In short
- Almost every company uses AI somewhere. Few have changed how they work because of it.
- The change happens process by process, in four stages: Explore, Adopt, Redesign, Run.
- Most efforts stall between Adopt and Redesign: the tool is in place, and the process is unchanged.
- Each practice on this site is tagged with the stage it belongs to.
For what AI-first means, see the guide. The four stages are this site’s way of sorting practices. They are not a maturity ladder.
1. Explore
Find out where you stand and where AI could help. Look at tasks, not job titles. Ask several groups of people, not one executive.
You are through this stage when: You have picked one or two processes to start with, and you have recorded how they perform today.
- 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)
2. Adopt
Put AI to work on those processes, with people checking the output. Train the people who use it. Name who is in charge.
You are through this stage when: The numbers have moved against the starting figures, and staff use the tool without being pushed.
- 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)
- 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)
- 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)
3. Redesign
Change the process itself. Decide again who does each step, in what order, and where a person checks.
You are through this stage when: The whole process, start to finish, is faster or better. One faster step is not enough.
- 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)
4. Run
Keep it working. Tools and models change, skills fade, and errors creep in. This stage has no end.
You are through this stage when: You review results, skills and risks on a schedule, and you act on what you find.
- 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)
Why most efforts stall
- Wide use, little profit. In McKinsey’s 2025 survey, 88% of organizations used AI in at least one business function. About 6% credited it with 5% or more of operating profit.
- Projects get dropped. In an S&P Global survey reported in 2025, 42% of companies said they had abandoned most of their AI initiatives, up from 17% a year earlier (CIO Dive).
- Availability is not change. Across the Danish labour market, chatbots were widely adopted without any measurable change in earnings or hours.
- Judging on cost alone backfires. Klarna said in 2024 that its AI assistant did the work of 700 customer-service agents. In 2025 its chief executive said cost had been “a too predominant evaluation factor”, that quality had suffered, and that the company would hire more human agents (Customer Experience Dive).
The common thread: results come from redesigning a specific process and measuring it. See redesign the workflow and measure against a baseline.
By company size
The stages are the same at every size. The pace and the cost differ. A small firm can go from Explore to Redesign on one process in weeks. An enterprise has many processes at different stages at once.