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How culture shapes AI adoption in events: 5 takeaways from Informa’s leaders
If you’re trying to drive AI adoption inside an events business, you’ve probably already realised something uncomfortable: this isn’t mainly a tools problem. It’s a people problem.
In this episode of The AI-genda, Meera Somji was joined by two leaders from inside Informa – the world’s largest B2B media and events company – who are shaping AI adoption from the inside out:
- Donna Lovitt-Edwards, Head of AI Fluency (building the learning strategy and capability programmes that make AI usable in day-to-day work)
- Rabi Atiti, Head of Inclusion (EMEA & India) and author of The Human Advantage (bringing the lens of psychological safety, belonging, and leadership behaviours)
Here are the 5 biggest takeaways if you want AI adoption to actually stick.
1) “AI fluency” isn’t knowing tools — it’s when AI becomes invisible
A lot of organisations confuse AI adoption with awareness (“we gave people access!”) or usage stats (“X% are active!”).
Donna’s definition is more useful, and more demanding: AI fluency is when AI becomes part of the workflow to the point it doesn’t feel like “extra”.
That changes how you design programmes. It’s not about one-off training or a big launch. It’s about habit formation: repeatable moments, practical use cases, and safe spaces to practise until it becomes normal.
Practical implication: Don’t design for “completion”. Design for everyday use in the moments people already work: planning, writing, analysing, synthesising, preparing meetings, responding to customers.
2) AI adoption fails when leaders treat it like a knowledge problem
One of the most powerful points in the conversation was this: organisations often respond to low adoption by doing more comms and more tool demos — as if people just haven’t heard the message.
But Rabi names what’s really happening: behaviour doesn’t change because someone attended a session. It changes when people feel a mix of relevance + safety + permission.
Or as she put it: we need to “sell the feeling, not just the product.”
Practical implication: If your internal AI push is full of features, prompts and productivity hacks — but light on emotion, identity, safety and trust — adoption will stall quietly.
3) People don’t resist AI — they resist the threat story around it
This was one of the most quote-worthy moments of the webinar, because it’s what so many people feel but rarely say out loud.
Rabi’s framing: people aren’t resisting AI. They’re reacting to what AI can signal:
“Am I about to be replaced?”
“Will I look stupid using this?”
“Will I get in trouble if I make a mistake?”
“Does leadership actually mean it when they say ‘experiment’?”
Practical implication: Your adoption strategy needs to explicitly address fear – not dismiss it. Make it normal to be a beginner. Name what’s changing. Be honest about what AI can and can’t do. And reassure people about what “good” looks like in early-stage experimentation.
4) The #1 leadership move is surprisingly simple: give people time
There are a lot of “AI leadership behaviours” lists floating around right now. But Donna’s answer was refreshingly grounded:
If leaders want adoption, they must give teams the gift of time.
Not just permission in theory — actual time in practice: protected learning moments, chances to practise together, space to iterate, and explicit permission to fail without penalty.
Donna also describes the operating rhythm that makes this real: build hypotheses, test in small structured ways, learn quickly, and move on.
Practical implication: Adoption doesn’t happen because you bought a tool. It happens because leaders made space for the organisation to learn.
5) The leaders who lean in share one trait: intellectual humility
Rabi made a sharp distinction between leaders who move towards AI and leaders who hesitate.
The leaders who lean in tend to be:
comfortable not having all the answers
curious
secure enough to learn in public
willing to experiment visibly
She names that trait as intellectual humility, and it matters because AI challenges old leadership identities built on expertise and control.
Practical implication: If you want to predict adoption success, don’t start by auditing the tech stack. Start by looking at leadership culture: do leaders learn out loud, or only perform certainty?
What to do next (if you want AI adoption to stick in 2026)
If you’re leading AI adoption inside an events business, here’s a simple checklist based on the conversation:
Are you designing for workflow habits, not training completion?
Are you tackling the fear story directly — not hoping it goes away?
Do people have time to practise, not just permission?
Are leaders modelling learning out loud?
Are you treating this as culture change, not tool rollout?
Want to see what this looks like in practice?
If you’ve read this far, you’re clearly thinking seriously about AI, leadership, and how to make change actually stick — not just in theory, but in real organisations.
Our next AI-genda session builds directly on this conversation.
I’ll be joined by Sharon Roessen, COO at Terrapinn, to explore why events may be one of the most AI-resilient — and opportunity-rich — parts of the media ecosystem, and what “playing offence” with AI really looks like at scale.
We’ll dig into:
where AI genuinely creates value in events
where human connection still outperforms automation
and how leaders are designing systems, teams, and cultures that deliver outcomes — not just adoption metrics