Staff Vice President (VP) Enterprise AI Governance, Risk & Operational Excellence

Elevance Health•Indianapolis, IN

8d•$206,080 - $370,944•Hybrid

About The Position

The Staff Vice President, Enterprise AI Governance, Risk & Operational Excellence is the enterprise executive responsible for transforming technology and AI governance into a disciplined, responsive, and scalable operating capability. This leader will establish the mechanisms that allow Elevance Health to innovate with speed while maintaining trust, transparency, regulatory compliance, and clear accountability. This executive will connect Legal, Compliance, Risk, Security, Data, Technology, and the Business to resolve complex issues quickly, translate requirements into practical controls, and embed governance directly into enterprise workflows. The role combines strategic judgment with hands-on operational leadership across Responsible AI, enterprise governance, regulatory readiness, process automation, workforce enablement, and value realization.

Requirements

Requires an BA/BS degree in Information Technology, Computer Science or related field of study and a minimum of 12 years of experience leading enterprise digital and/or product development business transformations work including large programs and multi-disciplinary teams partnerships such as leading technology teams and/or cross functional project (technology and operations) teams, significant large-scale project management; or any combination of education and experience, which would provide an equivalent background.
Health insurance industry experience strongly preferred.

Nice To Haves

Experience with healthcare operations, health data, clinical or administrative AI use cases, and healthcare regulatory expectations.
Experience governing third-party AI products, cloud services, foundation models, vendors, and externally sourced data.
Understanding of model risk management, data governance, cybersecurity, privacy engineering, product development, and software delivery practices.
Experience with governance, risk and compliance platforms, workflow automation, control monitoring, analytics, and executive dashboards.
Graduate degree in business, technology, risk, law, public policy, data science, or a related field. Relevant certifications may complement, but do not replace, demonstrated operating experience.

Responsibilities

Own and continuously advance the enterprise operating model for Responsible AI, including decision rights, risk-tiering, review pathways, escalation mechanisms, human oversight, and ongoing monitoring.
Translate Responsible AI principles into enforceable standards, repeatable controls, evidence requirements, and practical guidance for technology and business teams.
Establish governance across the AI lifecycle, from intake and use-case assessment through development, deployment, monitoring, change management, and retirement.
Ensure AI governance addresses traditional, generative, agentic, and emerging AI capabilities without creating unnecessary barriers to responsible innovation.
Anticipate, assess, and operationalize evolving federal and state requirements affecting AI, data, privacy, technology, and healthcare operations.
Partner with Legal, Compliance, Privacy, Risk, Government Affairs, and Internal Audit to convert regulatory interpretation into clear enterprise requirements, controls, ownership, and evidence.
Maintain a traceable view of obligations, policy decisions, risk acceptances, exceptions, control performance, and remediation actions.
Prepare the organization for regulatory inquiries, audits, examinations, and executive or board-level oversight with reliable documentation and transparent reporting.
Chair or lead decision forums that bring together Legal, Compliance, Risk, Security, Data, Technology, Operations, and Business leaders to resolve issues at the right level and pace.
Create clear decision rights, escalation paths, service-level commitments, and accountability so issues do not stall across organizational boundaries.
Frame complex tradeoffs in business terms, clarify residual risk, and drive documented decisions that leaders can execute.
Build trusted relationships while maintaining the independence and judgment required to challenge plans that create unacceptable enterprise risk.
Redesign fragmented, manual, and duplicative governance processes into integrated, technology-enabled workflows.
Use workflow automation, intelligent routing, reusable controls, evidence capture, dashboards, and AI-enabled decision support to improve speed, consistency, transparency, and user experience.
Establish baseline performance and measurable improvement targets for review cycle time, aging, rework, exception volume, control effectiveness, and stakeholder experience.
Eliminate low-value activity, simplify handoffs, and embed governance into product, engineering, procurement, data, and operational delivery processes.
Integrate AI and technology governance with enterprise risk management, compliance, privacy, cybersecurity, data governance, model risk, third-party risk, and business continuity practices.
Define risk taxonomy, appetite, thresholds, control objectives, issue-management practices, and escalation criteria appropriate to AI and emerging technology.
Ensure controls are risk-based, testable, auditable, and proportionate to the materiality and intended use of each capability.
Create closed-loop mechanisms to identify systemic issues, prioritize remediation, monitor residual risk, and prevent recurrence.
Enable associates and leaders to use AI safely and effectively through role-based guidance, education, access controls, communications, and embedded support.
Partner with product and business leaders to ensure governance enables adoption rather than operating as a separate compliance layer.
Define and report measures that connect governance to adoption, productivity, quality, risk reduction, member impact, and enterprise value.
Promote a culture in which responsible innovation, accountability, and measurable outcomes reinforce one another.
Provide concise, decision-oriented reporting to senior executives and governance bodies on adoption, risk exposure, compliance, control performance, unresolved decisions, and value realization.
Establish an enterprise governance maturity roadmap with clear priorities, milestones, ownership, and outcome measures.
Maintain an integrated policy, standard, procedure, and control library that is current, usable, and consistently applied.
Build a high-performing team of governance, risk, operations, automation, and Responsible AI practitioners with clear accountability and strong business orientation.