The role of coaching in AI adoption

AI has moved from experimentation to expectation in remarkably little time, while many workforces are still navigating the behavioural adjustments this moment demands. Organisations are investing heavily, leaders are pushing for deeper integration, and new tools appear almost weekly – yet real adoption continues to lag behind. This friction rarely lies in the technology itself, but in how individuals and teams adapt their behaviour around it. 

Recent UK government research makes this tension difficult to ignore, finding that despite 73% of employees reporting they have used AI in some form, just 21% feel confident using it at work. That is a striking disconnect, and one that should give every business leader pause. If the majority of workforces lacks confidence with AI, no amount of investment in tools or infrastructure will close that gap on its own. 

The real barrier to AI adoption 

When organisations hit friction in their AI rollouts, the instinct is often to revisit the technology: reassess platforms, tweak training programmes, or increase spending. But this misdiagnoses the problem. Integrating AI into everyday working life requires shifts in judgement, habits, and confidence that do not emerge automatically from a training session. The challenge becomes less about deploying tools and more about enabling people to work differently with them. 

This is compounded by a significant disconnect at leadership level. While organisations often assume their people are further along than they are, 56% of employers whose businesses are currently using or planning to use AI rate the overall level of knowledge in their business as “beginner” or “novice.” Change fatigue makes this challenge even harder still. When employees are navigating multiple transformation initiatives simultaneously, AI adoption becomes one more demand placed on already stretched cognitive and emotional bandwidth, and without adequate support, initial enthusiasm tends to fall away quickly. 

Why coaching belongs in the conversation 

If AI adoption is fundamentally a behavioural challenge, then behavioural change needs to be at the heart of the solution. This is where coaching enters the picture in a way that training alone cannot replicate. 

Traditional change management approaches tend to treat resistance as an information problem: provide the facts, outline the process, and adoption will follow. But uncertainty, confidence, and disrupted habits play a far greater role than information gaps ever will. Coaching operates at a different level of intervention. Rather than focusing exclusively on knowledge transfer, it helps individuals work through uncertainty, build balanced trust in AI tools, and translate abstract guidance into concrete action in their roles.  

This is well established in workplace behavioural science, which identifies a handful of conditions that determine whether people genuinely embrace new technologies. This includes believing it will improve their performance, feeling it is manageable to use, seeing peers and leaders model it, and having the right structures in place to support them. To this end, coaching directly addresses each of these in a way that training programmes rarely do. 

This is especially relevant for AI, where effective usage depends heavily on judgement and contextual awareness. Many employees grasp AI tools conceptually, yet this understanding does not always translate into confident application, particularly when decisions carry greater consequence or scrutiny. Concerns about accuracy, credibility, or role implications can dampen willingness to experiment. Coaching creates the space to work through these anxieties and maintain engagement over time, rather than allowing early setbacks to give way to hesitation or disengagement. 

What coaching reveals about working with AI 

AI changes the cognitive demands of work, influencing how information is generated, how options are evaluated, and how expertise is applied. Knowing how to operate AI tools is only part of the challenge. Users must also develop judgement about when to rely on AI, when to question its outputs, and how to integrate insight that has AI generated into human decision making. 

Coaching oriented approaches are particularly well suited to these demands because they focus on how people think and decide in practice. They cultivate critical thinking, encourage reflection, and help individuals recognise the cognitive habits that shape their judgement. Beyond individual capability, effective coaching also develops the broader competencies that AI adoption demands at an organisational level – namely, change agility, resilience, and the kind of growth mindset that allows people to treat early experimentation as learning rather than failure.  

Together, these capabilities create the conditions for something more fundamental – rather than treating AI as a static tool to be learned, coaching creates space to examine how it influences reasoning and behaviour, supporting more deliberate and considered usage over time. 

Scaling support with AI coaching 

One of the most common limitations of coaching has been its reach across an organisation. Traditionally a resource concentrated at leadership level, its benefits have rarely extended across an entire workforce – but AI powered coaching tools directly challenge this. It can deliver scalable, personalised support to every employee regardless of seniority or location, offering real time feedback, prompts for reflection, and behavioural reinforcement that sustains development well beyond isolated training moments. 

This democratisation matters enormously in the context of AI adoption. Rather than concentrating support at the top of the organisation while the wider workforce is left to navigate change unaided, AI coaching ensures that the human foundations of adoption are built consistently and at scale. 

The human factors that slow transformation 

Not all aspects of behavioural change lend themselves to automation, and it is important to recognise this. Challenges involving identity, ethical uncertainty, or complex interpersonal dynamics require the kind of nuanced human interpretation that technology cannot replicate. The most effective organisations are combining AI coaching with human coaching, using each for what it does best. AI provides the scale and consistency; human coaches bring the depth and contextual judgement needed for more complex development needs. 

Tools can be introduced rapidly, but habits, judgement, and confidence evolve more gradually. The organisations that will lead on AI are not necessarily those with the most advanced technology, but those that invest in the human foundations that make adoption sustainable and lasting.