In short
- Decide how far you are going: better tools, redesigned functions, or new products.
- Companies that report the most value from AI aim for growth or innovation, not only savings.
- Buying usually beats building for smaller firms.
- Do not base the plan on a maturity score, an exposure score or another company’s memo.
For what AI-first means, see the guide.
Three levels of ambition
The consultancy BCG separates three plays (BCG, 2024):
- Deploy off-the-shelf tools across the workforce.
- Reshape whole functions around AI.
- Invent new products and services built on AI.
BCG estimates the productivity gain from Deploy at 10–15% and from Reshape at 30–50%. The framework is useful as a question: which of the three are you planning for? Only the second and third change how the company works, which is what AI-first means.
Aim beyond cost
In McKinsey’s 2025 survey, 80% of respondents said their company set efficiency as a goal for AI. The companies reporting the most value often set growth or innovation as goals as well. They were more than three times as likely as others to say they intend to use AI to transform their business.
The opposite case is on record too. Klarna judged its customer-service AI mainly on cost and later said quality had suffered (Customer Experience Dive).
Buy or build
- The case for buying. A 2025 MIT study, as reported by Fortune, found that buying AI tools from specialised vendors and building partnerships succeeded about 67% of the time, far more often than internal builds (Fortune).
- A result that points the other way. In a survey of 179 leaders in Poland, internal builds did markedly better at the proof-of-concept stage than purchased software.
- Size settles most of it. A firm with no data or IT specialists cannot maintain what it builds. For micro, small and medium-sized firms the practical model is to buy, set up and govern.
What changes in the business model
AI-first can change a business model in three places.
- Cost to serve. Routine work costs less. The saving is real only if quality holds, so measure both.
- Speed. Work that took days can take hours. That can become something you sell: faster quotes, faster answers, faster delivery.
- New offerings. Some companies build products on their own data. Stripe, for example, says a payments model trained on its transaction data raised its detection of one type of card fraud by 64% (TechCrunch).
Most companies will change the first two long before the third. Work out which tasks are involved before you promise anything: map tasks before deciding what to automate.
What not to base the plan on
- A maturity score. Evidence that a higher score leads to better results is thin and disputed. See do not treat a maturity score as a forecast.
- An exposure score. A figure for how much of a job AI “could” do shows potential. Across 23 countries, more exposed occupations did not lose employment overall (study).
- Another company’s memo. An announcement says what a company intends. It does not say whether it worked. See what does not count as evidence.
- Reported returns. Return-on-investment figures from surveys of business leaders are opinions. Your own baseline is worth more.
Practices
Practices on strategy and on finding out where you stand. What to do first comes first.
- 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)
- 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)
- 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)
- 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)