AI Governance Lead

Role Description

We are seeking an experienced governance leader to help regulated-industry clients define, refine and/or execute policy and accountability frameworks that govern how AI is used inside the enterprise. This role needs to determine how to establish policy and gain commitment on core AI related decisions such as:

• What the enterprise is willing to delegate to AI
• Who is accountable when AI acts
• What standards AI implementations must meet
• How governance tenets support the safe adoption of AI

AI Governance as a discipline is still emerging, therefore we are looking for a seasoned practitioner that has deep experience in adjacent categories - enterprise data governance, corporate governance, regulated-industry compliance, or similar. The Lead will work as part of a unified AI and Data Governance framework, partnering closely with a client’s existing Data Governance function and with a Data & AI Architect who owns the technical environment that implements components of the Governance Framework.

This is a high-visibility position embedded with VP and Director level stakeholders. The role is often responsible for leading the FSFP engagement team as well as the project itself.

Key Responsibilities

• AI Governance:
• Policy Authority for AI: Define what AI the enterprise will and will not use, on what basis, and under what conditions. Includes prohibited uses, permitted uses, and the criteria that distinguish them. Author policies where needed.
• Decision Rights for AI: Define what decisions the enterprise is willing to delegate to AI, where humans must remain in the loop, where humans review after the fact, and where AI can act autonomously. Establish how those lines are drawn, who has authority to redraw them and how they are tracked and measured.
• AI Use Case Intake and Risk Classification: Establish the intake process for AI use cases, including AI impact assessments and risk classification, and match the level of oversight to the level of risk.
• Accountability Framework: Establish who is accountable when AI acts, including model and use case owners in the business, independent risk and compliance review, and audit assurance. Map this to the client’s existing corporate accountability structures rather than building a parallel one.
• Third-Party AI Position: Define how the enterprise governs third-party and vendor AI, including procurement criteria, vendor assessment standards, contractual terms, and ongoing review.
• Collaboration and Stakeholder Management:
• Unified Operating Model with Data Governance: Build an integrated operating model with the client’s Data Governance function: one accountability structure, one place AI use cases get reviewed, one set of policies, and shared metadata. The goal is reinforcement, not a parallel program competing for the same stakeholders.
• Stakeholder Alignment: Serve as the primary governance advisor to VP- and Director-level stakeholders; translate strategic intent into actionable governance commitments. Manage alignment across data governance, AI engineering, legal, privacy, security, and business stakeholders, including senior leadership communication and the operation of governance forums.
• Engagement Leadership: Lead the engagement team and own both the engagement roadmap and engagement deliverables from development to adoption. Own engagement planning, status, risks, and deliverables. Partner with the Data & AI Architect and the client’s teams to keep policy and technical tracks in sync.

Qualifications

• 8 to 12 years of progressive experience in enterprise data governance, model risk management, corporate governance, AI/ML lifecycle oversight, or a closely related governance, risk, or compliance discipline, with engagement or program leadership scope.
• Demonstrated experience setting policy and accountability frameworks at enterprise scale, not only operating frameworks set by others.
• Working knowledge of how authority, accountability, and decision rights are structured in large enterprises.
• Experience defining oversight structures with first-line ownership in the business, independent risk and compliance review, and audit assurance.
• Track record managing a cross-functional mix of stakeholders; governance, business, technical and functional.
• Strong executive communication. Able to engage senior stakeholders, frame trade-offs, and drive decisions.
• Comfort working embedded alongside an in-house client team, balancing facilitation with hands-on delivery.
• Bachelor’s degree in a relevant field (Information Management, Risk, Law, Business, Public Policy, Philosophy, or similar), or equivalent professional experience.

Preferred Qualifications

• Experience in regulated industries such as agriculture, life sciences, pharmaceuticals, financial services, or healthcare.
• Direct experience building AI Governance practices, even at smaller scale or as an internal program inside a single enterprise.
• Hands-on experience with the EU AI Act, ideally having translated obligations into enterprise policy rather than only interpreting them.
• Working knowledge of NIST AI Risk Management Framework and ISO/IEC 42001, including how they map to internal policy.
• Familiarity with generative and agentic AI risks (hallucination, prompt injection, data leakage, IP exposure, autonomous decision oversight) and how enterprises are setting policy on them.
• Experience operating or facilitating a Data Management Committee, AI Governance Committee, or equivalent enterprise governance forum.

What Success Looks Like

• Clear AI accountability in place. Named model and use case owners in the business, defined independent risk and compliance review, and audit assurance, all mapped to existing corporate accountability structures.
• A unified AI and Data Governance operating model adopted by the client, with shared accountability, intake, policies, and metadata between the two functions.
• Coordinated operation of the Data Management Committee and AI Governance Committee, with cross-cutting issues handled jointly rather than falling between the two.
• Live AI use case intake and risk classification applied to both predictive and generative AI use cases.
• Defined enterprise position on generative AI and agentic risks, on third-party AI, and on the conditions under which the enterprise accepts each.
• A documented position on the EU AI Act, NIST AI RMF, and ISO/IEC 42001 that the client’s team can defend and maintain.
• Operating documentation and capabilities the client’s team can carry forward after the engagement closes.

Benefits

• Medical, dental and vision insurance
• 401(K) savings plan
• Disability coverage
• Life and accident coverage
• Vacation time and paid holidays