Want to implement AI? Focus on organizational culture

The AI narrative might suggest it’s your ticket to transformation. But real transformation demands more than buying into that narrative. In my experience, whether AI succeeds or stalls inside a business comes down to one thing: culture.

I know, “it’s all about culture” can be used to dodge the practical change that’s required or as an excuse as to why change didn’t stick.

This makes it easy to dismiss. But culture, the shared beliefs, norms, behaviors, and expectations that shape how people work, is the silent force behind whether AI becomes business-critical or just another failed initiative collecting dust.

I’m seeing this play out already – failed AI proofs of concept, endless conversations but no action, inertia leading to procrastination.

The painful bit? If you don’t get this culture thing right, it won’t matter how good your AI technology is.

AI doesn’t work in a vacuum

Most AI conversations I’m seeing start with tools, models, platforms, maybe even governance frameworks. But those things don’t operate in isolation. They live within your organization's existing ecosystem, and that ecosystem is heavily shaped by culture.

If your teams are siloed, they won’t share the data AI needs to perform. If your decision-making is overly risk-averse, good AI ideas will be endlessly piloted but never scaled. If your people are terrified of job loss, they’ll resist adoption every step of the way.

You can’t drop AI into your business in a culture that isn’t fit for purpose and expect it to take off. You have to set yourself up for success and adjust the environment along the way.

Cultural foundations you’ll need

The first cultural shift needed is from control to curiosity. AI works best when people are able to experiment, explore, and iterate. But in many organizations, control is the default: tightly governed change, slow approvals, hierarchical or committee-led decision-making.

That kills innovation. To get the best from AI, you need to create a culture where people are encouraged to try and ask “what if?” rather than avoiding innovation and asking, “what’s the process?”.

The second shift is from competition to collaboration. AI doesn’t belong to one team. It touches operations, marketing, finance, HR, and everything in between. But too often, functions work in silos.

Data gets hoarded. Technology, data and business teams don’t speak the same language. Priorities clash. People pull in different directions.

You need a culture where people genuinely work together, where data is shared, not protected, where business problems lead the conversation, not just the latest algorithm.

The final foundational shift is from fear to confidence. There’s a lot of anxiety around AI: fear of the unknown, fear of job loss, fear of getting it wrong. If your culture amplifies fear, adoption will stall. If it builds confidence, people will lean in.

That means being transparent about how AI will be used. It means involving people early, not just asking them to embrace the change once it’s decided. And it means creating safe spaces for learning, testing, and even failing.

How do we shift the culture?

Cultural change is behavioral, it’s structural, and it’s ongoing. I heard a great quote recently by John Edwards, the Information Commissioner, in the context of implementing AI where he said, “If it isn’t uncomfortable, you’re not doing it right”, and it’s so true when it comes to culture change in the wake of AI.

A good starting point is to properly diagnose the current state. That means going beyond the surface and understanding the beliefs and behaviors that already exist around data, decision-making, tech, and change. It’s about being honest and curious about what’s really going on, not what people wish was happening.

From there, leaders need to model the right behaviour. Culture change needs top-down and bottom-up change, but there is huge power in ‘the top’ leading the way, day-to-day. Leaders need to ask better questions, champion collaboration, and be visibly open to new ways of working.

But it’s not just about leadership. You also have to embed change in how people actually work. AI can’t be something extra on top of the day job. You’ve got to look at incentives, decision rights, performance metrics, the plumbing of the business, and align it all with the behaviors you want to see.

Perhaps most importantly, celebrate progress, not just perfection. Becoming an AI-enhanced business doesn’t happen overnight. Momentum builds when people see that small wins matter, that experiments are safe, and that their contribution counts.

Culture is the strategy

If your AI strategy doesn’t include cultural change, it’s not a strategy. It’s a wish. Yes, the models matter. The infrastructure matters. The data quality definitely matters. But none of it will land unless the people using it are ready, willing, and supported to change how they work.

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