AI Strategy and Portfolio Principal

Blue Cross Blue Shield Association•Chicago, IL

2d•$130,300 - $188,950•Hybrid

About The Position

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.

Requirements

BS ; or equivalent experience
10+ years of experience in enterprise technology strategy, governance, portfolio management, or related disciplines
5+ years AI, analytics, or machine learning leadership experience
Experience managing enterprise technology portfolios
Experience leading cross-functional initiatives
Experience presenting to executive leadership
Experience managing strategic technology investments
Experience in the healthcare or payer industry
Ability to translate complex AI concepts into clear business terms for executive audiences, and to influence and build consensus without direct authority.
Strong understanding of: Generative AI, large language models, machine learning, AI agents, and foundation models
Strong understanding of: RAG architectures and prompt engineering concepts
Strong understanding of: Model lifecycle management and model governance
Strong understanding of: Enterprise architecture, technology strategy, and portfolio management
Strong understanding of: Analytical and financial-modeling

Nice To Haves

MS
Cloud AI platforms: Microsoft Azure AI, Amazon Web Services AI services, and Google Cloud Vertex AI
Foundation models: OpenAI, Anthropic Claude, Google Gemini, and Meta Llama
AI gateway technologies, AI observability platforms, and model monitoring tools
AI FinOps and technology portfolio management
Enterprise Architecture and Agile portfolio management
Healthcare interoperability, AI procurement, and third-party model evaluation
Certified Project Management Professional (PMP) - PMI
Microsoft Certified: Azure AI Engineer Associate - Microsoft

Responsibilities

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.
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.
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 and forecasting AI spend; support development of AI chargeback models; support enterprise AI licensing with Procurement; and develop and report key AI usage metrics.
Define and recommend methods to measure financial, operational, employee-productivity, including (for e.g.) time saved, reduced administrative burden, and indirect provider and member experience gains.
Serve as internal consultant helping business and technology teams select the right AI solution collaborating with key stakeholders evaluating foundation models, commercial and open-source large language models, AI agents and agent frameworks, RAG architectures, AI orchestration and prompt-management platforms, and AI evaluation tooling.
Collaborates with partners across technology, including, AI architecture, cloud AI platform, model-evaluation, API-integration, and AI-security expertise to provide, defensible recommendations to business teams.
Lead by influence across the enterprise, partnering with business leaders and other key stakeholders to advance responsible, value-driven AI adoption.
Uses influencing-without-authority, consensus-building, negotiation, change-management, facilitation, and executive-presentation skills, together with program-leadership discipline, to align diverse stakeholders and drive enterprise outcomes without direct reporting authority.