Couchbase, Inc. (NASDAQ: BASE), the developer data platform for critical applications in our AI world, today released the findings from its eighth consecutive survey of global IT leaders. The study of 800 senior IT decision-makers from enterprises with 1,000 or more employees, in sectors from finance to healthcare to gaming and more, found that businesses unable to effectively use AI in a timely manner could lose on average 8.6% of their revenue per month. Within our sample, that equates to an average annual loss of almost $87 million 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, while 64% are concerned that they are not taking advantage of AI as quickly as they could be due to “decision paralysis.”
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 GenAI, agentic AI and other forms of AI will surge by 51% in 2025 to 2026, compared to 35% growth in overall digital modernization. It 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.
“The evolution from GenAI to agentic AI is creating vast opportunities for enterprises that can harness these technologies effectively,” said Julie Irish, Chief Information Officer at Couchbase. “Creating and operating innovative AI applications at scale is essential for successful enterprises. 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.”
Key findings include:
- Falling behind the AI wave has significant consequences: 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. These issues had real consequences, eating up 17% of AI investment and setting strategic goals back by six months on average.
- Closing the data understanding gap is key to control: 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). Conversely, those with greater understanding are more confident, and are 33% more likely to be prepared for agentic AI.
- 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. 75% of enterprises have a multi-database architecture, which makes it more difficult to ensure accurate, consistent AI output; 61% do not have the tools to prevent proprietary data from being shared externally, which increases security and compliance risks; and 84% lack the ability to store, manage and index high-dimensional vector data needed for efficient AI use. To address these challenges, all surveyed enterprises are consolidating and simplifying their AI technology stacks to make controlling AI easier and more efficient.
- Encouraging experimentation contributes to AI success: Corporate attitudes about AI have a notable impact on its 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), GenAI (35%) and other forms of AI (35%) is almost even, despite agentic AI and GenAI 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.
- Inability to keep up with AI increases risk of being replaced: Enterprises recognize AI’s potential for disruption, allowing smaller organizations with a better grasp of the technology to replace larger, less agile competitors. 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.
“The data reveals both tremendous opportunities and significant risks presented by AI,” continued Irish. “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. The key is having robust controls in place and an architecture that suits enterprises’ purposes. When enterprises build the right foundation to support critical applications containing AI workflows, and target use cases with a clear ROI, CIOs will be best positioned to turn AI into a genuine competitive advantage.”
“A modern developer data platform is essential for enterprise AI success,” added Matt McDonough, SVP of product at Couchbase. “With capabilities like vector search, integrated AI Services and support for agentic AI development, Couchbase empowers customers to develop agentic systems and applications at scale, while delivering compelling price-performance. By supporting the management of all data types involved in AI interactions, our platform helps enterprises unify AI, operational, analytical, vector and mobile workloads into a single, multipurpose architecture. This holistic approach not only enhances data visibility, control and protection, but also gives developers the tools they need to innovate with the next wave of AI technologies.”
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