What Can Go Wrong Without Governance 

Weak or absent governance introduces several risks that can impact care quality, compliance, and trust: 

• Unreliable outputs. Poor data quality leads to flawed AI recommendations and insights. 

• Compliance violations. AI tools that are opaque or unexplainable can expose the organization to regulatory risk. 

• Loss of trust. A single AI-related mistake can reduce confidence among clinicians, patients, and partners. 

• Ethical missteps. Without oversight, AI systems may reinforce bias or misuse sensitive data. 

• Wasted investment. AI tools that are not built on a solid governance foundation often fail to scale or deliver return on investment. 

The consequences are especially serious in healthcare, where data integrity, transparency, and patient safety are non-negotiable. 

What Healthcare Leaders Can Do

To realize the benefits of AI, healthcare organizations need to approach it as a data-centric initiative. Governance should be tightly integrated with any AI strategy, not treated as an afterthought or separate track. 

The following steps can help healthcare leaders build the foundation needed for safe, scalable AI. 

1. Start With a Governance Maturity Assessment 

Before committing to AI projects, assess the current state of your data governance. A maturity assessment helps identify gaps in data quality, ownership, and alignment. 

Start with a well-scoped use case and apply strong governance around it to build experience and trust. 

• Focus on areas where AI is already being used or explored

• Evaluate accessibility, reliability, and documentation standards 

• Assess the readiness of structured and unstructured—from sources like notes, transcripts, and imaging—data

• Identify gaps in stewardship and accountability 

• Use assessment results to inform a phased improvement plan 

2. Secure Executive Sponsorship and Build a Governance Coalition 

Data governance requires leadership support and cross-functional engagement. Assigning an executive sponsor ensures authority, alignment with strategic goals, and access to critical resources.

Form a governance council representing IT, clinical, compliance, and operational domains to develop policies, oversee execution, and report progress to senior leadership. The sponsor should champion governance across the enterprise, remove barriers, and help reinforce its strategic importance.

• Designate a sponsor such as the CIO, CDO, or CMIO 

• Allocate funding for technology, training, and stewardship

• Align governance with business outcomes to reinforce its strategic value

• Encourage collaboration across departments and disciplines 

• Ensure the sponsor regularly communicates governance value to executive peers  

Governance must be addressed early as an organizational decision and long-term commitment. Once the framework is in place, data consumers begin to see improved reliability, stronger outcomes, and real momentum.

PHIL HEFFLEYManaging Director, Analytics & Insights 

3. Develop Practical Policies and Embed Them in Workflows 

Effective governance policies must be clear, actionable, and part of day-to-day work. Define how data will be accessed, shared, and used for AI, and make sure policies are tied to operational checkpoints. 

For example, require governance approval before deploying AI tools, and integrate data quality checks into EHR systems and reporting workflows. 

• Create policies covering data access, AI usage, retention, and review 

• Require governance checkpoints before AI tools are deployed

• Include data quality reviews in reporting and EHR workflows

• Define clear responsibilities for approvals and audits

4. Empower Data Stewards and Strengthen Data Literacy 

Governance depends on the people closest to the data. Data stewards—whether analysts, managers, or directors—have the expertise to detect inconsistencies, validate quality, and identify risk. 

Stewardship isn’t about adding new roles—it’s about recognizing and formalizing existing accountability. Data stewards already exist across your organization; governance simply gives them a name, a framework, and support. 

Good governance involves cross-functional teams and clear data ownership roles (not just the IT department). 

At the same time, investing in data literacy across teams helps ensure that everyone understands how their work contributes to trustworthy AI. 

• Identify stewards within clinical, operational, and technical domains 

• Provide training on governance tools and responsibilities 

• Help clinicians understand the downstream impact of documentation 

• Foster a culture that views data quality as a shared responsibility 

5. Implement Supporting Technology Strategically 

The right tools can help enforce governance standards and scale them across the organization. However, technology decisions should follow governance strategy—not drive it. 

Look for solutions that support your policies and make it easier to monitor, document, and improve data and AI performance. 

• Ensure a trusted source of truth with tools that provide visibility into where data comes from and how it’s used

• Implement platforms for monitoring data quality and AI model behavior 

• Ensure technology aligns with the organization’s governance policies, not the other way around

6. Measure Progress, Adapt, and Sustain the Program 

Governance is a long-term capability that must evolve over time. Track impact through measurable indicators, adjust as needed, and share progress to maintain engagement and support. 

Highlight success stories that show how governance contributed to safer or more effective AI use. These examples build confidence and help reinforce the importance of ongoing investment. 

• Define metrics like data quality scores, AI approval rates, and resolution times

• Review policies regularly in response to changes in regulations or technology 

• Highlight success stories to build momentum and trust

• Treat governance as a continuous improvement process 

The Bottom Line 

Strong data governance is essential for any healthcare organization planning to use AI responsibly. Without it, AI projects are more likely to produce unreliable results, create compliance risks, and fail to deliver long-term value. 

Yes, establishing data governance takes time, but taking the time to focus on governance is a solid investment—one that may accelerate results over time. A solid data governance foundation helps healthcare leaders gain insights faster, improve patient outcomes, and increase trust across teams. Projects move forward more smoothly and are less likely to stall, flounder, and fail.

Now is the time to act. Whether through a governance maturity assessment, a cross-functional task force, or targeted training, taking the first step today sets the stage for future success with AI.