Jarred Griffiths, MartinJenkins' AI Transformation Practice Lead, argues that supporting your team through AI-driven change can be simpler than you think.
Where to start when you need to guide your team through an AI transformation? Everything I’ve learnt so far tells me the most useful starting point is simply setting aside time for the team to get together and talk through what AI means for them.
When supporting a client organisation recently I got a chance to work together with an astute team leader who wanted to tackle that kind of foundational discussion with their people. The day went well, and in this article I want to talk about exactly how and why.
But before I get onto that, let’s start with some context
Leaders are now having to quickly make sense of what AI means for their team’s work and the value it brings. AI models are also improving quickly, and so the ground is also constantly moving under our feet.
Take coding. Anthropic has said its LLM Claude will write code better than humans by the end of this year. Quality is already at parity, but it’s delivered in a fraction of the time.
If you have a software business for example, it’s not tenable to run it in the same way you always have. Ignoring AI is a fatal strategy when your competitors can eclipse your productivity. That’s true now of just about any sector or profession, like policy, communications, finance, HR, or legal. AI is challenging all of us to rethink what we do.
Of course, as a team leader you’ll know that your people have routinely seen that kind of “Everything’s-changing-and-we-all-need-to-adapt” commentary, and that they’re understandably concerned about what AI will mean for them. The real challenge for leaders is not only supporting their people through this complexity, but finding ways to involve all their people in making the change.
I think of this as putting people at the heart of your AI transformation.
Unfortunately, what’s often happening is that AI becomes either a high-level strategy discussion or a set of disconnected experiments across a team. Neither approach builds confidence: both leave many of your people out in the cold, and they don’t shift the whole organisation or team into rethinking how work is done.
Start with where your team is really at
That recent workshop was productive because it got all the team together and got them talking. But first on the agenda wasn’t strategic goals, it was how the team were all experiencing and thinking about AI tools right now.
From the outset, the team leader both understood and explicitly acknowledged that people in the team were at different points in their use of AI. Some were already using tools in their day-to-day work, while others were less confident and still working out where to start.
Rather than trying to standardise that and push a single way of working, the discussion acknowledged the difference and made room for talking about it.
This might seem overly simple, but it really mattered to the team. It signalled that this was something everyone could work through together, not something that would be imposed on them. It also made it easier for people to be honest about what they did and didn’t know.
Recognise that people are already using AI
Within the team, people were already using AI in practical ways. They were experimenting, improving small parts of their workflow, and working out where AI helped them best. What hadn’t happened yet was bringing all those lessons together.
The team leader focussed on understanding how people were using AI day to day and then on creating the conditions for that to be shared more widely across their team. Gathering that knowledge together meant that the team’s next steps with AI could build on what the team already knew was working.
This also had a positive levelling-up effect within the team. The more confident were encouraged to share what they had learned. Others could see what was possible in a way that was grounded practically in their own work. Over time, that approach helps lift everyone’s capability, so that no-one in the team is left behind.
Make it a shared exercise, not something imposed from the top down
In the discussion, the leader set clear expectations that the team would engage with AI, but was equally clear that the team themselves had to be involved in shaping what that looked like in practice.
This leads to better outcomes, as the people doing the work are often best placed to identify where change will have the most impact.
This team discussion also helped to build trust, through acknowledging and respecting people’s concerns. People were encouraged to talk openly about how they were using AI, where they saw opportunity, and what they were worried about. When people can see that their experience is being taken seriously, they’re more likely to trust a change process and engage with it.
Think beyond individual use and consider implications at a team level
The workshop discussion shifted from isolated individual use to exploring what AI use could mean for the future of how the team worked.
Rather than focussing only on productivity or cost, the discussion looked at how AI could take pressure off routine tasks. This can free people up to spend more time on the parts of the role that require judgement and an understanding of context, or that depend on collaboration and relationships with others.
For this particular team, this was a powerful and inspiring conversation. Much of the value these team members provide lies in their understanding of their organisation, in how they work with their leaders, and in their ability to make sense of complex situations. Those strengths aren’t easily replaced, but they are often squeezed out by more transactional or reactive work. AI presents an opportunity to shift that balance.
Instead of thinking only about how to take out costs, this approach to AI allows us to genuinely re-think how we do our work, and where AI could enable us to have more impact overall. The prospect of being freed from the day-to-day grind of repeatable tasks excited this team – and they began to think about where this time could be reinvested.
The leader created room for the team to have a proper discussion
When it comes to AI, there’s a tendency to look for a defined model or a set of best practices. Those things have their place, but for most teams a good starting point is much simpler: having a good talk about it, where everyone in the team is involved, and different perspectives are heard – including all the concerns and doubts.
The team leader in our workshop discussion didn’t get stuck in litigating whether AI was good or bad, but instead moved to something more practical. The team, together, were able to explore how they could adopt AI in a way that works for their team. Not only that, but in a way where everyone could learn and support each other.
People could see how their own experiences connected to the broader picture. That, more than anything else, is what made the approach effective.
You don’t have to be an expert on AI to do this: it’s something any leader can do. It starts with creating the room for your people to surface what’s already happening and learn from each other, and then start shaping what comes next.
