VP, AI Operation Model and Execution

VP, AI Operating Model and Execution

Data & Analytics | Shared Services

Purpose: 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.

Overview

AI Operating Model
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

AI Strategy, Adoption & Scaling
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

AI Experience Design
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

Skills & Attributes Critical for Success
Deeply conversant in AI, with the discernment to know which automation lever fits which problem, understands multi-agent orchestration, tool use, and autonomy levels, and recognizes when a simpler approach is the better fit
Fluent in agent architecture and orchestration, understands how autonomous and semi-autonomous agents are built, deployed, and governed
A builder and change leader who modernizes how organizations work, not just by shipping tools, but by reshaping operating models
Comfortable leading in ambiguity, influencing without authority, and operating in large, complex enterprises
A compelling communicator who can earn executive sponsorship and bring skeptical stakeholders along
Biased toward action: you ship early, learn fast, and iterate continuously
Experienced in consulting, product strategy, platform, or process/people change leadership roles where adoption mattered as much as delivery

Background & Experience
10+ years in technology, data, AI, product, or change leadership roles
Proven track record leading cross-functional initiatives in large enterprises; regulated industry experience preferred
Experience building or deploying AI products, knowledge platforms, or enterprise productivity tools
Experience redesigning team operating models and workflows to incorporate new ways of working
Preferred candidates will have experience with AI agent workflows and defining human oversight roles
Demonstrated ability to lead diverse technical teams and drive human adoption of complex systems
Intellectual curiosity and business judgment prioritized over specific degrees
Bachelor’s or advanced degree in Human Computer Interaction, Cognitive Science, Computer Science, AI/ML, or related field preferred
Portfolio, writing, or examples that show how you think about human–AI collaboration
Deep practitioner-level fluency with LLMs and AI tools – you should be a power user yourself
Strong grounding in user research, prototyping, and iterative delivery
Working knowledge of AI/ML concepts, design thinking, and change management; familiarity with AI ethics

Why This Role Matters
Insurance runs on knowledge (judgment, experience, interpretation, and history). Today, much of that knowledge is fragmented across people, emails, and documents. AI can unlock it, but only if it is designed for users at the center. You will help define the tools and experiences that drive how work evolves at Chubb, an exciting “pace car of the possible”.

Chubb Canada does not use artificial intelligence (AI) tools to assess, screen, or select applican