We are seeking a Principal Program Manager to lead enterprise-wide AI strategy and transformation at scale. In this role, you will be the single-threaded owner of a multi-org AI adoption program, responsible for defining the vision, building the operating model, and driving measurable productivity impact through AI-powered automation and GenAI tool integration across a workforce of thousands.
You will operate as a strategic leader at the intersection of AI product adoption, operational transformation, and organizational change management. This role requires you to stitch together a fragmented landscape of AI experiments, 1P/3P tools, and automation initiatives into a coherent strategy — setting priorities, building coalitions across VP-level organizations, and creating the measurement frameworks that demonstrate AI's business impact.
You will build and lead a federated program model spanning multiple working groups, influence without authority across senior leadership, and serve as the connective tissue between engineering/product teams building AI capabilities and the operational organizations adopting them. This is not a role for someone who simply tracks projects — it requires a strategic thinker who can shape the "how" and "what" of AI transformation, navigate ambiguity, and drive organizational alignment at scale.
Key job responsibilities
Define and own the end-to-end AI transformation strategy, roadmap, and operating model across multiple VP-level organizations
Build and lead a federated program structure (working groups, SPOCs, steering committees) that drives accountability while respecting distributed ownership
Design productivity measurement frameworks that quantify AI's impact — including throughput metrics, complexity weighting, automation categorization, and baseline methodology
Orchestrate the AI experimentation portfolio — connecting teams working on similar use cases, prioritizing investments, recommending scaling vs. sunset decisions, and synthesizing learnings
Drive 1P AI platform adoption strategy, identifying high-impact use cases, removing adoption barriers, and partnering with product teams to shape tool roadmaps
Lead executive communication — translating complex operational and AI impact data into compelling narratives for VP+ leadership through MBRs, QBRs, and strategic planning cycles
Establish the governance model for AI experimentation including security review pathways, responsible AI guardrails, and scaling criteria
Partner with analytics and data science teams to build dashboards and tracking mechanisms that provide real-time visibility into AI-driven efficiency gains
Inform annual planning (OP1/OP2) by synthesizing experiment outcomes, common problem statements, and solution extensibility into resource and investment recommendations
Serve as the cross-org "air traffic controller" — maintaining a bird's-eye view of all AI initiatives, identifying duplication, driving reuse, and ensuring initiatives sum to program-level goals
