Senior Manager - AI Product Management
Edward Jones
The Senior Manager, AI Product Manager leads a team responsible for a portfolio of AI products and reusable AI capabilities, including AI models, agents, retrieval systems, knowledge assets, platform capabilities, and other emerging AI technologies. This role combines the strategic product scope and expertise expected of a Lead AI Product Manager with direct accountability for associate leadership, team performance, and capability development.The Senior Manager treats AI itself as the product—owning product strategy, discovery, prioritization, adoption, lifecycle management, and measurable outcomes for reusable AI capabilities that serve multiple business areas, products, customer experiences, and enterprise workflows. The role ensures that AI capabilities are designed as durable, scalable products rather than one-time solutions.
Operating with significant autonomy, the Senior Manager shapes product direction, translates enterprise priorities and customer needs into roadmaps and investment decisions, and ensures measurable value creation. The role balances hands-on product leadership with responsibility for leading, coaching, and developing AI Product Managers and establishing clear priorities, accountability, and effective ways of working across the team.
The Senior Manager partners closely with leaders and colleagues across EJ Labs, Engineering, Data, Product Management, Architecture, Experience, Risk, and Business and Functional areas. The role influences enterprise AI direction, aligns interconnected products and capabilities, and ensures AI products remain reusable, explainable, reliable, well-governed, and aligned with responsible AI practices.
What You'll Do
- Lead a team of AI Product Managers responsible for a portfolio of strategic AI products and reusable enterprise capabilities.
- Define product strategy, value propositions, outcomes, priorities, roadmaps, investment needs, and success measures for assigned AI product and capability domains, balancing customer value, enterprise benefit, cost, risk, feasibility, and time to value.
- Translate enterprise priorities, customer needs, market signals, and technical insights into scalable product strategies, requirements, evaluation criteria, and prioritized work.
- Establish and lead rigorous continuous discovery practices across the portfolio, ensuring teams build deep understanding of customer needs, workflows, pain points, and desired outcomes and use primary customer research, prototypes, experiments, evaluations, proofs of concept, and pilots to test assumptions and guide product decisions.
- Use customer evidence, product analytics, technical findings, and business measures to guide prioritization, investment, scaling, optimization, and retirement decisions.
- Own the full product lifecycle from discovery and launch through adoption, operation, modernization, and retirement, including integration, enablement, documentation, change, support, and monitoring strategies.
- Apply systems and platform thinking to ensure reusable AI products have clear customers, use cases, boundaries, interfaces, dependencies, supported uses, limitations, ownership, controls, and operating expectations—and operate coherently within the broader enterprise AI ecosystem.
- Establish portfolio-level analytics and observability practices, ensuring teams define and use measures for customer value, business outcomes, adoption, reuse, quality, reliability, cost, operational performance, scalability, and risk to make evidence-based product decisions.
- Influence and align product, engineering, data, architecture, cybersecurity, experience, risk, compliance, and business leaders around shared outcomes, resolving competing priorities, barriers, conflicts, and dependencies that affect effective operation and scale.
- Apply AI product expertise, technical fluency, and sound judgment to challenge assumptions, evaluate alternatives, make timely decisions, and balance tradeoffs across customer value, business outcomes, feasibility, cost, risk, scalability, and time to value.
- Recognize and manage AI limitations and risks while embedding responsible AI, privacy, security, legal, regulatory, accessibility, records, and model-risk requirements throughout the product lifecycle.
- Communicate product strategy, health, progress, priorities, risks, dependencies, assumptions, tradeoffs, and outcomes to senior leaders and stakeholders, serving as a trusted advisor across business, technology, product, risk, and operations.
- Establish clear team priorities, roles, decision rights, performance expectations, and accountability; allocate capacity and coordinate staffing, sequencing, and dependencies with product leadership and delivery partners.
- Select, onboard, coach, develop, and retain AI Product Management talent; build deep product craft across the team through regular feedback, performance management, targeted development experiences, career pathways, and differentiated coaching.
- Establish and promote reusable Product Management frameworks, standards, evaluation practices, governance, and customer-centered ways of working, while strengthening organizational capability through coaching, mentoring, communities of practice, and shared lessons.