Beyond Playbooks: Why Your AI Journey Can't Be Copied

We've all come across those trendy AI implementation frameworks, right? You've seen them before: those step-by-step guides, maturity models, and best practices that have really helped some companies get their AI journey going. They have definitely made a significant impact on some businesses.

But here's the thing: AI is getting eager, a paradigm-shifting. It's no longer just about plugging in some technology via API. Each company is like its own universe, with its own odd and exciting ways of doing things. And that's when it turns interesting. We're seeing that success with AI is more than just following a playbook; it's about something deeper, something that challenges our entire approach to implementation.

The Plot Twist: Enter Non-ergodicity

Let me explain why with a counterintuitive math principle that could change the way we think about AI project success: ergodicity. In quite simple terms: a system is ergodic when the average outcome over time for one person equals the average outcome across many people at one point in time. Sounds obvious, right? But here's where it gets interesting: most complex systems, including the path to AI implementation in organisations, aren't ergodic at all.

Think of it this way: Imagine you had 100 clones of your company, each trying the same AI project to some degree differently at the same time. You'd get a bunch of different outcomes (that's your ensemble average). Now imagine your company trying that AI project 100 times in sequence, learning and adapting each time (that's your time average). In a non-ergodic world, these two scenarios give you totally different results. Think about flipping a coin to grow your money: heads you gain 50%, tails you lose 40%. The average outcome looks positive, right? But if you play this game long enough, most players will end up losing money.

Follow Your Path: "Find Your Greatness"

Let's go back to 2012, when Nike captured something thorough that's even more relevant today:

"Greatness isn't reserved for the chosen few in their ivory towers. Greatness is for everyone, but you've got to find your own way to it."

They weren't just selling shoes; they were selling wisdom that perfectly captures what we're learning about AI implementation. Every organization has its own potential for AI "greatness". It's not about matching what Google, OpenAI, or Anthropic is doing. It's about discovering your singular walkway in this emergent AI landscape.

Let me share a real story: A retail company tried to replicate Amazon's AI-driven inventory management system. They had similar technology and similar data, but after six months, the project was failing. Why? Their customer behavior patterns, supplier relationships, and operational constraints created a completely different context. The path that worked for Amazon couldn't be replicated because the underlying system wasn't ergodic.

Three Rules for Your Journey

1. Stop looking for "best practices" and start focusing on your context. Company A's successful chatbot implementation might have worked because of specific customer behaviors, data quality, and organizational capabilities that are unique to them. If it works for them, it won't necessarily work for me.
2. Instead of copying solutions, copy the process of discovery. The most successful AI implementations come from organizations that develop a deep understanding of their unique context and challenges, together with technology. As if that were not enough, new technologies change the context.
3. Create small, controlled experiments before scaling. Each successful AI implementation is like a survivor of many possible trails. Start small, learn what works in your specific context, and then scale based on real evidence.

Your Journey Map

This isn't about mastering a complex theory; it's about adopting a mindset that embraces uniqueness and context. One that sees each AI implementation not as a recipe to follow but as a journey to discover. Here's a journey map:

1. Map your unique organizational DNA before starting any AI project. What makes you different? What are your constraints and advantages?
2. Create success metrics that matter in your context, not someone else's. Think about that technology has changed the context. Choose the right metrics.
3. Build flexibility into your strategy and be ready to adapt as you discover your own path.

And remember the following:

In non-ergodic systems, the journey matters more than the destination. Your AI strategy should focus on learning and adaptation rather than rigid replication of others' successes.