Research shows that AI is rapidly exposing, rather than hiding, historical gaps in data management, governance, and security.
The report surveyed more than 1,200 C-level executives and IT leaders in 15 countries, including 307 respondents in the United States and Canada. Eighty-four percent stated that the complexity of their data infrastructure environments is growing rapidly or too rapidly to manage.
As data environments become more complex, organizations are finding it increasingly difficult to maintain visibility, control, and accountability for their systems. With leaders predicting that AI investment will grow by 76% over the next two years, these challenges are intensifying, increasing the pressure on data security and governance. Among business and IT leaders in the US and Canada:
Only 43% have predictive or automated infrastructure operations, which limits their ability to manage complexity.
57% say the complexity of their data makes it difficult to identify a security breach.
59% fear that a critical data loss would be catastrophic.
50% say their systems are complex enough that executives would lose sleep if they fully understood the risks.
“AI is raising the bar for how organizations govern and manage their data,” said Octavian Tanase, Chief Product Officer at Hitachi Vantara. “As AI becomes more integrated into business operations, leaders are recognizing that governance, visibility, and control are just as important as performance. Organizations that have invested in automation and infrastructure optimization are moving forward with greater confidence, while others are seeing the complexity widen the gap between those who can manage it effectively and those who cannot.”.
The Great AI Gap:
AI adoption is nearly universal—98% of organizations are using, testing, or exploring it. However, readiness to scale and realize value varies considerably. The findings reveal a clear divide between organizations with strong data management foundations and those struggling to keep pace with the accelerating pace of AI.
In the US and Canada, 42% of organizations consider themselves data mature (leaders), while 58% are in defined, emerging, or fragmented stages of data management. This difference directly impacts performance and return on investment (ROI) in AI:
84% of data mature organizations report measurable ROI in AI, compared to 48% of laggards.
59% attribute the success of their AI projects to data quality (75% in mature organizations vs. 47% in those with weak practices).
59% of mature organizations consider AI critical to their business, compared to 18% of those with weak foundations.
Data-mature organizations demonstrate greater preparedness.
Organizations with strong foundations share distinct practices, particularly in leadership alignment, infrastructure modernization, and operational discipline. 87% report a clear vision from management, treating data and AI as strategic priorities.
Furthermore:
65% have automated infrastructure (vs. 27% of less mature organizations).
82% have sustainable design and built-in resilience (vs. 19%).
The study also underscores the importance of leadership: although 96% acknowledge needing external help with data infrastructure, many organizations fail to translate that need into a coordinated, long-term strategy.
“When AI becomes central to the business, leadership must treat databases as a strategic requirement,” said Sheila Rohra, CEO of Hitachi Vantara. “AI succeeds when data is reliable, well-governed, and resilient.”
