Enterprise AI Governance, Value Lead

Insulet Corporation

. Lead governance, oversight, and value realization of enterprise AI products and capabilities across Insulet .

Posted 11/4/2026full-timeActon • Massachusetts • United StatesSenior💰 $197,900 - $296,825 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in enterprise AI governance frameworks, risk management, and lifecycle processes, with a strong focus on value realization and compliance in regulated industries. Proficient in integrating AI principles into product development and operational mechanisms while ensuring stakeholder alignment and effective communication.

Highest-signal resume keywords

AI Governance FrameworksGenerative AIFinOps PracticesRisk-Based ControlsHealthcare Industry Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Product Lifecycle ManagementModel Risk ManagementData GovernancePortfolio GovernanceControl AutomationCloud Cost ManagementPredictive ModelsAI RegistriesCompliance StandardsEvidence Standards

Soft Skills

Influencing StakeholdersCross-Functional CollaborationPragmatic Governance Mindset

Tools & Technologies

Governance Workflow PlatformsModel Monitoring ToolsProduct AnalyticsPortfolio Management ToolsCloud Platforms

Industry Keywords

HealthcareMedical DevicesLife SciencesDigital HealthRegulated Industries

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Lead governance, oversight, and value realization of enterprise AI products and capabilities across Insulet
Own the enterprise AI Registry as the authoritative record of AI use cases, owners, models, agents, prompts, knowledge sources, connectors, vendors, controls, approvals, deployments, and lifecycle status
Define and operate AI intake, risk tiering, review, approval, release, monitoring, recertification, change-control, incident, and retirement processes
Establish governance for AI agents, including purpose, identity, access, autonomy thresholds, human approvals, monitoring, audit trails, escalation, and termination controls
Coordinate reviews with Legal, Privacy, Security, Quality, Regulatory, Clinical, Internal Audit, Data Governance, and Technology Risk
Define evidence standards for fairness, safety, transparency, explainability, privacy, security, robustness, output validation, source traceability, human oversight, and monitoring
Identify, assess, escalate, remediate, and report AI risks, control exceptions, incidents, and approval conditions
Establish value-led AI portfolio governance connecting intake, prioritization, funding, stage gates, adoption, benefits realization, and lifecycle decisions
Partner with Finance and business owners to validate benefit cases, track realized value, identify value leakage, and inform scale, pivot, pause, or retirement decisions
Define AI economics and FinOps standards for model selection, consumption, unit cost, cost allocation, vendor usage, capacity planning, and optimization
Develop portfolio scorecards covering value, adoption, risk, controls, cost, performance, reuse, and lifecycle health
Embed policy-as-code, automated evidence capture, continuous control monitoring, approval workflows, audit logging, and compliance telemetry into enterprise platforms
Advance continuous assurance by monitoring model behavior, agent activity, data access, control performance, consumption, and material changes
Create approved patterns, playbooks, assessment templates, control libraries, training, and self-service governance guidance
Establish certification and lifecycle standards for reusable AI assets
Lead governance readiness, internal reviews, audit support, issue remediation, and executive reporting
Build AI governance fluency across business sponsors, product managers, engineers, data scientists, risk partners, and functional AI leaders

Requirements

What you’ll need

Demonstrated ability to design and operate enterprise governance frameworks, cross-functional decision forums, risk-based controls, and lifecycle management processes
Strong understanding of generative AI, machine learning, large language models, retrieval-augmented generation, AI agents, data products, cloud platforms, and AI product lifecycles
Experience establishing portfolio governance, value measurement, benefits realization, financial management, or FinOps practices for technology or AI investments
Demonstrated ability to influence senior business, technology, legal, risk, finance, and regulatory stakeholders and convert complex requirements into practical operating mechanisms
Bachelor’s or Master’s degree in business, computer science, engineering, data science, information systems, risk management, law, public policy, or a related field
10+ years of experience in AI governance, technology risk, responsible AI, data governance, model risk, product governance, compliance, enterprise transformation, or adjacent disciplines
Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or regulated industries
Experience governing enterprise AI products, generative AI, copilots, autonomous or semi-autonomous agents, predictive models, or intelligent workflow automation
Experience with AI registries, governance workflow platforms, model monitoring, control automation, cloud cost management, product analytics, or portfolio management tools
Familiarity with responsible AI principles and recognized AI risk, quality, privacy, security, and regulatory frameworks relevant to enterprise deployment
Experience integrating governance requirements into product development, software delivery, data platforms, cloud environments, and third-party technology management
Pragmatic governance mindset balancing innovation velocity, business value, patient and user impact, regulatory expectations, and enterprise risk

Benefits

Comp & perks

Incentive compensation eligibility
Medical, dental, and vision insurance
401(k) with company match
Paid time off (PTO)
Additional employee wellness programs