Digital Economy Dispatch #250 -- Why You Might Be Focused On The Wrong AI

We're living through an “AI revolution”. And there’s no doubt that it is having an impact across every aspect of our lives. But as we get to grips with the implications of this disruption, I wonder if we're looking at it through the right lens.

Walk into any meeting today, and the conversation inevitably turns to ChatGPT, Gemini, Co-Pilot, Claude, or the latest locally developed large language models. These are spectacular tools with incredible capabilities. So no wonder CEOs are asking how to integrate generative AI into everything from customer service to strategic planning. Marketing departments are experimenting with AI-generated content, and HR teams are exploring AI-powered recruitment tools.

Don't get me wrong, generative AI is remarkable technology. But our collective fixation on it may be causing us to miss the forest for the trees. While we've been mesmerized by AI that can write poetry and generate images, some of the most transformative applications of artificial intelligence are happening in areas that generate fewer headlines but potentially far greater impact. It is in these areas where leaders and decision makers should be looking for long term sustained success with AI.

The Generative AI Gold Rush

The numbers tell the story of our current obsession. Microsoft invested $10 billion in OpenAI as part of a multiyear partnership that followed earlier investments in 2019 and 2021. Google scrambled to launch Bard to compete with ChatGPT, with the company's CEO reportedly declaring a "code red" situation for its search business. Amazon, Meta, and countless startups have poured billions into developing their own large language models. The message from Silicon Valley has been clear: generative AI is the future, and every business needs to get on board or risk being left behind.

This pressure created by the PR push from these AI technology providers has created a kind of tunnel vision. Companies are rushing to implement chatbots, experiment with AI writing assistants, and explore automated content generation. While these applications have genuine value (and I've seen impressive personal productivity gains in my own work) they represent just one facet of what AI can do. It's as if we discovered fire and became so fascinated by making torches and staring into the flames that we forgot about heating, cooking, and metallurgy.

The Quiet Revolution in Predictive AI

While the world has been captivated by AI that generates text and images, some of the most significant breakthroughs are happening in areas where AI excels at what humans struggle with most: making sense of vast amounts of data to predict complex, uncertain futures.

Take long-range weather forecasting. Traditional meteorological models, constrained by computational limits, could barely provide reliable forecasts beyond a week. But AI systems like DeepMind's GraphCast are now producing 10-day weather forecasts that outperform traditional models, using a fraction of the computational resources. This isn't just about knowing whether to pack an umbrella to hide from the rain, accurate long-term weather prediction has profound implications for agriculture, energy planning, disaster preparedness, and supply chain management.

Similarly, in healthcare AI is revolutionizing medical imaging in ways that dwarf the impact of any chatbot. Just look at some of what is happening.  Algorithms can now detect early-stage cancers that human radiologists miss, predict which patients are at risk of developing specific conditions years before symptoms appear, and identify subtle patterns in medical scans that suggest previously unknown disease markers. Google's AI system recently demonstrated the ability to predict acute kidney injury up to 48 hours before it occurs, potentially saving countless lives.

Financial markets present another fascinating case study. While everyone talks about AI to speed up admin or reduce fraud in financial transactions (important use cases, but hardly revolutionary), the real breakthroughs are in systems that can process thousands of economic indicators, news sources, and market signals to identify patterns invisible to human analysts. These systems aren't replacing financial advisors; they're uncovering relationships and predicting market movements in ways that fundamentally change how we understand economic systems.

Beyond Prediction: AI in High-Stakes Decision Making

Perhaps even more intriguing are AI applications in scenarios where the stakes are highest and uncertainty greatest. Consider maritime shipping, where AI systems now optimize routes for thousands of vessels simultaneously, accounting for weather patterns, fuel costs, port congestion, and geopolitical risks. These systems don't just save money; they reduce emissions and improve global supply chain resilience.

In urban planning, AI is being used to model the complex interactions between transportation, housing, employment, and environmental factors to predict how policy changes will affect cities over decades. This is light-years beyond generating a planning document; it's about understanding the deep, interconnected systems that govern how millions of people live and work.

The energy sector offers another compelling example. AI systems are now managing entire electrical grids, predicting energy demand, optimizing renewable energy integration, and preventing blackouts by identifying potential failures before they occur. As we transition to more complex, renewable energy systems, this kind of predictive management becomes critical infrastructure.

The Risk of Narrow Focus

I also think that our current focus on generative AI, while understandable, carries real risks. First, it may lead to misallocation of resources and attention. Companies investing heavily in chatbots and content generation may miss opportunities to apply AI to their core operational challenges, improving supply chain efficiency, enhancing quality control, or better understanding customer behaviour patterns.

Second, the emphasis on human-like AI interactions may cause us to undervalue AI's greatest strength: its ability to process and find patterns in data at scales impossible for humans. The most transformative AI applications often work behind the scenes, making millions of micro-decisions that collectively create massive improvements in efficiency, accuracy, or insight.

Third, our fascination with AI that mimics human creativity may blind us to applications where AI's new forms of intelligence (its ability to think in ways fundamentally different from humans) offers the greatest advantage. The patterns AI discovers in climate data, genetic sequences, or economic indicators often reveal insights that human intuition would never reach.

A Call for Strategic Vision

So, as leaders, we need to resist the temptation to view AI primarily through the lens of generative models. Instead, we should ask: Where in our organization do we deal with complex systems, uncertain predictions, or vast amounts of data that currently overwhelm human decision-making capacity? These are often the areas where AI can create the most value.

This doesn't mean abandoning generative AI. These tools have legitimate applications and will continue improving. But it does mean taking a more strategic, comprehensive view of AI's potential. Consider commissioning an AI audit that looks beyond content generation to identify where predictive analytics, pattern recognition, or system optimization could transform your operations.

Look for applications where AI can augment human judgment in high-stakes decisions, help navigate uncertainty, or uncover insights in your data that conventional analysis misses. These applications may be less flashy than an AI assistant, but they're often more transformative for your business and more defensible as competitive advantages.

The Bigger Picture

I know all too well that day-to-day financial pressures to make near term savings and incremental gains can be hard to resist in most commercial settings. Yet, we face a difficult choice. We can continue to focus primarily on AI that speaks and writes like humans, or we can embrace the full spectrum of AI's revolutionary capabilities. As AI continues to drive forward, the organizations and leaders who take the broader view, who recognize AI as a powerful tool for prediction, optimization, and pattern recognition in complex systems, will be best positioned for the next phases of the digital transformation.

The AI revolution is indeed here, but it's bigger, more diverse, and more profound than the current generative AI hype suggests. Our challenge as leaders isn't just to implement the AI tools everyone is talking about, but to identify and leverage the AI capabilities that others are overlooking. In a world increasingly defined by complexity and uncertainty, that broader vision of AI may be our greatest competitive advantage.