AI’s potential is undeniable, but the reality inside many organisations today tells a different story. There’s enthusiasm, investment, and lots of talk, but often not much to show for it.
In Miro’s survey of over 2,000 knowledge workers, more than 80% said AI could benefit their work. Yet half described their companies as “all talk and no action.” And a third admitted to feeling anxious about what AI means for their future.
This tension isn’t new. According to Gartner’s “Hype Cycle,” we’re in what’s known as the trough of disillusionment. It sounds dramatic, but really it’s just the part where reality sets in; the moment when early excitement crashes into the complexity of real-world implementation. And it’s a signal that we’re in the phase where true transformation begins.
So what now? How do we move from this so-called trough of disillusionment to uncovering the real value of AI for our organisations?
In this article, I’ll explore three shifts leaders can make to navigate this moment. First, start with strategy – not shiny new tools – by anchoring AI initiatives to your biggest business priorities. Second, build a culture where AI works for the team, making collaboration – not just automation – the goal. And third, develop practical skills and confidence across your organisation through training that’s inclusive, accessible, and tailored to real business needs.
Build strategy before you buy (or build) solutions
Let’s be honest, many AI investments today are happening backwards. Organisations are trialling tools without a clear view of what problems they’re trying to solve or how success will be measured.
Our research at Miro shows that while enthusiasm is high, many teams lack a shared strategic direction. This misalignment shows up fast: tools get rolled out without training, expectations aren’t clear, and teams are left unsure whether they’re allowed or even expected to use AI at all.
To move forward, leaders will need to anchor AI adoption to their broader business goals. That starts with asking a few hard questions:
- What specific workflows or decisions do we want AI to improve?
- Where is the greatest friction in our current processes?
- Do we have the data and technical support to scale these efforts?
When organisations build strategy first, they’re more likely to connect AI adoption to meaningful outcomes – whether it’s improving customer experience, reducing delivery friction, or freeing up time for deeper thinking.
And the gap is real: while 82% of UK knowledge workers are optimistic about AI’s potential for their role, half say that their company is ‘all talk and no action’ when it comes to AI. To get out of the trough, leaders need to start with clarity on business priorities to ensure AI investments are aligned to solving real, measurable problems.
Create an AI-first culture
If strategy sets the direction, culture is how it comes to life. It shapes how we behave, make decisions, and interact with new technologies like AI.
To build an AI-first culture, we first need to clarify what culture really means in each of our businesses – and it’s not what’s written on the office walls. Culture is how things get done day to day, and how team members come together as part of a shared goal and mission. It’s the shared values, expectations, and norms that influence how we collaborate.
An AI-first culture builds on this by integrating AI into daily work. It encourages teams to experiment openly, share learnings, and apply AI not just for personal productivity, but for collaborative problem-solving.
This distinction matters. Too often, AI is just seen as a personal tool: write faster, automate admin, respond to emails. But its real power lies in helping us work better together; by synthesising insights, aligning decisions, and generating ideas as a team. When we work in partnership with AI, the power and potential of teams increases.
We saw this in The Cybernetic Teammate, a field experiment led by Professor Ethan Mollick and researchers from Harvard and Wharton. Professionals at Procter & Gamble were asked to solve innovation challenges, some with and some without AI. The teams using AI didn’t just work faster – their ideas were more original, more creative, and the team had a more rewarding time working together. AI helped them bridge gaps in expertise and sparked better conversations.
Culture is what makes that kind of collaboration possible, but it needs clarity. In our survey, 37% of workers in the UK and globally said they want leaders to set clearer guidance around AI. That doesn’t mean rules for the sake of it, but it does mean clearer guidance and what’s in bounds, what’s encouraged, and whom to ask when you’re unsure.
An AI-first culture gives teams permission to explore, but also the support to do it well.
Developing inclusive, team-wide skills
In addition to developing a clear strategy and nurturing an AI-first culture, leaders must prioritise AI upskilling to realise the full benefits.
Our research revealed that 35% of global workers rate their AI skills as “non-existent.” At the same time, 75% of UK workers want to build those skills this year. The motivation is there. What’s needed is time dedicated to upskilling and the structure to support this.
Crucially, this learning and development must be inclusive by design. Neurodivergent team members may benefit from interface customisation, chatbot-based support, or AI that reduces cognitive load. Others may benefit from multimodal tools that combine visual and verbal input. When inclusivity is embedded in the way AI is introduced and supported, the whole team will gain confidence and see the benefits.
When teams are empowered to use AI confidently – and when those tools are designed to be accessible to everyone – it raises the baseline of what’s possible. Teams aren’t just working faster; they’re contributing in new ways, across traditional role boundaries.
That changes the dynamic of a team. It means a junior designer can shape early concepts, or a product marketer can pull insights from research without waiting on someone else. It levels the playing field – not by flattening expertise, but by making collaboration easier and more effective.
Climbing out of the trough
Post-hype, the trough of disillusionment doesn’t sound like a nice place to be stuck – but there’s reason to be hopeful.This is the moment when early excitement fades and teams are left with harder, more important questions: What do we really want from AI? And how can we make it work in the day-to-day?
Because the real work of AI isn’t about chasing the next tool or feature, it’s about creating the right conditions for long-term success. That starts with a strategy that aligns adoption with meaningful outcomes. It relies on a culture that encourages experimentation, learning, and shared accountability. And it requires teams to build inclusive, practical skills that make AI part of how people work together – not just faster, but smarter.
Done right, these shifts don’t just help organisations move beyond disillusionment. They build the momentum towards, and foundation for, a way of working where AI truly enhances how teams think and build together.
