AI Strategy and Portfolio Principal

Job Description Summary

This role is responsible for the enterprise management, governance, optimization, and value realization of Artificial Intelligence capabilities across the organization. Serving as the enterprise subject matter expert for AI portfolio management, model governance, AI technology selection, and Responsible AI practices, the role ensures AI investments align with strategic priorities, regulatory expectations, and business outcomes while maximizing return on investment and managing technology cost. This role serves as an enterprise advisor and governance leader, partnering across the organization to guide the responsible adoption, management, and optimization of emerging technologies and AI-enabled capabilities. Through influence and collaboration, the role helps ensure investments align to strategic priorities, appropriate governance practices are followed, risks are effectively managed, and measurable business value is realized.

Job Description

• Enterprise AI Strategy & Portfolio Management. Own the enterprise AI product/project portfolio. Develop and maintain the enterprise AI portfolio strategy and AI capability maturity roadmap, prioritizing investments and balancing cost against value across initiatives. Establish the AI enablement process, support BCBSA teams with AI business cases, identify and eliminate duplicate AI capabilities across departments, and optimize enterprise AI spending. Applies enterprise thinking, strategic planning, investment prioritization, and cost-benefit and portfolio-optimization analysis to deliver the AI portfolio roadmap, executive portfolio dashboards, and a repeatable AI business case methodology.

• AI Model Lifecycle Oversight. Act as operational lead and subject matter expert supporting the enterprise AI Review Board, ensuring responsible AI policies are followed within Technology Operations. Review AI use cases in support of business-team enablement, maintain the enterprise AI inventory, and define AI approval workflows for tool and model selection in collaboration with Enterprise Architecture and Enterprise Engineering. Govern models across their lifecycle by providing the frameworks and templates teams use to monitor the production model inventory, model applicability, drift, hallucination rates, and quality; review retraining schedules and retirement; and conduct periodic model reviews to ensure ongoing business fitness (this role provides oversight frameworks and does not itself perform statistical monitoring). Brings risk-assessment, data-governance, AI-security, model-evaluation, and performance-measurement discipline, and uses facilitation and consensus-building to align stakeholders on model management decisions.

• AI Financial Management & Value Measurement. Partner with FinOps to bring financial and value transparency to the AI portfolio. Report on AI operating expenses, token and GPU utilization, and cost avoidance; recommend approaches to optimizing LLM usage costs...