We are at a crossroads when it comes to expanding the role and potential of AI in organizations. Everybody wants it, but many see difficult-to-climb hurdles embedded within their organizations. And holding back may be costly. These are the findings of a new survey published by Couchbase of 800 senior IT decision makers from enterprises with 1,000 or more employees.
The heat is on. Just about every survey respondent (96%) feels there is a “deadline by which their organization needs to have embraced AI,” and that is within a year to the next 6 months.
The survey finds that businesses unable to effectively use AI in a timely manner could lose, on average, 8.6% of their revenue per month. That equates to an average annual loss of almost $87 million for each company that participated in the survey.
AI is still the Wild West in many cases, the study also confirms. More than 1 in 5 managers (21%) admit to having “zero” or “insufficient” control over AI use. At the same time, 64% are concerned that they are not taking advantage of AI as quickly as they could due to “decision paralysis.”
At the root of this is a major data understanding gap, the survey finds. Seven in 10 respondents (70%) admit their understanding of the data—the quality and real-time accessibility of data needed to power AI—is “incomplete.” In addition, 62% admit they do not fully understand the nature of AI risks—especially through security or data management issues.
Data initiatives weigh heavily on AI success, with the right data architecture being crucial for AI, the survey’s authors explain. At present, enterprises say their current architecture has an average life span of 18 months before it can no longer support in-house AI applications.
A sizable majority (84%) say they lack the ability to store, manage, and index high-dimensional vector data needed for efficient AI use. In addition, 3 in 4 enterprises (75%) report having a multi-database architecture, which makes it more difficult to ensure accurate, consistent AI output. A majority (61%) also indicate they do not have the tools to prevent proprietary data from being shared externally.
Falling behind the AI wave has significant consequences, the survey demonstrates.
Almost all respondents in the survey (99%) 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 consume at least 17% of AI investment and also set strategic goals back by 6 months, on average, the survey’s authors calculate.
Tellingly, the leading fear among executives and professionals is that the risks with AI may be too high, which was cited by close to half (45%). Data issues present the second-leading challenge, with 42% citing this as a roadblock to their AI efforts.
A deep understanding of data underpinning AI strategies is essential, the survey’s authors point out. “Poor quality data can give rise to hallucinations or introduce biases. If data isn’t recorded and accessible in real time, AI will make conclusions based on outdated information or be unable to share timely advice.” Plus, without effective data governance in place, AI can easily become a security and compliance risk. Every enterprise has encountered AI issues that stem from a lack of data control.
Still, the potential for success through AI is clearly visible to most. At least 78% of respondents believe early AI adopters will become industry leaders. Another 73% see AI as already transforming their technology environment.
Current investment patterns reflect this urgency. AI spend on technologies including generative, agentic, and other forms of AI will expand up to 51% over the next 12 months. AI will also 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 survey’s authors state.