Start here: five moves, in order
Do these five things first, one after the other. Each links to the full practice and the evidence behind it.
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Pick the one process that takes the most staff time or causes the most delay. For the first months, that one process is your whole programme.
Weeks, a few people Start with one costly process, not a company-wide rollout
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Before you change anything, time the task for two weeks and note the result in a spreadsheet. Measure again once people are past the learning period. Note sales or fees per job as well.
Days, one person Measure time saved per task against a baseline
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Set one rule: no client or personal details in any free AI tool. Buy a business plan for the tool you use most.
Days, one person Decide what data may go into which AI tools
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Write the rules on one page: which tools are allowed, what never goes in, who checks the output. It takes the owner an hour. Talk it through with the team.
Days, one person Write a one-page AI-use policy staff will actually read
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Have the owner or a senior colleague read everything that goes to a customer before it is sent.
Weeks, a few people Keep a person checking anything that goes to a customer or carries legal weight
Then: all 22 practices for this size
See what happens when these steps are skipped Primus Solicitors: 18 court citations that did not exist, taken from a client’s AI research
What is specific to this size
- Fewer small firms have started. Those that have use AI as heavily as large firms. In the euro area, 41% of micro firms (under 10 employees) used no AI in late 2025, against 13% of large firms. About 7–8% of firms made significant use of AI at every size (ECB firm survey). In the European Union, 17% of small enterprises (10–49 employees) used AI in 2025, against 55% of large ones (Eurostat). Eurostat leaves out firms with fewer than 10 employees. In Canada, 19.9% of businesses with 1–4 employees used AI in the year to spring 2026, against 27.8% of those with 100 or more (Statistics Canada).
- Figures depend on the question. In bank records, 17.7% of small US firms that bank with Chase had paid for an AI service by December 2025 (JPMorganChase Institute). 46% of US small firms with employees told a Federal Reserve survey that they used AI. 66% told a survey by the US Chamber of Commerce, a business lobby that argues against state AI laws (claim check). In the US Census Bureau’s survey, fewer than 20% of firms with four or fewer employees used AI (Census Bureau). Before you compare your firm with a figure, ask what was counted.
- Count the sale, not the hour. Among US small firms with employees that use AI, 71% reported higher productivity and 31% reported higher sales. 77% saw no change in labour costs (the same Federal Reserve survey). In a survey by Clio, which sells legal software, 86% of solo lawyers and 78% of small law firms had made no pricing change because of AI (Clio). If you bill by the hour, an hour saved is an hour you do not bill, unless you change how you price.
- Watch what you stop buying, and who sells it to you. 18% of US small-firm users spent less on outside services, in the same Federal Reserve survey. On a large freelance platform, demand for writing and translation fell 20–50% below its earlier trend after ChatGPT arrived, and short jobs fell most (study). If you sell short, well-defined jobs, other firms’ savings are your lost orders.
- Buy, don’t build. Small firms rarely have data or IT specialists. AI-first at this size means running core tasks on ready-made tools, not custom systems. Among small firms that paid for AI, the median spend was about $28 a month in 2025, in the Chase records. Buying does not move the risk to the vendor. In the United States, insurers can now add standard wording that removes generative-AI losses from general liability cover, trade press reports (Big I). Ask your broker at renewal whether AI is excluded. Budget for AI price rises in the software you already pay for: Google put its Gemini AI into all Workspace business plans and raised the price of Business Standard by $2 per user per month (Google).
- Pick one task. The gains measured in studies came from tasks that are frequent, well defined and checked by a person. Making a chatbot available to everyone did much less. In a randomised trial with 640 Kenyan small-business owners, owners who acted alone on an AI business adviser’s answers did no better on average, and the weakest did nearly 10% worse. Whether starting narrow or using AI widely gives more is not settled. In an OECD survey, firms that used AI across the business reported large effects far more often than firms that used it for single tasks. The firms rated those effects themselves, and the OECD says the sample is not representative (OECD). US hiring data show no difference between light and deep use (Federal Reserve Bank of New York).
- Speed is your advantage. A small firm can try a new way of working, measure it and drop it or keep it within weeks. If you sell on a marketplace, the platform decides which AI runs on your listings. On one large retail platform, AI that the platform built into its own tools raised sales by up to 16.3%, and the sellers did nothing (randomised experiments; some authors worked for or advised the platform).
- Hiring can go either way. In payroll records analysed by Gusto, which sells payroll software to small firms, US firms with fewer than ten employees that adopted AI grew headcount about 10% more over a year than firms that knew about AI and did not use it. The new hires were mostly hands-on, customer-facing staff. The records show what went together, not what caused it (study). Among US start-ups with about 11 staff on average, those whose work was most exposed to AI cut employment by about 8%, mostly in junior roles (study). The two studies do not settle why. One reading is that firms selling hands-on services hire, and firms selling digital output cut juniors.
- Firms of one. In the Chase records, 15.3% of firms with no employees had paid for AI, against 26.1% of firms with employees. Among firms with employees, smaller ones paid as often as larger ones. In the United States, applications for one-person businesses rose 26.8% in AI-exposed sectors from early 2024 to early 2026, in a descriptive analysis by the Mercatus Center that does not show the cause (Mercatus). On Product Hunt, a site where people launch new software, solo launches rose after ChatGPT, while teams increasingly hold the top rankings (Kim, Kang and Song).
- How customers find you is changing. When Google showed an AI summary, people clicked a normal search result on 8% of visits, against 15% without one. The figures come from the browsing records of 900 US adults, collected by the Pew Research Center (Pew).
Read the guide’s section on this size for the figures and their sources.
All practices for this size
This is the full list to come back to after the five moves: 22 practices that fit companies with 1–49 employees. They are grouped by stage. In each group, what to do first comes first. Filter them by theme, timing or evidence level.
Explore
Find out where you stand and where AI could help.
- Map tasks before deciding what to automate Break jobs into tasks and look at each one. Published scores rate how much of a job AI could do, its “exposure”. Treat a high score as a place to look, not as a decision to automate. Do first · Effort: low (days, one person)
- Ask AI vendors for proof before you buy, and test on your own material Do not take an AI vendor’s accuracy figure or “fully compliant” promise on trust. Ask how it was tested and on what, run a small test on your own material, and check the terms on your data and on leaving. Do first · Effort: medium (weeks, a few people)
- Know the rules that already apply to your AI use Most of the rules that bind your use of AI are not AI laws. You answer for what your AI says and does under the consumer, privacy, advertising and professional rules you already follow. List your AI uses and match each to its rules. Do first · Effort: low (days, one person)
- Start with one costly process, not a company-wide rollout Pick one process that visibly costs time or money. Apply AI there, with a person checking the output, and expand only when the numbers move. Do first · Effort: medium (weeks, a few people)
- Start with ready-made AI in software you already use, before building your own Your office, accounting, support and sales software probably has AI features now. Try those on one task, with a few licences and a measured result, before you pay anyone to build something custom. Do first · Effort: low (days, one person)
Adopt
Put AI to work on real tasks, with people checking the results.
- Keep a person checking anything that goes to a customer or carries legal weight Before AI output leaves the company or is relied on in a decision, a named person checks it against real sources and signs it off. Make the check a step in the process, with time allowed for it. Do first · Effort: medium (weeks, a few people)
- Train staff on the tools you actually chose Show every user what your chosen AI tool is, where it goes wrong, and what they must check. Use your own tasks and your own rules, give people time to practise, and include temporary staff. Do first · Effort: medium (weeks, a few people)
- Budget for the work AI creates AI removes some work and adds other work: checking its output, keeping records, handling exceptions and looking after the tools. Count that work and give it an owner before you count the saving. Do first · Effort: low (days, one person)
- Decide what data may go into which AI tools Sort your data into two or three groups and say which AI tools each group may go into. Client secrets and personal data stay out of public tools. Where a mistake would be serious, block the upload instead of relying on a rule. Do first · Effort: low (days, one person)
- Measure time saved per task against a baseline Record how long a task takes and how good the result is before you add AI. Without that starting figure you cannot tell a real gain from a hopeful one. Do first · Effort: low (days, one person)
- Write a one-page AI-use policy staff will actually read Put the rules for AI on one page: which tools are allowed, what must never be typed in, who checks the output, and who to ask. Then build the most important rule into the work itself, because a page alone gets ignored. Do first · Effort: low (days, one person)
- Make the AI show what its answer is based on A score or an estimate with no reason attached is hard to judge. Choose and set up AI tools so that each answer comes with what it rests on: the marked part of the image, the similar past cases, the passage in the document. Do next · Effort: medium (weeks, a few people)
- Claim only what your AI has been tested to do If you tell customers that your product or service uses AI, or how well it works, hold the evidence before you say it. Regulators treat “AI-powered” and “98% accurate” as factual claims like any other. Do next · Effort: low (days, one person)
- Give AI assistance to newer staff first When you roll out an AI assistant for a well-defined task, start with your less experienced people. Studies find they gain the most. Do next · Effort: low (days, one person)
- Put your own records in order before you ask AI to predict from them An AI that estimates a price, a time or a next step learns from your past jobs and customers. Get those records complete, consistent and in one place first. In the cases here, that was most of the work. Do next · Effort: medium (weeks, a few people)
- Check AI advice on money, tax and law before you act on it When you use an AI tool as an adviser, check its advice on money, tax and law against a reliable source or a professional before you act. In a small firm, nobody else will catch a wrong answer. Do next · Effort: low (days, one person)
- Set the limits before the tool acts Before an AI tool sets a price, sends a message or takes a step for you, write down what it must never do, how far it may go, and what needs a person. Build those limits into the tool, then test inside them. Do next · Effort: low (days, one person)
Redesign
Change how the work is done, not only the tools.
- For each kind of task, test whether a person with AI beats either one alone Do not assume that adding a person to AI, or AI to a person, gives the best result. Test it for each type of task before you settle the process. Do next · Effort: medium (weeks, a few people)
- Redesign the workflow instead of adding a tool to it Giving people an AI tool inside an unchanged process changes little. Decide again who does each step, in what order, and where a person checks. Do next · Effort: high (months, several teams)
Run
Keep it working: skills, quality and reviews over time.
- When AI output goes wrong, correct it fast and in the open Decide now what happens when an AI error gets out: who is told, who withdraws or corrects the work, and who checks how far the problem goes. In the documented cases, firms that owned up quickly were treated far better than firms that defended the error. Do next · Effort: low (days, one person)
- Check now and then what staff can do without the AI On a sample of cases, have people do the work without the AI and compare the result with their usual work. Start with the people who check AI output and the people still learning the job, and set the tools that learners use to give hints, not answers. Do later · Effort: medium (weeks, a few people)
- Keep the entry-level tasks that newcomers learn on Before you automate routine work, check whether it is how juniors learn the job. If it is, keep some of it for them, or replace the learning another way. Do later · Effort: medium (weeks, a few people)
Company cases
These are named companies of this size, with numbers and a public source. All cases.
- Delphia and Global Predictions: fined for saying they used AI that they did not have Two investment advisers advertised AI they did not have. One said its AI learned from client data. The other called itself the first regulated AI financial advisor. The US securities regulator fined them $225,000 and $175,000.
- EIST: a six-person metalworking shop cut quoting time from two hours to ten minutes A metalworking shop with five employees and an owner used AI to price custom parts from 3D files and its own production history. A quote that could take two hours now takes about ten minutes. Most of the project was spent cleaning the firm’s data, and the owner still checks every quote.
- Garfield.Law: a regulator authorised an AI-run law firm for small debt claims, with limits The regulator of solicitors in England and Wales authorised its first law firm that delivers legal services through AI. It helps businesses recover unpaid debts through the small-claims court. The regulator set limits: the AI may not propose case law, takes no step the client has not approved, and named solicitors stay accountable.
- Gilberts: a smaller audit firm started with the AI in its existing tools and two firm rules A smaller UK audit and accountancy firm began with the AI features in tools it already had, on low-risk internal tasks. A partner reports that summarising a long document now takes 10 to 15 minutes where it took 45. The firm bars client data from public AI tools and treats every AI output as a first draft.
- Primus Solicitors: 18 court citations that did not exist, taken from a client’s AI research A solicitor at a small law firm filed a statement with 45 case citations. Eighteen of the cases did not exist. His client had produced the research with public AI tools, and nobody at the firm checked it. The court referred him to his regulator.
- SAM: a two-person advisory firm cut the paperwork on each client file from six hours to two and a half A two-person firm that advises clients on retirement turned each client file into a fixed sequence of steps and gave the routine ones to AI. Administrative time per file fell from about six hours to two and a half. Set-up cost about €3,000 and 60 hours, and the adviser still approves important letters.
Worked example
This example is a medium-sized company. The steps are the same at any size: map the tasks, sort them, name owners, and pilot in stages.