Realizing the Value of AI Starts With Data Governance and Leadership Support
By Mark Leifer
Mark Leifer is data and analytics manager for Tegria.
AI dominates healthcare conversations. Vendors are knocking. Leadership is pressured to act. Pilots are sprouting across the industry. EHR vendors like Epic, Meditech, and Oracle are rolling out exciting AI tools that are embedded directly into their platforms.
Whether your organization is adopting those EHR-native tools or building a custom solution, one thing is clear: data governance is foundational.
Amid the AI buzz, many health systems remain stuck in the proof-of-concept phase, unable to scale or sustain results. Gartner reports that by 2027, 60% of organizations will fail to achieve the full value of their AI initiatives due to poor data governance.
In my experience, this isn’t a technology failure. It’s because the organization isn’t ready, and leadership hasn’t made data governance a priority.
Without Governance, AI Can’t Deliver Results
Imagine your organization rolls out a shiny new AI tool for clinical decision support. The logic is sound. It integrates with the EHR and the demo wowed the C-suite. But six months in, utilization is low, analysts distrust the data, and compliance wants to know who approved it.
This isn’t hypothetical. In fact, it’s a common pattern. AI stalls not because the tech fails, but because data governance was never embedded in the foundation. Behind that missing foundation is a lack of executive sponsorship.
Governance Needs a Seat at the Leadership Table
If AI is going to succeed in healthcare, data governance can’t live in the shadows. It needs executive backing, visibility, and resources.
Once an organization sets clear, business-aligned goals for data and AI, the next most important success factor is strong executive sponsorship. Ideally, that sponsor is someone with a C-level title — like a CIO, CMIO, or chief data officer — who can connect the dots between business strategy and the operational work of governance.
Modern data governance should emphasize accountability, clear decision-making authority, cultural alignment, and measurable outcomes rather than focusing solely on control. Executive sponsors are critical to bridging those priorities across business and IT. Their role is not to manage the day-to-day, but to model support, prioritize funding, and align governance with organizational goals.
When leaders show up to governance councils, reference it in strategy discussions, and reward good data practices, the signal is clear: This matters.
Culture, Not Control, Is the Real Barrier
Governance must move from fixing data to enabling confident use of data across the enterprise, from “AI as a cool tool” to “AI as a governed system.” Developing a strong data culture happens through modeling, incentives, and stewardship that’s embedded into real workflows. Without that cultural groundwork, even well-designed AI tools will flounder. Teams won’t know who owns the data. Trust will be low. People won’t feel confident using the outputs. Worse, they may not feel safe raising concerns when something looks off.
Build a Coalition, Not a Silo
Executive sponsorship is step one. Step two is building a data governance coalition that spans departments. This coalition — ideally a formal data governance committee — should include IT, clinical leadership, compliance, operations, and analytics. Too often, these groups are working in silos. This structure ensures that governance is positioned as a value enabler and a risk mitigator for AI adoption, rather than bureaucracy.
When it comes to AI, the governance committee should help define approval processes, monitor model performance, and ask questions about transparency, bias, and explainability. But they should also help build buy-in, provide feedback loops, and support training across the organization.
Is Your Culture Ready for AI?
Here are four signs that it may not be:
- No one can clearly answer who owns governance for AI tools.
- A promising AI pilot was shelved due to unclear accountability or lack of trust.
- Data decisions are made in silos or based on influence, not strategy.
- Governance is viewed as red tape, not a strategic capability.
If these sound familiar, you have work to do, but these are fixable problems.
Three Practical Moves To Build Executive-Led Data Governance
If your organization wants better AI outcomes, here’s what I recommend:
- Appoint a C-level sponsor for governance and AI readiness. This person should connect governance to business strategy. Not manage the weeds, but advocate visibly and consistently.
- Stand up a formal data governance committee that includes stakeholders from across the organization. Give it real authority, diverse voices, and a regular meeting cadence.
- Make cultural change part of the plan. Train people, talk about successes, and share stories where good governance led to better outcomes. Help teams see data governance as something that supports their work, not slows it down.
Final Thought
AI won’t transform healthcare if we treat it like a series of disconnected tech pilots. It must be guided by strategy, grounded in governance, and shaped by people who understand the intersection of data, operations, and clinical care. That kind of alignment demands executive leadership, cultural change, and above all, trust. And trust begins with governance.
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