For decades, organizations have relied on early-career talent to do the routine, lower-risk work that supports the business and to serve as a training ground for future leaders. Think of Peggy Olson’s trajectory on Mad Men, from novice assistant to Don Draper’s protégé to confident copy chief at an advertising agency—a climb that began because routine work kept her in the room where judgment was practiced, and where her own could be noticed.

In real life, advances in automation and AI are changing the composition of entry-level work itself. Tasks such as research, documentation, data cleanup, basic coding, and preliminary analysis are being streamlined or absorbed into AI systems. These are precisely the activities through which young employees have traditionally built instincts, developed judgment, and earned the right to take on more.

At the same time, fears are growing about the impact of AI on jobs. In the 2025 Women in the Workplace report by McKinsey and LeanIn.Org, entry-level workers, particularly women, reported feeling the most worried of all age groups about how AI use will affect their jobs. The anxiety is broad based: Among graduating seniors, pessimism about starting a career climbed to 62 percent, from 46 percent, in just two years, and three-quarters of the pessimists pointed to firms hiring fewer entry-level workers as the reason.¹

The outlook for entry-level hiring is still evolving, but early indicators suggest a tightening market. Unemployment among recent US college graduates has increased since 2019, while the number of entry-level roles is declining, particularly in AI-exposed occupations. As of the first quarter of 2026, the unemployment rate for recent college graduates stood at roughly 5.7 percent, and about four in ten were underemployed—working in jobs that do not typically require a degree.² Research from the Stanford Digital Economy Lab finds that workers ages 22 to 25 “in the most AI-exposed occupations have experienced a 16 percent relative decline in employment even after controlling for firm-level shocks.”³

How much of this softening is attributable to AI remains genuinely contested: Federal Reserve Bank of New York economists estimate that the rise of remote work—which makes it harder to train novices at a distance—accounts for much of the increase in young-graduate unemployment, and Yale’s Budget Lab finds no clear economy-wide AI fingerprint yet, even as it flags the growing divergence between younger and older graduates as consistent with early-career effects.⁴

Whether the apprenticeship channel is being eroded by algorithms or by distance, the implication for employers is the same: The informal mechanisms that once turned novices into experts can no longer be taken for granted. These shifts point to a narrowing set of traditional entry points just as the nature of early-career work is being redefined.

For organizations, this is a moment of both uncertainty and choice. As AI reshapes how work gets done, the question is no longer how many entry-level roles to hire, but what those roles are designed to do. Companies can use this transition to rethink entry-level work as a foundation for building expertise in an AI-enabled environment—equipping early-career employees to design, develop, and steer AI systems, not just operate within them.

This article outlines a four-part approach spanning knowledge management, role design, learning in the flow of work, and managerial coaching. Organizations that move deliberately can accelerate how quickly employees build judgment and take on higher-value problem-solving, strengthening both individual experience and long-term performance.

There will always be a pipeline

Entry-level employees bring fresh perspectives and creative problem-solving to organizations, fueling growth and innovation. They are apprentices to more-seasoned mentors, building relationships that strengthen the culture for everyone. They become middle managers and senior leaders down the road, keeping the pipeline strong into the future.

The question of how to generate the next group of talent is not new. It is, however, being exacerbated as AI use cuts across so many different levels of knowledge work simultaneously. Two senior Microsoft engineering leaders, Mark Russinovich and Scott Hanselman, describe the dynamic bluntly: Agentic coding assistants give senior engineers an “AI boost,” multiplying their throughput, while imposing an “AI drag” on early-career developers who lack the judgment to steer and verify AI output. The resulting incentive—hire seniors, automate juniors—quietly dismantles the bottom of the talent pyramid on which every senior role depends. Their prescription is striking for its candor: Keep hiring early-career employees, accept that they initially reduce capacity, and make their growth an explicit organizational goal.⁵

McKinsey survey data shows that gen AI use is starting to affect the need for entry-level positions at some organizations (See Exhibit 1).

In a March 2026 survey of roughly 1,500 executives and senior talent leaders, those expecting AI to increase entry-level hiring in 2026 outnumbered those expecting it to decrease hiring by nearly three to one—though the same survey carries a warning, with a third of employers reporting that AI has already reduced the foundational, skill-building tasks juniors learn from.⁷

Organizations want to hire people who have the nascent skills to interpret the in-depth data generated by AI and who can work alongside agents. To create a strategy that guides these employees, leaders can focus on four areas.