A company can expand its workforce while eliminating jobs that otherwise would have existed. That distinction matters for HR leaders evaluating claims that AI creates jobs rather than displaces them. The question is whether AI helps an organization grow, what happens to its hiring plans, and which workers gain or lose opportunities along the way.
AI-intensive employers are hiring more
A study by Ramp and Revelio Labs examined AI spending and employment at more than 21,000 US companies. Firms making the largest sustained AI investments had roughly 10.2% more employees over the two years after adoption than comparable firms that adopted later. Their entry-level head count grew about 12%. Companies making smaller investments saw no statistically significant employment gains.
These results offer encouraging evidence for HR leaders. Intensive AI investment can accompany expansion across engineering, sales, administration and customer service. When productivity improvements help a company deliver better services or serve more customers, demand for workers can increase even as individual tasks become easier to automate.
Still, the strongest AI adopters were unusual employers before buying the technology. They tended to be larger, more engineering-intensive, better funded and faster growing. The researchers compared earlier adopters with later adopters to reduce this selection problem, but that approach cannot completely disentangle AI from other drivers of expansion. Statistically significant sector-level gains were concentrated in information businesses, rather than appearing consistently across the economy.
An HR director at a hospital, manufacturer or insurance company should therefore resist treating these findings as a staffing forecast. The study establishes a relationship between intensive AI spending and subsequent head count growth in its sample. The authors’ suggestion that these findings rebut job-loss predictions goes further than the head count data permit.
When hiring slows, payroll can still grow
Imagine a business expects enough demand to hire 100 employees. After automating some work, it hires 40 instead. Its payroll grows, and nobody needs to be laid off, but 60 planned opportunities disappear. A head count dashboard would record growth without showing the forgone hiring.
Displacement can also take the form of canceled vacancies, unfilled positions after attrition, reduced hours or discontinued contractor assignments. Separate research from Ramp found that spending shifted away from freelance marketplaces as companies increased AI spending. That pattern raises questions about workers who never appear on an organization’s employee roster.
The entry-level picture is especially important for succession planning. An August 2026 Stanford analysis found employment among 22- to 25-year-olds in highly AI-exposed occupations was 19% below where it would have been had it tracked their less-exposed peers. Researchers observed weaker hiring rather than a surge in separations. They caution that the evidence does not establish AI as the sole cause.
And a September 2026 Danish administrative data study found that firms adopting AI in 2023 had employment growth 11% lower than non-adopters by late 2025, with slower recruitment concentrated among smaller firms. These studies examine different workers, employers and outcomes, so their findings need not contradict Ramp’s research. Together, they warn HR against generalizing from the success of high-spending adopters.
If employers quietly eliminate junior openings, they may also weaken the pipeline of employees who develop judgment and leadership skills through experience. That is a workforce-planning problem even when today’s payroll looks healthy.
HR needs a different AI workforce scorecard
HR should track workforce changes against the staffing plan that would have existed without AI. Monitor positions approved but never posted, vacancies canceled, replacement hires forgone, changes in contractor spending and entry-level recruiting. Break out the results by role, department, tenure and skill level. Head count alone captures too little.
HR leaders should also work with finance and operations to compare labor changes with output, service quality, revenue, and employee workload. A team that handles more cases with fewer new hires may be more productive, but that result tells management little about whether employees face unsustainable demands or whether early-career opportunities are shrinking.
Finally, HR needs a voice in deciding how productivity gains get used. Employers can reinvest saved time in customer service, new offerings, employee development, and internal mobility. They can redesign junior roles around judgment, communication and effective AI use rather than allowing entry-level hiring to vanish by default. As I argue in my work on workplace AI adoption, employee engagement and practical training belong in these decisions from the outset.
The Ramp findings give employers a reason to explore AI’s growth potential. They give HR no reason to stop asking whose work is changing, whose opportunities are disappearing, and whether the organization is preparing its next generation of talent.