AI Adoption in Enterprise: How to Help Employees Embrace AI at Work

Every organisation seems to be talking about AI adoption. Boards want it on the agenda, executives want to see it reflected in strategy, and technology leaders are under pressure to demonstrate that the investments they’re making are delivering value. Yet despite all of this attention, AI adoption remains one of those phrases that gets used constantly but rarely gets explained. We hear that organisations need to “embrace AI” or that employees need to “adopt AI”, but very few people stop to ask what adoption actually looks like inside a business with thousands of employees, established ways of working and years of organisational change behind it.

The more we listen to organisations talk about adoption, the more we think we’ve misunderstood the challenge. We often assume that if people have access to AI tools, they’ll naturally start using them. We send out licences, run a few training sessions, publish a responsible use policy and assume the rest will take care of itself. When adoption doesn’t happen, we conclude that people are resistant to AI.

But we’re not convinced that’s true. Most people aren’t resisting artificial intelligence at all. They’re resisting another change programme.

Large organisations have spent decades asking employees to adapt to new systems, new processes and new ways of working. They’ve lived through ERP implementations, CRM migrations, collaboration platforms, expense systems, compliance programmes, digital transformation initiatives and countless mandatory training sessions. By the time AI arrives, many employees aren’t thinking, “Here’s an exciting opportunity.” They’re thinking, “Here’s the next thing I’m expected to learn while still delivering everything else on my to-do list.”

That changes how we should think about adoption. If the real challenge is behaviour rather than technology, then simply giving people another tool isn’t enough. People need a reason to change the habits they’ve built over years, and they need to see that the effort of learning something new is actually worth it.

People change when they see someone like them succeeding

One example that really stood out came from an enterprise technology company, who shared how they approached AI adoption internally. Rather than relying on a top-down mandate, they created an AI council made up of employees who were already enthusiastic about using AI, ran AI days and hackathons, published internal newsletters, and encouraged people to share practical examples of how AI was helping them in their day-to-day work. This wasn’t about the individual initiatives themselves but the philosophy behind them. They recognised that people don’t change because leadership tells them to. They change because they see someone they trust solving a problem they recognise.

We’ve seen exactly the same thing happen here. Our own adoption hasn’t been driven by a company-wide announcement telling everyone to use AI. It’s happened gradually through lunch and learns where people simply share what they’re working on. One person demonstrates how they’re using Perplexity to speed up research, another walks through a custom GPT they’ve built to remove repetitive tasks, while someone else shows an AI agent they’re experimenting with to automate part of their workflow. None of these sessions are polished demonstrations designed to impress people. They’re conversations between colleagues about things that have genuinely made their work easier, and that’s precisely why they work. AI stops feeling like an abstract technology and starts feeling like a practical way to solve everyday problems.

Start with your most curious employees, not your entire organisation

That idea of learning from peers came up repeatedly in conversations we had recently with leaders responsible for driving AI adoption inside large organisations. One approach that was particularly interesting was to avoid rolling AI out to everyone at once. Instead, identify a small group of people who are naturally curious and willing to experiment. Give them the tools, the time and the support to spend a quarter exploring how AI could improve their own work, not as a side project but as part of their day job. Encourage them to try different tools, build workflows, test ideas and learn from what doesn’t work as much as what does. Then let those people become examples for everyone else.

It’s a subtle shift, but an important one. People rarely become enthusiastic because they’re told to use a new technology. They become curious when they see a colleague producing better work, solving problems faster or removing hours of repetitive administration. Eventually someone asks, “How did you do that?” and the answer isn’t a corporate presentation about AI strategy. It’s simply, “I used AI.”

That feels far more authentic than another training programme because it removes some of the fear that naturally comes with new technology. People realise they don’t have to become AI experts overnight. They just need to find one or two aspects of their own role that could be improved. One leader described it in this way: ask people to look at their job and identify the parts they don’t enjoy doing. The manual administration. The repetitive checking. The copying of information between systems. The reports that take hours to prepare. Those are the places where AI can quietly start making a difference, and once people experience that benefit for themselves, confidence begins to grow naturally.

Give people permission to experiment before you expect results

Of course, organisations can’t expect that confidence to develop on its own. If we genuinely want people to experiment, we have to create the conditions that make experimentation possible. That means giving employees the skills to understand what’s possible, the tools to explore different approaches safely and, perhaps most importantly, the time to do it. Too often we tell people to innovate while expecting exactly the same workload, deadlines and performance. Experimentation becomes something squeezed into evenings or quiet Friday afternoons, and inevitably it falls down the priority list. If AI is going to become part of how people work, organisations have to make space for people to learn without feeling they’re falling behind on everything else.

There’s another shift that many organisations will eventually have to make. At the moment, many leaders encourage employees to use AI if they want to. That’s an important first step because it creates psychological safety, but over time encouragement has to evolve into expectation. Not in the sense that everyone must use ChatGPT every day, but in the sense that continually looking for better ways of working becomes part of the culture. Instead of asking departments to “use AI”, leaders should be asking what business outcome they’re trying to improve and whether AI can help achieve it. Marketing might focus on reducing campaign production time, finance on reducing manual invoice validation, customer service on improving response quality without increasing headcount, or operations on reducing the time spent gathering information from multiple systems. Suddenly the conversation isn’t about technology at all. It’s about improving performance, and AI becomes one of the ways of getting there.

AI adoption starts with leadership long before it reaches everyone else

None of this works, however, if leadership isn’t visibly changing alongside everyone else. One of the strongest messages we’ve heard was that AI adoption starts with the senior leadership team. Employees pay close attention to what leaders actually do, not just what they say. If the CEO talks enthusiastically about AI but continues to work exactly as they always have, the organisation quickly concludes that AI is optional. On the other hand, when leaders openly experiment, share what they’re learning and admit what they still don’t know, they create permission for everyone else to do the same. Culture has always spread through behaviour more than communication, and AI is no different.

Perhaps that’s why we’ve been asking the wrong question all along. We keep measuring AI adoption by how many people have logged into a tool or completed a training course, when the more interesting question is whether people are starting to think differently about work itself. Are they redesigning processes rather than simply speeding them up? Are they questioning whether manual tasks need to exist at all? Are they creating more capacity for higher-value work instead of simply becoming more efficient at low-value work?

The future of AI adoption isn’t about using more AI. It’s about redesigning work

That’s what real AI adoption looks like. It isn’t a software rollout, a proof of concept or another transformation programme. It’s a gradual shift in how people solve problems, learn from one another and rethink the work they do every day. The organisations that succeed won’t necessarily be the ones with the biggest AI budgets or the latest technology. They’ll be the ones that recognise that before people adopt AI, they first have to believe that changing the way they work is worth it. And that’s a challenge that has always been about people, long before it was ever about technology.