It’s time to rewrite how we build talent

<p><b>Old talent templates aren't enough anymore. In the AI era, companies that anticipate skilling needs before roles change will set the pace, and our research shows leaders are doing just that.</b></p>

<p>Companies are closer to solving the AI skills challenge than they may realize. The gap isn’t a failure of ambition—it’s the natural result of enterprise learning and talent systems built for a different era, now ripe to be reimagined.</p> <p>The talent infrastructure organizations spent the past decades building served its purpose: skill cycles measured in years, training programs rolled out in cohorts, job descriptions stable enough to hire against. That foundation is a starting point.</p> <p>AI has accelerated change in ways that demand a different response. Today, leaders are navigating three interconnected shifts: a workforce eager to learn and apply AI but in need of continuous support rather than periodic training; a hiring market where the skills profile of tomorrow is actively being shaped but not yet clearly defined; and a critical layer of middle management that needs to drive adoption and evolution across teams while increasing their own AI fluency.</p> <p>That combination points clearly to a structural opportunity and the organizations that seize it will be the winners.</p> <h3><b><span class="h4">The opportunity hiding in the data</span></b></h3> <p>New research from Cognizant and Pearson reveals both the challenge and the clear path forward. Today, 54% of HR leaders say they are proactively arranging AI upskilling in anticipation of roles evolving—and that number is growing. That also means nearly half of organizations are trying to adapt their learning programs using the same methods and technology as roles change around them.&nbsp;</p> <p>In the AI era, companies that anticipate skilling needs <i>before</i> roles change will set the pace. Closing the gap between <i>perceived</i> and <i>actual</i> preparedness is where the most ambitious transformation efforts begin.</p> <h3><b><span class="h4">First, define the skills you need</span></b></h3> <p>The instinct when facing a skills gap is to reach for learning and development. That instinct is right—but incomplete. Before you can upskill a workforce or hire effectively for the future, you need to have a starting point of view on what the future is likely to look like role by role, built flexibly so that it can be adapted.</p> <p>Nearly all (97%) HR professionals say their organization plans to redefine job roles to reflect how AI is changing day-to-day work. It is great to see the intent is there. Yet, 64% of respondents believe they can’t find the right talent because AI is rapidly changing the skills they need to hire for.</p> <p>Organizations should pair their learning investments with deliberate work to define what roles look like now, what they will look like in the future, and then create bridge programs to get people there.</p> <h3><b><span class="h4">Learning as infrastructure is an imperative</span></b></h3> <p>Most organizations built strong L&amp;D foundations for a world that changed more slowly and predictably. In our research, four out of five HR professionals recognize their L&amp;D programs need to evolve to keep pace with how quickly AI is transforming jobs. That recognition is the first step—and it’s happening everywhere.&nbsp;</p> <p>This moment invites a complete overhaul of learning: a shift from periodic programs to continuous, embedded, personalized development that evolves as the technology does. It means moving beyond “libraries of content” toward practice-based learning woven into daily work—development that feels less like training and more like continuous growth.</p> <p>There is also a human dimension that L&amp;D strategies have underweighted—and it’s one of the most powerful levers available to us: the most durable learning is social. Neuroscience tells us collaborative learning dramatically improves retention, application, and motivation compared with solitary consumption of digital content. Organizations that treat learning as a living, social capability—not a periodic event—will close the skills gap faster and build an advantage that compounds over time.</p> <h3><b><span class="h4">Hiring for future-oriented roles</span></b></h3> <p>Once organizations draft what AI-era competency looks like across roles, the hiring strategy sharpens considerably.</p> <p>The data offers a clear signal about where AI-era talent is increasingly coming from. Nearly seven in ten (69%) HR professionals say broad, interdisciplinary academic backgrounds matter more than deep specialization for entry-level employees, and 67% say they value liberal arts graduates more than they used to. In the AI era, the primary differentiator is the capacity to learn, adapt, synthesize across domains, and apply judgment in ambiguity.</p> <p>Nearly all (96%) HR leaders expect entry-level roles to evolve into positions where employees supervise or collaborate with AI systems within five years. That is not a distant horizon. The roles being filled today are the roles that will need to make that transition. Organizations that define AI-era competency clearly now—and hire, develop, and build partnerships with educational institutions accordingly—shape the pipeline before it arrives.</p> <p>The intent to redesign roles is there. What transforms that intent into competitive advantage is building the talent architecture to match it first, then hiring into it with clarity.</p> <h3><b><span class="h4">The human infrastructure of transformation</span></b></h3> <p>Middle managers are the connective tissue between strategy and daily execution—and they may be the most underinvested asset in AI transformation. Our research makes this point emphatically: 95% of HR professionals believe middle managers are the most crucial factor in ensuring that employees effectively use AI in their work. Moreover, 92% say middle managers are crucial to redefining job roles as AI reshapes day-to-day work.</p> <p>Most AI transformation efforts have been designed top-down—through executive alignment, tool deployment, and policy—and the middle management layer is ready to be activated. Equipping these leaders means more than adding AI literacy to a checklist. It means empowering them as learning architects who can spark collaborative exploration, normalize experimentation, and translate AI strategy into the daily choices teams make about how work actually gets done.</p> <h3><b><span class="h4">The window is open—and so is the path forward</span></b></h3> <p>I firmly believe the AI skills gap is one of the most energizing design challenges talent leaders have faced in years. What it takes is treating it as a systems opportunity, not a content problem.</p> <p>That means moving past “Are we offering enough training?” and asking a more foundational question first: “Do we know what we’re building toward?” The organizations that answer that question—and build their learning, hiring, and management practices around the answer—are defining what it means to be a great place to grow in the AI era.</p> <p>The window is open. The moment is now.</p>