Own the Intelligent Operations AI roadmap
· Define and maintain the product vision and roadmap for AI across enterprise systems, including Operations, Finance, and HR applications, aligned to business and AI strategy.
· Identify, prioritize, and sequence high-impact AI/automation use cases across supply chain, inventory, finance, accounting, and people processes.
· Manage the product lifecycle end to end — from opportunity framing through delivery, adoption, and measured outcomes — with tight feedback loops.
· Turn fragmented tools, data, and processes into cohesive, secure, and reliable AI solutions.
Partner cross-functionally with Operations, Finance, Accounting, HR, and People Development
· Act as the central product partner for these teams — assessing needs, defining requirements, and establishing success measures for AI initiatives.
· Redesign and transform business processes around agent-driven automation — not just bolting AI onto legacy workflows.
· Enable AI-driven capabilities across our enterprise stack: intelligent automation, predictive analytics, copilots and agents, anomaly detection, and AI-assisted workflows (e.g., invoice automation, demand forecasting, inventory optimization, recruitment, onboarding, finance close).
· Translate business and functional requirements into clear product requirements, specs, and acceptance criteria that engineering, data science, and IT can deliver against.
Drive delivery, adoption, and measurable value
· Design and lead pilots and structured experiments with clear hypotheses, rollout criteria, and measurable outcomes.
· Own backlog, roadmap, priorities, and communication of progress and outcomes to stakeholders up to executive level.
· Drive adoption: enablement, training, SOP updates, usage metrics, and continuous improvement from feedback.
· Build ROI cases, track realized value, and justify AI investments.
Operate within governance and partner with technical teams
· Work hands-on with enterprise platforms (e.g., ERP, finance, planning, OMS) and their integrations to ground AI solutions in real data, controls, and operational ownership.
· Operate within AI governance, data, security, privacy, and compliance standards; manage risks and escalate blockers early.
· Partner with engineering, data science, IT, security, and external vendors to evaluate solutions and determine paths to market