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The Impact of Legacy Data Infra on AI Investment

In a recent report released by Hitachi Vantara, it is highlighted that sluggish data infrastructures are contributing to an astounding $108 billion in wasted AI investments annually, specifically within organizations across the United States and Canada. This finding underscores the urgency for businesses racing to adopt artificial intelligence technology but hampered by outdated or inefficient data management processes.

Current Landscape

The survey compiled insights from over 1,200 C-level executives and IT leaders across 15 countries, including 307 respondents from the U.S. and Canada. An alarming 84% of these leaders reported a rapid increase in the complexity of their data environments, fueled by the exponential growth of data, new platforms, and AI technologies. This surge is proving difficult to manage regarding governance and security.

As organizations endeavor to scale their AI investments—expected to grow by 76% over the next two years—they are confronted with significant challenges related to maintenance of visibility, control, and accountability over their data systems. Notably, only 43% of leaders report having predictive or automated infrastructure operations. Consequently, it becomes increasingly challenging to detect data breaches, with 57% of respondents expressing concern over the obscurity brought about by complex data systems, as they struggle with the fear that a critical data loss could be catastrophic.

The Divide in AI Adoption

AI is almost universally integrated within organizations, with 98% actively employing, piloting, or exploring AI technologies. However, readiness to harness and realize the full potential and value of AI varies remarkably. The survey reveals a significant disparity between organizations that have established robust data management frameworks and those that are bogged down by inefficient systems that fail to keep pace with the growing demands of AI adoption.

Upon analyzing organizations in North America, only 42% are considered 'data-mature,' which refers to those that have streamlined and optimized their data management strategies. In stark contrast, 58% fall behind within defined, emerging, or fragmented classifications of data management, constituting what are termed 'data laggards.' This classification highlights how many organizations continue to lack the necessary structure, automation, and consistency to fully leverage their AI initiatives.

The difference between data leaders and laggards is not merely about the technological tools employed but hinges significantly on whether they have established a clear vision and leadership alignment necessary for effective data governance and management as they scale their AI endeavors.

Data-mature organizations report an impressive 84% measurable return on investment (ROI) from their AI initiatives, whereas only 48% of data laggards can claim the same. A strong emphasis on data quality emerges in the findings; 59% of respondents attribute successful AI projects to high-quality data, a figure that rises to 75% among organizations with mature data management strategies.

Characteristics of Data-Mature Organizations

Organizations that excel in data management showcase several key characteristics distinct from their less effective peers. Of those identified as data-mature in the U.S. and Canada, 87% report having a robust leadership vision that prioritizes data and AI initiatives as pivotal organizational focuses, rather than relegating them to siloed IT projects.

Automation is another area where data-mature organizations enjoy significant advantages. 65% of these companies report having automated infrastructure compared to only 27% among those with less mature data practices, thereby minimizing operational friction as AI processes scale. Additionally, 82% of data-mature organizations pursue sustainable design with built-in resilience, reflecting an infrastructure approach tailored to support long-term growth and risk reduction during AI adoption.

Conclusion

As AI continues to occupy a central role in business operations, leadership must regard data infrastructures not merely as technical concerns but as strategic imperatives. The Hitachi Vantara report emphasizes the notion that the success of AI initiatives relies heavily on the trustworthiness, governance, and resilience of the underlying data systems. Moving forward, simplifying the management of these environments and reinforcing governance while supporting a coherent data strategy will be crucial for organizations aiming to thrive in an increasingly AI-driven landscape.

For further details on how Hitachi Vantara is guiding customers towards effective data-driven approaches in modernizing their infrastructures, visit Hitachi Vantara.