Winning with AI: Why Literacy Must Be a Team Effort

In his powerful autobiography Rise, Siya Kolisi reveals one of the lesser-known secrets behind the Springboks’ 2019 Rugby World Cup triumph: distributed responsibility. While Kolisi wore the captain’s armband, he didn’t try to do it all. Instead, leadership was shared.

Each player on the field wasn’t just responsible for their role - they were accountable for a specific aspect of the game itself. A forward focused on physical dominance. A backline player owned breakdown speed. The wingers were tasked with mastering the kick strategy and kick chase. Kolisi himself took charge of understanding the referee’s style and behaviour. These responsibilities extended far beyond game time. Each player would study their assigned domain - analysing footage, drawing insights, and raising team awareness so that, come match day, they weren’t relying solely on instinct or last-minute calls. They were executing a well-distributed, deeply prepared game plan.

This philosophy of shared ownership and targeted learning is exactly what organisations need when it comes to AI.

AI Is a Team Game

The Gartner chart above reinforces a critical idea: AI literacy is not universal, but it is essential. Not everyone in your organisation needs to become a machine learning engineer - but everyone does need to understand what AI means for their context.

Let’s break it down.

• Executives must become fluent in AI value. They don’t need to build models, but they must understand how AI creates leverage - reducing costs, improving decision-making, unlocking new experiences, and enabling innovation at scale. A strong grasp of ethical considerations and governance is also critical. In rugby terms, this is the equivalent of understanding the game plan, tempo, and referee’s interpretation of the laws - not the mechanics of every scrum.

• AI Leads are the midfield generals. They need a broad and deep grasp across all four domains: value, foundations, engineering, and governance. They’re responsible for stitching together the AI vision, translating strategy into execution, managing technical complexity, and ensuring safety, ethics, and compliance. They are both playmakers and tacticians.

• AI Experts bring the precision. Like the analysts and coaches off the pitch, they must master the how of AI - its foundations, architecture, engineering practices, and operational management. But they can’t stay in the technical weeds - they must also understand enough about governance and business value to build AI that’s safe, trusted, and useful.

• Subject Matter Experts and front-line employees need literacy that empowers them to contribute, not just comply. These are your players on the ground - those who know the customer, the process, the domain reality. They should be equipped to ask the right questions, spot opportunity, challenge bias, and interpret AI insights in the context of their expertise. Their learning needs to be practical and applied - just like the wingers who owned the kick chase strategy but didn’t try to lead the scrum.

Why Decentralised Learning Drives Performance

When AI understanding is concentrated in a few experts or a central team, organisations suffer. Use cases stall. Trust breaks down. Adoption fades. It’s the equivalent of asking the team captain to run every line, kick every goal, and referee the match. Not only is it unrealistic, it’s a guaranteed path to underperformance.

By decentralising learning responsibilities:

• You build resilience, ensuring knowledge isn’t locked in silos.

• You accelerate innovation, because ideas come from every level.

• You reduce risk, as more eyes can spot issues of bias, misuse, or misalignment.

• And you maximise relevance, because every function sees AI through the lens of their own challenges and goals.

Just like the Springboks, who trained for individual mastery of different parts of the game, the most successful AI-driven organisations are those that train their people to own a piece of the puzzle.

How to shape your organisation

In rugby and in business, it’s tempting to centralise expertise. But real transformation happens when you decentralise responsibility. The Springboks proved that the path to greatness is built on shared ownership, deliberate preparation, and clarity of focus.

So as you shape your organisation’s AI journey, ask:

• Who’s responsible for understanding value?

• Who owns governance?

• Who’s driving foundational capability?

• Who’s bridging the gap between technology and domain?

When everyone is learning what matters most to their role, you stop relying on a few experts to carry the weight, and start playing as a team built to win.