• Run the intake-to-roadmap loop for Enterprise AI Enablement: partner with stakeholders to capture use cases, clarify scope/ownership, and shape opportunities into well-defined epics and user stories (with clear outcomes and acceptance criteria) • Prioritize what matters most by applying the strategy’s prioritization model (top-down themes + bottom-up Business Unit needs), ensuring we build workflows that create measurable time savings and adoption—not one-off tools. • Own execution hygiene for the backlog: keep the backlog crisp, sequenced, and “build-ready” for AI engineering, including lightweight discovery, dependency mapping, and release notes for internal users • Enable “Right tools for the right work” by partnering with IT Ops / Procurement / Security to support tool inventory signals, capability-to-tool guidance, and practical rollout communications that reduce tool sprawl and compliance gaps. • Build “metrics that matter” into every initiative: define baseline + success metrics (adoption, time saved, workflow cycle time), and support quarterly impact reporting with clear, decision-ready summaries. • Ship workflow enhancement with BU partners: support delivery of production-grade agents/workflows by keeping BU owners aligned on outcomes, readiness, and change management—so adoption sticks after launch. • Contribute to org-wide AI fluency by partnering on enablement assets that make the compliant path the easiest path.