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

Location: Acton, Massachusetts

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.
• Create approved patterns, playbooks, assessment templates, control libraries, training, and self-service guidance that 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.
• Required Skills and Competencies
• 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.

Preferred Skills and Competencies

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.

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undefinedBachelor’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.