Product Manager - AI

Key Responsibilities

• Own the Consumer Bank AI product roadmap and Portfolio ensuring each initiative is tied to a specific business outcome: cost reduction, revenue growth, customer retention, operational efficiency, or experience improvement.
• Work with business line leaders across Consumer Banking to identify the highest-value problems where AI can deliver a step-change in performance.
• Prioritize AI investments using a rigorous cost-benefit framework, making explicit tradeoffs between complexity, data readiness, regulatory risk, and expected return.
• Translate strategic priorities into a sequenced roadmap that technology teams can execute against, with clear milestones and measurable outcomes at each stage.
• Maintain a forward-looking view of AI capabilities and market developments, identifying where Valley should invest ahead of the curve and where it should wait for the technology to mature.
• Serve as the product counterpart to Valley's AI and technology teams, co-owning delivery from problem definition through deployment and optimization.
• Define clear problem statements, success criteria, and evaluation frameworks before any model development or vendor selection begins, ensuring technology investment is preceded by business clarity.
• Evaluate AI vendors and platform partners, assessing their capabilities against Valley's specific use cases and holding them accountable to business outcomes, not just technical benefits.
• Drive the transition from pilot to production, owning the organizational, process, and experience changes required to make AI solutions scale beyond proof-of-concept.
• Define KPIs and measurement frameworks for every AI initiative before it launches, establishing baseline metrics and target outcomes that create clear accountability.
• Build reporting that connects AI performance to business results, making the ROI of AI investment visible and defensible to senior leadership.
• Monitor deployed AI solutions continuously, identifying degradation, drift, or unintended consequences and driving rapid response when performance falls short.
• Conduct regular post-implementation reviews to capture learnings, quantify realized value, and inform future investment decisions.
• Ensure AI capabilities are integrated into customer and teammate experiences in ways that feel intuitive and add genuine value, not bolted on as features.
• Partner with Channel Product owners across Consumer to embed AI into the product experiences they own, creating intelligent moments across the customer lifecycle: personalized guidance, proactive alerts, smarter search, faster service resolution.
• Advocate for the customer and teammate perspective in every AI design decision, ensuring that AI outputs are trustworthy, useful, and contextually appropriate.
• Champion consistency across channels, so that AI-powered experiences in digital banking, the contact center, and advisor tools feel like one bank, not separate systems.
• Own the end-to-end Voice AI product strategy across inbound and outbound use cases, including call containment, payment reminders, servicing, and authentication, with explicit accountability for containment rates, transfer accuracy, and customer satisfaction.
• Balance cost takeout objectives with customer trust and regulatory exposure, explicitly deciding where Voice AI should fully automate vs. where human escalation is required.
• Ensure Voice AI experiences feel consistent with Valley’s brand voice, disclosures, and service standards across channels (voice, chat, digital).
• Own the “last mile” experience: how Voice AI hands off to agents, how errors are explained, and how customers regain trust when the AI is wrong.
• Establish clear Voice AI success metrics beyond containment, including: Incorrect containment / premature containment rate, Transfer quality (context passed to human agents), Customer recontact rates after AI interactions.
• Define fraud AI KPIs that explicitly capture: Net fraud loss avoided, Investigator effort saved, Customer impact from false positives.
• Require prelaunch baselines and postlaunch validation so fraud models are defensible in audits and model risk reviews.

Responsibilities Include But Are Not Limited To

• Own product strategy and roadmap, aligning initiatives to business goals, customer needs, and performance insights.
• Manage end-to-end digital experiences, identifying and prioritizing improvements that enhance usability, conversion, engagement, and efficiency.
• Define and track key performance metrics (e.g., adoption, conversion, activation, engagement, satisfaction) and use data to inform decisions.
• Leverage data and AI-driven insights to identify opportunities for personalization, automation, and improved customer outcomes.
• Translate business needs into product requirements, including user stories, acceptance criteria, and backlog prioritization.
• Partner with technology and design teams to deliver high-quality, scalable solutions in an agile environment.
• Drive ongoing optimization, including experimentation (A/B testing), iterative improvements, and continuous learning.
• Collaborate across stakeholders to ensure alignment with business, regulatory, risk, and operational requirements.
• Act as the voice of the customer, ensuring solutions are intuitive, integrated, and aligned to customer expectations.
• Evaluate emerging technologies, including AI and automation capabilities, to enhance product functionality and operational efficiency.
• Monitor product performance post-launch, identifying issues, risks, and opportunities to improve outcomes.