AI in the enterprise is no longer experimental; it’s operational. A 2026 Foundry survey of large-enterprise decision-makers, commissioned by OpenText, found that half of organizations are already actively using GenAI, with the remainder in active testing or planning.
Organizations are actively tracking AI adoption, measuring employee AI competency, and factoring usage into performance reviews and hiring decisions—practices that now extend well beyond the tech sector into manufacturing, financial services, and professional services.
And the urgency to manage that adoption responsibly is growing: the Foundry survey found that 89% of enterprise leaders say the need to adopt AI has directly accelerated their focus on information governance—making governance not a back-office concern, but a front-and-center strategic priority.
As AI moves from elective to essential across industries, information governance plays a vital role in ensuring that adoption translates into reliable, measurable productivity gains—not just activity.
Let’s explore how information governance helps organizations realize the benefits of trusted AI, setting users up for success and enabling AI projects to flourish.
Information governance solutions drive more value from AI
With organizations under growing pressure to show a measurable GenAI ROI, it’s important to remember that AI is only as reliable as the information it can access. The readiness gap is real: 72% of enterprises in the Foundry survey admit they face foundational challenges with information sprawl and inconsistent content quality—even as they push ahead with AI deployment. Without structure, governance, and oversight, AI tools can produce inconsistent, incomplete, or risky outputs.
Pushing widespread GenAI adoption requires the careful application of information governance solutions. Unlike employees, AI tools don’t instinctively understand the big picture of your business, such as company policies, privacy standards, document hierarchies, or what information is deemed important.
AI needs context and curation to deliver trusted and relevant results. This means having strong controls across all data, both structured and unstructured, as well as knowing where your information lives, how it’s classified, who has access to it, and how it fits into broader business processes. It also means maintaining clear version control—so AI is always grounding its responses in the current, authoritative document, not a superseded draft or an outdated policy buried in content sprawl.
AI governance is part of information governance strategies
AI governance—an extension of information governance solutions—requires that AI be treated as another actor on your data.
As an example, let’s say employee A runs a report, using GenAI to analyze the findings, and creates a presentation based on those insights. In a separate instance, employee B runs the same report using a different AI tool and gets different results. Which one is right?
The problem often isn’t AI—it’s a lack of governance over how AI accesses information, what it retrieves, and how it processes it.
Plus, user variability introduces additional inconsistency and risk. Let’s imagine an employee pulls a report from Salesforce, exports it to a PDF, and feeds it into an AI tool. On the surface, that seems reasonable. But what if the report wasn’t filtered correctly? What if a column was missing? What if the wrong dataset was used? What if there is related information in SAP or another system that provides additional context?
A strong information governance solution removes that variability. Governance frameworks ensure metadata is applied automatically, content is labeled accurately, retention policies determine what information remains in scope for AI to use—and what has expired or been superseded—and AI works from trusted, official information.
Context is everything
Information governance software helps bring data into context by integrating systems, labeling information properly, and enriching it with accurate metadata. When information is consistently categorized and aligned with business processes, AI has a far better chance of generating accurate and complete responses.
Context also plays a critical role in AI grounding. Through retrieval-augmented generation (RAG), AI responses are limited to relevant, approved documents. For grounding to be trustworthy, that content pool must include only permissibly retained, current information—not outdated versions, expired records, or redundant copies that accumulate through content sprawl. When grounding is tightly coupled with content management permissions, retention policies, and metadata, accuracy improves, and risk decreases.
Privacy and security can’t be afterthoughts
GenAI can make some tasks look effortless, but the risks are significant and well-documented: 83% of enterprise leaders cite data and security risks as a top GenAI concern, and 96% flag at least one specific security or privacy issue related to using GenAI for content management—with IP protection and regulatory compliance topping the list. The risk isn’t always malicious. An employee uploads a customer report or product brief into an AI tool that hasn’t been vetted against corporate security standards—a routine-seeming action with significant consequences. That AI can effectively operate as a super-user, pulling and surfacing data well beyond what any individual employee would be permitted to access directly.
It’s important to remember that commercial large language models (LLMs) do not inherently understand your company’s privacy definitions or compliance requirements. They can only respect the controls you establish. This means that information governance solutions cannot be tacked on after AI has accessed the information, but need to be embedded in the content layer itself.
By combining strong content management and information governance software, AI has governed, policy-aligned content to operate safely, using the most official and up-to-date information.
Trusted AI needs trusted information governance
Similar to a new employee figuring out the ropes, AI doesn’t instinctively grasp corporate standards or operating procedures, know which version of a document is the latest and greatest, or fully understand internal definitions of security and privacy. But with the right information governance software in place, it doesn’t have to.
When content is well-managed, securely stored, richly labeled, and tightly integrated across business systems, AI accuracy and reliability increase.
The governance gap is real—and closing it is urgent. 78% of enterprises say their information governance practices are still developing or inconsistent, even as AI adoption accelerates. Discover how OpenText information governance solutions classify, organize, and protect business content to deliver trusted, policy-aligned AI results—at any scale.
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