Senior Product Owner - AI-Enabled Enterprise Solutions

Honda Canada Inc.•Markham, ON

5d•Hybrid

About The Position

We are seeking a Senior Product Owner to drive the delivery of enterprise IT solutions while actively enabling AI‑assisted and intelligent automation initiatives. This role acts as the primary bridge between business stakeholders, IT delivery teams, and AI/data partners, ensuring solutions are well‑defined, prioritized, governed, and aligned to business outcomes. The role spans traditional enterprise application delivery as well as emerging Copilot‑style, AI‑enabled, and agent‑assisted workflows. The ideal candidate combines strong business analysis and product ownership fundamentals with a practical understanding of how AI can augment decision‑making, reduce manual effort, and modernize enterprise processes.

Requirements

4–6+ years of experience in Business Systems Analysis, Product Ownership, or IT delivery in an enterprise environment.
Strong experience delivering enterprise applications using Agile, waterfall, or hybrid methodologies.
Demonstrated experience writing clear requirements, user stories, process flows, and acceptance criteria.
Practical, working knowledge of AI, automation, analytics, and Copilot‑style tools (no model development required).
Strong stakeholder management, communication, and facilitation skills.
Hands‑on experience with Jira, Confluence, and Lucid (required).

Nice To Haves

Experience supporting AI‑enabled, automation, or intelligent workflow initiatives.
Familiarity with agent‑based automation or advanced workflow orchestration concepts.
Experience in domains such as automotive, finance, sales, contact center, or technical operations.
Exposure to Kepner‑Tregoe, Lean, Six Sigma, or process re‑engineering methodologies.
Relevant certifications (CSPO, SAFe POPM, PMP, or equivalent).

Responsibilities

Own and manage the product vision, roadmap, and backlog for assigned enterprise IT initiatives across business domains.
Elicit and refine business requirements through workshops, interviews, and process reviews.
Translate business needs into epics, features, user stories, and acceptance criteria using enterprise tools (e.g., Jira, Confluence).
Document business processes, system flows, and functional logic using clear diagrams and narratives (e.g., Lucid).
Assess enterprise systems end‑to‑end, from user experience through integrations, data flows, and downstream impacts.
Balance business value, technical feasibility, regulatory obligations, and delivery risk.
Support Agile, waterfall, and hybrid delivery models, including formal governance and design documentation where required.
Write and execute functional test cases and support user and business acceptance testing.
Analyze business processes to identify where AI, Copilot‑style tools, automation, or agent‑based solutions can reduce manual effort and improve outcomes.
Identify repetitive, rules‑based, or information‑heavy activities suitable for AI augmentation or automation.
Partner with stakeholders to frame AI opportunities in measurable business terms (e.g., productivity improvement, cycle time reduction, decision support).
Leverage AI tools (e.g., Copilot, prompt‑based assistants) to support requirements analysis, documentation, ideation, and early solution exploration.
Identify decision points where AI can act as decision support or a semi‑autonomous “digital teammate”, with clear human oversight.
Elicit and define requirements for AI‑enabled initiatives, including: Business objectives and success criteria, User interaction models (human‑in‑the‑loop vs. automation), Inputs, outputs, decision boundaries, and escalation rules.
Translate business needs into AI‑ready user stories, acceptance criteria, and prompt or logic descriptions suitable for AI or agent‑based implementations.
Identify and document data requirements for AI use cases (source systems, data quality, metadata, access constraints) in collaboration with SMEs and data teams.
Work iteratively with technical and AI teams to validate feasibility, refine scope, and adjust requirements based on risk and complexity.
Support testing and validation of AI outputs, including quality, accuracy, explainability, and exception handling.
Ensure AI‑enabled features are appropriately governed, transparent, and aligned with enterprise AI, data, and security policies.
Capture and maintain AI use‑case documentation (purpose, scope, data inputs, user impacts, controls) to support governance and audit needs.
Identify and factor in ethical, legal, data privacy, and compliance considerations when defining AI requirements.
Collaborate with governance, legal, risk, and data teams to support AI approval, lifecycle management, and traceability.
Serve as the primary point of contact between business, IT, vendors, and AI/data teams.
Facilitate alignment on scope, priorities, risks, dependencies, and outcomes.
Translate complex technical and AI concepts into clear, business‑focused language.
Support change management and adoption by clearly defining how AI‑enabled capabilities fit into daily workflows and decision‑making.
Contribute to improving overall AI literacy through documentation, walkthroughs, and practical examples.