Product Owner--AI Enablement

AI Enablement Product OwnerLocation: must come into the office at least 1 day/week in Chicago, IL or Lake Mary, FLActs like a Product Owner, thinks like a Business Analyst, and works hands-on like an AI builder. Quick FactsEngagement: Contract-to-hire (intent to convert based on performance and business need)Focus: Deliver AI-enabled productivity and workflow solutions through use-case intake, prompt engineering, testing, and refinementPrimary toolchain: Microsoft 365 Copilot, Copilot Studio (Lite), Power Platform (Power Automate / AI Builder), Google GeminiDomain: Commercial P&C Insurance (experience strongly preferred)Delivery model: Agile / Scrum; partner with Product Owners, business stakeholders, and technology teams Role SummaryThis role is a hybrid execution position supporting the rollout of Generative AI initiatives across business operations. You will serve as the bridge between business stakeholders and delivery teams—intaking and shaping AI use cases, translating needs into clear requirements and prompts, and iteratively testing and refining outputs until they are reliable, repeatable, and fit-for-purpose. You will help establish a disciplined prompt lifecycle (design → test → refine → version) and enable end-users through guidance, examples, and adoption assets. What Success Looks Like (First 60–90 Days)Rapidly learn our business workflows and stakeholder priorities; build trust through crisp communication and follow-throughDeliver a prioritized pipeline of high-value AI use cases with clear problem statements, outcomes, and constraintsCreate a reusable prompt library (templates + examples) aligned to business rules and expected outputsEstablish and run prompt test plans (happy path, edge cases, negative tests) and document results and improvementsImprove output quality and consistency via iterative refinement; reduce rework and increase stakeholder confidenceEnable adoption by producing quick-start guides, FAQs, and training artifacts that make AI usable for day-to-day work Key ResponsibilitiesProduct Owner / Agile DeliveryOwn and manage a backlog of AI enablement work (use cases, prompts, automation components, adoption materials) with clear priorities and acceptance criteriaFacilitate discovery and refinement sessions with stakeholders and SMEs; translate needs into user stories and iterative deliverablesPartner with Scrum Master and delivery teams to plan sprints, remove blockers, and ensure incremental value is deliveredValidate outcomes with business users; accept work based on measurable criteria and real-world usabilityBusiness Analyst / Requirements & Process ThinkingIntake and triage AI requests; clarify the “job to be done,” define scope, assumptions, constraints, and success measuresMap current-state workflows and identify where AI can reduce effort, improve quality, or accelerate cycle timeDefine business rules and output standards (tone, structure, decision logic) to drive consistent AI behaviorDocument requirements, risks, dependencies, and change impacts; keep stakeholders alignedAI Builder / Prompt Engineering & QualityDesign, write, and maintain prompts and prompt templates for Microsoft 365 Copilot, Copilot Studio (Lite), and Google GeminiDevelop prompt evaluation rubrics and test suites; run structured testing across scenarios and document resultsIteratively refine prompts based on feedback, business rule updates, and model/tool changes; perform regression testing to prevent quality degradationCreate reusable patterns (prompt libraries, prompt chaining, examples) to scale consistent outcomes across teamsCollaborate with technical partners on automation flows (Power Automate) and integration points where applicableAdoption, Change Management & EnablementCreate user enablement assets (playbooks, examples, FAQs, short trainings) to help teams safely and consistently use AI toolsGather user feedback and usage insights; translate findings into backlog improvementsPromote a culture of experimentation with guardrails—helping users understand when and how to rely on AI outputsGovernance, Risk & Responsible UseOperate within existing enterprise governance processes for data handling, security, and complianceApply practical safeguards in prompt design (clear constraints, source grounding when available, and human review expectations)Escalate risk concerns and partner with appropriate stakeholders (privacy, security, legal, compliance) as needed Required Qualifications5+ years of experience as a Product Owner, Business Analyst, or similar role delivering business-facing technology solutionsDemonstrated experience translating business needs into clear requirements and acceptance criteria in an Agile environmentHands-on experience using Generative AI tools to produce strong business results (prompt writing, iteration, evaluation, and refinement)Strong written communication skills (ability to translate business rules into unambiguous instructions for both people and AI)High attention to detail, strong analytical thinking, and a naturally inquisitive, experiment-driven mindsetAbility to manage multiple workstreams, prioritize effectively, and deliver outcomes with minimal oversight Preferred QualificationsCommercial P&C insurance experience (underwriting, operations, policy/billing, claims, distribution, or adjacent functions)Experience with Microsoft 365 Copilot and Copilot Studio; familiarity with prompt/agent design in governed enterprise environmentsExperience working across multiple AI platforms (e.g., Copilot and Gemini) and adapting prompts to different model behaviorsFamiliarity with responsible AI concepts (privacy, security, bias, traceability) and partnering with governance stakeholdersCertifications: SAFe PO/PM, CSPO/PSPO, and/or Microsoft AI-900 (or equivalent) Key Skills & CompetenciesUse-case framing: define outcomes, constraints, and measures of successPrompt engineering: structured prompts, templates, examples, and iterative refinementEvaluation discipline: test plans, rubrics, regression testing, and documentationStakeholder management: clear communication, facilitation, and expectation-settingProcess thinking: mapping workflows, identifying failure modes, and designing practical controlsDelivery execution: backlog management, prioritization, sprint planning, and acceptanceChange enablement: playbooks, training, and adoption support Interview Signals We ValueYou can describe at least one AI use case you delivered end-to-end (problem → prompt(s) → testing → refinement → adoption).You have a practical method for determining output quality (accuracy, completeness, consistency, tone, compliance).You can explain how you iterate when outputs are inconsistent (adjusting constraints, adding examples, changing structure, or decomposing tasks).You demonstrate curiosity and self-directed learning (side projects, experimentation, or continuous upskilling).