For data and analytics leaders navigating the modern enterprise landscape, artificial intelligence has fundamentally raised the performance bar for everything operating beneath the surface. Data quality, enterprise-wide integration, and—above all else—data governance are no longer viewed merely as compliance checkboxes to satisfy internal audits or regulatory mandates. Instead, they have transformed into genuine strategic foundations that dictate operational success. Organizations that have failed to treat data governance as a primary operational enabler are now feeling that pressure acutely as they attempt to scale ambitious AI initiatives.

That harsh market reality formed the central premise of a recent panel discussion at the Data Integrity & AI Forum. Entitled AI Is Stress Testing Data Governance: Are You Seeing the Same?, the virtual session brought together three distinct industry voices with highly complementary perspectives on the modern data challenge: Nicola Askham, a renowned independent data governance coach; Kevin Guyot, Vice President of Data Analytics at Lake Michigan Credit Union; and David Woods, Senior Vice President of Global Services at software and data integrity firm Precisely.

The conversation illuminated several critical themes that modern enterprises must grapple with, highlighting the urgent questions that data leaders should be asking themselves as artificial intelligence adoption accelerates across global industries.

Why Data Governance Produces Trust, and What Happens Without It

One of the most telling signals from the 2026 State of Data Integrity and AI Readiness report—published by Precisely in partnership with Drexel University’s LeBow College of Business, drawing on insights from more than 500 global data and analytics leaders—revolves around the direct correlation between formal data governance programs and organizational data trust.

According to the research, data leaders operating within organizations that maintain formal governance programs report "high" or "very high" trust in their enterprise data at a striking rate of 71%. By contrast, that figure plummets to just 50% at organizations operating without a formalized governance program in place. In practical terms, venturing into modern data analytics without a governance framework leaves enterprise data trust little better than a coin toss.

The consequences of this trust deficit become exponentially magnified the moment generative and autonomous artificial intelligence enters the corporate ecosystem. Modern machine learning models, large language models, and autonomous AI agents consume immense volumes of data at unprecedented speeds. When fed untrustworthy, uncontextualized, or low-quality data, these advanced systems generate flawed outputs, hallucinations, and strategic miscalculations at a massive scale and with alarming velocity.

Kevin Guyot’s experiences leading the data analytics division at Lake Michigan Credit Union cut straight to the heart of why this trust gap persists across industries. For Guyot, the fundamental differentiator between organizations that genuinely trust their underlying data and those that merely hope for the best does not lie in passive documentation or static PDF policy manuals. Rather, it depends entirely on whether data governance is actively embedded into how business decisions are actually made on a daily basis.

Governance cannot simply be something that sits neglected on a web page or a corporate intranet, Guyot explained during the forum. It requires true institutional ownership and must be woven into the fabric of day-to-day decision-making. When an organization achieves that level of integration, teams begin to cultivate genuine confidence in the integrity of their information assets.

David Woods of Precisely noted that the primary driver behind enterprise data governance has shifted dramatically over the past two to three years. What was historically viewed as a niche, industry-specific concern concentrated primarily within heavily regulated sectors like financial services and healthcare has rapidly evolved into a universal corporate requirement.

According to Woods, trust has definitively emerged as the universal prerequisite for modern governance, with artificial intelligence acting as a powerful accelerant. Feeding an AI model poor or disconnected data causes enterprise trust to erode rapidly. Consequently, governance has transitioned from a defensive compliance exercise into an absolute business necessity for any organization looking to make its data work effectively.

This urgency is further underscored by a notable confidence-reality gap highlighted within the research data. While an overwhelming 87% of surveyed data leaders proudly describe their organizations as being fully "AI-ready," a substantial 43% simultaneously cite underlying data readiness as their single greatest barrier to achieving tangible AI success.

Industry experts emphasize that this dichotomy does not represent an internal contradiction, but rather a fundamental measurement problem. Many enterprises are currently evaluating their organizational readiness against outdated criteria. As businesses increasingly adopt Agentic AI—where autonomous software systems make complex operational decisions and take actions without direct human oversight—these hidden data gaps transform from abstract risks into immediate, consequential operational failures.

Does Your Governance Program Have a Data Strategy Behind It?

While the survey data indicates that 83% of organizations have some form of data governance program established, the widespread persistence of data readiness roadblocks suggests that the mere existence of a program is insufficient. The critical question facing executives is no longer whether governance exists, but whether that governance is actively performing the right foundational work.

During a live audience poll conducted during the forum, participants repeatedly flagged a familiar organizational dysfunction: a pervasive lack of clear ownership and accountability. David Woods pointed out a recurring pattern he encounters when enterprise clients reach out to Precisely for professional guidance: data governance initiatives that have been constructed entirely in isolation, divorced from a coherent overarching data strategy.

Organizations that treat governance and strategy as completely separate, disconnected workstreams consistently struggle to articulate their actual business value, Woods observed. They find it nearly impossible to point to concrete outcomes where governance served as an actual accelerator and enabler of business success. The moment an organization successfully bridges those two workstreams, leadership begins to witness measurable improvements in enterprise trust scores and the business results that follow.

Nicola Askham reinforced this sentiment, arguing bluntly that it matters very little how polished or comprehensive a data governance program appears on paper if nobody within the broader business operations can clearly articulate what specific problems it solves.

Askham recounted a recent coaching interaction in which a corporate client proudly listed "a better understanding of our data" as a primary operational benefit of their new governance initiative. Her immediate response to the client was simple: "So what?"

While a governance program will inherently yield a better understanding of enterprise data, Askham explained, that is rarely what operational business leaders actually care about hearing. The crucial follow-up question must always be focused on business alignment: How does understanding that data help them do their jobs better? What specific business problems will be resolved? What tangible business value will ultimately be delivered?

At Lake Michigan Credit Union, Kevin Guyot noted that achieving top-down alignment requires anchoring governance not in technical data management theories, but directly in core business goals, priorities, and strategic objectives. Governance must be framed as the operational mechanism that makes those broader business outcomes achievable.

Ultimately, it comes down to communicating the foundational why to the broader organization, Guyot explained. Teams need to understand what clean, well-governed data will actually empower them to do. They need to recognize how it accelerates decision-making, reduces the exhausting hours spent cross-referencing mismatched reports, and shifts the organizational focus away from manual data wrangling and toward strategic analysis.

The research study’s four-tier maturity profile framework illustrates this compounding effect of strategic alignment. To successfully ascend to the elite "Innovator" designation, organizations must intentionally design their data governance and data strategy to evolve as parallel, interconnected workstreams rather than executing them in rigid, sequential phases.

How Do You Fit AI Governance into an Existing Data Governance Program?

One of the most fiercely debated questions among modern data leaders revolves around organizational structure: Should artificial intelligence governance be treated as an entirely separate discipline, or should it be absorbed directly into established enterprise data governance programs?

Industry perspectives remain divided, though practical experience suggests that the solution is rarely a pure, mutually exclusive binary choice.

Nicola Askham noted that traditional data governance teams are increasingly being tasked with absorbing sweeping AI governance responsibilities, frequently without receiving any corresponding expansion in headcount, operational budget, or specialized training. While legacy data governance professionals generally feel comfortable managing the data quality components of AI initiatives, they often feel entirely unequipped when it comes to specialized technical tasks like rigorous model validation or screening algorithms for unintended bias. Because these complex tasks fall outside their core skill sets, many data governance teams feel overwhelmed, yet they reluctantly take on the burden out of a conviction that if they do not handle it, nobody else will.

At Lake Michigan Credit Union, Kevin Guyot’s approach involves consolidating internal governance councils wherever possible while implementing a rigorous, cross-functional checkpoint system before any new artificial intelligence use case is permitted to move forward. These evaluations go far beyond basic technical feasibility, forcing leadership teams to answer harder fundamental questions. Does deploying AI genuinely make sense for this specific operational capacity? If so, does the organization possess data of sufficient quality and context to allow the AI to act reliably? And, perhaps most importantly, just because the organization can deploy AI in a given scenario, should it?

Guyot emphasized that these evaluation frameworks must deliberately anchor every technological deployment to the organization’s core corporate values, ethical standards, and established governance principles.

David Woods observed from hundreds of customer engagements that many organizations initially approached AI governance by simply compiling a list of approved software tools and establishing a basic use case evaluation process—only to realize far too late that they had completely neglected the underlying data foundation.

If an enterprise’s existing data governance framework already incorporates rich business context, clear data lineage, and robust quality rules, Woods argues, it has already built the exact foundation that artificial intelligence demands. Organizations should start by pulling those existing assets forward into their AI strategies. However, the governance framework must continuously evolve.

Echoing Guyot’s emphasis on ethical considerations, Woods noted that the unique dimension introduced by AI governance is the critical question of corporate responsibility: Should we do this? Answering that question effectively requires bringing entirely new personas and cross-functional roles—including legal counsel, ethics officers, and human resources leaders—into governance councils that were traditionally dominated exclusively by IT and data professionals.

The broader institutional shift highlighted by Precisely’s research indicates that data governance must graduate from being an isolated administrative activity performed by a specialized back-office team to a deeply embedded operational practice woven directly into everyday business processes. Industry statistics indicate that approximately nine out of ten corporate AI use cases still fail to reach full production deployment, with root causes tracing almost invariably back to foundational data deficiencies. Organizations that successfully push AI models into production are precisely those that recognized early on that governance is not a bureaucratic gate blocking AI deployment, but the stable foundation upon which successful deployment relies.

Where to Start with Data Governance for AI

As organizations look toward the remainder of the year, data leaders face mounting pressure to prioritize their strategic investments. Industry experts emphasize several consistent focal points for enterprise roadmaps.

Kevin Guyot reiterates that leadership must continually return to foundational data governance as the absolute starting point, because it serves as the underlying mechanism that renders every subsequent technological capability possible. Building a robust governance framework establishes the vital business context required for AI models to consume enterprise data reliably, make rational choices, and minimize costly hallucinations. Without a shared corporate terminology and verifiable data quality guaranteed by a formal governance program, true AI readiness remains entirely out of reach.

Nicola Askham stresses that corporate financial investment must directly mirror strategic priorities. In far too many organizations, that alignment remains broken; Askham points to concerning instances where corporate data governance leaders have actually been laid off as part of sweeping enterprise cost-cutting measures designed to fund flashy new AI investments. This represents a fundamental misunderstanding of corporate technology dependencies, as data governance and artificial intelligence are not competing budgetary priorities, but mutually dependent pillars.

David Woods recommends that data and analytics leaders heavily prioritize the enhancement of their enterprise semantic layers—the contextual architectural layer that provides AI systems with a coherent, governed interpretation of what organizational data actually means. When properly maintained, a contextualized semantic layer transforms passive governance into active organizational intelligence.

Organizations that successfully build a contextualized semantic layer exposed securely through a robust governance framework will swiftly leapfrog their market competitors, Woods noted. For forward-thinking executives, focusing continuous energy on ensuring that the semantic layer remains fully accessible not only to human business and IT users, but critically to autonomous Agentic AI systems, represents the single most impactful investment an enterprise can make.

The Organizations That Get Data Governance Right Will Successfully Scale AI

The overarching narrative emerging from both recent industry research and the experiences of front-line practitioners is unmistakable: data governance is no longer a peripheral compliance program running parallel to an organization’s AI strategy. It has unequivocally become the operational platform upon which all sustainable artificial intelligence initiatives must run.

The enterprises making genuine, measurable progress are those intentionally constructing governance and business strategy in tandem. By embedding governance directly into daily workflows and confronting difficult questions regarding not only technical feasibility, but ethical responsibility and data trustworthiness, these market leaders are proving that the strongest technological futures are built upon rigorous data foundations.