Why your team has AI licenses but still isn’t using AI

Most companies have access to AI, but few teams use it every day. Six steps to move from AI access to AI adoption, based on our work with fast-growing teams.

Almost every company has access to AI by now. The tools are purchased, the licenses are running, and somewhere in the organisation a handful of people are doing impressive things with them. Ask how many people use AI as part of their daily work, though, and the answer is usually a lot smaller than the investment suggests.

The research points the same way. In Randy Bean’s 2026 AI & Data Leadership Executive Benchmark Survey, 93% of senior data and AI leaders at more than 100 Fortune 1000 and global organisations named culture and change management as the biggest barrier to AI adoption. Only a small minority pointed to technology. Managers feel the gap too. Research from the Chartered Management Institute among more than 1,000 UK managers shows that 68% of organisations are still experimenting or running pilots, while over 80% of managers say both they and their team would perform better if they understood how to manage AI.

And that naturally made me wonder:

Why this hits scale-ups so hard

You might expect this to be a corporate problem, with too many layers and too many stakeholders. We see the same pattern at fast-growing companies. When a business scales quickly, internal capacity has to keep up, and AI should help with that. To discover what AI can do for your work, though, you need time to explore it, and that’s the one thing a growing team rarely has.

So the enthusiasts run ahead, their knowledge stays with them, and the rest of the team keeps working the way it always has. Nobody refuses to use AI. There’s just no shared way of working, no shared set of tools, and nobody whose job it is to make it stick.

Six steps from AI access to AI adoption in teams

1. Diagnose before you build.
The first request is often about a specific tool or task. Before acting on it, find out what’s behind it: which tasks cost the most time, where quality gets lost, and what the team needs. The answer often turns out to be different from the original brief.

2. Know your starting point.
Map where every team member stands today and what would help them move forward. It shapes the content of your sessions and turns progress into something you can show.

3. Train on real work.
Generic AI courses rarely change how people work on Monday morning. Build every session around use cases from the team’s own daily tasks and test them on real client cases, so people see the difference in their own work.

4. Build a structure that fits your team.
Most people want to use AI and have no wish to build with it. They need a system that works for them, so they can learn to direct AI without learning to code. You don’t need to turn everyone into a builder. You need a few people who can.

5. Create shared tools.
Instead of everyone writing their own prompts, build a shared library of AI tools, such as Claude skills, based on how your team already works and communicates. It keeps output consistent and makes good work easy to repeat.

6. Appoint internal owners.
Choose a small group of AI ambassadors from within the team. They gather needs, build alongside their colleagues, and keep the knowledge in-house once external support ends. That’s what turns a project into lasting capacity.

What this looks like in practice

Billy Grace, the attribution platform for performance marketers, already used AI every day. With the company growing fast, they wanted to move from individual use to a shared way of working across CS and Sales. Ten sessions, a shared skill library, and five AI Innovation Ambassadors later, the team came up with its own next AI projects, and output across both teams is more consistent, with less manual work.

You can read the full case here.

The step you can’t do alone

Steps 2 to 6 are things most teams can start on themselves. Step 1 is the hard one. From inside your own team, it’s difficult to see the question behind the question, because you’re the one asking it. That’s usually where an outside view makes the biggest difference.

Every team is different, and the right starting point depends on where yours stands today. We’d love to hear where AI is stalling in your organization and think along about that first step. Pull up a chair and let’s talk.

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