As technology, media and finance leaders gather in Sun Valley this week, much of the conversation will focus on artificial intelligence — its extraordinary pace, its business potential and its implications for nearly every industry. But beneath this excitement lies a quieter strategic question that may ultimately be more important: If AI democratizes knowledge, what might become the source of lasting competitive advantage?
The answer isn’t just better technology. It’s a better experience. AI excels at analytical thinking and integration of complex data. It can collect vast amounts of information, identify patterns across complex data sets, augment human judgment, and even simulate empathy in customer interactions. The abilities that have distinguished high-performing professionals only recently are quickly becoming stakes on the table.
As AI compresses the value of routine cognitive work, the priority is shifting toward the kind of thinking machines that still struggle to replicate: connecting seemingly unrelated ideas, reframing problems, and generating new solutions. Here, deep experience becomes a strategic asset and not just a professional accreditation. Humans still outperform technology in their ability to connect disparate ideas and see things differently. This is especially true for people with deep expertise who consistently outperform both non-experts and AI at solving complex problems within their field.
Their advantage is not just that they know more. Years of deliberate practice fundamentally reshape their way of thinking. Experts recognize meaningful patterns more quickly, retrieve relevant knowledge more efficiently and evaluate problems with richer, more coherent mental models. Rather than approaching every decision from scratch, they rely on sophisticated internal frameworks that allow them to find more adaptive, and often more innovative, solutions.
However, this feature is highly domain-specific. As psychologist Timothy Salthouse notes, an expert is “one who is constantly learning more and more about less and less.” The complexity of expert thinking can become surprisingly fragile. Performance often declines quickly once experts cross the boundaries of their specialty.
Deep experience can also create blind spots and limit their thinking. Research shows that on truly novel problems, non-experts sometimes outperform specialists in fields ranging from medicine to forecasting. Nearly 60% of revolutionary innovations originate outside the industries they ultimately transform, a reminder that new perspectives often challenge assumptions that experts no longer question. This is one of the defining talent challenges of the AI era. Organizations need experts who continue to learn across boundaries.
The key to building expertise in the world of AI is breadth of experience. Our research shows that exposure to a wide range of challenges helps people build richer mental models while enhancing cognitive abilities that allow them to learn from new situations rather than simply rely on past experience. Breadth turns technical competence into adaptive expertise.
For organizations, this means that talent strategy can no longer focus exclusively on deep specialization. It must deliberately combine depth and breadth. Connecting experts from different disciplines is one way to achieve this. Cross-functional communities of practice allow professionals to borrow ideas from neighboring fields, exposing them to problems, perspectives, and ways of thinking that they would never encounter within their own silo. Companies like Procter & Gamble have long embraced this model of “constructive disruption,” recognizing that creativity often arises at the intersection of disciplines rather than within them.
Technology can also strengthen these connections. The same AI tools that dominate conversations at Sun Valley may be even more valuable for helping experts collaborate more effectively. Companies like HumanCorps are increasingly using emerging AI tools to identify unexpected relationships between experts across functions and accelerate the exchange of ideas. The goal is not to replace expertise with AI, but to use AI to help accumulate expertise.
This challenge becomes even more pressing when considering how expertise develops in the first place. Much of the repetitive work that served as apprenticeships for future experts is now disappearing, as artificial intelligence automates entry-level tasks. At the same time, overreliance on AI for research, analysis, and reasoning threatens to weaken the cognitive muscles that support deep expertise: critical thinking, pattern recognition, and independent judgment. Without intentional intervention, organizations may face an expertise gap a decade from now, not because of a lack of talent, but because fewer professionals have acquired the necessary expertise to become true experts.
Organizations can counter this erosion through more intentional talent development. Ed-tech companies like TalentXTools are building company-specific business simulations as an engaging way to provide exposure to a wide range of relevant challenges – a digital alternative to experience. Built-in mechanisms that support three-loop learning and idea gathering accelerate the learning process, making it a more efficient way to build essential skills and expertise.
Likewise, there has been a renewed commitment by companies to early-career talent strategies. For example, SAP is intentionally recalibrating its approach to talent with the specific goal of leveraging early-career expertise to scale innovation. When designing these types of talent programs, we typically seek to incorporate job rotation, strategic projects, and knowledge immersion to provide breadth, while coaches and mentors act as accelerators of learning.
Data collected through diagnostics combined with both processes can also be used to more accurately target formal education and skills development. This increases returns as participant engagement, as formal learning components feel more personalized and relevant.
With experience being a vital driver of value creation and sustainable competitive advantage, targeted talent programs – both digital and in-person – provide essential underpinnings. It helps organizations develop cutting-edge capabilities and deep domain knowledge. When combined with connect-the-dots mechanisms across domains and business environments designed for slow thinking and dynamic collaboration, this deep expertise can complement the speed and efficiency of AI with innovation that enables business leaders to build the future-readiness needed to stay ahead.
When executives leave Sun Valley, they will likely have new ideas about models, chips, infrastructure and ways to reduce AI costs. These conversations are important, but the organizations that outperform over the next decade may not be the ones with the largest compute budgets. The long-term difference may be less clear: whether organizations will continue to produce people capable of creative thinking. Technology may increasingly provide the answers. Competitive advantage will go to organizations that continue to ask better questions.
Future-ready talent: Building the talent pipeline for sustainable business success by Tanya Lennon and Rick Roy He is Out on the 28thy July, published by Kogan Page.
