The Six Things Every AI Strategy Document Should Answer

Strategy is about saying no.

Not which tools to evaluate. Not which vendors to pilot. Not what the AI steering committee decided to name itself. A real AI strategy document answers the questions that force actual decisions — and in doing so, it names what you're choosing not to do.

Most AI strategy docs skip that part entirely.

I read a lot of these documents. They're full of market analysis, capability assessments, and governance frameworks. What they're missing is the six things that would actually make them useful.

What problem are you solving?

Not "AI is changing the industry." Not "we want to move faster." A specific, ownable problem.

Good: "Our sales team spends 40% of their time on qualification calls that close at under 10%. We want to automate that first conversation."

Bad: "We want to use AI to improve operational efficiency across the business."

The specificity of the problem determines everything downstream. Vague problems produce vague strategies that nobody can execute, and nobody can hold anyone accountable to.

Where is AI load-bearing vs. decorative?

Every AI strategy has a core bet: is AI the thing that makes this work, or is it a layer on top of something that already works?

An AI that writes email subject lines is decorative. An AI that qualifies leads and triggers sequences based on intent signals is load-bearing. The first is an upgrade. The second is the product.

Good answers name the use cases where AI is doing the job, not assisting with it.

What does success look like in 90 days?

Not "increase efficiency." Not "improve AI maturity score."

Good: "By day 90, we have one AI-assisted workflow in production, handling at least 200 transactions per week, with a human review rate below 15%."

The 90-day mark matters because it's long enough to ship something real and short enough that the team stays honest. A strategy with no 90-day success definition is a roadmap to nowhere.

Who owns this?

Not "the AI working group." Not "cross-functional stakeholders."

Someone has to own the decisions. Who says the model is good enough to ship? Who decides when a human stays in the loop? Who breaks the tie when engineering and product disagree?

I've watched AI strategies collapse because nobody had clear authority. The document described shared responsibility, which is code for no responsibility.

What are we not going to do?

This is the question every AI strategy skips. The best ones don't.

Good: "We are not building models. We are not doing AI research. We are not automating any customer-facing communication without a human review gate in year one."

These commitments protect your team from scope creep, vendor pressure, and the pull of every impressive demo. A strategy without a not-to-do list is hedging, not making choices.

What's the exit criteria?

When do you stop? When do you call this a success? When do you kill it?

Most AI projects don't end dramatically. The pilot that was supposed to run for six weeks is still technically running eighteen months later because nobody defined done.

Good: "If we haven't hit our 90-day metric by month five, we reassess the problem definition before spending more. If we hit it, we scale to two additional workflows by Q3."

Exit criteria turn optimism into commitment.

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Six questions. Most AI strategy documents answer two of them, then fill the rest with vendor assessments and org charts.

The document isn't the strategy. But if you can't answer these six things clearly, you're not ready to start building.