AI Program Manager, IT Dept

This position will work one day per week onsite in San Francisco. 

The AI Program Manager serves as an organization-wide expert and strategic advisor leading UCSF’s enterprise AI program across clinical care, research, education, and administrative operations. The incumbent partners with Enterprise AI product, technical, and leadership stakeholders to translate institutional priorities and complex business needs into adoption plans, enablement programs, stakeholder engagement strategies, feedback loops, and sustainable operating practices. The role evaluates organizational readiness, workflows, stakeholder needs, and performance data to identify opportunities that improve institutional effectiveness, workforce experience, quality, and mission outcomes.

The incumbent coordinates the adoption, enablement, communications, stakeholder engagement, value tracking, and operational readiness components of a high-impact portfolio of AI initiatives from intake through implementation, adoption, value realization, and transition to operations. The role supports prioritization and resource planning by preparing analysis, maintaining visibility into progress and risks, facilitating multidisciplinary coordination, and escalating complex or politically sensitive issues for leadership decision-making.

The AI Program Manager leads awareness and adoption of approved AI tools by communicating available capabilities, appropriate use cases, limitations, support resources, and product roadmaps. Working with senior leaders and clinical, research, academic, operational, technology, and risk stakeholders, the incumbent advances responsible AI adoption, informs vendor and product decisions, leads organizational change, and represents UCSF in relevant University and industry forums. The role works in close partnership with Enterprise AI product leadership, who retain accountability for product strategy, platform roadmap, prioritization recommendations, and product/vendor decision-making. 

Department Overview 

UCSF Enterprise Information and Analytics (EIA) advances the institution’s data and analytics capabilities in support of patient care, research, education, and administrative operations. EIA develops and maintains enterprise platforms and services that integrate data from across UCSF, promote consistent and trustworthy information, and support informed decision-making. Its portfolio includes enterprise data warehousing and data lakes, data integration, business intelligence, reporting and analytics, data governance, and data catalog services. EIA also establishes the policies, standards, and processes that enable institutional data to be accessed and used responsibly.
 

Enterprise AI builds on this foundation by delivering secure, scalable, and responsible artificial intelligence capabilities to the UCSF community. The program integrates AI strategy, governance, platforms, portfolio management, adoption, training, communications, and stakeholder engagement. It supports enterprise services such as UCSF ChatGPT Enterprise and the Versa API, providing secure access to generative AI and multiple AI models. In partnership with technology, privacy, security, legal, compliance, clinical, academic, and operational stakeholders, Enterprise AI identifies and prioritizes high-value opportunities, communicates available capabilities and roadmaps, accelerates responsible adoption, and measures institutional value.

REQUIRED QUALIFICATIONS

• Bachelor's degree in related area and / or equivalent experience / training.
• Minimum of 10 years of progressively responsible experience in organizational consulting, strategic planning, or organizational change, including leading complex cross-functional initiatives.
• Expert knowledge of enterprise program management, organizational effectiveness, strategic planning, and change management, with demonstrated ability to lead complex transformation initiatives from strategy through implementation, adoption, and operational transition.
• Knowledge of generative AI capabilities, limitations, and adoption patterns, including associated privacy, security, safety, ethical, accessibility, and responsible-use considerations.
• Demonstrated ability to design and operate portfolio intake, evaluation, prioritization, governance, business-case, and value-realization processes.
• Advanced analytical and problem-solving skills, including the ability to evaluate complex organizational needs, workflows, risks, dependencies, resource requirements, and expected benefits.
• Demonstrated ability to lead multidisciplinary teams and influence senior leaders and technical, clinical, research, academic, administrative, operational, and governance stakeholders without direct authority.
• Demonstrated ability to design and scale adoption and enablement programs and communicate complex AI capabilities, risks, use cases, and roadmaps through training, demonstrations, communities of practice, and other engagement methods.
• Strong technical fluency across enterprise applications, systems integration, data and analytics platforms, information security, cloud services, or enterprise architecture, with the ability to translate between technical teams and organizational stakeholders.
• Demonstrated ability to establish performance measures and use quantitative and qualitative data to evaluate adoption, user experience, risk, program health, and institutional value.
• Ability to exercise independent judgment, establish priorities, and resolve highly complex or politically sensitive issues involving ambiguity, competing interests, and multiple constituencies.

PREFERRED QUALIFICATIONS

• Advanced degree in related area
• Certifications
  • Project Management Professional (PMP) or Program Management Professional (PgMP) certification
  • Specialized certifications in AI/ML project lifecycle delivery (e.g., PMI-CPMAI, CAIPM)
• Knowledge of product and vendor management practices, including capability assessment, requirements development, roadmap evaluation, deployment readiness, and vendor performance management.
• Experience leading enterprise technology programs in healthcare, academic medicine, research, higher education, or another regulated environment, including familiarity with applicable data governance, privacy, security, and compliance requirements.
• Experience with enterprise generative AI platforms, large language model services, AI-enabled workflows, or related emerging technologies.