Vice President AI Transformation

The Vice President, AI Transformation Office (AI TO) leads the enterprise artificial intelligence and machine learning (AI/ML) transformation program and serves as the operational leader of the AI Transformation Office. The role executes the enterprise AI strategy on behalf of, and reporting to, the AI Steering Committee and establishes the central operating model for AI/ML governance, investment portfolio management, use-case oversight, regulatory readiness, responsible AI, and value realization.

This executive will translate enterprise priorities into a disciplined portfolio of AI/ML initiatives, including generative AI, predictive and classification models, forecasting, fraud detection, clinical prediction, risk stratification, and risk adjustment models. The role coordinates decisions across business, clinical, technology, finance, legal, compliance, enterprise risk, information security, sourcing, and regulatory stakeholders and ensures that approved investments produce measurable financial, operational, quality, member, and provider outcomes.

Essential Functions

AI Leadership

• Build, lead, and scale the AI Transformation Office team in alignment with the size and complexity of the enterprise AI investment portfolio.

• Recruit, develop, engage, and retain high-performing talent in AI governance, portfolio management, program delivery, value realization, risk coordination, and change management.

• Lead cross-functional teams and workgroups through influence, clear decision rights, disciplined accountability, and transparent communication.

• Create a culture that balances innovation and speed with responsible AI, regulatory compliance, financial stewardship, and measurable outcomes.

• Serve as a trusted advisor to executive leadership on AI opportunity, investment, risk, operating model, and workforce implications.

AI Portfolio, Funding and Prioritization

• Establish and manage the AI investment portfolio, including initiatives funded through an AI-specific funding envelope, while operating alongside and in coordination with the enterprise Capital planning governance and stage-gate process.

• Develop and maintain consistent criteria to evaluate strategic alignment, total cost, time to value, implementation readiness, reusability, scalability, member and provider impact, administrative savings, quality, risk adjustment, and regulatory risk.

• Oversee business-case development, funding recommendations, resource planning, investment sequencing, and portfolio performance.

• Ensure Transformation Office-approved initiatives continue through the organizational capital planning Stage Gate approvals, in cooperation with enterprise planning and governance partners.

Use-Case Intake and Governance

• Serve as the single enterprise intake point for relevant AI and machine learning use cases, including vendor and delegated entity solutions with embedded AI.

• Apply defined entry criteria, triage use cases, determine required governance, and scale oversight based on risk and regulatory disclosure or pre-approval requirements.

• Maintain an enterprise AI inventory as the source of truth for governance, monitoring, and regulatory reporting.

• Structure intake so every reported use case can be assessed for applicable internal governance, regulatory pre-approval, and disclosure requirements.

• Determine the internal approvals required for each AI/ML use case and manage the process for obtaining them.

• Coordinate required clearances across Sourcing, Legal, Compliance, Information Security, the IT Architecture Review Board, and Enterprise Risk Management as necessary.

• Integrate internal clearances with intake triage and external regulatory disclosure or pre-approval requirements.

Responsible AI, Risk and Regulatory Readiness

• Operate the process for assessing compliance, legal, information security, architecture, procurement, sourcing, and enterprise risk implications of AI use cases.

• Apply the AI-specific risk-management process based on use-case risk. Partner with Compliance and Enterprise Risk Management, which are accountable for risk-assessment criteria and procedures, and consult Legal and Information Security.

• Establish and maintain the process requiring business and clinical owners to submit and periodically update risk assessments, risk-management plans, and supporting documentation.

• Collect required use-case information, prepare draft disclosure and pre-approval submission documents, and provide them to the appropriate Compliance team for review and submission to the applicable regulator.

• Maintain the enterprise AI inventory, risk classifications, monitoring procedures, and auditable, defensible operating procedures aligned with enterprise policy.

AI Delivery Lifecycle and Execution Enablement

• Provide lifecycle governance, standards, and stage-gate oversight for internal AI builds and third-party AI solutions, defining what good looks like and confirming requirements are met.

• Partner with Information Technology, which is responsible for underlying engineering execution, including build, environments, data pipelines, CI/CD, DevOps, testing infrastructure, and production operations.

• Establish clear, non-duplicative accountabilities and handoffs across the Transformation Office, IT, business and clinical owners, Compliance, Legal, Enterprise Risk, Finance, and enterprise governance bodies.

• Promote scalable, reusable, secure, and responsible AI delivery practices across the enterprise.

Value Realization and Performance Management

• Own the methodology for measuring AI value and track realized benefits against approved commitments.

• Establish portfolio dashboards covering investment, delivery, adoption, risk, regulatory status, operating cost, and outcome performance.

• Monitor performance of deployed AI solutions and lead corrective action when results, controls, or adoption do not meet expectations.

• Partner with Finance and IT to define and support reporting for AI licenses, consumption costs, ongoing run costs, agent usage, and total cost of ownership.

Workforce Enablement and Change Management

• Drive enterprise AI literacy, education, adoption, and responsible-use practices.

• Lead change management for AI-enabled operating model and workflow changes.

• Partner with Human Resources and functional leaders on employee upskilling, AI literacy, productivity, workforce readiness, change management, and enterprise adoption.

• Bachelor's degree in business, healthcare administration, information systems, computer science, data and analytics, engineering, or a related field required.

• Master's degree in business administration, healthcare administration, technology, analytics, or a related discipline preferred.

• 12 or more years of progressive leadership experience in enterprise transformation, healthcare, operations, technology, portfolio management, strategy, consulting, or a related field.

• Demonstrated experience leading complex, cross-functional transformation programs and executive governance processes.

• Experience managing significant investment portfolios, business cases, budgets, value realization, and performance reporting.

• Experience operating in a highly regulated environment and partnering with Legal, Compliance, Risk, Information Security, Finance, and Technology.

• Demonstrated ability to communicate with executive leaders, boards, regulators, and diverse business and technical stakeholders.

• Executive-level strategic thinking with the ability to translate strategy into portfolio choices, operating plans, measurable outcomes, and clear accountabilities.

• Strong understanding of enterprise AI, risks, governance, delivery lifecycle, economics, and adoption requirements.

• Financial acumen, including investment prioritization, total cost of ownership, benefits measurement, budget management, and return-on-investment analysis.

• Knowledge of healthcare payer operations and the regulatory expectations applicable to AI-enabled decisions, reporting, transparency, and oversight.

• Exceptional stakeholder management, facilitation, written communication, executive presentation, negotiation, and influence skills.

• Ability to lead through ambiguity, establish new processes, resolve cross-functional issues, and maintain momentum in a rapidly evolving environment.

• Commitment to responsible AI, member and provider trust, data protection, information integrity, transparency, and human oversight.

• Leadership experience in artificial intelligence, machine learning, digital transformation, automation, advanced analytics, or emerging technology.

• Healthcare payer, managed care, Medicaid, Medicare, or other government-sponsored healthcare experience.

• Experience establishing responsible AI governance, model risk management, AI regulatory reporting, or enterprise AI portfolio management.

• Experience scaling new enterprise functions, operating models, centers of excellence, or transformation offices.