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 and forecasting AI spend; support development of AI chargeback models; support enterprise AI licensing with Procurement; and develop and report key AI usage metrics (e.g., cost per transaction, per employee, and per AI interaction). 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. Applies financial modeling, cost-benefit analysis, KPI development, and executive reporting so leadership dashboards can answer where the organization is investing, what value it is getting, which AI initiatives are succeeding, and which should stop.

• AI Technology Advisory & Architecture Guidance. 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

• Cross-functional Leadership & Stakeholder Partnership. 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.

The posting range for this position is:

130,300.00 - 188,950.00

Qualifications:

Education

• Required BS ; or equivalent experience
• Preferred MS

Experience

• Required 10+ Years 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

Knowledge Skills and Abilities

• 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
• RAG architectures and prompt engineering concepts
• Model lifecycle management and model governance
• Enterprise architecture, technology strategy, and portfolio management
• Analytical and financial-modeling

Preferred Skills:

• 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

Certifications & Licenses

• Preferred: Certified Project Management Professional (PMP) - PMI
• Preferred: Microsoft Certified: Azure AI Engineer Associate - Microsoft

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