Dive Brief:
- Companies with concrete artificial intelligence disclosures are seeing faster revenue growth, according to a recent report from Carnegie Mellon University and technology firm Larridin.
- Public companies with the most specific descriptions of their AI initiatives in their most recent 10-K filings achieved a revenue-growth advantage of 8 percentage points year over year compared with those that provided the least detail, the study found.
- “A lot of these companies are now being very specific about AI,” Larridin founder and CTO Ameya Kanitkar said in an interview. This reflects more mature AI adoption, he said, adding that companies are moving beyond AI experimentation and are increasingly focused on identifying “high-value” use cases.
Dive Insight:
The report, released Aug. 12, is the first to show that strong AI adoption “correlates significantly” with faster revenue growth, though not necessarily with better operating margins or future stock performance, Larridin, provider of an AI return-on-investment measurement platform, said in a press release.
The study controlled for industry, company size and prior revenue growth. To ensure the results were not disproportionately influenced by a small group of high-performing AI-chip companies, the main analysis excluded Nvidia, Broadcom, AMD, Micron, and Intel. Researchers also tested the full sample with those companies included and found that the study’s overall conclusions remained unchanged, Larridin said.
The researchers scored companies on a five-point scale based on the specificity of their AI disclosures. Of the roughly 500 companies analyzed, more than 150 received one of the two highest scores for disclosure concreteness, including five that received the top score for reporting deployed AI use cases with quantified business results.
Visa, one of the company’s that earned a specificity score of five, saw a 17% revenue spike year over year after disclosing that nearly 26,000 employees had launched AI-powered chats and described pilot programs involving AI agents and transactions.
Conagra Brands, by contrast, had a 2% revenue decline after it discussed AI more generally, including the use of AI and connected-data improvements to upgrade its operations. Larridin gave Conagra a specificity score of 2.5.
A study announced in June by AI startup Blue Bridge Group AI reached a different conclusion on the correlation between AI disclosures and stock gains. That study found that companies with concrete AI strategies delivered stronger stock performance than peers relying on vague or merely aspirational narratives.
“Comparing the two studies is really an apples-to-oranges comparison,” Kanitkar said. “Our findings simply mean that we did not find a link between AI adoption and stock performance based on the specific correlation methodology we used... It does not mean that a link cannot exist.”
