AI Change Analyst

Introduction Welcome to Gallagher - a global community of people who bring bold ideas, deep expertise, and a shared commitment to doing what’s right. We help clients navigate complexity with confidence by empowering businesses, communities, and individuals to thrive. At Gallagher, you’ll find more than a job; you’ll find a culture built on trust, driven by collaboration, and sustained by the belief that we’re better together. Whether you join us in a client-facing role or as part of our brokerage division, our benefits and HR consulting division, or our corporate team, you’ll have the opportunity to grow your career, make an impact, and be part of something bigger. Experience a workplace where you’re encouraged to be yourself, supported to succeed, and inspired to keep learning. That’s what it means to live The Gallagher Way. Overview The AI Change Analyst owns how change moves through AJ Gallagher's AI portfolio. Reporting to the Director of AI Technology, you will manage change requests across every AI project and production system the team runs - RAG pipelines, Azure AI Foundry and Copilot Studio agents, and M365 Copilot integrations - from intake and impact assessment through approval, scheduling, deployment, and post-implementation review. AI systems change in ways traditional applications don't: a model version is retired, a prompt is reworded, a knowledge source is re-indexed, an agent gains a new tool - and any of these can change what users see without a line of application code changing. You will make sure each of those changes is assessed, approved by the right people, and traceable, working with AI developers, engineering leadership, the AI Support Manager, and business stakeholders so that for any AI system in production the team can show what changed, who approved it, and why. You will also help build change control into the delivery tooling itself via Azure DevOps pipelines, environment approvals, and work item traceability so the governed path is also the fastest one. How you'll make an impact Own Change Request Intake and Lifecycle: Run intake, logging, and tracking for change requests across every AI project and production system - scope changes to in-flight initiatives as well as changes to live systems - ensuring each request states its scope, rationale, affected systems, test evidence, and rollback plan before it enters review, and following it through to closure. Assess Impact and Risk: Evaluate each change for its effect on users, downstream integrations, data access, cost, and compliance; classify it as standard, normal, or emergency and route it to the right approvers. For scope changes, quantify the effect on schedule, cost, and dependent projects, and work with the AI Business Analyst to keep requirements traceability current. Bring AI-Specific Changes Under Control: Treat model version upgrades and model retirements, prompt and agent instruction changes, Azure AI Search index and knowledge-source changes, and new agent tools or connectors as changes in their own right, and require evaluation results showing no regression in groundedness or accuracy before they are approved. Run the Change Process: Maintain the AI team's change policy, request templates, and catalog of pre-approved standard changes; facilitate the team's change review meetings, prepare the evidence approvers need, record decisions, and maintain the change calendar, including release windows and freeze periods. Coordinate Changes Across Projects: Identify conflicts and dependencies between changes planned by different initiatives - shared Azure AI Search indexes, shared Foundry resources, shared connectors and APIs - and sequence them so one team's release doesn't break another's. Build Change Control into Azure DevOps: Partner with the Senior AI Engineering Lead and AI developers to configure and maintain environment approvals and checks, branch policies, and pipeline templates so that production deployments require a linked, approved change and every change can be traced from request to work items, pull requests, pipeline runs, and sign-offs without manual reconstruction. Manage Emergency Changes and Post-Implementation Reviews: Coordinate expedited approvals with the AI Support Manager when an incident requires an urgent fix, and run post-implementation reviews on failed, rolled-back, and emergency changes, feeding what you learn back into the process. Liaise With Enterprise IT Change Management: Represent the AI team in Gallagher's enterprise IT change process, ensuring AI changes that touch shared infrastructure, identity, networking, or line-of-business systems are raised and approved through that channel as well. Report on Change Health: Produce recurring reporting for the Director of AI Technology on change volume, lead time, success and rollback rates, and emergency change frequency across the portfolio, and flag trends that signal delivery or quality risk. Keep the Change Record Audit-Ready: Maintain change records, approvals, and supporting evidence to the standard required for AI compliance reviews, model risk assessments, and internal audit, and keep the AI system inventory accurate as changes land. About You Experience: 3 to 5 years in IT change management, release management, or project change control, including hands-on management of change requests in a software delivery environment. Change Management Practice: Working knowledge of change enablement - risk classification, standard/normal/emergency changes, approval workflows, change calendars, rollback planning, and post-implementation review. Azure DevOps: Hands-on experience with Azure DevOps Boards and Pipelines (or an equivalent such as GitHub Actions or Jira with CI/CD) - work item queries and dashboards, linking work to code and deployments, and enough YAML fluency to read a pipeline and configure approvals and checks alongside engineers. Technical Fluency: Able to read a pull request, pipeline log, or release note well en