The question boards can no longer ignore in the age of AI
The advance of AI is making enterprise data governance increasingly urgent, in a scenario shaped by new regulations, cybersecurity risks, and geopolitical changes.
By: Adrian Johnson, Chief Revenue Officer (CRO) at Hitachi Vantara.
Across industries, organizations are paying increasing attention to who has access to their data and what this means from the perspectives of privacy, regulatory compliance, and operational resilience.
Geopolitical changes in our interconnected world, growing cybersecurity threats, and new regulations are driving increasingly in-depth discussions about where data is stored and where it flows.
A global study in 2026 found that 76% of organizations expect data sovereignty to become more important, while legal requirements are identified as the main external factor driving this trend.
This growing regulatory pressure means executives and other IT decision-makers are increasingly focused on where enterprise data is stored, which laws and regulations apply to it, and how to ensure that their organizations have the data infrastructure needed to meet those requirements today — and in the future.
For example, organizations may have multiple copies or versions of the same data distributed across different environments, without a clear view of which is the current version, who has access to it, or which regulations apply to it. This can create compliance, security, and operational risks.
The rapid adoption of enterprise AI is also adding urgency to data governance. AI systems often use large volumes of data from different areas of an organization, increasing the importance of how that data is classified, protected, and managed.
In recent years, organizations have discussed data monetization and tried to manage the so-called data deluge. Now, AI offers an opportunity to extract even greater value from that data. But to achieve the right outcomes with AI, organizations need to be able to locate the data they need, manage it securely and in compliance with regulations, and ensure that it is clean, structured, and properly understood.
If an AI system uses outdated, duplicated, or incorrectly classified data, the result can be inaccurate recommendations, unreliable insights, or the exposure of information that should have been restricted. If an organization cannot properly and reliably manage, protect, and use its data, its AI initiatives will struggle to deliver results that can be trusted.
This is not mere speculation. Recent research shows that the accuracy and trustworthiness of AI-generated results continue to stand at around 40%.
This helps explain why many AI projects still fail to achieve their objectives. Effectively managing AI requires managing the data that underpins it. Without that visibility and control, businesses risk scaling AI faster than their ability to govern the data on which they depend. The goal is not to slow AI adoption, but to ensure that the right data governance and infrastructure are in place to support it.
The infrastructure challenge: keeping pace with AI.
Many organizations are already at a disadvantage because the data infrastructure they rely on was not designed for AI. It was built to support core banking systems and other industry-specific platforms, enable customer relationship management, and support other traditional aspects of the business.
Recent industry research found that 59% of organizations have infrastructure that limits AI effectiveness. And when selecting that infrastructure, companies certainly did not anticipate that geopolitical concerns — such as data sovereignty and AI sovereignty — would have the impact they have today on technology decisions.
This leaves organizations needing to exercise greater control over rapidly growing volumes of data, precisely when AI is placing new demands on the infrastructure used to store, manage, and protect it.
Data governance: non-negotiable in the face of AI and geopolitical risks.
Proper data governance can be particularly complex when an organization relies on hyperscalers and other software-as-a-service (SaaS) platform providers to support its operations, as most businesses currently do.
As data is distributed across an increasing number of platforms and environments, it becomes more difficult to track where it resides and how it moves.
Data may be flowing through platforms and systems without the organization having full visibility or control over it. It could even cross into another legal jurisdiction without the company knowing.
Considering the global size and reach of hyperscalers, it is not difficult to imagine this happening.
First step: understand the regulations your data is subject to.
Whether an organization operates in banking, healthcare, manufacturing, or another sector, it needs absolute clarity about the legal jurisdictions and regulatory requirements that apply to its data.
Especially in regulated industries, these requirements can determine where certain information is stored, who can access it, how long it must be retained, and how its use must be documented.
It is necessary to understand how data is classified, identify which data is most important, and determine the best way to control and protect it to ensure regulatory compliance, resilience, and business outcomes.
The answer lies in the modern data storage platforms currently available. Today, for example, companies can deploy a unified data platform that eliminates information silos by combining block, file, object, and mainframe storage within a single data and control plane.
This not only gives organizations greater visibility and control over their data environments, but also provides greater simplicity and ease of management, scalability, and, overall, helps reduce costs and energy requirements. Ultimately, it saves time and money while providing greater visibility into where data resides.
The best approach is to have a coherent strategy and modern data infrastructure that makes it possible to visualize, classify, and control data, thereby ensuring a greater level of governance and compliance with applicable regulations.
As AI adoption accelerates, organizations that know where their data is, how it is governed, and whether their infrastructure can respond to new demands will be in a much stronger position to manage risks and derive greater value from AI.
A more unified approach across storage environments can provide the visibility needed to manage data consistently as these requirements evolve.
Establishing these foundations correctly is what enables organizations to move faster without giving up the control and resilience they need.
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