AI Use in the Workplace: Statistics and Studies

This page lists research studies, ideally peer-reviewed and published, on the contributions of AI to productivity (or lack thereof) in the workplace. Please send relevant studies, comments and suggestions to sree@cs.rochester.edu.

2025

The GenAI Divide: STATE OF AI IN BUSINESS 2025

Aditya Challapally, Chris Pease, Ramesh Raskar, Pradyumna Chari, MIT NANDA

Jul 2025

Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach.

Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise- grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.

Source

Peer reviewed: No

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Joel Becker, Nate Rush, Beth Barnes, David Rein, Model Evaluation & Threat Research (METR)

Jul 2025

We conduct a randomized controlled trial (RCT) to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower. We view this result as a snapshot of early-2025 AI capabilities in one relevant setting; as these systems continue to rapidly evolve, we plan on continuing to use this methodology to help estimate AI acceleration from AI R&D automation

Blog Source

Peer reviewed: No

Large Language Models, Small Labor Market Effects

Anders Humlum (University of Chicago), Emilie Vestergaard (University of Copenhagen)

May 2025

We examine the labor market effects of AI chatbots using two large-scale adoption surveys (late 2023 and 2024) covering 11 exposed occupations (25,000 workers, 7,000 workplaces), linked to matched employer-employee data in Denmark. AI chatbots are now widespread—most employers encourage their use, many deploy in-house models, and training initiatives are common. These firm-led investments boost adoption, narrow demographic gaps in take-up, enhance workplace utility, and create new job tasks. Yet, despite substantial investments, economic impacts remain minimal. Using difference-in-differences and employer policies as quasi-experimental variation, we estimate precise zeros: AI chatbots have had no significant impact on earnings or recorded hours in any occupation, with confidence intervals ruling out effects larger than 1%. Modest productivity gains (average time savings of 3%), combined with weak wage pass-through, help explain these limited labor market effects. Our findings challenge narratives of imminent labor market transformation due to Generative AI.

NBER Working Paper No. w33777

Peer reviewed: No

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Last updated: Aug 25, 2025