The AI Era: Why Contextualising and Applying Knowledge Matter More Than Having It

In the late 1990s, Garry Kasparov, one of the greatest chess grandmasters of all time, lost to IBM's Deep Blue. It was a defining moment that signalled the beginning of an era where machines could outperform humans in raw computation. Today, AI chess engines can beat the best players in the world, but interestingly, human-AI hybrid teams still outperform AI alone. Why? Because knowing isn't enough. Contextualising and applying knowledge in the right way is what makes the difference.

We've always been told that asking the right questions is the key to success. In school, in business, in leadership—every great mentor has stressed its importance. But in the AI era, this principle isn’t just useful—it’s critical.

AI has changed the game. We no longer live in a world where expertise is measured by how much we know. AI can absorb and process more information in seconds than any human could in a lifetime. But can it apply that knowledge in the right context? Can it decide when to act and what truly matters? That’s where human intelligence still holds its edge.

We now live in a world where AI doesn't just play chess—it’s making billion-dollar investment decisions, diagnosing medical conditions, and generating content like this article. The question is no longer who knows more, but who can apply knowledge more effectively.

Why 'Having Knowledge' Is No Longer a Competitive Advantage

For centuries, expertise was measured by knowledge accumulation. The scholar who memorised books, the lawyer who recalled every case, and the investor who studied historical charts were the gatekeepers of knowledge. But today, AI has democratised access to information.

The numbers speak for themselves:

• By 2025, AI-generated data will reach 181 zettabytes, up from 79 zettabytes in 2021.
• AI adoption in enterprises has surged by 270% in the last four years, yet 47% of businesses struggle to translate AI insights into action.
• AI-driven automation is expected to replace 85 million jobs by 2025, but it will also create 97 million new roles requiring contextual intelligence and complex problem-solving.

In this landscape, knowledge itself is no longer a scarce resource—but the ability to apply it effectively is.

"In an AI-driven world, intelligence is not about knowing everything; it’s about knowing what matters and applying it where it counts.

The Power of Asking the Right Questions

AI can give you answers—but only if you ask the right questions.

This is where human intelligence comes in. The ability to frame problems, challenge assumptions, and think critically is the defining skill of the future. Those who thrive in the AI era won’t be those who simply consume information, but those who can contextualise it and ask the right questions to unlock value.

Consider these real-world examples:

• In Business: AI can predict consumer behaviour, but a CEO must ask why a trend is emerging, how it will impact their industry, and what their company should do about it before competitors do.
• In Medicine: AI can analyse millions of medical images, but a doctor must determine which cases need further testing, when to intervene, and how to ensure ethical patient care.
• In Finance: AI-driven trading models process market data faster than any human. Yet, investors like Warren Buffett continue to succeed because they ask: What risks aren’t in the data? What market forces is AI overlooking?

"The future belongs to those who don’t just seek answers, but those who know which questions to ask."

Applying Knowledge at the Right Time, in the Right Place

Knowing something is like having a key. But if you don’t know which door it opens, it’s useless.

The difference between success and failure lies in timing and application. Two companies prove this point perfectly:

Airbnb – Asking the Right Question at the Right Time - In 2008, the hospitality industry was ruled by hotel chains. The prevailing wisdom? Travellers wanted standardisation and reliability. But Airbnb’s founders asked a different question: What if people value unique experiences over predictability?

By applying their knowledge of digital platforms and trust-based transactions, they built a $100 billion company that transformed the way we travel.

Kodak – Having Knowledge but Failing to Apply It - In 1975, Kodak invented the world’s first digital camera—decades ahead of its time. But instead of using that knowledge to lead the digital revolution, they clung to their film business.

Kodak didn’t lack knowledge—they lacked the ability to apply it at the right moment. By the time they realised their mistake, it was too late. In 2012, Kodak filed for bankruptcy.

"Knowing something is one thing. Knowing when to act on it is what defines success."

The Future of Intelligence: Context Over Content

As AI continues to evolve, the value of content retention is diminishing. The real measure of intelligence is shifting towards contextual intelligence—understanding how and when to apply knowledge.

The future will demand:

• Lawyers who don’t just memorise case law but can ask AI the right legal questions and interpret laws contextually.
• Data scientists who don’t just build models but know how to frame the right problems for AI to solve.
• Doctors who don’t just diagnose diseases but understand how to balance AI-driven diagnostics with human judgment, ethics, and patient care.

"AI can process data, but human intelligence must decide what data matters."

The New Competitive Advantage

We are moving into an era where AI will be our collaborator, not just a tool. But the real winners will be those who understand that AI is only as good as the questions we ask it.

• It’s no longer about how much you know—it’s about how well you apply what you know.
• It’s no longer about memorisation—it’s about contextual intelligence.
• It’s no longer about finding answers—it’s about asking better questions.

"AI can generate answers in milliseconds. But it takes human intelligence to ask, ‘Are we even solving the right problem?’"

Kasparov, the chess grandmaster who once lost to AI, later reflected:
"Machines have calculation; humans have understanding. That is the edge we must sharpen."

And that is the edge that will define the future of intelligence.