Enterprises That Fall Behind in AI Race Risk $87 Million Annual Loss, Couchbase Survey Reveals
70% Admit 'Incomplete' Understanding of AI Data Requirements While 21% Have 'Insufficient' or 'Zero' AI Control
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
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
Additional Resources
- To download the full report, click here.
- To download the graphic that highlights key findings from the report, click here.
- To learn more about how organizations can fully realize the potential of agents, click here.
- To learn more about how Couchbase empowers customers to develop agentic systems and AI applications, click here.
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As industries race to embrace AI, traditional database solutions fall short of rising demands for versatility, performance and affordability.
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