Beyond the technology: How to build a culture that supports AI adoption

Despite billions invested, the vast majority of AI initiatives never scale. McKinsey estimates that while almost every organisation is investing in AI, only 1% consider themselves truly mature in its deployment and suggest that up to 80% of AI projects fail to deliver expected ROI. What we’re seeing is consistent: this isn’t just a technology challenge - it’s about how organisations support their people to adapt. Many organisations are still treating AI as a tick-box exercise - often without the support needed to unlock its full value.

The myth of “just implement the tech”

There’s a persistent belief that deploying AI tools will automatically drive productivity. Yet most organisations stall long before value is realised. Why? Because AI often highlights where organisations need to evolve, such as:

• Leadership priorities that aren’t always aligned
• Gaps in communication between teams
• Unclear priorities or problems to solve
• Natural hesitation or uncertainty from employees
• Limited investment in building confidence and capability

RAND research found that more than 80% of AI failures stem from organisational issues like unclear problem definition and misalignment, not technical limitations. Similarly, studies suggest as many as 84% of failures are driven by leadership.

Roadblock 1: Leadership misalignment

One of the most consistent barriers to AI success is the gap between leadership ambition and organisational reality. Senior leaders are investing heavily in AI. But they’re not always equipping their people with the support they need to use it effectively. Recent research shows that while 66% of leaders prioritise AI skills, only a third of employees have received training. At the same time, employees are often more ready than leaders realise. McKinsey found that workers are already using AI tools regularly and are eager to build their skills. However, support doesn't always keep pace with that momentum. This disconnect can lead to a familiar pattern:

• Leadership sets strategy in isolation
• Teams experiment informally
• No consistent adoption emerges

AI becomes fragmented rather than delivering the impact teams are ready to create.

What works instead:

Organisations that succeed treat AI as a leadership capability, not just a technology investment. They align senior leaders around clear use cases, shared language, and a roadmap that helps their people apply AI in meaningful ways.

Roadblock 2: A technology-first mindset

Another major blocker is the obsession with tools. Many AI programmes start with: “What can this technology do?” rather than “What problem are we solving?” This leads to pilots that look impressive but struggle to deliver meaningful value. Research consistently shows that organisations that succeed with AI focus far more on people and processes than on the technology itself. In fact, best practice suggests that up to 70% of AI success depends on people and process change, not algorithms. When organisations treat AI as a product rather than an organisational capability:

• Invest in tools without evolving how people work
• Layer AI onto processes that were already under pressure
• Struggle to integrate solutions into day-to-day work

What works instead:

High-performing organisations redesign workflows around AI. McKinsey highlights that workflow redesign is one of the strongest drivers of AI value creation. This is where culture shifts, from using AI to working differently because of AI.

Roadblock 3: Skills gaps and employee resistance

AI adoption rarely fails because people can’t use the tools. More often, it's because people haven't been given clarity on how or why to use it in their role. Without the right support, it's natural for people to default to avoidance or inconsistent use. What can be seen as resistance is often rooted in uncertainty, particularly around job security and expectations. Where communication isn't clear or consistent, adoption can slow significantly. At the same time, there is a growing divide between “AI-native” employees and the rest of the workforce. McKinsey notes that many employees are already embracing AI tools, while decision-makers hesitate.

What works instead:

Organisations need structured, role-relevant learning that connects directly to how their people work day-to-day.

Learning should be practical, contextual and aligned to business outcomes.

This is where Corndel’s approach stands apart. Our AI programmes focus on embedding capability in the flow of work, helping people apply AI to real tasks in their day-to-day roles, not hypothetical scenarios. As Kathryn Dolan, Chief People Officer of Mitie says: “We don’t just focus on the technical skills for AI, we focus on the human skills. Our objective is that we use AI to streamline productivity but also create more capacity for our people to really harness those skills that are uniquely human. Critical thinking, problem solving, creativity, innovation, empathy. These are those skills that we think will become even more critical in the future.”

Roadblock 4: Lack of ownership and governance

AI introduces new risks, and many organisations are still building their approach. Recent data from TechRadar shows that:

• 59% of organisations don't know how quickly they could shut down AI systems in a crisis
• Only 38% assign clear accountability at board level

What works instead:

Successful organisations treat AI governance as part of their culture, not just a compliance one. They define ownership, build trust, and create transparency so people understand how AI is used and where they fit.

From tick-box to transformation

The organisations unlocking real value from AI aren't with the most advanced tools. They are the ones that have made the hardest shift to:

• Align leadership around clear priorities
• Invest in building confidence and capability at every level
• Build cultures of experimentation, trust and shared learning

In short, they treat AI as an organisational capability.