents the workflow, who gets trained next, and what the second pilot will be — so that a success becomes a capability instead of an anecdote.
Decided when: the pilot has a named successor. See scaling a successful pilot.
Turning six decisions into 90 days.
Once the decisions exist, the sequence is fairly mechanical:
- Days 1–30 — establish the floor. Measure the baseline on your chosen workflow. Publish the data rule. Find out what AI use is already happening, without penalty.
- Days 31–60 — run one controlled pilot. One workflow, one team, the success standard fixed in advance. Resist widening it; a pilot that grows mid-flight stops being measurable.
- Days 61–90 — measure and document. Compare against the baseline, write down what made it work, and show the result to the people who were waiting for proof.
Ninety days is not arbitrary. It is long enough to produce real evidence and short enough that leadership attention has not moved on before you have something to show.
Three ways strategies go wrong.
Writing the document first. A strategy document produced before any workflow has been touched encodes assumptions nobody has tested. Decide, test, then write the document from what you learned.
Choosing breadth over depth. Five shallow pilots produce five opinions. One properly measured pilot produces evidence. The organizations that move fastest are usually the ones that started narrowest.
Mistaking activity for progress. Licences bought, tools trialled, meetings held — none of it accumulates unless it is anchored to a workflow with a baseline. The seven AI adoption challenges covers the rest of this failure pattern.
Start from where you actually are.
Decisions two through five are easier to make once you know which capability pillar is weakest. The AI Readiness Score measures all four — strategy and leadership clarity, governance and risk awareness, workflow integration, and capability development — and returns a structured starting point rather than a guess.
Take the Free Assessment →Related resources.
AI Adoption Challenges →
What these six decisions are designed to prevent.
The AI Adoption Curve →
Who to pick for the first pilot, and why.
Your First 30 Days →
What to do before you touch a single tool.
Common questions.
Six decisions, then ninety days of evidence.
The AI Capability Rollout Framework turns those decisions into a running system — 13 tools covering baseline, guardrails, pilot design, measurement, and the executive briefing that follows.