Enterprise AI Investments Fail to Deliver Expected Returns, Despite Rising Spending

Corporate technology executives remain uncertain about whether their AI deployments are meeting expectations, despite years of investment and growing spending on the technology. While an SAP and Oxford Economics report shows incremental improvement in satisfaction with AI return on investment this year, C-suite leaders express skepticism that the systems they have put in place are realizing their full capabilities.

Bringing any new technology across an organization presents technical hurdles, but company leaders point to non-technical obstacles as the primary culprits behind lackluster results. Constraints on AI infrastructure, bloated technology budgets, weak data strategies and an imbalance between risk and potential value are among the factors preventing enterprises from reaching their goals.

The organizational dimension represents another critical barrier. Rolling out AI requires sweeping change across the workforce, operations and systems, according to Ismail Amla, SVP of Kyndryl Consult. "The classic categories of people, process and technology have become much more complex as human change gets overlayed atop each component," Amla said during a media briefing this week.

CIO Dive has assembled reporting examining the obstacles standing in the way of enterprise AI success and the strategies some technology leaders are using to overcome them.