How to Build an AI Strategy for a Small or Mid-Sized Business

An AI strategy for a small or mid-sized business comes down to three decisions: where AI pays, what it may touch, and who owns it. Add a choice of tools and a list of what to leave alone, and you have a strategy. It should fit on a few pages and turn into a 90-day plan your team can run.

What is an AI strategy for a small business?

An AI strategy for a small business is a plan for where AI will create value, what it is allowed to do, who is responsible for it and which tools it will use. It ranks a small number of opportunities, sets rules for data and approval, and turns the top priorities into a 90-day plan with owners.

It is not a technology roadmap or a vision statement. For a business of 10 to 200 people, the strategy is mostly about choices. Which three jobs first? Which data stays out of AI tools? Who decides when something goes wrong? Which assistant does the company standardise on?

Most small businesses skip this step because AI arrived bottom-up. Staff started using assistants on their own, and the business found itself with AI use but no plan. That is normal. The strategy's first job is to take what is already happening and point it somewhere.

Why does a small business need an AI strategy now?

Because AI is already in the business, whether or not anyone planned it. Staff are using assistants, often on personal accounts, in different ways and with company data. Without a strategy, that use stays fragmented, risky and unmeasured. With one, the same effort is aimed at the work that pays.

The numbers show how far adoption has run ahead of planning. According to the National AI Centre, 43% of Australian SMEs reported some level of AI adoption across December 2025 to February 2026. The Australian Bureau of Statistics, which counts AI in use more strictly, found about 11% of small and micro businesses using it in 2024-25, against about 35% of large businesses. Either way, adoption is rising and the gap with larger firms is real.

Leaders feel the absence of a plan. According to Microsoft and LinkedIn's 2024 Work Trend Index, 60% of leaders worry their company lacks a vision and plan for AI. The same survey found 59% concerned about quantifying AI's productivity gains.

What should an AI strategy cover?

A small business AI strategy should cover five things: the ranked opportunities, the rules for data and approval, the owners, the tool choices, and what to leave alone for now. Each takes a page or less. Together they turn scattered AI use into a plan the leadership team can stand behind.

The last row is underrated. A strategy that says no to some ideas protects time and money for the ones that matter. The guide to AI work worth delaying covers how to decide.

How do you find where AI pays in a small business?

List the jobs your team does every week, then score each on return, effort and risk. The best first opportunities are frequent, have a clear output, depend on knowledge the business already holds, and can be reviewed quickly by a person. Rank them, and start with the top one or two.

Good candidates usually look like this:

• Frequent. Weekly reports, proposals, follow-ups, content and research.
• Defined. Someone can describe how they do it well.
• Knowledge-based. The inputs exist in your CRM, documents or calls.
• Reviewable. A person can check the output in minutes.

Score each on three axes. Return asks how much time, money or revenue is at stake. Effort asks how hard it would be to set up. Risk asks what happens if the output is wrong. A weekly client report scores well: high frequency, clear output, easy to review. Automated pricing decisions score badly: high risk, hard to review.

The guide to where AI pays first walks through the scoring in more detail.

What rules does an AI strategy need?

At minimum, three rules: what data may go into which AI tools, which outputs need a person's approval before they go out, and which accounts staff may use for company work. Write them on one page, in plain language, so everyone can follow them without a training course.

Data. Say plainly what never goes into a public AI tool, such as customer personal details, employee records and confidential financials. In Australia, the privacy regulator recommends as best practice that organisations do not enter personal information into publicly available generative AI tools.

Approval. Decide what a named person must approve before it is published, sent or spent. Everything else, such as drafts, research and summaries, can run freely.

Accounts. Company work happens in company accounts. That keeps data under control and keeps what people build inside the business.

These rules belong in a short AI policy the team will actually read.

What does a 90-day AI plan look like?

A 90-day plan takes the top one or two opportunities and runs them properly: set baselines, give the AI the business knowledge it needs, build and launch the workflow with a named owner, train the people who use it, and measure the result. At day 90, decide what to expand, change or stop.

Beyond 90 days, a 12-month roadmap sequences the rest of the shortlist, with owners named. The guide to an AI roadmap template shows how to lay it out.

Should you build an AI strategy yourself?

Many small businesses can, using the steps above. Outside help is worth it when leaders disagree on priorities, when nobody has time to run the process, or when the business needs the opportunities ranked against what others have learned. Either way, the leadership team must own the decisions.

There is some evidence for bringing in experience. MIT NANDA's 2025 study found that external partnerships succeeded about twice as often as internal builds. That supports getting help. It does not mean handing over the decisions.

Skein runs a fixed-scope AI strategy diagnostic: listen, map, rank and plan. It ends in a ranked shortlist of three to five opportunities, a 90-day plan and a 12-month roadmap with owners. That suits a growing B2B business already paying for AI that cannot say what it returns. A business with clear priorities and the time to run the process itself may not need it.

Frequently asked questions

What should a small business AI strategy include?

It should include a ranked shortlist of three to five opportunities, rules for which data goes into which tools and what needs approval, named owners, the tools the business will standardise on, and a list of what to leave alone for now. The top priorities then become a 90-day plan.

How do I create an AI strategy for my business?

List the jobs your team does weekly, score each on return, effort and risk, and pick the top one or two. Set rules for data and approval, name owners, choose your tools, then run a 90-day plan with a baseline so you can measure the result and decide what to expand.

How long does it take to build an AI strategy?

For a small or mid-sized business, the decisions can be made in weeks rather than months, depending on how many people are interviewed and how aligned the leadership team is. The strategy should be short. The time goes into ranking opportunities honestly and agreeing the rules.

Do small businesses need an AI policy?

Yes, even a short one. Staff are likely using AI already, sometimes with company data in personal accounts. A one-page policy saying what data may go into which tools, which accounts to use, and what needs approval protects the business and makes safe use easy.

What is the difference between an AI strategy and an AI roadmap?

The strategy is the set of decisions: where AI pays, what it may touch, who owns it and which tools to use. The roadmap is the sequence: which opportunities run when, over 90 days and 12 months, with owners. Strategy comes first, and the roadmap puts it on a timeline.

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