New study of 500 enterprises conducted by Larridin and Carnegie Mellon University reveals organizations with concrete evidence of AI deployments and results grew revenue faster than those with weaker signals
Larridin, Inc.®, the platform measuring AI-powered work across the enterprise, today announced findings from a new study, Do AI Adoption Signals Predict Company Performance?,conductedin collaboration with researchers at Carnegie Mellon University.
For the first time, researchers have identified measurable connections between companies’ visible AI adoption signals and subsequent revenue growth. Examining more than 500 publicly traded U.S. companies (excluding the five largest AI chipmakers), the study found that companies providing the most specific AI disclosures in public 10-K filings achieved an 8% advantage in revenue growth compared with those that provided the least detail.
The strongest finding centers around “narrative concreteness,” or how clearly a company describes deployed AI systems and quantifiable results in its regulatory filings. The results suggest that companies documenting named AI deployments and measurable outcomes tend to grow faster than organizations relying primarily on aspirational or generalized AI language.
“Generalized AI investment alone tells us little about a company’s ability to create value,” said Ameya Kanitkar, co-founder and CTO of Larridin. “What matters is identifying where AI is being deployed, measuring adoption and workforce proficiency, understanding how customers and employees are benefiting, and connecting those efforts to quantifiable business results.”
AI Evaluation Signals Predict Growth
The study, Do AI Adoption Signals Predict Company Performance?, evaluated companies using seven AI-related signals, including Larridin’s scores on adoption, proficiency, impact, and maturity index, as well as three measures derived from SEC filings and hiring data: narrative concreteness, investment intensity, and AI-hiring builder rate. Researchers compared those signals with revenue growth, operating margins, and stock returns while controlling for industry sector, company size, and prior growth momentum. All seven AI signals show a relationship to revenue growth.
However, most of the broader adoption and composite scores became less predictive after accounting for the company’s industry sector, company size, and existing momentum. Narrative concreteness, the extent and depth of information about AI deployments in the 10-K filing, carries information not explained by those factors.
The study is the first to show that a signal of AI adoption correlates significantly with faster revenue growth, though not with better operating margins or future stock performance. This means that companies demonstrating the clearest understanding of how they are investing in and deploying AI technology appeared better positioned to grow the top line, but those investments had not yet translated into measurable cost efficiencies or investor returns.
The findings also do not support the speculative narrative that companies are adopting AI primarily to reduce headcount. While the study did not directly measure layoffs, the absence of a relationship between AI adoption and margin expansion suggests that AI is not yet functioning as a broad-based labor-cost reduction strategy. At this stage, companies appear to be using AI more to expand capabilities than to reduce costs. As the paper concludes, “in this sample, AI adoption is a top-line story, not yet a cost story.”
“The study suggests companies are using AI primarily to expand capabilities, improve customer experiences, and create new growth opportunities,” said Shixiang (Woody) Zhu, Assistant Professor at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy, who led the research team. “At this stage, AI’s measurable impact is appearing more clearly in revenue growth than in operating margins or stock performance, indicating that its value goes beyond cost reduction.”
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