• Opportunity discovery & prioritization: Run process discovery with supply chain teams (planning, scheduling, inventory, procurement, logistics) to map and size AI opportunities; build a sequenced backlog tied to measurable outcomes. • Hands-on POCs to MVPs: Rapidly build POCs and evolve the best into MVPs using AWS + Databricks + Dataiku + OpenAI, integrating with enterprise data sources and real workflows. • Roadmap ownership: Produce and maintain a capability roadmap (90 days/6 months/12 months) with dependencies, data readiness needs, and adoption/change requirements. • Kinaxis-enabled use cases: Design solutions that complement and enhance Kinaxis workflows (exception management, scenario narratives, decision support, insight generation, planning explainability). • Balance quick wins vs foundations: Deliver short cycle wins that work today while shaping requirements for scalable infrastructure as IT builds out data/AI foundations (reusable components, standard integration patterns, agent orchestration approaches, monitoring/evaluation patterns). • Vision and transition plan: Define a north-star vision and pragmatic transition plan bridging today's constraints to the medium/long-term target state (platform, governance, integration patterns, reusable AI assets). • Production-minded engineering: Define architecture, evaluation metrics, guardrails, monitoring, and last-mile integration patterns; partner with platform/data teams while remaining personally hands-on. • Drive ChatGPT adoption: Build role-based prompt libraries, playbooks, templates, and enablement sessions; measure adoption and iterate based on usage and impact. • Stay current and apply responsibly: Maintain working knowledge of the rapidly evolving AI landscape (GenAI, agents, evaluation, toolchains) and translate relevant advances into practical solutions aligned to enterprise guardrails.