Falling Behind in AI Adoption Comes at Substantial Cost — Campus Technology

Report: Falling Behind in AI Adoption Comes at Substantial Price

A current report from Couchbase has cautioned that enterprises that don’t maintain tempo in AI adoption face potential monetary losses. The “Couchbase FY 2026 CIO AI Survey” calculated a median annual affect of as much as $87 million for organizations that fall behind. The examine, carried out by Coleman Parkes in April 2025, polled 800 international IT decision-makers throughout monetary providers, retail, manufacturing, telecommunications, healthcare, power and utilities, gaming, and journey and hospitality, to look at developments in AI adoption, funding methods, and challenges.

Survey Highlights

Key findings of the report as introduced in a news release embody:

Falling behind the AI wave has important penalties: 99% of enterprises have encountered points that disrupted AI initiatives or prevented them outright, together with issues accessing or managing the required knowledge; notion that the chance of failure had turn out to be too excessive; and an lack of ability to remain on funds. These points had actual penalties, consuming up 17% of AI funding and setting strategic objectives again by six months on common.
Closing the info understanding hole is essential to regulate: 70% of enterprises admit their understanding of the info (e.g., the standard and real-time accessibility of information) wanted to energy AI is “incomplete,” contributing to 62% not totally understanding the place they’re in danger from AI (e.g., by means of safety or knowledge administration points). Conversely, these with better understanding are extra assured, and are 33% extra prone to be ready for agentic AI.
Knowledge structure is evolving and requires consolidation: The precise knowledge structure is essential for AI. But enterprises say their present structure has a median lifespan of 18 months earlier than it might now not assist in-house AI purposes. 75% of enterprises have a multi-database structure, which makes it harder to make sure correct, constant AI output; 61% would not have the instruments to stop proprietary knowledge from being shared externally, which will increase safety and compliance dangers; and 84% lack the power to retailer, handle and index high-dimensional vector knowledge wanted for environment friendly AI use. To handle these challenges, all surveyed enterprises are consolidating and simplifying their AI expertise stacks to make controlling AI simpler and extra environment friendly.
Encouraging experimentation contributes to AI success: Company attitudes about AI have a notable affect on its success. Enterprises that encourage AI experimentation have 10% extra AI initiatives enter manufacturing and incur 13% much less wasted AI spend than enterprises with a extra restrictive method.
New developments in AI are quickly reaching parity: The proportion of AI spend on agentic AI (30% of complete), generative AI (35%) and different types of AI (35%) is nearly even, regardless of agentic AI and gen AI being a lot newer ideas. This means enterprises are investing closely in maintaining with AI improvement as 66% fear that AI and totally different approaches to AI are evolving quicker than their organizations can maintain tempo.
Lack of ability to maintain up with AI will increase threat of being changed: Enterprises acknowledge AI’s potential for disruption, permitting smaller organizations with a greater grasp of the expertise to switch bigger, much less agile rivals. Greater than half (59%) of IT leaders are involved that their organizations threat being changed by smaller rivals, but on the identical time 79% consider they will do the identical and displace their bigger competitors.