Companies worldwide risk billions in lost revenue if they are too slow to adopt artificial intelligence. At the same time, many CIOs expect that rapid and thoughtful deployment of AI will enable them to outperform larger competitors and capitalize on new market opportunities.
The latest edition of the Couchbase FY 2026 CIO AI Survey shows that organizations lose an average of 8.6 percent of their revenue if they lag behind in the adoption of artificial intelligence. This equates to around $87 million per year per company. At the same time, CIOs expect their AI budgets to grow by 51 percent in 2025 and 2026, significantly more than the 35 percent increase for digital modernization as a whole.
The survey results show that AI is no longer seen as experimental technology, but as a key part of the strategic direction. This also explains why 96 percent of respondents say there is a hard deadline for implementing AI, with 87 percent seeing it approaching within six months.
Implementing AI is a challenge
Despite the optimism of 73 percent of CIOs, organizations are encountering obstacles almost everywhere. No less than 99 percent have experienced problems that delayed or even halted AI projects. On average, this represents a delay of more than five months in achieving strategic objectives. Globally, this amounts to 5.84 months and a cost impact of $42 million. The figures are similar in Europe, while the United States scores slightly better with 5.5 months and $38 million. This difference suggests that American organizations are quicker to move to production.
The survey also emphasizes the importance of a culture of experimentation. Companies that encourage experimentation get 10% more projects into production and lose 13% less of their AI budgets. The message is that innovation benefits from controlled testing and learning, as long as there are clear frameworks for governance and security.
Another major theme is data. Seventy percent of CIOs say that their own understanding of the data requirements for AI is inadequate. Without high-quality, easily accessible, and well-managed data, development stalls. Only a small minority has a mature vector database, even though this type of technology is crucial for generative and agentic AI. The average lifespan of existing AI architectures is estimated at only 18 months. It is therefore not surprising that three-quarters of organizations are focusing on consolidating their technology stack to reduce complexity and risks.
The results paint a mixed picture. There is a high willingness to invest and enthusiasm prevails, but the risks and obstacles are real. Those who want to use AI successfully will now have to build a solid data foundation and create space for controlled experiments. Only then can the substantial wave of investment in AI be converted into a tangible competitive advantage.