Businesses that are unable to effectively use AI in a timely manner could lose on average 8.6% of their revenue per month, according to a report from Couchbase.
Couchbase commissioned an online survey conducted in April 2025 by Coleman Parkes, an independent market research organization. Respondents included 800 senior IT decision-makers across enterprises in the United States, United Kingdom, France, Germany, Turkey, Japan, India, Australia and Singapore.
The company said that within the study sample, average losses equate to almost $87 million per year per company.
A significant number of enterprises are at risk — 21% admit to having “zero” or “insufficient” control over AI use, allowing employees too much or too limited access to tools and increasing risk. Meanwhile, 64% are concerned that they are not taking advantage of AI as quickly as they could be due to “decision paralysis.”
Couchbase said the stakes are high, with 78% of respondents believing early AI adopters will become industry leaders and 73% reporting AI is already transforming their technology environment.
Investment reflects this urgency. AI spend on technologies including generative AI, agentic AI and other forms of AI will surge by 51% in 2025 to 2026, compared to 35% growth in overall digital modernization.
This will account for more than half of all digital modernization spend. Enterprises with control over their AI, and most importantly the data behind it, will be best positioned to capitalize on AI.
“Creating and operating innovative AI applications at scale is essential for successful enterprises,” said Julie Irish, CIO at Couchbase. “The right data strategy, including methods to ensure high data quality, scalability and accessibility, is more important than ever to ensure companies unlock the value of AI.”
The study found that falling behind the AI wave has significant consequences, as 99% of enterprises have encountered issues that disrupted AI projects or prevented them outright, including problems accessing or managing the required data; perception that the risk of failure had become too high; and an inability to stay on budget.
Closing the data understanding gap is key to control, as 70% of enterprises admit their understanding of the data (e.g., the quality and real-time accessibility of data) needed to power AI is “incomplete,” contributing to 62% not fully understanding where they are at risk from AI (e.g., through security or data management issues).
Data architecture is evolving and requires consolidation. The right data architecture is crucial for AI. Yet enterprises say their current architecture has an average lifespan of 18 months before it can no longer support in-house AI applications.
Encouraging experimentation contributes to AI success. Enterprises that encourage AI experimentation have 10% more AI projects enter production and incur 13% less wasted AI spend than enterprises with a more restrictive approach.
New developments in AI are rapidly reaching parity. The proportion of AI spend on agentic AI (30% of total), generative AI (35%) and other forms of AI (35%) is almost even, despite agentic AI and generative AI being much newer concepts. This suggests enterprises are investing heavily in keeping up with AI development as 66% worry that AI and different approaches to AI are evolving faster than their organizations can keep pace.
The inability to keep up with AI increases risk of being replaced. More than half (59%) of IT leaders are concerned that their organizations risk being replaced by smaller competitors, yet at the same time 79% believe they can do the same and displace their larger competition.
“While 73% of CIOs are excited about AI’s potential and feel compelled to use it more, the enterprises that master their data will be the ones that truly capitalize,” said Irish. “The key is having robust controls in place and an architecture that suits enterprises’ purposes.”
