Enterprise AI Governance & Value Lead - Hybrid (Acton, MA)

Insulet Corporation•Acton, MA

12h•Hybrid

About The Position

The Enterprise AI Governance & Value Lead will lead the governance, oversight, and value realization of enterprise AI products and capabilities across Insulet. As part of the Enterprise AI and Data organization, this role will establish operating mechanisms that help AI scale responsibly and economically, with clear accountability. The role translates enterprise policies and principles into practical controls throughout the AI lifecycle and connects governance, risk, economics, and value realization. The lead will create an enterprise AI control system that provides visibility into AI use cases, models, agents, assets, and deployments; calibrates governance to risk; enables lower-risk innovation to move faster; and ensures AI investments deliver measurable, sustained business value. The position requires strategic and operational leadership, and collaboration across business, technology, legal, risk, finance, regulatory, and other functions. The role is eligible for hybrid working arrangements and requires on-site work from an Insulet office.

Requirements

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.

Nice To Haves

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.
A pragmatic governance mindset that balances innovation velocity, business value, patient and user impact, regulatory expectations, and enterprise risk.

Responsibilities

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 intake, risk-tiering, review, approval, release, monitoring, recertification, change-control, incident, and retirement processes for AI products and capabilities.
Establish governance for AI agents, including authorized purpose, identity, tool and data access, autonomy thresholds, human approvals, runtime monitoring, audit trails, escalation, and termination controls.
Coordinate reviews with Legal, Privacy, Security, Quality, Regulatory, Clinical, Internal Audit, Data Governance, and Technology Risk, maintaining clear accountability.
Define evidence standards for fairness, safety, transparency, explainability, privacy, security, robustness, output validation, source traceability, human oversight, and ongoing monitoring.
Establish mechanisms to identify, assess, escalate, remediate, and report AI risks, control exceptions, incidents, and conditions of approval.
Establish value-led portfolio governance linking 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 decisions to scale, pivot, pause, or retire AI investments.
Define AI economics and FinOps standards for model selection, token and compute consumption, unit cost, cost allocation, vendor usage, capacity planning, and optimization.
Develop portfolio scorecards showing value, adoption, risk, control status, cost, performance, reuse, and lifecycle health.
Partner with AI Platform Engineering and product teams to embed policy-as-code, automated evidence capture, continuous control monitoring, approval workflows, audit logging, and compliance telemetry into enterprise platforms.
Advance continuous assurance through automation that monitors model behavior, agent activity, data access, control performance, consumption, and material changes.
Create approved patterns, playbooks, assessment templates, control libraries, training, and self-service guidance to help teams meet governance requirements by design.
Establish certification and lifecycle standards for reusable prompts, agents, workflows, models, connectors, and other AI assets before they are shared or scaled.
Lead governance readiness, internal reviews, audit support, issue remediation, and executive reporting for the enterprise AI portfolio.
Build AI governance fluency across business sponsors, product managers, engineers, data scientists, risk partners, and functional AI leaders.