Sr. Director, Enterprise AI Engagement

McKesson•Irving, TX

1d•Onsite

About The Position

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve – we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow’s health today, we want to hear from you. We are seeking Sr. Director, Enterprise AI Engagement who acts as the front door to the enterprise AI practice—ensuring stakeholders have a clear path to engage, adopt, and scale AI capabilities across the organization.

Requirements

Degree or equivalent experience.
Typically requires 13+ years of professional experience and 6+ years of diversified leadership, planning, communication, organization, and people motivation skills (or equivalent experience)
Proven ability to drive enterprise AI adoption through structured enablement, education, and stakeholder engagement models
Strong experience building and running program management offices (PMOs) for complex, cross-functional technology initiatives
Demonstrated success acting as a senior interface between business and technology, influencing executives and driving alignment
Deep understanding of AI/ML lifecycle, with the ability to partner effectively with engineering and data science teams without owning model development directly
Experience designing scalable onboarding, support, and lifecycle management models for enterprise AI products
Strong communication and storytelling skills, with the ability to translate complex AI concepts into clear business value narratives
Track record of building learning ecosystems, knowledge repositories, and communities of practice to drive continuous capability building
Candidate must be authorized to work in the U.S, now or in the future, without the support from McKesson.

Responsibilities

Lead enterprise-wide AI adoption by establishing a scalable engagement model that serves as the front door to the enterprise AI practice.
Build and operationalize learning, education, and enablement programs to upskill business and technology teams, accelerating AI fluency and usage across the organization.
Partner with business and technology leaders to translate enterprise priorities into structured AI programs, ensuring alignment with strategic objectives and measurable outcomes.
Own the enterprise AI Program Management Office (PMO), driving prioritization, roadmap alignment, and execution governance across AI engineering and solution delivery.
Establish standardized operating rhythms, intake frameworks, and delivery processes to ensure transparency, speed, and consistency across the AI portfolio.
Coordinate across data science, engineering, product, and business teams to ensure seamless execution and alignment on timelines, dependencies, and outcomes.
Oversee support models for enterprise AI products and solutions, ensuring reliability, scalability, and sustained adoption post-deployment.
Define and implement frameworks for onboarding, user adoption, and lifecycle management of AI capabilities across business units.
Partner with engineering and platform teams to ensure AI solutions are production-ready, maintainable, and aligned with enterprise standards.
Act as the primary point of contact for stakeholders across the enterprise, providing a clear and consistent interface into the AI organization.
Drive executive alignment and shared accountability across business and technology leaders to accelerate adoption and value realization.
Navigate complex matrixed environments to align priorities, resolve conflicts, and ensure coordinated execution of AI initiatives.
Design and lead enterprise change management strategies to embed AI into decision-making and operational workflows.
Establish continuous learning ecosystems, including training programs, playbooks, and communities of practice to scale AI knowledge and best practices.
Foster a culture that balances speed, accountability, and continuous improvement, enabling teams to adopt and scale AI effectively.
Define and track KPIs for adoption, engagement, delivery performance, and business impact across the AI portfolio.
Build transparent reporting mechanisms and executive-level dashboards to communicate progress, risks, and outcomes.
Continuously refine engagement, delivery, and support models based on feedback and performance insights.