How to Create an AI Strategy for Your Company (One-Page Template)

Most companies do not have an AI problem. They have a focus problem. Different teams are testing different tools, nobody knows what is working, and leadership keeps asking “what is our AI strategy?” without a clear answer.

A good AI strategy does not need a consulting project or a 60-page deck. It needs to answer 5 questions on one page: what you want to achieve, where AI will help, what data you need, who will make it happen and how you will manage the risks. This guide walks through each part and gives you a template to fill in.

Why you need an AI strategy now

AI adoption is growing but uneven. The US Census Bureau’s Business Trends and Outlook Survey found that 19.8% of US businesses reported using AI in a business function as of May 2026, with use much higher among firms with 100 or more employees than among the smallest firms. In other words, many of your competitors are still early, and a focused plan can put you ahead.

Without a strategy, you tend to get the worst of both worlds: scattered spending on tools that overlap, sensitive data in unapproved apps, and pilots that never turn into results. A one-page strategy fixes that by giving everyone the same priorities.

The one-page AI strategy template

Copy this structure into a document and fill in each section. Keep each to a few lines.

Now let’s go section by section.

Set goals and choose use cases

Step 1: Start with business goals, not technology

Write down the 2 or 3 business outcomes that matter most this year: grow revenue, reduce costs, improve customer experience, reduce errors or free up staff time. Make each one measurable.

Bad goal: “Use AI in marketing.” Good goal: “Publish twice as much product content per month without adding headcount.”

Every AI project in your strategy should connect to one of these goals. If it does not, it waits.

Step 2: Find and rank your use cases

Collect ideas from every team. Ask: “What repetitive, time-consuming or error-prone task do you do every week?” You will get a long list. Common candidates include customer support replies, sales emails, document summaries, invoice processing, forecasting, content drafting, code assistance and internal knowledge search.

Then score each one. A simple scoring sheet:

The example scores are illustrative. Pick your top 3 to 5 and start with 1 or 2 that score high on value and feasibility and low on risk. High-risk uses, like decisions about people’s jobs, credit or health, need more governance and usually come later.

For ideas, our guides to generative AI for small business and AI agents cover the most common starting points.

Get your data and tools ready

Step 3: Check your data

AI is only as good as the data it works with. For each priority use case, ask:

• Where does the data live? CRM, help desk, ERP, shared drives, spreadsheets.
• Is it accurate and complete? Duplicates, missing fields and outdated documents will produce bad AI output.
• Who can access it? AI tools connected to your files can surface documents people should not see. Fix permissions first.
• Is any of it sensitive or regulated? Customer personal data, health, financial or student records need specific protections. See whether it is safe to put company data into AI.

Many AI strategies stall here. Budget time for data cleanup; it pays off for every future project.

Step 4: Decide on tools, and build vs buy

Start with what you already pay for. Office suites, CRMs, help desks, accounting and design tools increasingly include AI features. Then decide:

• Buy when a mature product solves the use case well. This is the right answer for most small and midsize companies.
• Configure when you need AI grounded in your own documents, for example an assistant that answers from your policies. Techniques like retrieval-augmented generation make this possible without training a model.
• Build only when the use case is core to your competitive advantage and you have the engineering capacity.

Choose business or enterprise plans with clear data terms, and avoid buying several tools that do the same job.

Plan for people and risk

Step 5: Prepare your people

AI strategies fail more often on people than on technology. Cover these roles:

• Executive sponsor: A leader who owns the strategy and clears roadblocks.
• AI lead: The person who coordinates projects, vendors and the policy. In a small company, this may be part of someone’s existing job.
• Champions: 1 or 2 enthusiastic people per team who test tools and help colleagues.
• Everyone else: Practical training on approved tools, good prompting, checking output and data rules.

Be honest about how roles will change. Staff adopt AI faster when they see it removing tedious work rather than threatening their jobs.

Step 6: Set your risk and governance rules

Keep this proportional to your size and risk. At minimum:

• An AI acceptable use policy covering approved tools, data rules, human review and disclosure. Use our AI acceptable use policy outline.
• Human review for anything customer-facing or high-stakes.
• Vendor review of data use, retention and security before buying.
• Legal checks for regulated uses such as hiring, lending, insurance and health, where state AI laws may apply. This is not legal advice.

If you want a recognized framework, the voluntary NIST AI Risk Management Framework organizes AI risk work into 4 functions, Govern, Map, Measure and Manage. Larger organizations sometimes adopt ISO/IEC 42001, the international standard for AI management systems.

Key takeaway: A strong AI strategy is short and specific. It links every AI project to a business goal, starts with 1 or 2 measurable pilots and sets clear rules on data and review before scaling.

Measure, pilot and review

Step 7: Run measured pilots

Before any pilot starts, record a baseline: time per task, cost per ticket, error rate, conversion rate or whatever your goal measures. Run the pilot for a set period, often 30 to 90 days, with a small group. Then compare results against the baseline and decide whether to scale, adjust or stop. Our guide on how to measure AI ROI gives a worked formula.

Review the strategy every quarter. AI tools change quickly, and so will your priorities.

A 90-day plan to get started

• Days 1 to 30: Name a sponsor and AI lead, survey current AI use, set 2 or 3 goals, collect and score use cases, and publish a short AI policy.
• Days 31 to 60: Choose 1 or 2 pilots, clean the data they need, set up approved tools with proper settings, capture baselines and train pilot users.
• Days 61 to 90: Run the pilots, measure results, share what you learned and decide what to scale. Update the one-page strategy.

Conclusion

An AI strategy is a short list of decisions: which goals matter, which use cases come first, what data and tools you need, who owns the work and how you will manage risk. Put it on one page, start small with measured pilots, and revisit it every quarter. That discipline is what separates companies that get real value from AI from those that just collect subscriptions.

Sources

• US Census Bureau, Large Firms With at Least 20 Employees Biggest AI Users (May 2026) — national AI use rate and differences by firm size
• NIST AI Risk Management Framework — voluntary framework with Govern, Map, Measure and Manage functions
• ISO/IEC 42001:2023, AI management systems — international standard for managing AI in organizations