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

Job Summary

How do you scale AI across an enterprise while ensuring it remains responsible, accountable, and delivers measurable business value?

At Insulet, we’re looking for an Enterprise AI Governance & Value Lead to help answer that question and shape how AI scales across the organization.

This is a high-impact opportunity to establish the governance, oversight, and value framework behind enterprise AI, creating the mechanisms that allow teams to innovate with speed while managing risk, accountability, investment, and business value.

You’ll help build an enterprise approach that:

🔹 Creates visibility and accountability across AI use cases, models, agents, assets, and deployments
🔹 Translates Responsible AI principles into practical, scalable controls
🔹 Applies the right level of governance based on risk
🔹 Enables lower-risk AI innovation to move faster
🔹 Ensures AI investments are tied to measurable and sustainable business outcomes
🔹 Brings together governance, risk, economics, and value realization across the AI lifecycle

We’re looking for a forward-thinking leader who can operate equally well at the strategic and operational levels, collaborate across the enterprise, and help create the foundation for AI to scale responsibly and deliver lasting value.

If you’re passionate about shaping how enterprises govern, scale, and realize value from AI, this is an opportunity to make a significant impact.



The Enterprise AI Governance & Value Lead will lead the governance, oversight, and value realization of enterprise AI products and capabilities across Insulet. This role will establish operating mechanisms that enable AI to scale responsibly, economically, and with clear accountability, while translating enterprise policies and principles into practical controls embedded throughout the AI lifecycle.

As part of the Enterprise AI and Data organization, this role requires expertise in responsible AI, AI risk management, portfolio governance, value realization, AI economics, regulatory and control environments, and cross-functional operating models.

The ideal candidate will be a forward-looking leader who can operate at both strategic and operational levels. This leader will be accountable for creating an enterprise AI control system that provides visibility into every AI use case, model, agent, asset, and deployment; calibrates governance to risk; accelerates lower-risk innovation; and ensures AI investments deliver measurable and sustained business value.

Responsibilities

AI Registry, Risk, and Lifecycle Governance

• Own the enterprise AI Registry as the authoritative record of AI use cases, business 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.
• Partner with Legal, Privacy, Security, Quality, Regulatory, Clinical, Internal Audit, Data Governance, and Technology Risk to coordinate reviews and maintain 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.

AI Portfolio Governance, Economics, and Value Realization

• Establish a value-led governance process that connects AI portfolio 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, token and computer consumption, unit cost, cost allocation, vendor usage, capacity planning, and optimization.
• Develop portfolio scorecards that provide leadership visibility into value, adoption, risk, control status, cost, performance, reuse, and lifecycle health.

Governance Automation, Assurance, and Enterprise Enablement

• 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 governance from periodic reviews toward continuous assurance by using automation to monitor model behavior, agent activity, data access, control performance, consumption, and material changes.

…