Report finds high-intensity AI adopters increased headcount 10.2% overall, 12% for entry-level roles

A recent report from Ramp and Revelio Labs analyzed workforce records and enterprise AI spending across nearly 22,000 companies to assess the relationship between AI adoption and employment. The study defines “high-intensity adopters” as firms spending an average of $30 per employee per month on AI during the first three months of investment. Among these companies, overall headcount increased by 10.2%, with entry-level roles expanding by 12%.

Job growth was observed across multiple functions, including engineering, sales, administration, customer service, finance, marketing, and scientific roles. The strongest hiring gains occurred in the information sector, which includes software, internet, media, and tech-adjacent industries.

The report’s authors caution that the findings do not indicate AI universally creates jobs, but they do counter claims that AI will lead to broad job losses. They note that the data skews toward fast-growing, tech-forward firms that may have had expansion momentum independent of AI adoption.

The analysis also challenges the notion that AI disproportionately eliminates junior roles. While other research, such as a Goldman Sachs report, has found that AI contributed to the elimination of approximately 16,000 net jobs per month over the past year—with entry-level and Gen Z workers disproportionately affected—this study finds that entry-level headcount rose by 12% among high-intensity AI adopters.

The report suggests that in software and technology firms, AI can reduce costs or accelerate core workflows—such as coding, debugging, building internal tools, producing documentation, and supporting product development—thereby increasing the return on expanding the entire firm rather than just specific teams.

However, firms that purchased AI subscriptions or ran pilots without sustained investment did not see headcount gains, according to the report. This indicates that the benefits of AI adoption may accrue primarily to companies with the capital, technical staff, founder networks, and management bandwidth to implement and scale AI effectively.

The authors speculate that the gap between firms with resources to invest in AI and those without may continue to widen, potentially leaving less-resourced companies at a competitive disadvantage.