Product Owner--AI Enablement Local Only

AI Enablement Product Owner

Location: must come into the office at least 1 day/week in Chicago, IL or Lake Mary, FL

Acts like a Product Owner, thinks like a Business Analyst, and works hands-on like an AI builder.

Quick Facts

Engagement: 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 refinement

Primary toolchain: Microsoft 365 Copilot, Copilot Studio (Lite), Power Platform (Power Automate / AI Builder), Google Gemini

Domain: Commercial P&C Insurance (experience strongly preferred)

Delivery model: Agile / Scrum; partner with Product Owners, business stakeholders, and technology teams

Role Summary

This 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-through

Deliver a prioritized pipeline of high-value AI use cases with clear problem statements, outcomes, and constraints

Create a reusable prompt library (templates examples) aligned to business rules and expected outputs

Establish and run prompt test plans (happy path, edge cases, negative tests) and document results and improvements

Improve output quality and consistency via iterative refinement; reduce rework and increase stakeholder confidence

Enable adoption by producing quick-start guides, FAQs, and training artifacts that make AI usable for day-to-day work

Key Responsibilities

Product Owner / Agile Delivery

Own and manage a backlog of AI enablement work (use cases, prompts, automation components, adoption materials) with clear priorities and acceptance criteria

Facilitate discovery and refinement sessions with stakeholders and SMEs; translate needs into user stories and iterative deliverables

Partner with Scrum Master and delivery teams to plan sprints, remove blockers, and ensure incremental value is delivered

Validate outcomes with business users; accept work based on measurable criteria and real-world usability

Business Analyst / Requirements & Process Thinking

Intake and triage AI requests; clarify the “job to be done,” define scope, assumptions, constraints, and success measures

Map current-state workflows and identify where AI can reduce effort, improve quality, or accelerate cycle time

Define business rules and output standards (tone, structure, decision logic) to drive consistent AI behavior

Document requirements, risks, dependencies, and change impacts; keep stakeholders aligned

AI Builder / Prompt Engineering & Quality

Design, write, and maintain prompts and prompt templates for Microsoft 365 Copilot, Copilot Studio (Lite), and Google Gemini

Develop prompt evaluation rubrics and test suites; run structured testing across scenarios and document results

Iteratively refine prompts based on feedback, business rule updates, and model/tool changes; perform regression testing to prevent quality degradation

Create reusable patterns (prompt libraries, prompt chaining, examples) to scale consistent outcomes across teams

Collaborate with technical partners on automation flows (Power Automate) and integration points where applicable

Adoption, Change Management & Enablement

Create user enablement assets (playbooks, examples, FAQs, short trainings) to help teams safely and consistently use AI tools

Gather user feedback and usage insights; translate findings into backlog improvements

Promote a culture of experimentation with guardrails—helping users understand when and how to rely on AI outputs

Governance, Risk & Responsible Use

Operate within existing enterprise governance processes for data handling, security, and compliance

Apply 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 Qualifications

5 years of experience as a Product Owner, Business Analyst, or similar role delivering business-facing technology solutions

Demonstrated experience translating business needs into clear requirements and acceptance criteria in an Agile environment

Hands-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 mindset

Ability to manage multiple workstreams, prioritize effectively, and deliver outcomes with minimal oversight

Preferred Qualifications

Commercial 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 environments

Experience working across multiple AI platforms (e.g., Copilot and Gemini) and adapting prompts to different model behaviors

Familiarity with responsible AI concepts (privacy, security, bias, traceability) and partnering with governance stakeholders

Certifications: SAFe PO/PM, CSPO/PSPO, and/or Microsoft AI-900 (or equivalent)

Key Skills & Competencies

Use-case framing: define outcomes, constraints, and measures of success

Prompt engineering: structured prompts, templates, examples, and iterative refinement

Evaluation discipline: test plans, rubrics, regression testing, and documentation

Stakeholder management: clear communication, facilitation, and expectation-setting

Process thinking: mapping workflows, identifying failure modes, and designing practical controls

Delivery execution: backlog management, prioritization, sprint planning, and acceptance

Change enablement: playbooks, training, and adoption support

Interview Signals We Value

You 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).