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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Name an executive owner for AI. Add a small group of senior people from several functions to set priorities, and an AI lead in each business unit.
Days, one person Name one accountable AI owner
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Say which data may go into which AI tools, using the data classes you already have. Back the rule with technical controls on uploads and downloads.
Days, one person Decide what data may go into which AI tools
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Set a standard list of questions for every AI purchase, with security and legal review. Test each tool on your own material and keep the test set.
Weeks, a few people Ask AI vendors for proof before you buy, and test on your own material
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Before you approve a tool, put a figure on training, redesign and checking time. Expect data quality and integration to take a larger share once pilots are rolled out more widely.
Days, one person Budget for people and process, not just tools
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Take one process where a pilot has shown a gain on a single step. Redesign the whole process around it, and name one owner for all of it.
Months, several teams Redesign the workflow instead of adding a tool to it
What is specific to this size
- Most firms have started. In RSM’s 2026 survey of mid-sized firms in the United States and Canada, 86% said they had partly or fully built AI into their operations. That survey defines size by revenue, not headcount (RSM).
- Data and integration are the sticking points. Among firms whose pilots had only moderate or limited success, the most-cited barriers to rolling them out more widely were data quality (53%) and integration with existing systems (47%), in the same survey. Giving each person a licence is not integration. In a randomised experiment in 66 firms, an office AI assistant saved email time and did not change what people worked on (study page).
- The size advantage is contested. BCG’s 2026 study found that large companies were more likely to report significant revenue gains from AI, spent a larger share of revenue on it, and hired AI specialists faster (BCG).
- A common structure. A named executive owner and a small group of senior people set priorities. Business units get their own AI leads, backed by a central team for shared tools, data and rules.
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: 29 practices that fit companies with 250–999 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)
- Do not treat a maturity score as a forecast An AI maturity or readiness score describes where you say you stand. Evidence that a higher score leads to better results is thin and disputed. Use it to start a discussion, not to predict returns. Do next · Effort: low (days, one person)
- Assess readiness from several sources, not one executive’s rating A readiness check filled in by one senior person reflects that person’s view. Ask staff and managers as well, and compare the answers with what you can observe. Do next · Effort: medium (weeks, a few people)
- Measure readiness and resistance separately Staff can be keen on AI and worried about it at once. Ask about the two separately. A single “readiness” score hides the worry. Do next · Effort: medium (weeks, a few people)
Adopt
Put AI to work on real tasks, with people checking the results.
- Protect psychological safety during an AI rollout AI projects can make staff afraid to speak up and unwilling to share what they know. Say early what AI means for jobs, and consult the people affected. Do first · Effort: medium (weeks, a few people)
- Budget for people and process, not just tools Licences are the small part of the cost. Plan most of the time and money for training, redesigning work, and helping people through the change. Do first · Effort: low (days, one person)
- 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)
- Name one accountable AI owner Give one named senior person the responsibility for AI: priorities, budget, risk and results. Shared responsibility tends to mean nobody decides. 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)
- Test AI knowledge instead of asking staff to rate their own People judge their own skills poorly. To find out what your staff know about AI, use a short knowledge test or a second opinion, not only a self-rating. 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)
- Run two tracks: hand some tasks to AI, give experts AI help on others Treat AI work as two programmes. One automates repeatable digital tasks whose results are easy to check. The other gives experts AI help on work that cannot be handed over. Judge each by its own measure. Do next · Effort: medium (weeks, a few people)
- 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.
- Butler Snow: a firm with an AI policy, and a partner who did not follow it The firm’s policy required written approval before using ChatGPT for legal research. A partner used it without approval and filed five citations it had made up. The court removed three lawyers from the case. The firm paid for a check of 2,400 citations.
- Duolingo: 148 new language courses in about a year, after 12 years for the first 100 Duolingo replaced a slow, manual way of writing language courses with one built around AI. It launched 148 new courses in under a year, after taking about 12 years over its first 100. The same week, its chief executive told all staff what “AI-first” would mean for contractors, hiring and headcount.
- META-Regalbau: sensors and AI analysis of order picking paid back in about a year A shelving manufacturer with about 300 employees had staff wear small sensors so that AI could analyse how orders are picked in its warehouse. The findings shaped the layout of a new warehouse and save about €22,000 a year. Managers, the works council and all staff were told the purpose from the start.
- NSW Reconstruction Authority: a temporary worker uploaded data on 2,031 people to ChatGPT A temporary staff member uploaded a spreadsheet of flood-recovery applicants to ChatGPT, which the agency had not approved. It held personal and health details of 2,031 people. The agency then wrote AI guidance, tightened training and blocked such uploads.
- Rue La La: machine-learning price recommendations raised revenue by about 10% in a test Rue La La, an online fashion retailer, had a tool built that predicts demand for products it has never sold and recommends a price for each. In a five-month test, researchers estimated that revenue on the tested products rose by 9.7%. The retailer set firm limits on what the tool could do before it let it near a price.
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.