In short: adopting AI in a company is a process of changing how you work, not buying a tool. It has five stages: building the team’s awareness, mapping everyday tasks, choosing and wiring in solutions, training people and embedding habits, and measuring the effects. It’s an ongoing process - because by the BCG 10-20-70 rule about 70% of the value comes from people and processes, and only 10% from the algorithms themselves. That’s why companies that “buy AI” often see no effect - while those that rebuild a concrete process around it do.
Adopting AI is a process, not a purchase
The most common misunderstanding is: “we’ve adopted AI because we bought access.” A subscription alone doesn’t change how the team works. Access to the best model is just 10% of success - the rest is understanding where AI genuinely helps and teaching people how to use it.
The data shows it. On one hand, AI is already everywhere: according to McKinsey 72% of companies have adopted AI in at least one function, and 65% regularly use generative AI. On the other, simply buying the tool rarely is enough. Gartner predicts that at least 30% of generative AI projects will be abandoned after the pilot stage by the end of 2025 - most often due to poor data quality, unclear business value and rising costs. RAND, citing industry estimates, reports that over 80% of AI projects fail - twice as often as IT projects without AI.
The difference between those that succeed and the rest rarely sits in the model itself. It sits in whether the company treated adoption as a change in how it works, run in stages. So we break it into five steps.
Stage 1: awareness and team education
We start from the top - the owner, then managers, then employees. The team needs to know three things:
- What AI is and where it shows up - not just a chat window. Also assistants built into tools (in the office suite, in the CRM), agents that carry out whole tasks, and automations running in the background.
- What its limits are - models can get things wrong (“hallucinate”), don’t offer 100% certainty, and require care with data. Awareness of the limits guards against blind trust.
- What the real uses are - where AI helps in your industry, and where it won’t add value.
Without this foundation, the later stages hang in the air: people either fear the tool or trust it uncritically. There’s a third scenario too, the most common today - they use it quietly. A Microsoft study found that 75% of knowledge workers already use AI at work, and 78% bring their own tools, often without the company knowing. That’s “Shadow AI”: company data ends up in random apps, with no rules and no control. Awareness turns it from a hidden risk into a manageable advantage.
Stage 2: mapping the work
Next we look at what the team actually does. We break down everyday tasks and ask: what repeats most often, what takes the most time, where the bottlenecks form. It’s a map on which we mark the spots with the most potential - usually tasks that are repeatable, measurable and low-risk. For example, in a B2B company that map often shows the biggest time sink isn’t selling itself, but preparing offers, answering repetitive enquiries and manually re-keying data between systems. That, rather than the “strategic” areas, is where the first real gain sits.
This stage is crucial because it decides where it’s even worth adopting AI. Skipping it ends in “adopting AI for the sake of AI”. How to pick that first process in practice, we break down in a separate post: where to start adopting AI.
Stage 3: choosing and wiring in solutions
For the mapped spots we choose concrete solutions that genuinely speed up the work. But choosing isn’t the end - a tool bought and left alone ends up on the shelf. So the third step is actually wiring AI into the daily process: so that using it is a natural part of work, not an extra task on the side.
In a manufacturing or B2B company that spot is often handling repetitive enquiries, drafting first versions of documents, an initial check of quality data, or tidying up information from machines and systems. The common thread: time leaks there every day, and a single mistake doesn’t cost you a client.
Stage 4: training people and embedding habits
The best solution won’t work if the team doesn’t change its habits. So we teach people specific uses - on their own tasks, not abstract examples - and help cement the new way of working. This closes the “people” pillar - the same one that, by the 10-20-70 rule, holds about 70% of the entire rollout’s value.
It’s also the most often skipped stage. Buying licences for the whole team is easy; getting anyone to still use them a month later is harder. So we measure not the number of seats bought, but whether the new way of working took hold.
Stage 5: measuring and iterating
Finally we check whether it worked. We compare “before” and “after” on the mapped tasks: how long they took before, how long they take now. That’s the difference between “we’ve adopted AI” and “AI actually works”. Where the effect is there, we scale to more areas; where it isn’t, we adjust or drop it.
Measuring also protects the budget. Without a “before” number you can’t tell real savings from a feeling that “it’s faster” - and it’s exactly that unclear business value that most often sinks AI projects after the pilot.
What sets AI adoption apart from a regular IT project?
A regular IT project has an end: you deploy a system, sign off, close it. AI adoption doesn’t work that way - both the tool and the way you work around it keep changing. It’s closer to continuous improvement than a one-off install.
You can see it best in manufacturing. Factories in the World Economic Forum’s Lighthouse network that rebuilt their processes around AI and automation achieve on average 40% higher labour productivity and 41% fewer product defects. That effect doesn’t come from one better machine, but from changing how people work and how information flows. That is exactly what “adoption as a process” means.
Three misconceptions that cost the most
Most failed rollouts trace back to the same three wrong assumptions:
- “AI is a one-off purchase.” It’s not a product off the shelf, but a way of working you have to maintain. S&P Global reports that in 2025, 42% of companies abandoned most of their AI initiatives - up from 17% a year earlier. Usually not because the tool was bad, but because nobody closed the loop on the process around it.
- “It’s an IT-department project.” Value is created where the work is done - in sales, support, on the production floor. IT helps technically, but it’s the process owner who decides whether the new way of working takes hold.
- “Tool first, idea later.” The reverse order. First a task worth improving, then a tool chosen for it - not the other way round.
Why is it an ongoing process?
Because AI tools change every quarter, and their capabilities grow with them. Adoption has no single “end” - once built, awareness and habits let a company absorb each new improvement faster and cheaper than a competitor starting from scratch.
Key takeaways
- Adopting AI is a change in how you work, not buying a tool.
- Five stages: awareness → mapping → choosing and wiring in → training and habits → measuring.
- Most of the value (about 70%) comes from people and processes, not technology.
- Just buying the tool is a common dead end - at least 30% of generative AI projects are abandoned after the pilot.
- It’s an ongoing process - because tools and capabilities keep growing.