How Leaders Can Adapt to the Forces Shaping the Next Generation of Work

Matt Sigelman, President, The Burning Glass Institute and Senior Advisor with The Harvard Project on the Workforce

To listen to the member call click 

The Central Challenge of the AI Era: Skills, Not Jobs

In this first session of the year, we examined how artificial intelligence is reshaping work at its most fundamental level and what this means for organizational resilience. Drawing on large-scale labor market data, Matt Sigelman outlined why the defining challenge of the AI era is not job loss, but the rapid reconfiguration of skills, tasks, and career pathways—and why leaders must rethink how work is designed, how talent develops, and how productivity is measured in order to remain competitive.

Rapid Skill Change Is the New Normal

At the core of this shift is the fact that AI is not primarily eliminating jobs; it is fundamentally reshaping the skills required within them. Even before generative AI, the average role had already replaced more than 37% of its core skills within five years, and in the most disrupted jobs nearly three-quarters of required skills had changed. AI is dramatically accelerating this pace. As a result, organizational resilience in the AI era will depend less on adopting new technologies and more on how effectively leaders anticipate skill change and redesign roles, learning systems, and talent pathways accordingly.

AI Changes How Work Is Done Before It Changes Jobs

The most significant impact of AI is occurring at the task and skill level, not the job level. Historically, general-purpose technologies change how work is performed long before they alter employment levels. AI follows this same trajectory: it first enables new tools, then embeds into workflows, and only later reshapes occupations and creates new categories of work. In the near term, AI is unbundling jobs into tasks—automating some while augmenting others—freeing human capacity for higher-value work rather than triggering widespread job displacement.

Automation and Augmentation Are Happening Inside the Same Roles

Importantly, automation and augmentation are not opposing forces. Data shows that the roles experiencing the strongest automation effects are often the same roles seeing the greatest augmentation effects. In practice, AI removes certain tasks while expanding the scope, complexity, and value of the remaining work. This explains why demand is declining for skills that AI can fully automate, while rising for skills that allow people to work effectively alongside AI. In addition, some skill sets were augmented and automated at the same time making you more effective and efficient at the same time. There will be certain skills that all of us will need to have.

The Rise of the AI-Enabled Generalist

As AI democratizes access to advanced tools, organizations should expect the rise of a new breed of generalist. Skills once confined to specialists—such as data analysis, coding, advanced analytics, and design—are becoming baseline requirements across many roles, much as spreadsheet literacy did with Excel. Rather than reducing the need for these skills, AI raises the minimum level of proficiency required to apply them with judgment and impact.

Career Pathways Are Being Rewired

These changes are actively rewiring career pathways. Because jobs are defined by skills, shifts in skill composition alter which roles are adjacent and which transitions are viable. Early evidence already shows increased cross-industry mobility alongside the erosion of traditional skill bridges between roles. Organizations that rely on static job architectures and legacy career ladders risk misaligned pipelines, stalled mobility, and emerging capability gaps.

The Expertise Upheaval: Fewer On-Ramps, Greater Demand for Experience

One of the most profound implications is what Matt described as an “expertise upheaval.” In many professional roles, AI shortens learning curves for some tasks while simultaneously reducing demand for entry-level work that historically served as the training ground for expertise. This is pushing organizations away from pyramid-shaped talent models toward diamond-shaped ones, with increased demand for experienced talent in the middle. Without intentional redesign, this dynamic threatens long-term expertise development and weakens workforce resilience.

From Reskilling to Pre-Skilling

In this context, traditional approaches to reskilling are insufficient. Evidence suggests that reactive, large-scale reskilling efforts rarely produce strong outcomes. Instead, organizations must focus on pre-skilling—anticipating skill shifts early and enabling workers to build new capabilities while still in role. Skill development should function not only as remediation, but as a forward-looking mechanism for mobility, credibility, and transition.

The AI Revolution will Require an AI Workforce

Over the past few years, organizations have shifted toward leaner hiring models, driven by both the pandemic and AI. AI creates the opportunity to boost productivity and raise living standards, but doing so requires understanding how AI reshapes workflows, the skills people need to effectively use it, and how roles evolve as a result. These changes, in turn, open up new career transitions and raise important questions about organizational design.

AI as a Tool of Work, Not a Standalone Technology

To fully capture AI’s potential, leaders must treat AI not as a standalone technology initiative but as a tool of work, anchored in concrete business use cases. Effective deployment begins with strategic priorities, productivity bottlenecks, and workforce scale, then works backward to workflow redesign, role impact, and skill requirements. This places AI squarely within business leadership, with HR playing a critical enabling role by maintaining role architecture, tracking capabilities, and supporting transitions.

A New Paradigm of Productivity

Ultimately, AI creates an opportunity to rethink productivity itself. Historically, productivity efforts have focused on reducing labor costs (input). The AI era enables a different paradigm—one centered on increasing the value of human output. By redesigning work, strengthening foundational capabilities, and enabling people to take on more complex, higher-impact tasks, organizations can use AI to make work—and workers—worth more, not less.