AI is moving fast. Regulation is catching up. And many organizations (like a client I’m working with now) are grappling with a deceptively simple question: How much data governance do we actually need to support AI?
It’s a smart question. The reality is, without data governance, what I often think of as data enablement, AI doesn’t just underdeliver. It becomes risky, unscalable, and hard to trust. Sure, you can throw a giant pile of information at an AI engine and it might organize it for you. But the long-term value – the kind of AI that generates insight, drives decisions, and operates responsibly only becomes possible when governance comes first.
We see data governance not as overhead, but as the foundation for innovation. And in today’s environment, that foundation has never mattered more.
The Regulatory Wake-Up Call
In late 2023, the Biden administration issued an executive order outlining new AI safeguards. At the same time, the EU’s Artificial Intelligence Act moved toward final approval, signaling the start of an era where AI risk management is not optional. The U.S. also introduced the NIST AI Risk Management Framework (AI RMF 1.0) to guide responsible development.
As the White House noted, “governments globally are accelerating AI legislation… organizations must adopt governance tools to meet compliance requirements and respond to investigative requests.”
Meanwhile, EU DORA Article 6 mandates that financial institutions implement governance structures to manage ICT risk – linking AI risk directly to board-level accountability. For companies operating globally, these regulatory pressures are converging fast, and data governance is at the center of every framework.
Trust Is the New Currency
At the same time, trust in AI is under siege. Issues like bias, hallucination, and lack of explainability are no longer theoretical. They’re business risks. Consider this stat: ‘67% of executives report poor data quality has led to incorrect insights that negatively impacted business performance‘ (Deloitte). And when public AI missteps happen, the results are swift and costly:
- Google lost $100 billion in market value after its Bard chatbot made an error during a live demo
- ChatGPT was banned in Italy over privacy concerns
- Samsung workers accidentally leaked trade secrets via a GenAI tool
These aren’t tech flukes – they’re governance failures. The algorithms behind AI are only as trustworthy as the data they’re trained on, and flawed data leads to flawed outcomes.
Why Governance = Enablement
Data governance often gets miscast as a blocker. In truth, it’s what enables scale, trust, and performance. ‘The success of agentic AI hinges on an organization’s ability to establish a strong data foundation’ (CDO Times). That means governance must ensure that data is accurate, timely, complete, and protected. It must provide clear lineage, explainability, and role-based access. And it must evolve with your AI strategy.
‘Those who get this right will unlock AI’s full capabilities while mitigating risks – setting themselves apart from competitors who prioritize AI hype over foundational readiness’ (CDO Times). By strategically embracing these emergent AI-driven capabilities, organizations can save valuable development time, improve system reliability, and ultimately accelerate the delivery of business value.
Responsible AI as a Differentiator
Responsible AI isn’t just about compliance, it’s a competitive advantage. Executives who view governance as ‘just IT’ are increasingly left behind. In fact, 40% of highly regulated enterprises are already combining data and AI governance into unified frameworks (Forrester) – because doing AI right builds customer trust, brand equity, and long-term enterprise value.
‘As AI technologies advance, data governance becomes more than a checkbox for regulatory compliance—it becomes a matter of trust, ethics, and quality’ (Finextra). Companies that implement ethics boards, conduct regular impact assessments, and enforce transparent model design don’t just comply. They lead.
Make it Everyone’s Job
Data governance isn’t the CDO’s job, it’s the organization’s job. From legal and compliance to HR, marketing, and customer experience – AI touches every corner of the enterprise. That’s why modern governance frameworks are cross-functional by design.
Establish cross-functional compliance teams that include legal, IT, and business departments to monitor the evolving regulatory landscape, (CDO Times). Workforce enablement matters, too. Companies are reskilling operations teams in RPA and analytics, turning manual staff into ‘automation champions.’ That’s governance in action—not as constraint, but as empowerment.
How to Get Started
If you’re unsure how to start building a data governance foundation for AI, begin here:
- Inventory and classify your data – Know what you have, where it lives, and who has access to it
- Establish governance guardrails – Define roles, policies, and controls for data access, quality, privacy, and usage
- Design for explainability and auditability – Build transparency into your AI lifecycle with lineage tracking, bias checks, and model documentation
AI may be the future, but trust is what gets you there. Without governance, AI is brittle. With it, you unlock its full potential – responsibly, ethically, and at scale.
Trissential helps clients bridge that gap from innovation to responsibility. Let’s make sure your data, and your business, are ready.
Learn more about Trissential’s Data & AI Services: Artificial Intelligence | Data & Analytics | Hyperautomation
Talk to the Expert
Larry Odebrecht – Sr. Director, Data & Analytics
larry.odebrecht@trissential.com
