AI Business Analyst

Gallagher•Rolling Meadows, IL

About The Position

The AI Business Analyst is the link between AJ Gallagher's business units and the engineers, architects, and data engineers building the company's AI agents and pipelines. Reporting directly to the Director of AI Technology, you will serve as the dedicated business analyst across multiple concurrent AI initiatives at once rather than sitting inside a single delivery team, which means continually re-prioritizing discovery and requirements work as the portfolio shifts. Generative AI tools now draft much of the first-pass documentation, so this role exists to do what those tools can't: frame ambiguous business problems before a line of code or a prompt gets written, own the business context and edge cases that must be captured accurately in AI retrieval sources and agent logic, and critically validate that AI-generated requirements, process maps, and test coverage are complete and correct. You will partner closely with AI engineers, architects, and business stakeholders across each project's full lifecycle — not just at requirements handoff — in an environment where precision, traceability, and auditability are non-negotiable.

Requirements

3–5 years as a business analyst or similar role, ideally with meaningful time in insurance, financial services, or another regulated industry.
Demonstrated ability to translate ambiguous business needs into clear, testable requirements, user stories, and acceptance criteria that hold up under technical review.
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.
Hands-on experience with process mapping and workflow documentation tools (Visio, Lucidchart, or equivalent).
Experience owning UAT strategy and test design for technical systems, including test cases for edge cases and failure modes.
Experience working within Agile teams and managing backlogs, epics, and user stories in Azure DevOps or equivalent tooling.
Track record managing multiple stakeholders and competing priorities at once, across both business and technical audiences.
Bachelor's degree in business, information systems, or a related field, or equivalent experience.

Nice To Haves

Prior experience supporting delivery of AI/ML systems — RAG pipelines, conversational agents, or Copilot Studio-based solutions.
Understanding of how structured content, metadata, and document chunking affect retrieval quality and agent grounding.
The development team utilizes Azure technology. Understanding resources available and where AI fits in this landscape is critical.
Familiarity with Microsoft Copilot Studio, Power Platform, or similar low-code conversational AI tooling.
Experience with insurance claims, underwriting, or policy administration workflows, or with model risk management and compliance documentation (e.g., NAIC AI model governance).
IIBA certification (CBAP, CCBA), PMI-PBA, or equivalent business analysis credential.
Experience supporting more than one active initiative at a time without letting any of them stall.

Responsibilities

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.
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.
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.
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.
Translate validated requirements into user stories, functional specs, and acceptance criteria that AI engineers and architects can build directly against, reducing rework during sprints.
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.
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.
Maintain requirements traceability and documentation that supports AI compliance reviews, model risk assessments, and audit needs across your project portfolio.
Monitor delivered AI solutions against the requirements and KPIs that defined success, and feed gaps back into the backlog for the relevant project team.