Enterprises across the U.S. and Canada are losing significant value from AI investments as legacy data infrastructure struggles to keep pace with rising complexity, according to a new survey from Hitachi Vantara. The report estimates that weak data foundations contribute to $108 billion in wasted global AI investment annually, with 58% of surveyed organizations in the region saying they fail to realize meaningful value from AI initiatives.
The study surveyed more than 1,200 C-level executives and IT leaders across 15 countries, including 307 respondents in the U.S. and Canada. It found that 84% of regional organizations report data infrastructure complexity is growing rapidly or too quickly to manage, driven by expanding data volumes, platform sprawl, and accelerating AI adoption. Leaders expect AI investment to rise 76% over the next two years, intensifying pressure on governance, security, and operational visibility.
The research highlights a widening gap between “data-mature” organizations and those lagging behind. In the U.S. and Canada, 42% qualify as data-mature, while 58% fall into emerging or fragmented stages of data management. Data-mature organizations report higher AI ROI, stronger leadership alignment, and greater automation, underscoring that infrastructure readiness—not AI adoption alone—determines outcomes.
- 58% of U.S. and Canadian organizations say weak data foundations prevent them from realizing AI value
- 84% report rapidly rising data infrastructure complexity
- Only 43% have predictive or automated infrastructure operations
- 57% say data complexity makes breach detection harder
- 59% fear catastrophic impact from critical data loss
- 84% of data-mature organizations report measurable AI ROI, versus 48% of data laggards
- 65% of data-mature organizations report automated infrastructure, compared with 27% of laggards
“AI is raising the bar for how organizations govern and manage their data,” said Octavian Tanase, chief product officer, Hitachi Vantara. “Organizations that invest in automation and optimized data infrastructure are moving faster with confidence, while others are seeing complexity widen the gap between those that can manage it effectively and those that cannot.”
🌐 Analysis
The findings align with a broader industry shift toward treating data infrastructure as a strategic prerequisite for AI, rather than a back-office concern. As hyperscalers and enterprise vendors push agentic AI, autonomous operations, and large-scale model deployment, gaps in data governance and automation increasingly translate into measurable financial drag. Competitors across storage, data management, and hybrid cloud platforms are also emphasizing automation, resilience, and data quality as core enablers for AI ROI, suggesting rising pressure on enterprises to modernize foundational infrastructure in parallel with AI adoption.
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