AI Automation Cuts Entry-Level Hiring at 22% of Organizations

Nearly one in four organizations has stopped hiring for at least some entry-level positions as artificial intelligence automates tasks traditionally assigned to junior employees, according to new research from Gartner.

The finding comes from a fourth-quarter 2025 survey of 110 chief human resources officers. Twenty-two percent reported that at least one business leader in their organization has halted entry-level hiring due to AI automation. While 95% of surveyed organizations have deployed AI in some form over the past year, only 20% have achieved significant or transformational value from those investments.

The automation mismatch

Current AI implementations primarily target augmentation and automation of less complex tasks—work historically performed by entry-level employees. This shift creates a fundamental disconnect: the skill profiles of early-career talent no longer align with the organization's remaining low-complexity work.

"Organizations that respond by cutting their early career talent pipelines altogether risk creating significant workforce challenges down the road," said Kaelyn Lowmaster, Director Analyst in Gartner's HR practice. She advocates for redefining entry-level roles to enable earlier contributions to higher-value work rather than eliminating these positions entirely.

Companies that abandon early-career hiring face two critical costs. First, they must pay premiums to hire experienced external talent instead of developing workers internally. Second, they lose the low-risk learning environments where employees traditionally built skills, networks, and institutional knowledge over time.

Why it matters

This trend signals a fundamental shift in how organizations must approach talent development. As AI handles routine tasks, companies can no longer rely on gradual skill-building through repetitive work. The challenge extends beyond immediate hiring decisions—it threatens the entire pipeline of future mid-level and senior talent. Organizations that fail to redesign early-career roles may find themselves perpetually dependent on expensive external hires while lacking the institutional knowledge that comes from internal development.

Three strategic responses

Gartner recommends CHROs prioritize three approaches to bridge the gap between early-career capabilities and AI-enabled work complexity:

First, conduct detailed workforce analysis to understand how AI changes work distribution. Organizations should examine value streams supporting critical business capabilities and map AI's impact on required skills. This analysis helps identify high-value job families where redistributing tasks across roles will have the greatest effect.

Second, shift development strategies from gradual skill-building to accelerated capability development. A December 2025 Gartner survey of 3,086 employees found workers are 3.8 times more likely to achieve high skills preparedness when building on adaptable foundational skills. CHROs must partner with business leaders to design on-the-job learning solutions and drive employee versatility rather than narrow role mastery.

Third, provide robust support structures for early-career employees operating in more complex roles. With fewer opportunities to develop judgment through routine work, organizations need safety nets including tools, guidance, and peer connections that help employees navigate ambiguity while minimizing mistakes. Training should emphasize business acumen and how specific actions deliver business value.

"Understanding how AI is changing work helps organizations identify opportunities to shift tasks across roles," said Annika Jessen, Director Analyst in Gartner's HR practice. "Knowing where AI is freeing up time enables leaders to create new supervisory responsibilities and identify tasks that can safely shift to early career talent."

The findings were first reported by Gartner in a press release detailing the survey results.