Executive Director - Enterprise Data Management and AI

Position Summary:

APPLICATION PERIOD: This position will be posted for a limited two-week period. Applications must be submitted by September 2nd, 2026 to be considered.

The Executive Director of Enterprise Data Management and AI will facilitate and coordinate UT Medical's enterprise approach to data governance, data management, artificial intelligence oversight, and research enablement. This position works under the direction of the SVP/CIO and in collaboration with governance councils and executive sponsors.

This role serves as the coordinating leader to ensure data are treated as strategic organizational assets that are accurate, consistent, well-governed, and aligned to UT Medical's clinical, operational, financial, academic, and research priorities.

The Executive Director of Enterprise Data Management and AI will facilitate and coordinate with the governance bodies and executive sponsors in developing the Enterprise Data Management Program. The role will work collaboratively to create the structure, standards, and accountability necessary to strengthen trust in data, reduce fragmented reporting, improve data quality, support responsible AI adoption, and enable more effective decision-making across the organization.

This role will facilitate shared ownership and clear stewardship, governance, and accountability for enterprise data assets while enabling innovation and reducing unnecessary complexity, duplication, and manual reconciliation.

Key Responsibilities

Enterprise Data Strategy

• Partner with subject matter experts (SMEs) and other stakeholders to develop, maintain, and operationalize UT Medical's enterprise data strategy and roadmap for data governance, data management, analytics maturity, responsible AI oversight, and research enablement.

• Align data priorities with organizational goals related to patient care, quality, safety, access, growth, financial performance, operational excellence, academic medicine, and research.

• Partner with executive, clinical, operational, financial, academic, and research leaders to ensure data strategy supports enterprise decision-making and long-term organizational performance.

• Translate complex data and technology concepts into clear business value for executive and operational leaders.

Data Governance and Stewardship

• Recommend the enterprise data governance operating model, including cross-functional governance councils, stewardship workgroups, decision rights, escalation pathways, and approval processes.

• Organize and align enterprise standards for data ownership, stewardship, metric definitions, data quality, lineage, access, use, retention, and documentation.

• Coordinate development and implementation of data owner and data steward models across major organizational domains.

• Lead the certification process for enterprise metrics, data products, and authoritative sources of truth.

• Support governance councils in establishing enterprise priorities.

Enterprise Data Management

• Oversee the development and maintenance of an enterprise data asset inventory, including key systems, data domains, reports, dashboards, data products, and critical data elements.

• Establish standards for documenting source systems, lineage, business rules, calculation logic, refresh cadence, ownership, and stewardship.

• Identify and prioritize high-risk, high-value data quality issues that affect executive reporting, operational decisions, financial performance, regulatory reporting, patient care, or research.

• Promote reuse of trusted data assets and reduce duplication, manual reconciliation, and inconsistent reporting logic.

Responsible AI Oversight

• Coordinate and administer UT Medical's enterprise, cross-functional AI oversight process.

• Create an intake, inventory, prioritization, risk classification, review, approval, and monitoring process for AI use cases.

• Partner with SMEs including Legal, Compliance, Privacy, Information Security, Clinical Leadership, Research, Human Resources, Finance, and Operations to evaluate AI-related solutions.

• Ensure AI use cases are assessed for privacy, security, bias, explainability, clinical impact, operational impact, fiscal impact, reputational risk, and regulatory considerations.

• Facilitate and maintain standards for AI governance, including appropriate human oversight, performance monitoring, vendor review, and lifecycle management.

• Maintain an inventory of AI tools, pilots, models, vendor capabilities, and proposed use cases.

• Support responsible AI adoption that balances innovation, safety, ethics, compliance, and enterprise value.

Research and Academic Data Enablement

• Partner with research, academic, compliance, privacy, and technology SMEs and leaders to support responsible and efficient access to data for research and academic partnerships.

• Propose governance processes that enable research while maintaining appropriate safeguards for privacy, security, data use, and regulatory compliance.

• Support the development of data-sharing standards, research data request workflows, and collaborative models with academic partners.

• Enable the use of trusted data, analytics, and AI capabilities to strengthen UT Medical's academic and research mission.

Leadership and Program Management

• Lead the Enterprise Data Management (EDM) Program and team members.

• Establish clear roles, responsibilities, service expectations, performance measures, and professional development plans for the EDM team.

• Create a culture of partnership, transparency, accountability, service, and continuous improvement.

• Build strong relationships with leaders and frontline stakeholders.

• Through collaboration with SMEs and other stakeholders, champion change management efforts related to data governance, AI oversight, and enterprise data practices.

Decision Rights and Authority

• Recommend enterprise data governance standards.

• Define and maintain the data owner and data steward model.

• Establish prioritization methods for enterprise data work.

• Through collaboration with SMEs and other stakeholders, recommend approval of enterprise metric definitions and certified data products.

• Escalate unresolved conflicts regarding data ownership, metric definitions, AI risk, or source-of-truth decisions.

Position Qualification:

Required Qualifications

• Bachelor's degree in health administration, business administration, information systems, data science, computer science, analytics, public health, informatics, or related field.

• Minimum of seven years of progressive leadership experience in healthcare, data management, analytics, information technology, informatics, data governance, enterprise reporting, or related fields.

• Minimum of three years of experience leading teams, programs, or enterprise initiatives in a complex organization.

• Demonstrated experience developing or leading data governance, analytics, enterprise reporting, data strategy, data quality, or data management programs.

• Experience working with executive leadership and translating data strategies into operational, clinical, financial, or strategic value.

• Strong understanding of healthcare operations, clinical workflows, financial reporting, quality reporting, regulatory requirements, privacy, and data security considerations.

• Experience leading cross-functional change in a matrixed environment.

Preferred Qualifications

• Master's degree in health administration, business administration, information systems, data science, computer science, analytics, public health, informatics, or related fields.

• Experience in an academic health system, integrated health system, or complex healthcare delivery organization.

• Experience with enterprise data governance frameworks, data stewardship models, data quality programs, metadata management, master data management, or data cataloging.

• Experience with AI governance, responsible AI frameworks, predictive analytics, machine learning oversight, or clinical/operational AI use cases.

• Experience supporting research data enablement, academic partnerships, or secondary use of clinical data.

• Experience with electronic health record data, ERP data, financial systems, HR systems, quality data, operational data, and clinical reporting.

• Familiarity with healthcare platforms and tools such as Epic, Oracle Health/Cerner, Strata, Lawson/Infor, Tableau, Power BI, cloud data platforms, data warehouses, or enterprise analytics platforms.

Knowledge, Skills, and Abilities

• Ability to communicate effectively with senior leaders, physicians, operational leaders, technical teams, and frontline stakeholders.

• Ability to build trust across departments.

• Strong understanding of data governance, stewardship, data quality, data lineage, metadata, analytics, reporting, and data lifecycle management.

• Ability to develop practical operating models.

• Strong knowledge of healthcare data, including clinical, operational, financial, quality, workforce, regulatory, and research data.

• Ability to lead responsible AI oversight in a healthcare environment.

• Strong analytical, strategic, and critical-thinking skills.

• Ability to manage competing priorities and create transparent prioritization processes.

• Strong change management, facilitation, and relationship-building skills.

• Commitment to patient care, safety, quality, compliance, privacy, and ethical use of data and AI.