Role Purpose 

The AI Strategy role identifies, frames, validates, and prioritizes the AI opportunities that offer potential for significant enterprise revenue and margin growth. This individual acts as a organizational thought leader to our company's leadership in order to decide what opportunities to attack, how to attack them, how to prioritize them, and what to initiate, accelerate, or stop ahead of critical investment decisions.

This is a strategy and business-judgment role. It sits with SBU and functional leaders to turn financial, operational, and clinical problems into investment-ready theses: the problem, the customer or clinician, the first principles approach, the opportunity value, the investment required to unlock that value, the risk, and the conditions under which the work should proceed or cease. 

This is not a builder role. Solution design, technical feasibility, and build execution sit with AI Business Partners, Product Builders, enterprise architecture, and technology colleagues. AI Strategy owns the quality of the question and the quality of the bet. 

Key Responsibilities 

Problem Framing & Opportunity Assessment 

• Partner with enterprise and SBU, finance, operations, and clinical and pharmacy leaders to identify business problems where AI could create significant, even non-linear, value unlock. 

• Frame each opportunity in business terms: who is served, what changes, what value is at stake, what risk is introduced, and what would have to be true for the work to succeed. 

• Conduct rigorous qualitative and quantitative user and market research to validate the opportunity in order to derisk the bet.

• Distinguish problems for which AI offers non-linear advantages, (i.e. not just problems that need process, policy, data, or operating-model change), and conduct a first principles decomposition of the problem's points of leverage and AI's ability to unlock value against those points.

• Translate enterprise and SBU challenges into opportunities with measurable financial value, outcomes, and experiential improvements, not a list of use cases. 

• Bring a clear recommendation to proceed, sequence, partner, or stop. 

Portfolio Strategy & Investment Framing 

• Stack-rank opportunities on strategic alignment, customer or clinical value, feasibility (in partnership with product and technology colleagues), risk, cost, and speed to value, developing a robust and rigorous scoring methodology aligned to our organization's strategic needs.

• Develop business cases, investment theses, value realization models, and roadmaps that a business owner have data to stand behind. 

• Assess buy, build, partner, and hybrid paths as business choices, and flag duplicate investment across teams. 

• Explore non-traditional investment routes to solving problems, such as exploration of venture investment, incubation, or even M&A discussion, alongside colleagues from these areas.

• Define success metrics and measurement instrumentation before work is staffed, including adoption, productivity, financial impact, quality, customer experience, and clinical outcomes. 

• Revisit the AI portfolio as evidence arrives, and retire or reframe work that is not earning the next increment of investment, seeking to drive an "innovation rate of return" on the total body of work.

Progression Criteria & Value Discipline 

• Establish stage-gates and evidence expectations for concept, pilot, and broader adoption decisions. 

• Define what good enough to continue looks like in business and adoption terms, and name the owner who will be accountable if the work scales. 

• Identify the workflow, operating-model, and organizational changes required for value to show up, not only the technology change. 

• Use results from live work to refine what the enterprise should fund next. 

• Partner with the DD&AI program function and technology partners, including the AI Enablement team, on portfolio tracking and value reporting. Note that this role does not serve as the project or program manager, but facilitates key reporting around our AI IO return on invested capital and effort.

Healthcare Context & Responsible Use 

• Align recommendations with clinical and operational realities, regulatory context, and member or provider impact. 

• Partner with legal, compliance, privacy, information security, governance, and model risk management when an opportunity raises those questions. 

• Apply Responsible AI principles as decision criteria, including transparency, fairness, privacy, reliability, human oversight, and accountability. 

• Recommend where AI should automate, assist, recommend, or escalate, and name the human who remains accountable. 

• Raise risk early enough that it shapes the investment decision, not the launch review. 

Stakeholder Alignment & Influence 

• Advise business, clinical, product, and operations leaders on which AI bets are worth making and why, using rigorous analyses and data. 

• Communicate recommendations clearly and compellingly through business narratives, simple decision frameworks, and financial or outcome models. 

• Align stakeholders with competing priorities and variable AI fluency. 

• Represent AI Strategy in intake, planning, and investment conversations. 

• Mentor colleagues on problem-framing quality. Note that this role does not coordinate the delivery function. 

Qualifications & Experience 

Required 

• Bachelor's degree in Business, Finance, Healthcare Administration, Public Health, Information Systems, or a related discipline, or equivalent combination of education and experience. 

• Typically 8+ years in strategy, product strategy, management consulting, business operations, finance, digital transformation, or a related field inside a large or regulated enterprise. 

• Demonstrated experience framing business problems, writing investment cases, and helping leaders make fund, sequence, or stop decisions. 

• Ability to evaluate value, adoption risk, implementation complexity, and total cost of ownership at a decision-quality level, in partnership with technology counterparts. 

• Working literacy in what current AI capabilities can and cannot do, sufficient to challenge weak use cases and to know when a technology partner or AI expert needs to be in the room.

• Knowledge of what it takes to build great products, including an appreciation and intuition around the user's needs and experience expectations.  

• Experience operating in a matrix, with competing stakeholders and incomplete information. 

• Clear written and verbal communication with senior business leaders, with the ability to make a recommendation rather than only present options. 

Preferred 

• Master's degree, MBA, or equivalent advanced degree. 

• Experience in healthcare payer, provider, pharmacy, clinical, benefits, or healthcare services environments. 

• Familiarity with clinical workflows, claims, utilization management, prior authorization, care management, provider operations, or member engagement. 

• Deep expertise in leveraging AI to solve problems at a root cause level.

• Experience with Responsible AI, privacy, or regulatory considerations as they affect product and investment decisions. 

• Experience managing or advising an innovation or digital portfolio, including stopping or redirecting work. 

• Experience partnering with product, technology, and delivery teams without owning their work. 

Core Competencies 

• Problem Framing: Identifies the problem worth solving and states it so a leader can act. 

• Value Orientation: Anchors recommendations in rigorous financial (e.g. revenue, cost, margin, etc.), clinical outcomes, and experiential differentiation metrics, not just as interesting opportunities.

• Portfolio Judgment: Sequences work, stops work, and protects reuse over another isolated initiative. 

• Product-Mindedness: Fundamentally understands what it means to build great products.

• AI Literacy: Knows enough about developing AI solutions to decompose a use case and to pull in specialists when needed. 

• Healthcare and Operating Context: Understands how clinical teams operate in a regulated healthcare enterprise. 

• Responsible Use: Treats privacy, fairness, oversight, and accountability as part of the strategy, not a later review. 

• Trusted Advisor: Earns trust through clarity and integrity. The recommendation holds when the answer is no. 

What Success Looks Like 

• Business leaders treat AI Strategy as a go-to thought leader partner on what is worth doing, not as a ticket-taker or a project coordinator. 

• A prioritized set of AI opportunities is well documented with clear articulation of a compelling problem, audience, value, approach, risk, owner, and a proceed, sequence, or stop recommendation. 

• Funded work in partner areas has passed shared framing and validation criteria before teams are assigned or funds are allocated. 

• Duplicative or redundant or low-value ideas are stopped or merged, and that outcome is counted as success. 

• Handoffs to AI Business Partners, Product Builders, and Technology are clean: the bet is clear, the owner is named, and the success measures are agreed. 

• Responsible-use questions are raised before investment, not after a pilot is already underway. 

• Return on invested capital compounds as high value problems are attacked with rigor, measured, and investment decisions are made with discipline.