Chief Data & AI Officer; CDAO

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Michigan Medicine is seeking a Chief Data & Artificial Intelligence Officer (CDAO) to provide executive leadership for the enterprise data, analytics, and AI ecosystem
, enabling a data-driven, AI-enabled academic health system.

Reporting to the Chief Digital & Information Officer (CDIO), the CDAO is accountable for the strategy, governance, architecture, and enablement of data and AI capabilities across Michigan Medicine. This role ensures data is trusted, accessible, governed, and translated into actionable intelligence
, while enabling scalable, responsible adoption of artificial intelligence.

The CDAO plays a central role in supporting Michigan Medicine's evolution into a learning health system
, orchestrating data and AI capabilities across clinical, operational, academic, and research domains. The role emphasizes platform enablement over centralization
, fostering a distributed analytics and democratized AI model supported by strong governance, shared standards, and modern tooling.

The CDAO provides enterprise leadership across core domains including:

• Enterprise Data Strategy & Governance
• Data Platforms, Architecture & Engineering
• Clinical & Operational Analytics (including Epic Cogito)
• Artificial Intelligence & Advanced Analytics (AI/ML/GenAI)
• Machine Learning Operations (MLOps) & AI Lifecycle Management
• Data Literacy, Self-Service Analytics & AI Enablement
• Data Infrastructure & Data Use Enablement
• AI Governance, Ethics & Responsible AI
• AI Orchestration, Automation, and Agentic Monitoring

Key Responsibilities

Enterprise Data & AI Strategy

Define and execute a comprehensive enterprise data and AI strategy aligned with Michigan Medicine's clinical, operational, academic, and research priorities.

Position Michigan Medicine as a leader in AI-enabled healthcare delivery, academia, research, and operations
.

Serve as the executive advisor on data, analytics, and AI investments
, opportunities, and risks.

Data Governance, Strategy & Stewardship (Enterprise Ownership)

Partnering across the enterprise, establish and lead enterprise data governance
, including:

• Data ownership and stewardship models
• Data policies, standards, and controls
• Data quality, integrity, and trust frameworks

Define and enforce data management standards
, including:

• Metadata, cataloging, and lineage
• Data classification and access controls
• Master and reference data strategies

Ensure all data assets are secure, compliant, governed, and usable at scale
.

Lead enterprise data platform strategy and delivery, including:

• Data architecture and engineering
• Data pipelines, integration, and interoperability
• Scalable data environments supporting clinical, administrative, academic, and research workloads

Oversee clinical data infrastructure and architecture
, enabling:

• Longitudinal patient records
• Master Data Management
• Interoperability across systems and partners
• Support for advanced analytics and AI

Lead and optimize the Epic Cogito environment
, ensuring it is:

• Integrated into the broader enterprise data ecosystem
• Performing, scalable, and aligned with reporting and analytics needs

Partner with the CTO to ensure alignment between:

• Underlying infrastructure and cloud environments
• Integration and interoperability platforms (API management, middleware)

Analytics & Data Enablement (Distributed Model)

Enable a distributed analytics model by:

• Providing shared platforms, tools, and governed access
• Supporting domain-based analytics across clinical, operational, academic, and research teams

Lead enterprise analytics capabilities, including:

• Clinical, operational, and financial analytics
• Revenue cycle, administrative, and performance analytics
• Research and academic analytics
• Self-service BI tools and reporting environments
• Access trusted data
• Build insights independently
• Operate within governance guardrails

Artificial Intelligence & Advanced Analytics

Lead enterprise AI strategy, including:

• Predictive analytics
• Machine learning and deep learning
• Generative AI and agent-based systems

Identify, prioritize, and scale high-impact AI use cases across clinical, operational, and research domains.

Partner with CHIO to enable clinical decision support and AI in care delivery
.

Partner with CAO to embed AI into applications and workflows
.

MLOps, AI Orchestration & Agentic Monitoring

Establish and lead Machine Learning Operations (MLOps) capabilities, including:

• Deployment, monitoring, and lifecycle management
• Model versioning, retraining, and performance tracking

AI orchestration across systems and workflows

Agentic AI management and monitoring

Continuous validation of model performance, drift, and bias

Ensure AI is:

• Scalable and production-ready
• Continuously monitored and improved
• Integrated into enterprise workflows

AI Governance,…