Legacy Data Infrastructure Is Driving $108 Billion in Annual Wasted AI Investment, New Survey Finds
MWN-AI** Summary
According to a recent report by Hitachi Vantara, legacy data infrastructure is resulting in annual wasted AI investments of approximately $108 billion in the U.S. and Canada. This staggering figure stems from a survey of over 1,200 C-level executives and IT leaders, revealing that 58% of organizations struggle to derive value from their AI initiatives due to outdated and complex data environments. As enterprises accelerate AI adoption, data management gaps are becoming increasingly apparent, complicating governance and security.
The report highlights that 84% of organizations report a rapid rise in data complexity, which hampers their ability to maintain oversight and control. With investment in AI expected to increase by 76% over the next two years, this complexity is predicted to worsen, particularly for companies lacking automation in their data practices. The survey found that only 43% of respondents have predictive or automated infrastructure operations, and 57% reported difficulty identifying data breaches amidst the complexity.
There is a significant divide between organizations with mature data practices and those that struggle with fragmentation. Notably, 84% of data-mature organizations report measurable returns on AI investment, compared to only 48% among their data-lagging counterparts. High-quality data was identified as a critical success factor, as 59% of respondents attributed their successful AI projects to robust data practices.
In this context, leadership plays a vital role in fostering a strategic approach to data governance and management. As Sheila Rohra, CEO of Hitachi Vantara, states, treating data foundations as a strategic requirement is essential for successful AI implementation. The report emphasizes the urgent need for organizations to modernize their data infrastructure to harness the full potential of AI.
MWN-AI** Analysis
The recent Hitachi Vantara report highlights a staggering $108 billion wasted annually on AI investments due to the complexities of legacy data infrastructure. With 84% of organizations in the U.S. and Canada acknowledging rapid data complexity, the resulting governance, security, and operational challenges pose significant risks.
For investors and business leaders, this data underscores the urgency for a transformational approach to data management. Organizations that have yet to optimize their data foundations risk falling behind as AI adoption accelerates—98% of businesses are currently exploring or implementing AI. The disparity between those with mature data practices and those lagging is evident: 84% of leading organizations report measurable AI ROI compared to just 48% of their less prepared counterparts.
To minimize wasted investment, businesses should prioritize modernizing their data infrastructure. Key focus areas include enhancing automation, establishing robust governance frameworks, and ensuring that data quality is a central concern. Organizations with robust data management structures report high levels of preparedness, with 65% having automated operations which drastically reduce operational friction when scaling AI initiatives.
Additionally, it’s crucial for leadership to align an organization’s AI vision with data strategy. A strong executive presence advocating for strategic data practices can foster an environment where data is considered an asset rather than a mere technical necessity.
In conclusion, companies should evaluate their data practices and infrastructure readiness as a strategic priority. Those willing to invest in modernizing data management will not only recover wasted investments but also position themselves advantageously in a competitive landscape increasingly driven by AI. Efficient data handling translates to enhanced operational capability and improved ROI—an essential consideration in today’s digital-first economy.
**MWN-AI Summary and Analysis is based on asking OpenAI to summarize and analyze this news release.
PR Newswire
New Hitachi Vantara report finds 84% of organizations across the U.S. and Canada say data
complexity is rising rapidly or too quickly to manage, driven by growth in data, platforms and AI,
straining governance and security
SANTA CLARA, Calif., Jan. 27, 2026 /PRNewswire/ -- As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a new report from Hitachi Vantara, the data storage, infrastructure and hybrid cloud management subsidiary of Hitachi Ltd. (TSE: 6501). The research shows that AI is rapidly exposing long-standing gaps in data management, governance and security rather than masking them.
Click here to download the Hitachi Vantara State of Data Infrastructure Report
The report surveyed over 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 these organizations say the complexity of their data infrastructure environments is growing rapidly or too quickly to manage.
As data environments grow more complex, organizations are finding it harder to maintain visibility, control and accountability across their systems. With leaders expecting investment in AI to grow by 76% over the next two years, those challenges are intensifying, placing greater strain on security and data governance. Among U.S. and Canadian business and IT leaders:
- Only 43% report having predictive or automated infrastructure operations, limiting their ability to manage complexity;
- 57% say the complexity of their data makes identifying a data breach more difficult;
- 59% fear a critical data loss would be catastrophic; and
- Half (50%) say their systems are complex enough that executives would lose sleep if they understood the risks.
"AI is raising the bar for how organizations govern and manage their data," said Octavian Tanase, chief product officer, Hitachi Vantara. "As AI becomes more embedded in business operations, leaders are realizing that governance, visibility and control matter just as much as performance. As a result, we're seeing that organizations that have invested 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."
The Great AI Divide
AI adoption is nearly universal, with 98% of organizations using, piloting or exploring AI, but readiness to scale and realize value varies widely. The findings reveal a clear divide between organizations that have built strong data management foundations and those struggling to keep pace as AI adoption accelerates. The difference is not whether companies are using AI or how large a company is, but whether they pair a clear AI vision and leadership alignment with data environments that are structured, governed and built to scale.
In the U.S. and Canada, 42% of organizations are considered data-mature, or leaders, defined as having managed or optimized data practices. Meanwhile, 58% fall into defined, emerging or fragmented stages of data management, or data laggards, meaning their data environments lack the structure, automation or consistency required to maximize the value of AI initiatives at scale.
That gap is reflected directly in AI performance and return on investment (ROI):
- 84% of data-mature organizations report measurable AI ROI, compared with only 48% of data laggards.
- Data quality is the most commonly cited driver of AI success, with 59% of organizations attributing successful AI projects to the use of high-quality data – rising to 75% among data-mature organizations, compared to 47% for those with weaker data practices.
- Among organizations with mature data practices, 59% say AI is critical to their business, compared with just 18% of organizations with weaker data foundations.
Data-Mature Organizations Show Clear Differences in Data Infrastructure Readiness
Organizations with strong data foundations demonstrate shared practices that set them apart, particularly when it comes to leadership alignment, infrastructure modernization and operational discipline. Among organizations leading in data maturity in the U.S. and Canada, 87% report having a strong leadership vision, enabling data and AI initiatives to be treated as strategic priorities rather than siloed IT projects.
Companies with mature data practices are also significantly ahead in automation, with 65% reporting automated infrastructure, compared with just 27% of organizations with weaker data practices, reducing operational friction as AI scales. Additionally, 82% of data-mature companies report having sustainable design and built-in resilience, reflecting an infrastructure approach designed to support long-term growth, efficiency and risk reduction as AI adoption expands, compared with just 19% among organizations with weaker data practices.
The research further underscores the importance of leadership engagement. While 96% of these organizations say they need outside help with data infrastructure, many continue to struggle to translate that need into coordinated action, highlighting a gap between recognizing the challenge and executing a long-term data strategy.
"As AI becomes central to how every business operates, leadership has to treat data foundations as a strategic requirement, not just a technical concern," said Sheila Rohra, CEO, Hitachi Vantara. "This report makes clear that AI succeeds when the data behind it is trusted, well-governed and resilient. Our role is to help organizations simplify the management of their environments, strengthen governance and support a data strategy that can drive long-term growth."
For more information on how Hitachi Vantara is helping customers provide a data-driven approach to modern data infrastructure, visit www.hitachivantara.com.
Additional Resources
- Report: Hitachi Vantara State of Data Infrastructure Report
- Blog: Operationalizing Agentic AI with Hitachi iQ Studio and NVIDIA Nemotron 3
- Press Release: Hitachi Vantara Launches Virtual Storage Platform One Block High End Storage to Power the Next Era of AI and Mission-Critical Workloads
- Blog: From Science Fiction to Factory Floor: The Physical AI Revolution Takes Shape
- Webinar: Build Modern AI-Ready Infrastructure Without Starting Over
- Blog: The End of Data Infrastructure as We Know It. And That's a Good Thing.
- Press Release: U.S. Data Concerns Soar as AI Surges – 37% of IT Leaders Identify Data Quality as Major Barrier to AI Success
Connect With Hitachi Vantara
About Hitachi Vantara
Hitachi Vantara is transforming the way data fuels innovation. A wholly owned subsidiary of Hitachi Ltd., Hitachi Vantara provides the data foundation the world's leading innovators rely on. Through data storage, infrastructure systems, cloud management and digital expertise, the company helps customers build the foundation for sustainable business growth.?To learn more, visit www.hitachivantara.com.
About Hitachi, Ltd.
Through its Social Innovation Business (SIB) that brings together IT, OT(Operational Technology) and products, Hitachi contributes to a harmonized society where the environment, wellbeing, and economic growth are in balance. Hitachi operates globally in four sectors – Digital Systems & Services, Energy, Mobility, and Connective Industries – and the Strategic SIB Business Unit for new growth businesses. With Lumada at its core, Hitachi generates value from integrating data, technology and domain knowledge to solve customer and social challenges. Revenues for FY2024 (ended March 31, 2025) totaled 9,783.3 billion yen, with 618 consolidated subsidiaries and approximately 280,000 employees worldwide. Visit us at www.hitachi.com.
HITACHI is a trademark or registered trademark of Hitachi, Ltd. All other trademarks, service marks, and company names are properties of their respective owners.
SOURCE Hitachi Vantara
FAQ**
How does the report from Hitachi Vantara highlight the impact of legacy data infrastructure on AI investment, particularly for companies heavily reliant on Hitachi Ltd. ADR HTHIY's solutions?
What strategies can organizations implement to transition from data laggards to data-mature entities, as indicated in the Hitachi Vantara report, while leveraging the services of Hitachi Ltd. ADR HTHIY?
In what ways does the increasing data complexity reported by Hitachi Vantara affect governance and security measures that businesses need to adopt, particularly those considering partnerships with Hitachi Ltd. ADR HTHIY?
According to the findings from Hitachi Vantara, what role does leadership alignment play in maximizing AI ROI, and how can Hitachi Ltd. ADR HTHIY support organizations in this endeavor?
**MWN-AI FAQ is based on asking OpenAI questions about Hitachi Ltd. ADR (OTC: HTHIY).
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