AI Governance Program Manager

Career Guide
An AI Governance Program Manager builds and runs the policies, reviews, and operating routines that help an organization use AI safely, legally, and responsibly. The role connects business teams, technical teams, legal, security, and risk to reduce harm, improve transparency, and support compliant AI delivery at scale.

Key Responsibilities

  • Define AI governance goals, scope, and success metrics
  • Create and maintain AI policies, standards, and guidelines
  • Set up an AI intake process for new use cases
  • Run risk assessments for AI systems across their lifecycle
  • Coordinate model review and approval workflows
  • Partner with legal and privacy teams on regulatory requirements
  • Work with security teams on controls for data and model access
  • Establish documentation requirements for AI systems
  • Monitor compliance with internal policies and external rules
  • Track AI incidents and lead corrective action planning
  • Build training and awareness programs for teams using AI
  • Report governance status and risks to leadership
  • Manage a cross functional governance committee cadence
  • Maintain a roadmap for governance tooling and process improvements

Top Skills for Success

Program Management
Stakeholder Management
Risk Management
Policy Writing
Change Management
Communication
Regulatory Awareness
Privacy Fundamentals
Security Fundamentals
Vendor Risk Management
AI Lifecycle Knowledge
Model Risk Assessment
Data Governance
Responsible AI Principles
Model Documentation
Audit Readiness
Control Design
Incident Management

Career Progression

Can Lead To
AI Governance Lead
Responsible AI Lead
AI Risk Manager
Model Risk Management Lead
AI Compliance Manager
AI Program Director
Transition Opportunities
Product Operations Manager
Privacy Program Manager
Security Program Manager
Enterprise Risk Manager
Trust and Safety Manager
Technical Program Manager

Common Skill Gaps

Often Missing Skills
Hands on AI risk assessment experienceModel documentation ownershipGovernance metrics and reporting designAI incident response practiceData lineage knowledgeThird party AI evaluation experiencePolicy to control translation
Development SuggestionsBuild experience by leading a pilot governance workflow for a small set of AI use cases, creating a reusable risk assessment template, and producing an executive dashboard that tracks approvals, exceptions, and incidents. Pair this with a strong understanding of privacy, security, and documentation expectations.

Salary & Demand

Median Salary Range
Entry LevelUSD 105,000 to 135,000
Mid LevelUSD 135,000 to 175,000
Senior LevelUSD 175,000 to 230,000
Growth Trend
Strong growth. Hiring demand is increasing as AI adoption expands and new regulations raise expectations for oversight, documentation, and risk controls.

Companies Hiring

Major Employers
MicrosoftGoogleAmazonMetaAppleSalesforceIBMAccentureDeloittePwCJPMorgan ChaseBank of AmericaWells FargoUnitedHealth GroupCVS HealthPfizerSiemensSAPOracleServiceNow
Industry Sectors
TechnologyFinancial ServicesHealthcareInsuranceRetailManufacturingTelecommunicationsProfessional ServicesGovernment

Recommended Next Steps

1
Inventory current AI use cases and create a centralized register
2
Draft a lightweight AI policy and a set of practical standards
3
Implement an intake and approval workflow with clear owners
4
Create a repeatable AI risk assessment process and checklist
5
Define documentation requirements and a review cadence
6
Set up governance metrics and a monthly reporting rhythm
7
Run training sessions for product and engineering teams
8
Partner with legal, privacy, and security to align controls
9
Pilot an incident process for AI related issues and escalation
10
Identify tooling needs for tracking, evidence, and audits