From skills gap to human capital advantage: How leaders can keep pace with AI

<h2><span class="h6">Sixty percent of HR leaders say their learning and development programs cannot keep pace with AI job transformation. By treating learning as a strategic enabler rather than a support function, organizations will be better placed to succeed in an AI-first economy.</span></h2>

<p>Stable, prosperous societies are built on a shared understanding that meaningful work is available to those willing to develop the skills it requires, and that governments, institutions and employers will help people keep pace as the nature of that work changes.</p>
<p>AI is now testing that contract at a speed not seen before. Whether the gains of this transformation are broadly shared depends on how urgently we treat the obligation to bring the workforce along.</p>
<p>The strain is already visible. Sixty-four percent of HR leaders say they cannot find the right talent because AI is changing the skills they need to hire for so rapidly. That statistic is drawn from <a rel="noopener noreferrer" href="https://www.cognizant.com/us/en/insights/insights-blog/adapting-roles-for-an-ai-workforce" target="_blank">a new survey</a> commissioned by Cognizant and Pearson of 750 HR professionals at director level and above across the US, UK and India.</p>
<p>This is not simply a corporate hiring challenge. It is a sign that labor markets are struggling to keep pace with a transformation that is reshaping the relationship between skills, jobs and economic competitiveness. The result is widening productivity gaps between firms, mismatches between the skills education systems produce and those economies need, and increased pressure on public institutions.</p>
<p>Our research revealed that 60% of HR leaders agree that their company’s learning and development programs cannot keep pace with how quickly AI is transforming jobs. Yet 46% of organizations still do not proactively offer AI upskilling at all—they wait for employees to identify their own gaps or wait for roles to change before they respond.</p>
<p>Organizations understand the challenge, but many are still working out how to develop skills at scale. The demand for AI training from employees is growing: 91% of HR leaders report that requests for AI training have increased over the past year.</p>
<p>Organizations that treat learning as a strategic enabler rather than a support function will be better placed to adapt, capture the benefits of AI, and succeed in an AI-first economy.</p>
<h3><span class="h4" style="font-weight: normal;">AI requires embedded, not scheduled, skilling</span><b></b></h3>
<p>When the survey asked how they currently make time for AI learning, the results revealed a wide range of approaches and a clear gap between ambition and execution.</p>
<p>Eighteen percent of companies expect employees to develop AI skills on their own time, outside of working hours. Another 29% leave it to employees to find time organically within the workday. While 53% have established dedicated blocks of time, which is indicative of a more committed approach, this still treats learning as a discrete event separated from the flow of work.</p>
<p>All three approaches share the same underlying assumption: that upskilling is something that happens alongside work, rather than within it. That assumption itself may be part of the problem.</p>
<p>Success will depend less on how much time organizations allocate to training and more on whether learning is embedded into everyday work, available continuously, calibrated to the role and expected throughout the workday.&nbsp;</p>
<p>In our own organizations, we have approached this by rebuilding talent architecture around AI-first job families, establishing role-based requirements alongside recommended paths, and making progress visible across the enterprise. That combination of structure and flexibility distinguishes genuine learning infrastructure from a training calendar.</p>
<h3><span class="h4" style="font-weight: normal;">The middle manager role in activating AI skilling</span><b></b></h3>
<p>The data also highlights a factor that is often overlooked in upskilling strategies: the role of middle managers in determining whether learning infrastructure actually changes behavior.</p>
<p>Ninety-five percent of HR leaders identify middle managers as essential to ensuring that employees use AI tools effectively, challenging the recent trend of hierarchical flattening that has deprioritized that layer. In fact, 92% see them as critical to reimagining workflows and redefining job roles.</p>
<p>This carries an important implication for organizations that spent the past decade flattening management layers in the name of agility. Many are now rediscovering the importance of managers in scaling AI capability across their workforce.</p>
<p>While learning in the flow of work is increasingly recognized as an effective way to build capability, it requires managers who can model AI fluency, coach direct reports through change and translate organizational learning goals into team-level behavior. Successful organizations will explicitly equip middle managers with the skills, tools and support they need to help shape the workforce of the future.</p>
<h3><span class="h4" style="font-weight: normal;">Three shifts to close the AI skills gap</span><b></b></h3>
<p>The data points toward three shifts, and the sequence matters as much as the substance.</p>
<ol>
<li><b>The foundational shift</b> is an infrastructure challenge that belongs to CHROs and CTOs together: moving from episodic training events to continuous, workflow-integrated learning. Without this architectural change, upskilling investments will fall short regardless of scale, because the delivery mechanism is mismatched to the pace of AI change.<br>
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<li><b>The second shift,</b> from reactive to anticipatory upskilling design, is a workforce planning challenge. It becomes far more achievable once the right learning infrastructure is in place. Organizations cannot design learning paths around where roles are headed if they are still responding to gaps after they appear.<br>
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<li><b>The third shift</b> is the most consequential: 97% of organizations say they are planning to redefine job roles in response to AI. This is an ongoing process that raises fundamental questions about the social contract between employers and employees, and what obligations follow. It requires coordination between employers, educators, policymakers and workers to address questions that only become tractable when learning infrastructure and manager capability are firmly in place.</li>
</ol>
<h3><span style="font-weight: normal;" class="h4">The window for AI skills is narrowing</span><b></b></h3>
<p>The organizations best positioned to navigate AI change in the workforce will treat learning infrastructure as a core strategic enabler.</p>
<p>But the stakes extend beyond any single organization. When learning infrastructure fails at scale, the consequences ripple into even bigger consequences of widening inequality, declining competitiveness and eroding trust in the institutions we rely on to prepare people for the future.</p>
<p>That makes this a shared challenge: for business leaders who must build the systems, for policymakers who must create the conditions, and for educators who must reimagine what preparation looks like. Together, we can help build a future in which the benefits of AI are shared broadly, by workers, employers and society as a whole.</p>
<p><i>Statistics cited in this article are drawn from a survey commissioned by Cognizant and Pearson, conducted by Wakefield Research among 750 HR professionals at director level and above in the US, UK and India, at companies with 1,000 or more employees, conducted March 23 through April 3, 2026.</i></p>