New opportunity

AI Strategy and Portfolio Principal

Blue Cross Blue Shield Association (BCBSA) · Chicago, United States of America

About this role

The AI Strategy and Portfolio Principal manages Blue Cross Blue Shield Association’s enterprise AI portfolio, governance and value realization. The role advises business and technology teams on responsible AI adoption, technology choices and investments aligned with strategic priorities, regulatory expectations and measurable outcomes.

Responsibilities

Own the AI product and project portfolio, develop its strategy and capability maturity roadmap, prioritize investments, build a repeatable business-case methodology and executive dashboards, eliminate duplicate capabilities and optimize spending.
Support the enterprise AI Review Board, review use cases, maintain the AI inventory and establish approval workflows with architecture and engineering teams. Provide frameworks for monitoring model applicability, drift, hallucination rates and quality; review retraining, retirement and ongoing business fitness. This is governance oversight, not direct statistical monitoring.
Partner with FinOps and Procurement on AI operating expenses, token and GPU utilization, cost forecasting, LLM cost optimization, chargeback models and licensing. Define usage, financial, operational and productivity measures, including cost per transaction, employee and AI interaction.
Advise teams evaluating foundation models, commercial and open-source LLMs, AI agents, RAG architectures, orchestration and prompt-management platforms, and evaluation tools. Lead cross-functional decisions through influence, consensus-building and executive communication.

Qualifications

Required: BS or equivalent experience; 10+ years in enterprise technology strategy, governance, portfolio management or related disciplines, including 5+ years of AI, analytics or machine learning leadership. Experience managing technology portfolios and strategic investments, leading cross-functional initiatives, presenting to executives, and working in healthcare or the payer industry is listed.
Strong understanding of generative AI, LLMs, machine learning, AI agents, RAG, prompt engineering, model governance, enterprise architecture and financial modeling. Must communicate complex AI concepts to executives and build consensus without direct authority.
Preferred: MS; experience with Azure AI, AWS AI services, Google Cloud Vertex AI, OpenAI, Anthropic Claude, Google Gemini, Meta Llama, AI gateways, observability, model monitoring, AI FinOps, Agile portfolio management, healthcare interoperability, AI procurement and third-party model evaluation. PMP and Microsoft Azure AI Engineer Associate certifications are preferred.

The Chicago role is designated hybrid. The posting range is 130,300.00–188,950.00; the role is eligible for annual bonus incentive pay. Listed benefits include paid time off, 11 holidays, medical, dental and vision insurance, 401(k) matching and a lifestyle spending account.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI portfolio managementAI strategyResponsible AIModel governanceModel lifecycle managementTechnology portfolio managementInvestment prioritizationCost-benefit analysisFinancial modelingAI FinOpsKPI developmentExecutive reportingStakeholder managementCross-functional leadershipChange managementGenerative AILarge language modelsMachine learningAI agentsFoundation modelsRAGPrompt engineeringEnterprise architectureAI securityData governanceModel evaluationMicrosoft Azure AIAmazon Web Services AI servicesGoogle Cloud Vertex AIOpenAI models and services adhere to governance requirements and that AI methods

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Set the enterprise AI portfolio direction
Own the AI product and project portfolio, maintain its strategy and capability maturity roadmap, prioritize investments by cost and value, and identify duplicate capabilities. The posting calls for repeatable business cases and executive portfolio dashboards.
Establish responsible lifecycle oversight
Support the enterprise AI Review Board, review use cases, maintain the AI inventory, and define approval workflows with architecture and engineering. Provide frameworks for teams to assess model applicability, drift, hallucinations, quality, retraining and retirement; the role does not perform statistical monitoring itself.
Make AI spending and value visible
Partner with FinOps and Procurement on operating expenses, token and GPU utilization, forecasts, licensing and potential chargeback models. Define usage, financial, operational and productivity measures that help leaders decide which initiatives to continue or stop.
Guide defensible technology choices
Advise business and technology teams evaluating foundation models, commercial and open-source LLMs, agents, RAG, orchestration, prompt-management and evaluation tools. Work with architecture, platform, integration and security specialists rather than treating selection as a standalone product decision.
Align decisions without direct authority
Lead cross-functional decisions through facilitation, negotiation and executive communication. The posting emphasizes translating complex AI concepts into business terms and building consensus among stakeholders who do not report to this role.

Likely challenges to think through

Balancing enablement with review
A likely difficulty is making approvals useful to business teams without weakening responsible AI and lifecycle controls. Prepare to explain how risk-based review, a clear inventory and defined decision points could preserve both progress and oversight.
Judging value against variable cost
Token and GPU usage can change while benefits such as time saved or improved experience may be indirect. Prepare a way to distinguish measured outcomes from estimates, compare them with operating costs, and present continue-or-stop decisions to executives.
Reducing duplication without ignoring local needs
An enterprise roadmap may favor shared capabilities while departments have different use cases and constraints. Prepare a decision framework for when to reuse, consolidate or allow a distinct solution, including how to build agreement without direct authority.
Choosing technology that remains fit for purpose
Model and platform options may differ in cost, quality, integration and governance implications, and their suitability can change over time. Prepare to describe a comparative evaluation and periodic reassessment, rather than assuming one model or architecture fits every use case.

Practice your answers

Technical practice

Suggested practice: How would you prioritize an enterprise AI portfolio and build its capability maturity roadmap?

What to coverThe posting calls for balancing strategic priorities, cost, value and risk across AI investments. Explain how you would compare initiatives, sequence capabilities and make trade-offs visible to executives.

Suggested practice: What would a repeatable AI business case include, and how would you identify duplicate capabilities?

What to coverThe role supports business cases and aims to eliminate overlapping AI capabilities. Describe a consistent way to compare expected benefits, operating costs and alternatives, including when consolidation might outweigh a team’s preferred solution.

Suggested practice: How would you structure AI use-case reviews, inventory records and approval workflows?

What to coverThe role supports an enterprise AI Review Board and works with architecture and engineering on tool and model approvals. Address the information needed for a defensible review, clear decision ownership and how approved uses would remain visible in the inventory.

Suggested practice: How would you govern models after deployment without performing statistical monitoring yourself?

What to coverThe posting specifies oversight frameworks for applicability, drift, hallucination rates and quality, rather than direct monitoring. Explain what teams should report and how periodic reviews would inform retraining, continued use or retirement.

Suggested practice: How would you forecast and optimize AI operating costs?

What to coverThe role partners with FinOps on operating expenses, token and GPU utilization, LLM costs and chargeback models. Discuss useful cost drivers, forecast assumptions and the trade-off between lower spend and acceptable solution quality.

Suggested practice: Which measures would show whether an AI initiative delivers business value?

What to coverThe posting names financial, operational, productivity and usage measures, including cost per transaction and time saved. Explain how you would establish a baseline, distinguish usage from outcomes and present evidence for continuing or stopping an initiative.

Suggested practice: How would you compare commercial and open-source foundation models for a business use case?

What to coverThe role advises on model and technology selection, including commercial and open-source LLMs. Describe evaluation criteria such as task quality, governance fit, integration needs and ongoing cost, and how you would document a recommendation.

Suggested practice: When would you consider RAG or an AI agent rather than a simpler LLM approach?

What to coverThe posting calls for understanding RAG, prompt engineering, agents and evaluation tooling. Compare the business need each approach addresses with its added complexity, evaluation needs and potential failure modes.

Suggested practice: How would you evaluate orchestration and prompt-management platforms within an enterprise architecture?

What to coverThe role collaborates with architecture, cloud AI, API-integration and security specialists on technology recommendations. Explain how interoperability, governance visibility and the risk of duplicating existing capabilities would shape a platform decision.

Suggested practice: How would responsible AI, data governance and security affect a proposed healthcare or payer AI use case?

What to coverThe posting calls for risk-assessment, data-governance and AI-security discipline aligned with regulatory expectations. Describe the questions you would raise with relevant specialists and how unresolved risks would affect approval or investment advice, without assuming a specific policy or regulation.

Behavioural practice

Suggested practice: Tell me about a time you aligned stakeholders who disagreed on a technology investment.

What to coverThe role requires consensus-building without direct authority. Use a real situation, your actions and the result to show how you surfaced competing priorities and reached or escalated a decision.

Suggested practice: Tell me about a time you explained a complex AI or technology trade-off to executives.

What to coverThe posting emphasizes clear executive communication and portfolio dashboards. Use situation, action and result to show how you framed the decision in business terms and handled questions or uncertainty.

Suggested practice: Tell me about a time you introduced a governance or approval process across teams.

What to coverThe role helps establish AI enablement and approval workflows with multiple partners. Describe the situation, the actions you took to earn adoption and the result, including how you addressed friction.

Suggested practice: Tell me about a time evidence led you to recommend reducing or stopping an initiative.

What to coverThe posting expects portfolio reporting to identify which AI initiatives should stop. Give a real situation, your analysis and communication actions, and the result rather than claiming a decision was easy or universally accepted.

Suggested practice: Tell me about a time you negotiated competing cost, procurement and delivery priorities.

What to coverThe role partners with FinOps and Procurement on spending, licensing and value. Use situation, action and result to explain the trade-offs you made visible and how the parties reached an outcome.

Sharpen your knowledge

Topics to prepare

Enterprise AI portfolio strategy
Revise how a capability maturity roadmap, investment prioritization and duplicate-capability review fit together. Exercise: explain how you would choose between two overlapping initiatives with different costs and expected value. The posting assigns ownership of the enterprise AI portfolio and roadmap.
Responsible AI and model lifecycle governance
Revise the distinction between oversight and statistical monitoring. Exercise: outline what a review board should ask about model applicability, drift, hallucination rates, quality, retraining and retirement. The posting calls for governance frameworks and periodic business-fitness reviews, not direct statistical monitoring.
AI financial management and value
Revise how operating expense, token and GPU utilization, cost avoidance and chargeback relate to portfolio decisions. Exercise: explain when cost per AI interaction could be useful but insufficient without an outcome measure. These costs and measures are specified in the posting.
AI architecture trade-offs
Revise the decision factors for foundation models, commercial versus open-source LLMs, agents, RAG, orchestration and prompt-management platforms. Exercise: compare two approaches on fitness, evaluation, governance and cost. The role advises teams on these technology choices rather than being described as their implementer.
Healthcare and payer decision context
Revise how regulatory expectations, data governance and AI security can affect an AI investment decision. Exercise: describe questions you would raise before approving a hypothetical payer use case, without assuming a specific regulation or BCBSA policy. The posting requires healthcare or payer experience and emphasizes these considerations.
Influence without authority
Revise approaches to consensus-building, negotiation and executive communication across business and technology stakeholders. Exercise: explain how you would resolve disagreement between a business sponsor and architecture reviewers. The posting explicitly calls for cross-functional leadership without direct reporting authority.

Skills to revise

Build a portfolio prioritization brief
Practice ranking three hypothetical AI proposals by strategic fit, expected value, cost, risk and overlap; state what to fund, defer or stop. This prepares for the posting’s investment-prioritization and portfolio-optimization responsibilities.
Draft an AI review workflow
Practice creating a one-page use-case intake, inventory entry and approval path showing where the AI Review Board, Enterprise Architecture and Enterprise Engineering contribute. The posting calls for review-board support, an AI inventory and tool/model approval workflows.
Create a lifecycle oversight template
Practice specifying the evidence teams would report on applicability, quality, drift, hallucinations, retraining and retirement, plus questions for a periodic review. This matches the role’s framework-setting responsibility without treating hands-on model monitoring as part of the job.
Model AI costs and benefits
Practice a simple scenario with assumed usage, token and GPU costs, cost per transaction and time saved; show a spend forecast and the assumptions behind a business case. The posting calls for financial modeling, LLM cost optimization, usage metrics and value measurement.
Present an executive portfolio dashboard
Practice a short briefing showing investment, spend, measured outcomes, risks and a stop-or-continue recommendation for hypothetical initiatives. The posting calls for executive dashboards that show where BCBSA is investing and what value it is getting.
Write a defensible technology recommendation
Practice a one-page comparison of a hypothetical RAG approach and another LLM-based approach, including evaluation evidence, architecture fit, security considerations and cost. The posting calls for advising business teams in collaboration with architecture, evaluation and AI-security partners.

Your preparation checklist

Select evidence for the stated qualifications
Identify examples, if available, of enterprise portfolio management, AI or analytics leadership, strategic investment decisions, executive presentations, cross-functional work, and healthcare or payer experience. Note your specific role and outcomes without assuming a match.
Build a portfolio decision walkthrough
Prepare one project example showing how initiatives were inventoried, prioritized against strategy, assessed for duplicate capabilities, and translated into a roadmap. If you lack a direct example, use a clearly labeled hypothetical exercise.
Draft a sample business case and dashboard
The role calls for repeatable business cases and executive reporting. Create a compact example with costs, expected value, assumptions, success measures, and a recommendation to invest, revise, or stop.
Sketch a model-governance workflow
Draw an illustrative path from use-case review and AI inventory entry through approval, periodic review, retraining, and retirement. Show how teams would report applicability, drift, hallucination rates, and quality; the posting assigns this role oversight frameworks, not direct statistical monitoring.
Work through an AI cost-and-value scenario
Using explicitly hypothetical figures, outline how you would forecast token and GPU spend, compare cost per interaction or transaction with value measures, and discuss optimization, chargeback, and licensing with FinOps and Procurement.
Prepare a technology-selection recommendation
For a hypothetical business need, compare options such as a commercial or open-source LLM, RAG, or an agent. State evaluation criteria, architecture and security considerations, trade-offs, and what specialist input you would seek before recommending a choice.
Rehearse executive communication and employer questions
Practice a brief explanation of a contested AI investment decision and how you built consensus without direct authority. Ask how BCBSA currently prioritizes its AI portfolio, divides Review Board responsibilities, and measures value realization.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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