A recent study from Clari Labs, the research arm of Clari + Salesloft, shows that 87% of large enterprises failed to meet their revenue goals in 2025, even as investments in artificial intelligence reached record highs. The research highlights a significant gap between organizations’ ambitions for AI and the readiness of their data infrastructure.
Nearly half of surveyed enterprises reported that their revenue data is not prepared for AI applications, while 42% have yet to establish formal governance frameworks to ensure data accuracy and control. This lack of preparation means many companies are making important business decisions based on incomplete or unreliable information.
The report, which surveyed 400 CIOs, CROs, and RevOps leaders at North American enterprises with more than 1,000 employees, found that CIOs are becoming central figures in driving enterprise growth through AI-powered tools. However, fragmented systems and weak data governance continue to slow progress.
Steve Cox, CEO of Clari + Salesloft, commented on the findings: “We’re watching revenue evolve into one of the most disciplined systems inside the enterprise. AI doesn’t just need data; it needs context. The winners of the next decade will be the companies that trade fragmented signals for a unified revenue truth. True revenue predictability depends on every forecast, deal, and action being grounded in trusted, governed data that is aligned across the CIO, CRO, and RevOps.”
Among other key findings:
– Despite high levels of spending on AI technology by enterprises not using Clari + Salesloft products, challenges persist due to disconnected data sources. More than half said they experience conflicting pipeline signals from different systems.
– CIO teams now lead the selection and implementation of forecasting and revenue tools in nearly two-thirds (64%) of organizations surveyed.
– Almost all (96%) revenue leaders believe greater involvement by CIOs improves forecast accuracy.
– Over one-third (39%) update their forecast models only weekly or monthly; 42% still do not have formal frameworks for maintaining consistent and accountable data practices.
– While IT teams largely manage AI training and preparation (91%), less than a third (29%) of RevOps teams play a top role—potentially limiting effective adoption.
– Most CIOs and CROs now coordinate closely—61% meet daily or weekly—but nearly half identify trust and accountability as ongoing challenges.
The study suggests that achieving reliable outcomes from AI requires disciplined operating models standardizing how revenue data is managed across sales operations and technology functions.
Additional industry analysis indicates that companies with unified and well-governed revenue data have achieved up to 96% forecast accuracy along with substantial improvements in renewal rates and financial returns over three years.
These trends indicate that organizations focusing on robust data discipline are better positioned to turn AI investments into consistent business growth.
