Culture, not code: why people are the key to successful AI adoption

When AI enters the boardroom, the instinct is often to look at tools, models, and platforms. But the biggest success factor has little to do with tech and everything to do with culture.

In the latest episode of the Beyond Boundaries Podcast, Gareth Workman, Chief AI Officer at Kainos, sat down with Rory Hanratty, CTO of Axial3D, to explore what it really takes to build organisations where AI can thrive. Their conversation offered a grounded and honest take on why many initiatives falter and how the right mindset and leadership approach can make all the difference.

From hype to outcomes: the AI culture gap

The conversation began with a clear message: too many organisations are still implementing AI for its own sake. As Rory put it:

“You end up with something that’s like a cathedral to how great technology can be, but it actually solves no problems.”

This mindset is one of the most common and costly pitfalls. Whether in engineering teams or at the executive level, losing sight of user needs and business outcomes can lead to wasted investment, failed initiatives, and confusion.

In Rory’s words, the remedy is simple: “Start from the outcome you want to achieve, then figure out how you might solve that problem - with chainsaws, tractors, software or AI.”

What leadership needs to do differently

Leadership plays a pivotal role in building AI-ready cultures. It starts with understanding the technology - not in deep technical detail, but enough to ask the right questions and spot real opportunities.

More importantly, leaders must foster curiosity and psychological safety.

“Ask ‘how might we?’ - it’s a great way to open up better answers and create a more open culture. Especially when you’re dealing with something where outcomes aren’t guaranteed,” Rory explained.

Leaders also need to align closely with innovation teams, ensuring a two-way relationship rooted in shared goals, not just throwing problems over the fence.

Real success starts with a mindset shift

Both Gareth and Rory reflected on how successful AI initiatives often start with a mindset shift, from abstract innovation to solving tangible business problems.

Rory shared an example from Axial3D, where a single, focused challenge - reducing the time taken to segment medical scans - transformed how the organisation operated.

“We didn’t say, ‘let’s use AI.’ We asked, ‘how do we speed up segmentation?’ That shifted the whole company. Suddenly, machine learning was central to our value proposition.”

The key to this success wasn’t just the technology. It was the clarity of the problem and alignment across the organisation. Teams rallied around a defined business outcome that had human benefit at its core.

What leaders should do now

If you’re looking to embed a culture that’s ready for AI, here are nine priorities to focus on:

1. Start with outcomes, not tech
   Identify real business problems and ask, how might we solve them, possibly with AI. Keep the focus on practical, human-centred impact.
2. Build contextual understanding
   Avoid generic approaches. Make sure your strategy reflects your industry, operations and customer needs.
3. Invest in leadership learning
   Equip leaders with enough understanding to ask the right questions and support adoption.
4. Encourage curiosity and experimentation
   Create safe space for exploration - and for failure - especially with early-stage AI use cases.
5. Align innovation with business strategy
   Bridge the gap between technologists and business units. Shared goals and language are critical.
6. Move from exploration to execution
   Don’t let experimentation stall momentum. Build a clear pathway from proof of concept to real-world integration.
7. Embed trust-building mechanisms
   Build understanding and confidence in how AI is tested, validated, and introduced into workflows.
8. Plan for scale from the start
   Think beyond isolated use cases. Build governance, tooling and cultural readiness that can evolve with you.
9. Keep AI anchored in human value
   Whether it's speeding up diagnosis or improving productivity, AI must ultimately serve people and deliver meaningful outcomes - not just advance technology for its own sake.