What High-Growth Companies Do Differently When Learning from Other Industries

Growth rarely fails because leaders lack intelligence or ambition. It fails because decisions are made too far away from where value is actually shifting.

In many organisations, learning is treated as a reflective exercise – something that happens after strategy is set, or alongside execution as a cultural initiative. But for high-growth companies, learning is not reflective. It is directional. It is how leaders spot change earlier, decide faster, and move before competitors are forced to react.

The difference is not curiosity alone. Plenty of organisations are curious. The difference is curiosity with intent, reinforced by speed and execution.

While slower organisations look inward – refining frameworks, benchmarking peers, optimising existing models – high-growth companies look outward to answer a more urgent question: where is value already being created differently, and what does that mean for us now?

Sometimes, that question points to a different industry.

The Advantage Isn’t Just Insight — It’s Timing

Disruption doesn’t arrive neatly packaged inside industry boundaries.

New pricing logic, operating models, customer expectations, and scaling mechanics usually appear first somewhere else – in adjacent sectors, emerging markets, or unfamiliar operating environments. By the time those patterns become visible within an industry’s own data, the advantage has often already been claimed.

This is why outward-looking companies consistently outperform. 

Research from McKinsey & Company on adjacency-driven growth shows that organisations expanding beyond their core businesses are more likely to outperform those that remain confined to familiar territory. 

Specifically, when speaking on companies that managed to both outgrow their peers as well as be more innovative than their competitors, the report found that these companies “... build innovation into their overall strategy aspirations. They activate critical growth pathways within their core businesses and enter only those adjacent markets where they have the strongest competitive advantage. They pursue excellence in execution and invest in key innovation capabilities. And they use M&A, particularly programmatic M&A, to extend their innovation reach.”

This dynamic also explains why some organisations deliberately look far beyond their home markets when trying to understand where value is moving next.

In one recent example, a large Middle Eastern luxury and retail group approached us to deepen its understanding of how consumer behaviour, brand-building, and commerce are evolving at speed. Rather than studying competitors within its own category, the leadership team wanted to look outward – to South Korea – as a live laboratory for the future of beauty and consumer culture.

The rationale was simple. Korea has become one of the most influential ecosystems shaping global beauty trends, digital-native brand building, and fandom-driven commerce. In fact, in the first half of 2025, the country surpassed France to become the world's second-largest exporter of beauty products, trailing only the United States.

The learning objective was not to copy Korean brands or import surface tactics. It was to observe how an entire ecosystem operates under different rules – how culture becomes demand, how digital communities translate into loyalty, and how speed, experimentation, and iteration are embedded into everyday decision-making.

That kind of exposure compresses time. It allows leaders to encounter emerging patterns before they harden into industry consensus, and to ask the right strategic questions while there is still room to act.

Why Learning Breaks Down Inside the Organisation

Most corporate learning environments are designed for control.

The data is sanitised. The scenarios are hypothetical. The consequences are abstract. That makes them useful for alignment – but weak at changing behaviour.

Markets are not controlled environments.

Customers don’t behave rationally. Competitors don’t wait for internal consensus. Decisions are made under uncertainty, trade-offs are immediate, and feedback is often incomplete or uncomfortable. Yet many leadership teams encounter these realities only through summaries and dashboards.

The result is a familiar pattern:

• organisations talk about agility, but operate slowly

• they celebrate innovation, but reward predictability

• they endorse change, but defend legacy assumptions because the evidence never feels conclusive enough

High-growth companies close this gap by exposing leaders directly to live operating environments – places where decisions carry consequences and speed matters more than certainty.

Why Cross-Industry Learning Beats Best Practice

Best practice is rarely where growth comes from.

What works today is usually optimised for yesterday’s conditions. Organisations that focus too heavily on peer benchmarking often end up converging on the same models, the same assumptions, and the same constraints. That creates parity – not advantage.

High-growth companies study other industries differently. They are not looking for models to copy. They are looking for patterns to transfer.

• How do other sectors reduce decision latency?

• How do they design for speed instead of certainty?

• How do they allocate talent dynamically instead of hierarchically?

• How do they monetise behaviour earlier in the customer journey?

McKinsey’s research on growth leaders shows that top performers are more likely to build or acquire capabilities outside their traditional domain – not to replicate surface tactics, but to import underlying operating principles. The advantage comes from pattern recognition, not imitation.

Why Learning Expedition Exists

Learning Expedition was built around this reality.

Rather than abstract frameworks or generic programmes, it places leaders inside unfamiliar markets and operating environments – where decisions are real, trade-offs are visible, and assumptions are tested under pressure.

For us, the work doesn’t stop at exposure. What leaders observe is synthesised collectively, translated into priorities, and activated within a defined execution window.

Get in touch to learn more.