AI Business Analyst

Gallagher is a global company that helps businesses, communities, and individuals navigate complexity through brokerage, benefits, HR consulting, and corporate services. The AI Business Analyst translates ambiguous business needs into precise requirements for multiple concurrent AI initiatives, coordinates stakeholders and user acceptance testing, and supports governance, traceability, and solution performance validation.

Responsibilities:

• Own Requirements Across a Multi-Project Portfolio: Serve as the primary business analyst for several concurrent AI initiatives — RAG pipelines, AI agents, software integration — working directly with the Director of AI Technology to prioritize and sequence discovery and delivery work as project needs shift
• Frame Ambiguous Business Problems: Translate ambiguous requests from business stakeholders into precise, testable requirements before AI agent or software development begins, using generative AI tools to accelerate first drafts while owning the judgment on what's actually correct and complete
• Capture the Business Context AI Systems Need: Own the edge cases, exceptions, and regulatory nuance that must be encoded into RAG retrieval sources, agent prompts, and decision logic, structuring that content so it's usable by the systems consuming it, not just readable by a person
• Map Business Processes: Document current- and future-state process flows (Lucidchart or Mermaid diagrams) to identify where AI and automation create real value versus where a human-in-the-loop checkpoint is still required
• Write Specs Engineers Can Build From: Translate validated requirements into user stories, functional specs, and acceptance criteria that AI engineers and architects can build directly against, reducing rework during sprints
• Own UAT and Validation: Define test cases and acceptance criteria and coordinate user acceptance testing for AI agents and Copilot experiences, with particular attention to the edge cases and failure modes that AI-generated test coverage tends to miss
• Facilitate Cross-Team Alignment: Run workshops and backlog grooming between business units and the AI delivery team, and act as the point of alignment when priorities or interpretations conflict across the projects you support
• Support Governance and Traceability: Maintain requirements traceability and documentation that supports AI compliance reviews, model risk assessments, and audit needs across your project portfolio
• Track Solution Performance: Monitor delivered AI solutions against the requirements and KPIs that defined success, and feed gaps back into the backlog for the relevant project team

Requirements:

• • Experience: 3–5 years as a business analyst or similar role, ideally with meaningful time in insurance, financial services, or another regulated industry
• • Requirements Writing: Demonstrated ability to translate ambiguous business needs into clear, testable requirements, user stories, and acceptance criteria that hold up under technical review
• • Generative AI Fluency: Hands-on use of generative AI tools (e.g., Copilot, Claude, or similar) to accelerate analysis artifacts, paired with the judgment to critically evaluate their output
• • Process Documentation: Hands-on experience with process mapping and workflow documentation tools (Visio, Lucidchart, or equivalent)
• • UAT Ownership: Experience owning UAT strategy and test design for technical systems, including test cases for edge cases and failure modes
• • Agile Delivery: Experience working within Agile teams and managing backlogs, epics, and user stories in Azure DevOps or equivalent tooling
• • Multi-Stakeholder Management: Track record managing multiple stakeholders and competing priorities at once, across both business and technical audiences
• • Education: Bachelor's degree in business, information systems, or a related field, or equivalent experience
• • Analytical Rigor: Breaks ambiguous, cross-functional problems into precise, testable requirements
• • Healthy Skepticism: Applies sound judgment when evaluating AI-generated outputs rather than taking them at face value
• • Prioritization Under Pressure: Manages shifting priorities across multiple concurrent AI projects without losing track of any one of them
• • Cross-Functional Influence: Builds trust across business units and technical teams, and reconciles competing priorities without escalation fatigue
• • Comfort in a Regulated Environment: Operates naturally in an audit-sensitive setting where traceability matters as much as speed
• • Clear Communication: Writes and speaks in a way that both business stakeholders and engineers can act on without follow-up questions
• • AI/ML Project Exposure: Prior experience supporting delivery of AI/ML systems — RAG pipelines, conversational agents, or Copilot Studio-based solutions
• • RAG/Knowledge Base Familiarity: Understanding of how structured content, metadata, and document chunking affect retrieval quality and agent grounding
• • Azure infrastructure familiarity: The development team utilizes Azure technology. Understanding resources available and where AI fits in this landscape is critical
• • Copilot/Power Platform Exposure: Familiarity with Microsoft Copilot Studio, Power Platform, or similar low-code conversational AI tooling
• • Regulated Industry Background: Experience with insurance claims, underwriting, or policy administration workflows, or with model risk management and compliance documentation (e.g., NAIC AI model governance)
• • Certification: IIBA certification (CBAP, CCBA), PMI-PBA, or equivalent business analysis credential
• • Portfolio Juggling: Experience supporting more than one active initiative at a time without letting any of them stall