AI for small businesses: a sensible place to start

If you run a small business, you've almost certainly been told that AI will change everything. It's on the news, in your inbox, and probably mentioned by a supplier or two. It's natural to wonder whether you should be doing more, and what "doing more" would even look like.

Here's the reassuring part: most well-run small businesses are better placed than they think. You don't need a data science team, a big budget or a grand strategy to get real value from AI. You need a sensible starting point, a few everyday tasks, and some simple guardrails.

This article sets out a practical way to begin.

Start with friction, not technology

The most useful question isn't "what can AI do?" It's "where does our time go on work that doesn't need our full attention?"

Every business has tasks like this. They're necessary, but repetitive, fiddly or slow:

• Writing first drafts of emails, proposals and letters
• Summarising long documents, contracts or meeting notes
• Turning rough notes into something tidy and structured
• Pulling the key points out of a long email thread
• Answering "how do I do this in Excel?" questions

These are exactly the areas where today's AI assistants tend to be most helpful, and where the risks are easiest to manage, because a person is still reading and approving the result.

Spend ten minutes with a pen and paper, or better still ask two or three colleagues, and list the tasks that feel like friction. That list is your starting point.

Look at the tools you already have

Before buying anything new, it's worth checking what's already within reach. Many businesses already have access to AI features through software they pay for:

• Microsoft 365 and Google Workspace both now include AI assistants, with more capable options available as add-ons.
• Accounting, CRM and other business software increasingly include AI features for things like categorising transactions, drafting replies or summarising customer history.
• Standalone assistants such as ChatGPT, Claude and Gemini offer business plans with stronger data protections than their free versions.

Features and plans change frequently, so check the current details with each provider. What matters here is the principle: start with what fits naturally into the way you already work. An AI tool that sits inside the software your team uses every day is far more likely to be used than one that needs a new login and a new habit.

Run a small, low-risk experiment

Rather than rolling something out to everyone, pick two or three people and two or three tasks from your friction list. Give it a few weeks.

A good first experiment has three qualities:

1. It's low risk. The output is checked by a person before it goes anywhere important. Drafting an internal summary is a better first step than letting AI reply to customers on its own.
2. It's frequent. A task done several times a week will show its value quickly.
3. It's easy to compare. You can tell whether the result was faster, better, or both.

Ask the people involved to keep a few informal notes: what worked, what didn't, and roughly how much time it saved. You're not after precise measurement at this stage, just an honest sense of whether it's worth continuing.

Put a few simple guardrails in place

A little structure helps people feel confident. It doesn't need to be a long document. For most small businesses, a handful of clear points is enough:

• Use approved tools. Agree which AI tools people can use for work, and use business versions where possible, as they typically offer better protection for your information.
• Be careful with sensitive information. Be clear about what shouldn't be pasted into an AI tool, such as personal data about customers or staff, or commercially sensitive material, unless the tool has been set up and approved for it. The ICO's guidance on AI and data protection is a useful reference.
• Check the output. AI can be confidently wrong. Whoever uses the result is responsible for checking it, just as they would with a colleague's draft.
• Be open about it. Agree when it's appropriate to mention that AI helped, particularly in client-facing work.
• Know who to ask. Name someone people can go to with questions.

We'll cover how to write a short, positive AI use policy in a future article. For now, even an email to the team setting out these points is a good step.

Measure what matters

After a few weeks, sit down with the people involved and ask:

• Which tasks genuinely got easier or faster?
• Was the quality as good, or better?
• Where did it fall short, or need too much correction?
• Would we miss it if we stopped?

The answers will tell you where to go next. That might mean extending the same approach to more people, trying a new set of tasks, or investing in a more capable tool. And sometimes it means deciding that a particular use isn't worth it yet, which is a perfectly good outcome.

When it's worth getting help

Plenty of businesses make a good start on their own. It can be worth bringing in outside help when:

• You'd like an independent view of where AI would add the most value in your business
• You're considering paid tools, such as Microsoft 365 Copilot, and want to be confident they'll pay their way
• Your files and permissions need tidying before AI tools can be used safely and effectively
• You want to go beyond individual productivity into automating parts of a process

A sensible first step

If you do one thing this week, make it this: write down five tasks that take up time without needing much judgement, and ask one colleague to do the same. Compare the lists. The overlap is where AI is most likely to help first.

From there, you can choose one or two to try with the tools you already have, and you'll have taken your first practical step, at your own pace.

If you'd like a hand working out where AI could help most in your business, our AI Adoption service starts with exactly that conversation.