Position Responsibilities
1. Enterprise AI Governance and Responsible AI
• 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.
2. Regulatory Strategy, Compliance and Audit Readiness
• 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.
3. Cross-Enterprise Decision Leadership
• 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.
4. Governance Automation and Operational Excellence
• 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.
5. Enterprise Risk and Control Management
• 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.
6. AI Enablement, Adoption and Value Realization
• 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.
7. Executive Transparency and Governance Maturity
• 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.