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Senior Business Analyst, AI

Wolters Kluwer · Chicago, IL · $85,600–$149,400 a year · Full time

Posted Sep 22, 2026 · We last checked this listing on Oct 6, 2026

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Likely interview questions for this role

Written from this job description, not a generic list. Each one notes what the interviewer is really checking.

About your past

Tell me about a time you owned a product after launch and kept improving it. What signals did you watch?

What they are checking: Whether they are suited to post-release ownership, which is the heart of this role.

Tell me about a time you had to say no to a stakeholder about a feature.

What they are checking: Whether they can act as the primary decision maker for a product and keep relationships intact.

What would you do

Users report that an agent gives a wrong answer sometimes but not always. How do you turn that into a backlog item?

What they are checking: Whether they can handle nondeterministic AI issues and capture them with enough examples to act on.

Three stakeholders each send you an urgent enhancement request for the same agent. How do you prioritize?

What they are checking: Whether they use a clear prioritization method and can explain it to people who don't win.

An agent update improves one task but makes another worse. What do you do?

What they are checking: Whether they think about regression testing and tradeoffs before shipping AI changes.

About the work

In plain terms, explain how retrieval-augmented generation works and why it affects what goes in your backlog.

What they are checking: Whether they understand generative AI well enough to manage an AI product backlog, as the posting requires.

How do you decide whether an agent issue needs a prompt change, new training content, or an engineering fix?

What they are checking: Whether they can diagnose the type of problem and route it to AI Trainers, enablement or engineering correctly.

What metrics would you use to tell whether a live agentic AI product is delivering business value?

What they are checking: Whether they go beyond usage counts to outcomes like resolution, accuracy and time saved.

How would you write acceptance criteria for an agent behavior change, given that its outputs vary?

What they are checking: Whether they can define success for AI behavior using test sets and thresholds rather than exact outputs.

How do you triage incoming bugs versus enhancements for an AI product?

What they are checking: Whether they have an orderly intake process that keeps the backlog clear and actionable.

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The full job description

As published by Wolters Kluwer.

BASIC FUNCTION The AI / Agentic Product Owner is responsible for post-release ownership and continuous optimization of agentic AI products. This role ensures that deployed AI solutions deliver ongoing business value through effective backlog management, prioritization, and cross-functional coordination. The Agentic Product Owner serves as the primary decision-maker for live AI products, translating feedback, performance signals, and business needs into prioritized work. This role partners closely with AI Enablement, AI Trainers, engineering, and business stakeholders to continuously refine agent behavior, improve outcomes, and scale AI capabilities. This role also requires a foundational understanding of generative AI concepts (e.g., LLMs, RAG, prompt behavior) to effectively manage AI-driven backlogs and product decisions. ESSENTIAL DUTIES AND RESPONSIBILITIES Own Post-Release Product Lifecycle - Own and manage the post-release product backlog for agentic AI solutions - Continuously evaluate product performance and identify improvement opportunities - Ensure AI products evolve in alignment with business objectives and user needs - Drive ongoing optimization of AI capabilities after launch Backlog Management and Prioritization - Intake and triage feedback, bugs, and enhancement requests from stakeholders - Define, prioritize, and maintain a clear, actionable backlog - Balance competing priorities across defect resolution, AI performance improvements, and new features - Lead release planning and prioritization decisions for live AI products - Translate post-release feedback, usage patterns, defect trends, and business needs into prioritized backlog items - Make backlog trade-off decisions across multiple live agentic AI products, balancing urgency, business value, user impact, and delivery capacity Cross-Functional Coordination - Act as the central point of coordination across business stakeholders, engineering and QA teams, AI Enablement, and AI Trainers - Translate post-release feedback, business needs, and operational improvement opportunities into clear product requirements, user stories, and acceptance criteria - Ensure alignment on priorities, timelines, and expected outcomes - Coordinate with business SMEs, content owners, AI Enablement, AI Trainers, engineering, and QA to ensure backlog items are well understood and actionable - Facilitate prioritization discussions where feedback, defects, content gaps, AI behavior concerns, and enhancement requests compete for delivery capacity AI Performance and Feedback Integration - Collaborate with AI Trainers and AI Enablement to incorporate AI behavior analysis and performance insights into backlog decisions - Incorporate feedback into backlog prioritization - Ensure improvements are driven by user feedback, usage patterns, and quality issues - Leverage AI Trainer and AI Enablement expertise for analysis of agent behavior, prompt sensitivity, grounding issues, response quality, and improvement opportunities - Ensure AI-related issues are appropriately categorized, such as product defect, content gap, prompt/behavior issue, user experience gap, or training/adoption need Product Decision Ownership - Serve as the primary decision-maker for product direction, prioritization, and trade-offs - Ensure clear ownership of product outcomes post-launch - Own decisions regarding what gets fixed, enhanced, deferred, or escalated for live AI products - Partner with business sponsors and stakeholders to ensure post-release product decisions remain aligned to business value, user impact, and roadmap priorities Support Scalable Operating Model - Enable a distributed ownership model with clear backlog accountability per AI product - Contribute to scalable practices for managing multiple concurrent AI products - Effectively manage a portfolio of multiple live agentic AI products (typically 2–4 concurrently), balancing priorities across them - Help mature repeatable practices for intake, triage, prioritization, release planning, product health reviews, and transition to steady-state support JOB QUALIFICATIONS Education - Bachelor’s Degree or equivalent experience Preferred: - Business, Technology, or related field - MBA or advanced degree Experience - 7+ years in Product Ownership, Business Analysis, or related roles - Experience working in Agile environments - Experience coordinating across business, technology, QA, and operational stakeholders Preferred Experience - Experience working with AI/ML-enabled products or intelligent automation - Familiarity with AI behavior evaluation, prompt iteration, or model output analysis - Foundational understanding of generative AI concepts such as LLMs, retrieval-augmented generation (RAG), and prompt-driven workflows - Experience in platform, SaaS, or enterprise product environments - Experience translating user feedback, performance data, or support trends into actionable product improvements Key Skills - Strong prioritization and decision-making capability - Ability to manage ambiguity in AI-driven systems - Excellent communication and stakeholder influence - Analytical thinking and problem solving - Collaboration across business and technical teams - Foundational understanding of how generative AI systems behave, including variability, hallucination risk, and prompt sensitivity - Strong judgment in balancing user feedback, business value, AI quality concerns, and technical feasibility - Ability to distinguish between product issues, content gaps, AI behavior issues, defects, and user adoption needs - Ability to influence stakeholders and drive alignment without direct authority - Comfort operating in a developing AI product model where standards, ownership, and practices continue to mature Our Interview Practices To maintain a fair and genuine hiring process, we kindly ask that all candidates participate in interviews without the assistance of AI tools or external prompts. Our interview process is designed to assess your individual skills, experiences, and communication style. We value authenticity and want to ensure we’re getting to know you—not a digital assistant. To help maintain this integrity, we ask to remove virtual backgrounds and include in-person interviews in our hiring process. Please note that use of AI-generated responses or third-party support during interviews will be grounds for disqualification from the recruitment process. Applicants may be required to appear onsite at a Wolters Kluwer office as part of the recruitment process. Compensation: $85,600.00 - $149,400.00 USD This role is eligible for Bonus. Compensation range listed is based on primary location of the position. Actual base salary offer is influenced by a wide array of factors including but not limited to skills, experience and actual hiring location. Your recruiter can share more information about the specific offer for the job location during the hiring process. Additional Information : Wolters Kluwer offers a wide variety of competitive benefits and programs to help meet your needs and balance your work and personal life, including but not limited to: Medical, Dental, & Vision Plans, 401(k), FSA/HSA, Commuter Benefits, Tuition Assistance Plan, Vacation and Sick Time, and Paid Parental Leave. Full details of our benefits are available upon request.

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