VP, AI Operating Model and Execution

Chubb•Toronto, ON

About The Position

This role sits at the intersection of design, enterprise AI strategy, and has the mandate to scale adoption across the enterprise. Chubb is building AI infrastructure that will fundamentally change how work gets done. We are moving toward a future where employees can use AI tools and direct, supervise, and collaborate with AI agents in an effective, nimble way. The VP, AI Operating Model and Execution will serve as the connector among users, technologists, and senior leaders across Global Data & Analytics, advancing the vision, building early examples, and driving adoption of AI-native, agentic ways of working. This is not a theoretical or advisory role. You will design, build, deploy, and iterate, demonstrating what’s possible and collaborate with other teams to scale solutions across the enterprise. Our credibility and product insight depend on being exemplary AI users ourselves. This role exists to break through institutional inertia, challenge “the way we have always done it” and make our AI-team the most AI-augmented team at Chubb.

Requirements

Design and operationalize an AI operating model, defining which work is retained by employees or delegated to agents with human-above-the-loop workflows
Partner with Data & Analytics leadership to sequence the rollout, phasing where and when agents are introduced, what capabilities they require, and how human roles evolve alongside them
Help shape the agent ecosystem: agent roles, orchestration logic, tool/data access, and how agents connect and work together across the broader AI platform
Establish a framework and governance model for tracking human-to-agent work ratios across teams as the operating model scales
Define guardrails, escalation paths, and human oversight checkpoints for agent-driven decisions and actions
Lead change management for teams transitioning to a blended human-agent model, ensuring employees are equipped to supervise, direct, and collaborate with agents
Identify where AI can materially improve how people work, prioritizing impact over experimentation
Translate emerging AI capabilities into practical, human-centered solutions that deliver measurable outcomes
Partner across product, engineering, legal, compliance, and business teams to ensure responsible rollout
Implement feedback loops and metrics to track adoption, trust, and productivity impact
Navigate data governance, privacy, and security requirements in a regulated environment without stalling progress
Codify patterns, playbooks, and standards that can extend beyond Data & Analytics to the broader enterprise
Lead working sessions, labs, and workshops to educate, energize, and mobilize teams
Address cultural barriers preventing adoption e.g., “I am faster doing it myself” mindset or simply workflow inertia
Define intuitive interaction models for AI tools and agents across Data & Analytics
Design and refine end-to-end human–AI journeys, including feedback and learning loops
Conduct deep user discovery and usability testing to surface real friction points as AI tools and agents are introduced
Balance automation with transparency, explainability, and user control to build trust
Establish and evangelize clear design principles for responsible, human-first AI

Responsibilities

Design and operationalize an AI operating model, defining which work is retained by employees or delegated to agents with human-above-the-loop workflows
Partner with Data & Analytics leadership to sequence the rollout, phasing where and when agents are introduced, what capabilities they require, and how human roles evolve alongside them
Help shape the agent ecosystem: agent roles, orchestration logic, tool/data access, and how agents connect and work together across the broader AI platform
Establish a framework and governance model for tracking human-to-agent work ratios across teams as the operating model scales
Define guardrails, escalation paths, and human oversight checkpoints for agent-driven decisions and actions
Lead change management for teams transitioning to a blended human-agent model, ensuring employees are equipped to supervise, direct, and collaborate with agents
Identify where AI can materially improve how people work, prioritizing impact over experimentation
Translate emerging AI capabilities into practical, human-centered solutions that deliver measurable outcomes
Partner across product, engineering, legal, compliance, and business teams to ensure responsible rollout
Implement feedback loops and metrics to track adoption, trust, and productivity impact
Navigate data governance, privacy, and security requirements in a regulated environment without stalling progress
Codify patterns, playbooks, and standards that can extend beyond Data & Analytics to the broader enterprise
Lead working sessions, labs, and workshops to educate, energize, and mobilize teams
Address cultural barriers preventing adoption e.g., “I am faster doing it myself” mindset or simply workflow inertia
Define intuitive interaction models for AI tools and agents across Data & Analytics
Design and refine end-to-end human–AI journeys, including feedback and learning loops
Conduct deep user discovery and usability testing to surface real friction points as AI tools and agents are introduced
Balance automation with transparency, explainability, and user control to build trust
Establish and evangelize clear design principles for responsible, human-first AI