New Report Claims Companies That Adopt AI Also Add More Workers (Eventually)

Companies that rely on AI are also seeing positive impacts on their workforce.
Entry-level positions saw above-average growth, contrary to existing research
Workforce gains are not immediate – AI needs time to find its place within organizations

A new report challenges the notion that AI adoption has led to job losses, instead revealing that companies investing the most in AI are actually increasing their workforces.

The study combines business AI spending data from Ramp’s payments platform and workforce records from Revelio Labs to analyze more than 21,500 U.S. companies, making it one of the largest of its kind.

It concludes that high-intensity AI adopters increased their workforce by about 10% in the first two years after AI deployment, making AI good news for workers and the workforce.

AI Adoption Prompts Companies to Hire More Workers

Clearly, only high adoption of AI has a positive impact on workers, as companies making modest investments have not seen significant growth.

The study also highlights that the impacts are felt slowly: rather than seeing an immediate increase in employment, it takes time for companies to integrate AI, discover productive use cases and hire more workers.

High adopters are defined as those who invested approximately $33 per employee per month in the first three months after adoption, compared to approximately $3 for low adopters.

It also calls into question other recent research, claiming that entry-level employment actually grew 12% higher than average among high-intensity AI adopters. Other reports suggest that entry-level workers have been among the hardest hit.

Even as tech giants grab headlines, with Salesforce cutting nearly half of its support staff and Amazon notably cutting tens of thousands of workers, the Ramp/Revelio Labs report actually shows growth beyond just AI engineering roles, spanning sales, marketing, administration, finance, customer service and more.

Although the research cannot be used to predict long-term impacts on work, it at least serves to indicate that workers are not currently at risk of total layoff, even when changing roles.

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