AI Business Analyst

Gallagher•Rolling Meadows, IL

49d

About The Position

The AI Business Analyst connects Gallagher’s business units with the engineers, architects, and data engineers developing AI agents and pipelines. Reporting to the Director of AI Technology, this role supports multiple concurrent AI initiatives—including RAG pipelines, AI agents, and software integrations—and continually prioritizes discovery and requirements work as the project portfolio shifts. The analyst frames ambiguous business problems before development begins, captures the business context and edge cases needed for AI retrieval sources and agent logic, and validates AI-generated requirements, process maps, and test coverage for accuracy and completeness. The role partners with AI engineers, architects, and business stakeholders throughout the project lifecycle in an environment where precision, traceability, and auditability are essential. The AI Business Analyst translates stakeholder needs into testable requirements, user stories, functional specifications, and acceptance criteria; documents current- and future-state workflows; owns UAT and validation; facilitates cross-team alignment; supports governance and traceability; and tracks solution performance against requirements and KPIs. Generative AI tools may be used to accelerate initial documentation, with the analyst responsible for critically evaluating the outputs.

Requirements

3–5 years of experience as a business analyst or in a similar role.
Demonstrated ability to turn 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 such as Copilot or Claude, with the judgment to critically evaluate their outputs.
Hands-on experience with process mapping and workflow documentation tools such as Visio, Lucidchart, or equivalent.
Experience owning UAT strategy and test design for technical systems, including edge cases and failure modes.
Experience working in Agile teams and managing backlogs, epics, and user stories in Azure DevOps or equivalent tools.
Track record managing multiple stakeholders and competing priorities across business and technical audiences.
Bachelor’s degree in business, information systems, or a related field, or equivalent experience.

Nice To Haves

Meaningful experience in insurance, financial services, or another regulated industry.
Experience supporting delivery of AI/ML systems such as RAG pipelines, conversational agents, or Copilot Studio-based solutions.
Understanding of how structured content, metadata, and document chunking affect retrieval quality and agent grounding.
Familiarity with Azure infrastructure and the role of AI within the Azure technology landscape.
Familiarity with Microsoft Copilot Studio, Power Platform, or similar low-code conversational AI tools.
Experience with insurance claims, underwriting, policy administration workflows, model risk management, or compliance documentation such as NAIC AI model governance.
IIBA certification (CBAP or CCBA), PMI-PBA, or equivalent business analysis credential.
Experience supporting more than one active initiative at a time without allowing any to stall.

Responsibilities

Serve as the primary business analyst for several concurrent AI initiatives, prioritizing and sequencing discovery and delivery work with the Director of AI Technology.
Translate ambiguous business requests into precise, testable requirements before AI agent or software development begins.
Use generative AI tools to accelerate first drafts while evaluating whether the resulting analysis is correct and complete.
Capture edge cases, exceptions, and regulatory context for RAG retrieval sources, agent prompts, and decision logic.
Document current- and future-state process flows and identify opportunities for AI and automation, including where human-in-the-loop checkpoints are required.
Translate validated requirements into user stories, functional specifications, and acceptance criteria for AI engineers and architects.
Define test cases and acceptance criteria and coordinate UAT for AI agents and Copilot experiences, including edge cases and failure modes.
Facilitate workshops and backlog grooming between business units and the AI delivery team, and align stakeholders when project priorities or interpretations conflict.
Maintain requirements traceability and documentation for AI compliance reviews, model risk assessments, and audits.
Monitor delivered AI solutions against requirements and success KPIs, and feed identified gaps back into project backlogs.