At Sun Valley, A.I. Isn’t the Only Competitive Advantage That Matters

As leaders across technology, media and finance gather in Sun Valley this week, much of the conversation will center on A.I.—its extraordinary pace, its commercial potential and its implications for nearly every industry. But beneath the excitement lies a quieter strategic question that may ultimately be more important: if A.I. democratizes knowledge, what becomes the source of lasting competitive advantage?

The answer isn’t simply better technology. It’s better expertise. A.I. excels at analytical thinking and complex data integration. It can synthesize vast amounts of information, identify patterns across complex datasets, augment human judgment and even simulate empathy in customer interactions. Capabilities that only recently differentiated high-performing professionals are rapidly becoming table stakes.

As A.I. compresses the value of routine knowledge work, the premium shifts toward the kind of thinking machines still struggle to replicate: connecting seemingly unrelated ideas, reframing problems and generating novel solutions. That is where deep expertise becomes a strategic asset rather than simply a professional credential. Humans still surpass technology in their ability to connect disparate ideas and see things differently. This is especially true of people with deep expertise who consistently outperform both non-experts and A.I. at solving complex problems within their domain. 

Their advantage is not simply that they know more. Years of deliberate practice fundamentally reshape how they think. Experts recognize meaningful patterns more quickly, retrieve relevant knowledge more efficiently and evaluate problems through richer, more interconnected mental models. Rather than processing every decision from scratch, they draw on sophisticated internal frameworks that allow them to generate more adaptive, and often more innovative, solutions. 

However, this advantage is highly domain-specific. As psychologist Timothy Salthouse observed, an expert is “someone who continually learns more and more about less and less.” The sophistication of expert thinking can become surprisingly brittle. Performance often declines rapidly once experts move beyond the boundaries of their own discipline.

Deep expertise can also create blind spots and constrain their thinking. Research shows that on truly novel problems, non-experts sometimes outperform specialists across fields ranging from medicine to forecasting. Roughly 60 percent of disruptive innovations originate outside the industries they ultimately transform, a reminder that fresh perspectives often challenge assumptions that experts no longer question. This presents one of the defining talent challenges of the A.I. era. Organizations need experts who continue learning across boundaries.

The key to building expertise in an A.I. world is breadth of experience. Our research shows that exposure to a wide range of challenges helps people build richer mental models while strengthening the cognitive capabilities that allow them to learn from new situations rather than simply rely on past experience. Breadth transforms technical competence into adaptive expertise.

For organizations, that means talent strategy can no longer focus exclusively on deep specialization. It must deliberately combine depth with breadth. Connecting experts from different disciplines is one way to accomplish it. Cross-functional communities of practice allow specialists to borrow insights from adjacent fields, exposing them to problems, perspectives and ways of thinking they would never encounter within their own silo. Companies like Procter & Gamble have long embraced this model of “constructive disruption,” recognizing that innovation often emerges at the intersection of disciplines rather than within them.

Technology can strengthen these connections as well. The same A.I. tools dominating conversations in Sun Valley may prove most valuable for helping experts collaborate more effectively. Companies like HumanCorps are increasingly using emerging A.I. tools to identify unexpected relationships among experts across functions and to accelerate the exchange of ideas. The goal is not to replace expertise with A.I., but to use A.I. to help expertise compound.

That challenge becomes even more urgent when considering how expertise develops in the first place. Much of the repetitive work that historically served as the apprenticeship for future experts is disappearing as A.I. automates entry-level tasks. At the same time, overreliance on A.I. for research, analysis and reasoning risks weakening the very cognitive muscles that underpin deep expertise: critical thinking, pattern recognition and independent judgment. Without intentional intervention, organizations could face an expertise gap a decade from now, not because talent is unavailable, but because fewer professionals have accumulated the experiences necessary to become true experts.

Organizations can counter this erosion through more intentional talent development. Learning tech companies such as TalentXTools are building company-specific business simulations as an engaging way to provide exposure to a wealth of relevant challenges—a digital substitute for experience. The built-in mechanisms that support triple-loop learning and insight harvesting accelerate the learning process, making it a more efficient way to build foundational skills and expertise. 

Similarly, there has been a renewed commitment from companies to early-career talent strategies. For example, SAP is intentionally resetting its talent approach with the specific aim of fostering early-career professional expertise to scale innovation. When designing these types of talent programs, we typically seek to incorporate job rotations, strategic projects and immersions to provide breadth, while coaches and mentors serve as learning accelerators.  

Data harvested through diagnostics infused in both of these processes can also be used to target formal education and skills development with greater precision. This increases returns as participant engagement, as formal learning components feel more personalized and relevant. 

With expertise as a vital driver of value creation and sustained competitive advantage, targeted talent programs—both digital and in-person—offer essential scaffolding. They help organizations to grow sophisticated capabilities and deep domain knowledge. When paired with mechanisms for connecting the dots across domains and work environments architected for slow thinking and lively collaboration, this deep expertise can complement the speed and efficiency of A.I. with innovation that enables business leaders to build the future readiness needed to stay ahead.  

As executives leave Sun Valley, they’ll likely have new ideas about models, chips, infrastructure and ways to reduce A.I. costs. Those conversations are significant, but the organizations that outperform over the next decade may not be the ones with the largest compute budgets. The longer-term differentiator may prove to be something less visible: whether organizations continue producing people capable of original thought. Technology may increasingly supply the answers. Competitive advantage will belong to the organizations that continue asking better questions.

Future-Ready Talent: Building a Talent Pipeline for Sustained Business Success by Tania Lennon and Ric Roi is out on the 28th July, published by Kogan Page.