Commercial Bank AI Product Manager

Summary:

The AI Product Manager drives the planning, prioritization, and delivery of AI and data-enabled products that advance Commercial Banking’s sales enablement and service transformation agenda. This role owns the product plan for one or more priority AI initiatives end-to-end – from problem definition and business case through build, governance, launch, and measured adoption – partnering closely with engineering, data science, segment risk, compliance, and the central Enterprise Data & AI Strategy (EDSA) and Technology Innovation functions.

The AI Product Manager is accountable for translating Commercial Banking’s highest-value opportunities into a clear, resourced, and executable roadmap that delivers measurable value to bankers and clients.

• - Own end-to-end accountability for the broader product plan, stakeholder messaging, and investment case for assigned AI initiatives – not just the backlog or delivery schedule.
• - Build and defend the business case (cost, benefit, PPNR/revenue impact, and risk) required to secure and sustain funding through Huntington’s AI governance and prioritization process.
• - Serve as the credible, single point of accountability that segment leaders, technology partners, and executive sponsors can turn to for status, trade-offs, and outcomes.

• - Lead with user value: define the colleague or client benefit and the problem to be solved before selecting a capability, model, or vendor.
• - Use design thinking and direct engagement with relationship managers, service teams, and clients to validate needs, pain points, and workflow context before scoping solutions.
• - Translate validated needs into clear product requirements and acceptance criteria for cross-functional delivery teams.

• - Maintain one integrated view across initiatives, segments, data readiness, cross-functional dependencies, and adoption.
• - Partner across Commercial, Consumer & Regional Banking, Payments, Risk, and Technology Innovation to align priorities, sequencing, and shared data foundations (including ongoing CRM/Salesforce migration work).
• - Maintain a current, transparent roadmap and dependency map that leadership can use to make sequencing and resourcing decisions

• - Define success before launch: establish baselines, target metrics, business outcomes, and attribution methodology in partnership with Finance and Analytics before development begins.
• - Ensure measurement goes beyond usage counts to tie adoption to real business outcomes – revenue, banker productivity, client experience, and risk/quality.
• - Report progress against the defined baseline and value case throughout the product lifecycle, adjusting scope when tracking falls short of plan.

• - Build within workflows: design and deliver capabilities that meet colleagues where they already work (e.g., Salesforce, Copilot, core banking systems) rather than requiring new tools or habits.
• - Partner with HR, segment leaders, and change management to build role-specific training, manager reinforcement, and adoption reporting for each launch.
• - Continuously gather adoption and quality feedback post-launch and iterate on the product to close usage and value gaps.

• - Move from roadmap to execution: convert prioritized concepts into resourced, sequenced delivery plans with clear owners, milestones, and go-live dates.
• - Serve as product owner for one or more cross-functional pods (engineers, data scientists, analysts), prioritizing the backlog and ensuring iterative, quality delivery each sprint/quarter.
• - Navigate Huntington’s AI governance lifecycle (business…