An AI strategy is six decisions made in the right order. Here they are, each with an example and the mistake to avoid.
Key takeaways
- AI is everywhere, plans are rare. Most companies use AI somewhere, but formal adoption with a real plan is still unusual.
- Strategy is six decisions. The outcome, where AI belongs, your data, your rules, who owns it and how you measure it.
- Measure value, not activity. Research from MIT Sloan shows leaders often mistake quick productivity gains for lasting value.
Where should your business actually start with AI? Not with a list of apps. A list of apps is a shopping list, and a shopping list isn't a strategy.
Start with one business outcome you want to change this year, written as a number. Faster proposals. Fewer hours lost to reporting. Everything else in an AI strategy follows from that one choice. I call this the one-number rule: every later choice has to point back to it, or it waits.
By the end of this guide, you'll have made six choices, in order, and a checklist to keep you honest.
Why does a strategy matter now?
How many businesses use AI with an actual plan behind it? Far fewer than use it at all. McKinsey's 2025 State of AI survey found that 88% of organizations now use AI in at least one business function. Much of that use is shallow: a chatbot here, a note-taking app one person uses there.
Formal plans are much rarer. Federal Reserve research from April 2026 found that only about 18% of U.S. firms had formally adopted AI by the end of 2025. Yet roughly 78% of the U.S. workforce already works at companies using AI in some form.
So AI is already inside most workplaces, mostly without a plan. For your business, that's an opening. A clear, written plan puts you ahead of most of the market.
How to build an AI strategy in six decisions
Decision 1: Choose the business outcome
Pick one specific, measurable problem. Maybe proposals take too long, support requests swamp your staff, or monthly reports eat two days of spreadsheet work. Write it as a number you want to move.
Example: a marketing agency writes down one goal: send proposals within 48 hours of a discovery call, down from a week. When a new AI idea comes up, the owner asks one question: does this move the 48 hours? That question settles most debates in a minute.
Mistake to avoid: switching direction every quarter. Netflix shows the value of holding a course. In 2011 it split its DVD and streaming plans into separate subscriptions. It lost about 800,000 subscribers in one quarter, and its stock fell roughly 77%. It stuck with streaming anyway, and that choice built the company it became.
Decision 2: Decide where AI belongs first
The principles are the same in every team. The starting point isn't.
- Operations: data entry, status reports and recurring paperwork. The guide to AI for operations and process improvement shows how to find the best first target.
- Sales and marketing: how people search is changing fast. McKinsey estimates that $750 billion in U.S. revenue will flow through AI search by 2028, and about half of consumers already use it to research purchases. Your strategy needs a plan for being found.
- Customer experience: faster, more consistent service helps you keep customers. See how to use AI to increase customer lifetime value.
Mistake to avoid: starting in every team at once. Pick the team closest to your number and let the others wait a quarter. One small, visible win makes the next department far easier to bring along.
Decision 3: Check your data honestly
Find where the information for your goal lives. Then check three things: is it complete, is it consistent, and can other systems reach it?
Example: the agency finds its past proposals spread across three shared drives, each with its own naming habits. Before any AI can draft from them, someone spends a week building one tidy folder of the best examples.
Mistake to avoid: assuming the data is fine because your staff find things by memory. AI can't read your team's memory. A week of tidying up front usually saves a month of poor results later.
Decision 4: Write your rules before you scale
Decide in writing what gets a person's review before it reaches a client, and what data AI may use. Research cited by analyst Josh Bersin found that roughly 45% of AI queries produce wrong answers. That's why a review step comes before any client sees AI output.
Example: every AI-drafted proposal gets a senior team member's read before it goes out. Client budgets never go into public chatbots.
Mistake to avoid: writing rules after something goes wrong. The guide to using AI at work without risking data leaks has a starting policy you can adapt.
Decision 5: Make the owner the decision-maker
AI decisions have moved to the top. BCG's 2026 research found that nearly three-quarters of CEOs are now their company's main decision-maker on AI strategy, double the share from a year before.
In a small business, that means you. Then name one person who runs each AI-assisted task day to day.
Example: the agency owner sets the goal and signs off the rules. The operations lead runs proposal drafting and reports the number each month.
Mistake to avoid: treating AI strategy as an IT project. It changes how work gets done, so it belongs with the person who decides how work gets done.
Decision 6: Measure value, not activity
Research from MIT Sloan's Center for Information Systems Research is blunt about a common trap. Leaders often mistake productivity gains for real business value. A pilot looks promising, everyone declares success, and the work that drives margin and growth stays the same.
Track the target from Decision 1. For the agency, that's days from discovery call to proposal, plus the win rate on those proposals.
Mistake to avoid: counting logins. Usage tells you people opened the app. Your target tells you whether anything changed.
Review it monthly for the first quarter. If it moves, expand to the next team. If it stalls, look at the work and the data before you blame the software. A stalled number is information, and it usually points to an earlier decision.
What if your industry is cautious?
Even strict, heavily regulated industries are moving. Data from the Office of the National Coordinator for Health IT shows that 71% of U.S. hospitals used predictive AI tied into their core systems by 2024, up from 66% the year before.
Hospitals aren't known for moving fast. I find that encouraging for any business with strict client rules. Careful industries adopt AI by making Decision 4 early and taking it seriously. So what does this mean for you? Caution is a reason to write your rules first. It's a poor reason to wait.
Your AI strategy checklist
- One business outcome, written as a number to move
- One team chosen to start, closest to that number
- The data for that outcome found, checked and tidied
- Written rules for human review and client data
- The owner as decision-maker, plus one named person per AI-assisted process
- A monthly check on that target, kept separate from usage
If a line on this list is blank, that's your next task. Once it's complete, turn it into a 90-day plan with the AI roadmap template for small and medium businesses. Unsure whether your business is ready to start? The guide to AI readiness assessments explains what to check first.
Ready to build your AI strategy on a real plan?
You probably already have a hunch about which outcome matters most. The next step is turning that hunch into a plan with a target attached.
Our complimentary AI Readiness Assessment shows where your business stands today. It includes an estimate of the productivity value sitting in your current operations, so you know where to start.