Dive Brief:
- Data infrastructure issues are derailing enterprise AI investments, leading to $108 billion in wasted annual AI spend, according to a Hitachi Vantara analysis released Tuesday. The tech provider surveyed 1,200 IT decision makers for its report.
- More than 4 in 5 businesses with mature data estates reported ROI on their AI investments, compared with less than half of what the firm described as “data laggards.” Only 43% of U.S. leaders believe that their companies have predictive or automated infrastructure operations.
- Despite data limitations, surveyed IT leaders expect AI spending to rise by 76% in the next two years as businesses spin up in-house platforms and plan broader deployment efforts.
Dive Insight:
Data and infrastructure have been stubborn components of AI implementations, hindering the training of models for targeted use cases and creating bottlenecks for in-demand services.
Hyperscalers are sprinting to deploy capital, ramping up efforts in the past two years with plans to match available compute capacity with soaring demand for AI services. This year, the spending blitz continues — large cloud providers plan to increase their capital investments by nearly 40% in order to adequately service demand, according to a recent report from S&P Global.
In the enterprise, data sprawl could be hampering broader AI adoption, as data quality is the most commonly cited factor in successful AI implementations, according to Hitachi Vantara.
“The fastest way to waste AI budget is to chase models without fixing the data underneath them,” said Simon Ninan, SVP of business strategy at Hitachi Vantara, in an email to CIO Dive. The company works with customers to align AI spending to business outcomes, focusing on data performance, resiliency and availability, Ninan said.
“CIOs get the most out of AI spending by modernizing their data infrastructure, setting clear ROI benchmarks and treating AI as an operational system, not an experiment,” Ninan said.
CIOs and CISOs are grappling with information complexity as they look for a strong security posture amid adoption efforts, according to a Ponemon Institute study published in August. Nearly 3 in 4 leaders said reducing information complexity can help scale AI tools.
