Product Owner (AI)

Wesco•Jersey Village, TX

1d•$119,327 - $161,092•Onsite

About The Position

We are seeking an experienced and business-focused AI Product Owner to help identify, prioritize, and deliver high-impact Artificial Intelligence and Generative AI solutions across the organization. The AI Product Owner will serve as a critical bridge between business stakeholders, AI/Data Science teams, engineering teams, and technology leadership, translating business challenges and opportunities into scalable AI-enabled products and solutions. This role requires a strong combination of product ownership, business process understanding, Agile delivery, and knowledge of AI and GenAI. The ideal candidate is comfortable working with ambiguous business problems, identifying where AI can create measurable value, and translating those opportunities into clearly defined requirements, user stories, acceptance criteria, and product roadmaps. The Product Owner should be able to work with functional subject matter experts to understand existing processes and identify opportunities to simplify, automate, augment, or transform workflows using AI. Experience working with Supply Chain and/or Sales business functions is strongly preferred, particularly in identifying and implementing AI solutions that improve operational efficiency, decision-making, forecasting, customer engagement, and revenue growth.

Requirements

3–6+ years of experience in Product Management, Product Ownership, Business Analysis, Digital Transformation, Agile Delivery, or a related discipline.
Demonstrated experience owning or supporting technology products from business problem identification through implementation and adoption.
Experience translating complex or ambiguous business requirements into structured product requirements, user stories, and acceptance criteria.
Strong understanding of Agile/Scrum product development methodologies and backlog management.
Working knowledge of AI, Machine Learning, Generative AI, LLMs, analytics, or Intelligent Automation technologies.
Ability to work effectively with Data Scientists, AI/ML Engineers, Software Engineers, Architects, and business stakeholders.
Strong analytical and problem-solving capabilities with an ability to connect technical capabilities to measurable business outcomes.
Excellent stakeholder management, facilitation, communication, and presentation skills.
Experience using product and delivery tools such as Jira, Azure DevOps, Confluence, or similar platforms.

Nice To Haves

Previous experience working within Supply Chain, Sales, Commercial Operations, or related business functions.
Experience delivering AI/ML or GenAI products in an enterprise environment.
Understanding of AI/ML concepts including model evaluation, training and inference, data pipelines, prompt engineering, RAG, and LLM-based applications.
Familiarity with technologies such as Python, SQL, Snowflake, Azure/AWS/GCP AI services, APIs, and modern data platforms.
Experience developing business cases, ROI assessments, and value-realization frameworks for AI or digital initiatives.
Experience taking AI solutions from proof-of-concept/MVP to scalable production deployment.
Exposure to enterprise AI governance and Responsible AI frameworks.

Responsibilities

Partners with business leaders and functional stakeholders to identify, evaluate, and prioritize opportunities where AI, Generative AI, Machine Learning, and Intelligent Automation can create measurable business value.
Understand business processes, pain points, data availability, and operational requirements to determine appropriate AI solutions.
Translate strategic business objectives into actionable AI product roadmaps, capabilities, and use cases.
Evaluate potential AI initiatives based on business impact, feasibility, scalability, data readiness, implementation effort, and expected ROI.
Define MVP scope and support iterative development from proof-of-concept through production deployment and enterprise adoption.
Own and manage the AI product backlog, ensuring priorities remain aligned with business objectives and product strategy.
Translate business requirements into detailed epics, features, user stories, acceptance criteria, and functional requirements.
Continuously prioritize and refine the backlog based on business value, technical feasibility, stakeholder feedback, and evolving organizational priorities.
Work closely with Scrum Masters, engineering teams, data scientists, AI/ML engineers, architects, and business stakeholders throughout the product lifecycle.
Participate in Agile ceremonies including sprint planning, backlog refinement, sprint reviews, demos, and retrospectives.
Define clear acceptance criteria and validate that delivered solutions meet business requirements before release.
Partner with Data Science, AI/ML, engineering, and architecture teams to develop solutions leveraging technologies such as Generative AI, Large Language Models (LLMs), Machine Learning, NLP, predictive analytics, AI agents, and Intelligent Automation.
Help translate business requirements into appropriate AI capabilities and workflows.
Support evaluation of AI outputs for accuracy, relevance, reliability, usability, and business value.
Define appropriate product and model performance metrics and establish feedback mechanisms for continuous improvement.
Work with technical teams to address issues such as hallucinations, model performance, data quality, scalability, and integration with enterprise applications.
Establish measurable KPIs, success criteria, and business outcomes for AI initiatives.
Develop business cases and help quantify potential improvements in productivity, revenue, cost reduction, customer experience, forecasting accuracy, cycle time, and operational efficiency.
Monitor adoption and performance after deployment and identify opportunities for continuous improvement.
Gather feedback from users and stakeholders and incorporate insights into future product releases.
Communicate AI product performance, risks, dependencies, roadmap progress, and business outcomes to leadership and key stakeholders.
Ensure AI solutions are developed and deployed in alignment with organizational Responsible AI, security, privacy, data governance, and compliance standards.
Partner with appropriate governance, security, legal, risk, and technology teams throughout the AI product lifecycle.
Support evaluation of AI solutions for potential bias, accuracy, explainability, data quality, privacy, and other risks.
Establish appropriate human-in-the-loop controls and monitoring mechanisms where required.