Why Experimentation First Cultures Are Winning the AI Race

Be honest, did the photo get you to open this or was it the heading? In the past year, I have slowly gone insane with the tsunami of content about artificial intelligence!

Boards are asking about it, every agency and consultancy is an expert in the field, marketing teams are branding with it, and IT departments are scrambling to integrate it. But for all the hype, only a handful of companies are turning AI investments into scalable, meaningful outcomes. The question is: why them?

While many are focusing on tooling, vendor selection, or hiring AI specialists, one overlooked factor is proving to be a powerful predictor of success: a culture of experimentation. Organisations that already embrace iterative learning, rapid testing, and data-driven decision-making are adopting and scaling AI faster and with less friction than those that don’t.

At first glance, experimentation and AI might appear unrelated. But they rely on the same behavioural infrastructure. Both require a willingness to test hypotheses rather than assume answers. Both demand strong feedback loops, where data is used to inform, validate or discard ideas. Both reward teams that can act with speed, even if the path forward is unclear. In short: if you’ve built a business that knows how to experiment well, you’ve already built the foundations for doing AI well.

Let’s take Amazon. Its legendary “test everything” culture isn’t just about e-commerce optimisation, it’s a strategic enabler. The same frameworks that let them A/B test checkout flows have allowed them to deploy and refine AI across product recommendations, supply chain optimisation, and Alexa’s language models. Netflix is another strong example. Their personalisation engine is underpinned by continuous experimentation; it’s how they decide what content to promote, which thumbnails to show, and now, how to integrate AI tools into the creative and production process itself.

Closer to home, Canva shows how experimentation creates AI velocity. Known for its rapid iteration culture, Canva has rolled out a robust AI suite—including Magic Write and AI image tools—at a pace few enterprise companies could dream of. Their ability to ship fast and improve in public is a direct result of their testing-led mindset. 

This isn’t just theory. AI leaders themselves are highlighting the importance of curiosity, risk tolerance and iterative learning. Mustafa Suleyman, CEO of Microsoft AI, recently encouraged professionals to “be curious, try new tools, make mistakes.” Andrew Ng, one of the pioneers of modern AI, consistently talks about the importance of building “exploration systems at scale.” These aren’t tech quotes—they’re cultural mandates.

So what does this mean for companies racing to deploy AI? It means the organisations that treat AI as a plug-and-play tool are missing the point. AI isn’t a destination, it’s a capability. And like any capability, it needs structure, support, and the right behavioural scaffolding. You can’t outsource your way to AI maturity. You need to build the habits that let it thrive.

These habits already exist inside teams who experiment well. They’ve normalised failure. They value learning loops over long-term bets. They have operating models that allow teams to test ideas, analyse results, and pivot quickly. Most importantly, they’ve created environments where people feel safe to try, safe to learn, and empowered to act.

If your teams already work this way, AI will feel like a natural next step. If they don’t, no amount of tools or funding will make adoption easy.

In the end, AI won’t reward the loudest voices or the biggest budgets. It will reward the most those that have a culture of experimentation.