• Partner with functional leaders to identify high-leverage processes suitable for AI enablement. • Map workflows, quantify baseline performance (cost, time, quality), and prioritize opportunities based on ROI and feasibility. • Develop clear business cases for AI investments, including expected impact and risks. • Lead end-to-end deployment of AI tools and workflow redesign across pilot functions. • Redesign processes to embed AI natively rather than layering tools onto existing inefficiencies. • Coordinate cross-functionally to ensure adoption, training, and operational continuity. • Ensure compliance with data security, privacy, and risk standards. • Define standardized metrics to assess AI impact (e.g., cost per unit, throughput, error rates, headcount leverage). • Establish dashboards and reporting cadences to track performance. • Deliver sustained productivity improvements in pilot processes, with documented performance over at least six months. • Present regular updates to executive leadership on outcomes, risks, and expansion opportunities. • Develop and document a repeatable AI evaluation and implementation framework. • Create internal playbooks, governance guidelines, and training materials for responsible AI usage. • Establish an intake process for AI ideas across the company. • Expand the program beyond initial pilots as results are validated.