Senior Director, AI Enablement

McKesson•Alpharetta, GA

3d•$174,800 - $291,300

About The Position

McKesson is seeking a Senior Director, AI Enablement to lead the next stage of CoverMyMeds' AI transformation. The role owns AI strategy, governance, and AI engineering platforms, and builds and delivers AI products using an agentic, AI-native approach. It scales safe, measurable AI adoption across engineering and business teams while partnering with McKesson's Enterprise AI team to develop capabilities that can be reused across business units. This is a builder role combining technical depth, product delivery, strategic vision, and organizational change leadership. The Senior Director will define the AI enablement strategy and multi-year roadmap, manage a portfolio of concurrent AI initiatives and a multimillion-dollar budget, and report on AI adoption, productivity, and return on investment to executive leadership. The role leads AI products and agents from discovery through production and ongoing improvement, and promotes products and workflows designed around agents with human oversight where appropriate. The position chairs the AI Review Board and owns AI usage policy, model-selection frameworks, and token allocation and cost management. It ensures HIPAA, privacy, and security requirements are incorporated into AI development and delivery, working with Legal, Privacy, Security, and Compliance. It also leads reusable AI platforms and developer tooling, advances an AI-embedded software development lifecycle, and promotes shared AI assets across teams and business units. The role expands AI adoption beyond engineering, owns training and a champions network, advises senior leaders and the C-suite, and builds an engaged AI culture. It partners with McKesson Enterprise AI, architecture, product, platform, external technology partners, and vendors, and hires, develops, and retains a high-performing team.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent experience.
Typically 13 or more years of relevant software engineering and technology leadership experience, including 5 or more years leading engineering or platform teams.
At least 3 years of hands-on leadership in AI or ML, including generative AI, LLMs, and agentic AI in production environments.
Proven track record of driving AI adoption at scale with measurable outcomes in productivity, quality, or delivery speed.
Demonstrated experience building and delivering AI-powered products or agents to production with measurable business impact.
Experience establishing AI governance, responsible AI practices, or technology standards in a regulated environment.
Experience managing people, budgets, vendor relationships, and a portfolio of concurrent initiatives.
Knowledge of generative and agentic AI, including LLMs, RAG, agents, prompt and context engineering, and model evaluation.
Knowledge of agentic-native product design, including multi-agent workflows, tool use, human-in-the-loop design, and agent observability and evaluation.
Product delivery experience spanning discovery, MVP definition, rapid iteration, and production release.
AI platform engineering knowledge, including MCP, reusable skills and plugins, AI marketplaces, and LLMOps.
Knowledge of modern SDLC practices, including spec-driven development, CI/CD, DevOps, cybersecurity, automated testing, and code review.
Experience with cloud-native architecture (GCP or AWS), APIs, and integration platforms.
Knowledge of data foundations for AI, including data quality, tagging, and synthetic data.
Strategic vision and ability to turn it into an executable roadmap.
Executive communication and influence, including explaining complex technical topics simply.
Change leadership skills, including moving teams toward confident, everyday AI use.
Cross-functional partnership skills with Legal, Privacy, Security, Product, and business leaders.
Talent development skills and ability to build inclusive, high-performing teams.
Data-driven decision-making focused on measurable outcomes and ROI.

Nice To Haves

Master's degree in a technical field or an MBA.
Healthcare, pharmacy, or health technology experience, including HIPAA and patient data protection.
Experience with AI developer tooling such as Claude, Claude Code, GitHub Copilot, and Model Context Protocol (MCP).
Experience with FHIR and healthcare interoperability standards.
Experience in a matrixed, multi-business-unit enterprise.

Responsibilities

Define and own CoverMyMeds' AI enablement strategy and multi-year roadmap, aligned with business priorities and McKesson's enterprise AI direction.
Manage a portfolio of concurrent AI initiatives and a multimillion-dollar budget, prioritizing investments by business value, risk, and reuse.
Measure and report AI adoption, productivity, and ROI to executive leadership.
Own the end-to-end build and delivery of AI products and agents, from discovery and design through production release and ongoing improvement.
Promote agentic-native product and workflow design, with humans in the loop where appropriate.
Partner with Product and business leaders to identify high-value use cases and deliver products with measurable clinical, operational, and financial outcomes.
Use AI-accelerated delivery to move work from idea to production quickly while maintaining quality, security, and compliance.
Chair the AI Review Board and own AI usage policy, model-selection frameworks, and token allocation and cost management.
Ensure HIPAA, privacy, and security requirements are built into AI development and delivery processes.
Partner with Legal, Privacy, Security, and Compliance to enable safe and responsible AI adoption.
Lead teams building and operating reusable AI capabilities, including the AI Skills Marketplace, MCP servers, agents, and developer tooling.
Advance the AI-embedded SDLC, including spec-driven development, AI code review, automated vulnerability remediation, shift-left testing, and self-service developer onboarding.
Drive build-once, reuse-everywhere practices for shared AI assets across teams and business units.
Scale AI adoption beyond engineering to product, business, legal, finance, privacy, call center, and platform teams.
Own the AI training curriculum and champions network, from foundational AI literacy to advanced agentic workflows.
Advise senior leadership and the C-suite on AI strategy, capabilities, and risk.
Build a positive, curious AI culture through townhalls, ERG sessions, and hands-on enablement.
Partner with McKesson's Enterprise AI team and architecture, product, and platform leaders to align standards and avoid duplicate effort.
Work with external technology partners and vendors to evaluate and adopt emerging AI capabilities.
Hire, develop, and retain a high-performing team of engineers, architects, and enablement specialists.