AI adoption isn’t just a technology challenge — it’s very much a people challenge. In any team, people are in very different places. Some have a natural affinity for technology and see the benefits immediately, both at work and in their daily lives. Others find it less tangible or obvious. Some are super confident and comfortable with change; others are far more cautious.
As a leader, I’ve found this an odd challenge to navigate. With most technologies, you usually have a small group of experts to guide the rest of the organisation. With AI, we are almost all novices — myself included. I’ve embraced AI big time over the last 12 months and I feel comfortable leading the conversation, but I don’t have 20+ years of deep, specific AI experience to fall back on. Like many leaders, I’m learning alongside my team.
That’s why I’ve come to believe that the key isn’t about being the expert in every tool — it’s about creating the right culture. A culture of continuous learning. It’s something that my Microsoft learning paths and AI certifications have repeatedly stressed: you don’t treat AI like a one-off training, you build a culture that keeps evolving. Easier said than done — but absolutely essential.
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The Team Adoption Challenge
AI has exploded into focus everywhere in 2025. The speed of adoption has been phenomenal — exciting at best, and at times even a little scary. Some people are naturally curious and confident when it comes to technology, but many are more anxious. And the reasons vary: fear of “getting it wrong,” not knowing where to start, feeling like they’re already behind, or — perhaps the most damaging — worrying that AI will take their jobs.
News headlines don’t always help. For every story about breakthroughs and productivity gains, there’s another about job losses . That can make AI feel less like an opportunity and more like a threat, especially for those who don’t yet see the benefits.
In my own role leading a customer experience team, I’ve seen both sides of this. On the one hand, AI is full of potential use cases — from onboarding to training to product support. On the other hand, the shift can feel overwhelming if people don’t know how to start or aren’t confident that AI can really help them in their day-to-day roles.
What I’ve also noticed is that while many people are happy to play with AI in their personal lives — trying out a chatbot or experimenting with image generation — it’s a different story in a business context. In the workplace, it isn’t enough to “have a go.” You need to show a clear return on investment, tie it back to strategy, and integrate it into real processes. And that’s a much harder proposition.
The Three Ingredients of Culture
If there’s one thing I’ve learned about making AI work in teams, it’s that success depends far less on the tools and far more on the culture. From my experience, three ingredients matter most: curiosity, confidence, and continuous learning.
Curiosity
Teams need to feel safe to experiment. AI is still new territory for most people, and the only way to get value is to try it out. As a leader, what helped me was sharing my own AI discoveries with my team — something new I had learned, or something I thought might apply to our work. Just as importantly, I encouraged them to share their thoughts back. It became two-way curiosity. Over time, this made experimentation feel normal — almost the new default in how we worked together.
Confidence
Curiosity alone isn’t enough. Teams also need confidence, which comes from training and visible quick wins. And quick wins only happen if you have clear use cases to apply AI to. I was fortunate that my team was working on a new Learning Management System (LMS) in multiple languages. Suddenly, AI for translation, copy generation, and even image creation started delivering tangible results. Those wins built confidence fast — and once one person shared their success, the rest of the team quickly saw what was possible. It spread almost like word of mouth.
Continuous Learning
AI is not a one-and-done skill. Tools change, features evolve, and new use cases appear almost weekly. One day you think you’ve mastered something, and the next day it moves on. The sooner you accept that, the easier it becomes. For me, the lesson is to treat AI as an ongoing journey, not a destination. My Microsoft learning paths and certifications reinforced this: don’t think of training as a single event. Think of it as creating a culture where learning is constant, expected, and supported.
In short, curiosity gets people started, confidence keeps them going, and continuous learning ensures the team doesn’t stand still. Without all three, AI adoption risks becoming a short-lived experiment rather than a sustainable change.
A Practical Team Playbook
So how do you actually build this culture in practice? From what I’ve seen (and I don’t claim to be an expert here), it isn’t about rolling out the latest tool and hoping people adopt it. It’s about creating simple structures and habits that make AI part of everyday work. Four things stand out:
1. Create AI champions
Identify people in different functions who are naturally curious about AI and give them space to experiment. These champions don’t need to be experts — their role is to try things out, share what they learn, and inspire others.
2. Build knowledge-sharing spaces
Sharing discoveries is powerful, but it needs more than just ad-hoc conversations. This is an area I know I can do more as a leader. Creating structured places for knowledge-sharing — whether that’s a shared folder, a channel, or a regular “AI Clinics/Show & Tell” — helps curiosity spread. I’ve embraced AI training heavily over the past 12 months, and I’ve learned that running short, informal training sessions is an easy way to pass that knowledge on. It keeps learning continuous, and it shows the team that experimentation is encouraged.
3. Start with real but low-stakes experiments
Not every experiment needs to be tied to ROI from day one. In my team, trialling AI with customer emails has been a surprisingly effective starting point. We tested different prompting techniques to improve tone, and even used AI to help simplify responses into clearer, plainer English for customers whose first language isn’t English. The impact was immediate — small changes that made communication smoother and built confidence that AI can help in practical, day-to-day ways.
4. Celebrate the wins, however small
Confidence grows when success is visible. Highlight and celebrate when someone saves time, creates something faster, or discovers a clever new use case. These moments matter — they show the team that AI isn’t abstract, it’s real and it’s helping colleagues today. Word of mouth then takes over, and momentum builds.
None of these steps require big budgets or major structural change. They require leadership attention, consistency, and the willingness to model the behaviour yourself. When people see their leader experimenting, sharing knowledge, and celebrating wins, they feel encouraged to do the same.
Personal Reflection
Looking back on my own journey, I’ve realised that leading AI adoption has been very different from leading other types of change. Normally as a leader you can draw on years of accumulated expertise. With AI, we’re all learning together. That can feel uncomfortable at times — but it can also be liberating, because it means you can set the tone for how the team learns.
And because it’s all still so new, there are so many learning wins. Things that AI can help with today were almost unimaginable a year ago. That makes the experience not only useful, but at times almost magical — and definitely fun.
As a leader, I’ve also learned that it takes confidence to openly admit you’re on a learning journey yourself. But I believe that’s important. Teams don’t expect their leader to have every technical detail at their fingertips. What they need is a clear vision of what AI can help the organisation achieve — and the confidence that they’re being guided in the right direction. Being willing to say “we’re learning this together” creates trust. It shows that curiosity is not just encouraged — it’s modelled.
The launch of our new Learning Management System (LMS) has been a great example of this. Working across multiple languages gave us the perfect opportunity to test AI in practical ways. Translation, copy generation, even image creation — these all became live use cases where the benefits were immediate and visible. Seeing AI make a real difference in a project of that scale was a turning point. It moved AI from being “something interesting” to being “something essential” in how we deliver.
For me, the real progress has come not from being the AI expert, but from encouraging my team to experiment, to share, and to learn in the open. When teams feel supported to try, share, and learn together, AI adoption stops being overwhelming — and starts being exciting.
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Conclusion
AI adoption is often talked about in terms of tools and technologies, but in reality it’s a team sport. Tools will come and go, but what really matters is the culture you build around them.
For me, that culture rests on three things: curiosity, confidence, and continuous learning. Curiosity makes it normal to explore. Confidence grows through quick wins and real use cases. And continuous learning ensures the team keeps moving forward even as the technology evolves week by week.
As leaders, our role isn’t to be the ultimate experts. It’s to set the vision for what AI can achieve, to create the conditions where teams feel safe to experiment, and to have the confidence to admit that we’re learning alongside them. In many ways, that honesty is what builds the most trust.
AI is still new enough that every week brings fresh possibilities. That can be daunting, but it can also be magical. When teams feel supported, AI adoption becomes less about fear and more about excitement. And once that happens, the opportunities are almost limitless.
