Senior Business Analyst, AI

Wolters Kluwer•Chicago, IL

1d•$85,600 - $149,400•Onsite

About The Position

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.

Requirements

Bachelor’s Degree or equivalent 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
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

Nice To Haves

Business, Technology, or related field
MBA or advanced degree
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

Responsibilities

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
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
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
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
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
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