AI Adoption: Using the ADKAR Framework to Make Change Stick
Everyone's talking about AI tools. Leadership teams are excited about productivity gains. Vendors are promising transformation. And yet, here's what the data actually shows: around 90% of AI implementations fail to deliver their expected value.
That's not a technology problem. It's a people problem.
I've spent months working with mid-market UK firms on AI adoption, and the pattern is consistent. The organisations that succeed aren't the ones with the biggest budgets or the flashiest tools. They're the ones that treat AI adoption as a change management challenge: not a tech rollout.
Enter the ADKAR framework. It's not new, it's not sexy, and it won't get you excited at a conference. But it works. And if you're serious about making AI stick in your organisation, it's worth understanding why.
The Real Reason AI Rollouts Fail
Let's be honest about what's happening in most UK organisations right now.
Leadership sees the potential. They read the headlines, attend the webinars, and get genuinely excited about what AI could do for efficiency, decision-making, and competitive advantage. They greenlight a Copilot rollout or invest in a shiny new platform.
Meanwhile, employees are worried. They're reading different headlines: ones about job losses, skills gaps, and machines replacing humans. A recent analysis shows that while executives see opportunity, frontline workers see threat.
This worry versus excitement gap is killing your AI initiatives before they even get started.
You can have the best AI tools on the market. But if your people don't understand why they're being asked to change, don't want to change, or don't know how to change: you've wasted your investment.
What Is the ADKAR Framework?
ADKAR is a change management model developed by Prosci. It stands for:
Awareness – Understanding why the change is happening
Desire – Wanting to participate in the change
Knowledge – Knowing how to change
Ability – Demonstrating the skills and behaviours required
Reinforcement – Making the change stick long-term
Unlike process-focused change models, ADKAR puts individuals at the centre. It recognises that organisations don't change: people do. And people change in a specific sequence.
Skip a step, and you'll hit resistance. Push too fast, and you'll lose people. This is why so many AI rollouts stall after the initial excitement fades.
Applying ADKAR to AI Adoption
Here's what each stage looks like in practice when you're rolling out AI tools across your organisation.
1. Awareness: Why Are We Doing This?
Before anyone learns a new tool, they need to understand why it matters. This isn't about sending a company-wide email announcing "we're adopting AI." It's about answering the questions your people are actually asking:
Why now?
What problem does this solve?
How will this affect my job?
Most organisations skip straight to training. Big mistake. If employees don't understand the business case: or worse, if they think AI is being brought in to replace them: you've already lost them.
What this actually requires:
Transparent communication about the strategic reasons behind AI adoption
Honest conversations about what will and won't change in people's roles
Leadership visibility: not just an announcement from IT
The failure to build awareness early is one of the most common mistakes in AI change management.
2. Desire: What's In It For Me?
Awareness gets people's attention. Desire gets their buy-in.
This is where you need to shift from "here's why the company needs AI" to "here's why you might actually want this." And that means addressing fears head-on.
Most employees aren't resistant to technology. They're resistant to uncertainty. They want to know:
Will this make my job easier or harder?
Am I going to look incompetent while I learn?
Is this just more work on top of everything else?
What this actually requires:
Showing how AI reduces tedious tasks rather than adding complexity
Identifying early adopters who can model positive experiences
Creating psychological safety around experimentation and mistakes
Addressing the "what about my job?" question directly
You can't manufacture desire through mandates. You build it through trust and relevance.
3. Knowledge: How Do I Do This?
Once people want to engage, they need to know how. This is the training stage: but it's not just about clicking through an e-learning module.
Effective AI training looks different from traditional software training. AI tools are probabilistic, not deterministic. They require judgement, context, and iteration. Your people need to understand not just which buttons to press, but how to think about AI as a collaborator.
What this actually requires:
Practical, role-specific training (not generic overviews)
Clear documentation and quick-reference guides
Access to coaching and support during the learning curve
Permission to experiment without fear of breaking things
The UK's AI training gap is well-documented: 78% of workers are using AI, but only 24% have received formal training. That's a Knowledge gap waiting to cause problems.
4. Ability: Can I Actually Do This?
Knowledge and ability aren't the same thing. I can know how to play the piano in theory. That doesn't mean I can perform at a concert.
Ability is about translating knowledge into practice. It's where rubber meets road: and where many AI initiatives quietly fail.
This stage requires patience. People need time to fumble, make mistakes, and build confidence. They need space in their workload to actually practice. And they need support when they get stuck.
What this actually requires:
Protected time for learning and experimentation
Removal of conflicting priorities and outdated systems
Identification and support for those struggling to adapt
Realistic expectations about the learning curve
If your people are expected to adopt AI while maintaining their full workload with no additional support, you're setting them up to fail.
5. Reinforcement: Making It Permanent
Here's where most organisations drop the ball completely.
They run a training programme, see some initial adoption, declare victory, and move on. Six months later, people have quietly reverted to their old ways of working.
Reinforcement is about making the change stick. It means embedding AI into how work actually gets done: not as an optional extra, but as the default.
What this actually requires:
Integrating AI usage into performance expectations and reviews
Celebrating wins and sharing success stories
Addressing backsliding quickly and supportively
Continued leadership visibility and commitment
Regular check-ins to identify new barriers or skill gaps
Behavioural change doesn't happen overnight. It requires consistent reinforcement over time.
Using ADKAR to Diagnose Resistance
One of the most useful things about ADKAR is its diagnostic power.
When someone isn't adopting AI, you can pinpoint exactly where they're stuck:
Not using it at all? Probably an Awareness or Desire gap.
Using it badly? Probably a Knowledge gap.
Trying but struggling? Probably an Ability gap.
Used it for a while then stopped? Probably a Reinforcement gap.
This lets you target your interventions rather than throwing generic "more training" at everyone. Different people in different departments will face different barriers. ADKAR helps you identify and address them specifically.
The Bottom Line
AI adoption isn't a technology project. It's a people project.
The organisations that get this right: that treat AI as a change management challenge requiring structured support through Awareness, Desire, Knowledge, Ability, and Reinforcement: are the ones that actually capture the value everyone's promising.
The rest will join the 90% who invested in tools their people never properly used.
If you're a UK business leader planning an AI rollout, or struggling with one that's stalled, start by asking: where are my people actually stuck? The answer will tell you exactly where to focus next.
Ready to get AI adoption right in your organisation? Explore our practical resources for managers and L&D professionals, or read more about why AI implementations fail in UK organisations.
