Applied AI Product Manager

We are looking for an experienced Applied AI Product Manager with 8 to 10 years ofoverall experience to join our team. The Applied AI Product Manager is a senior individual contributor responsible for ensuring a product's value and viability within a product line. This role involves leading empowered, cross-functional product teams to solve moderate complexity customer problems that align withhigh value business needs. The Applied AI Product Manager is accountable for the product's success, from vision to execution, and collaborates closely with various functions and stakeholders to deliver valuable, viable, usable, and feasible solutions.

The AppliedAI Product Manager harnesses AI and agentic tools to compress theconcept-to-cash learning loop - automating analysis, prototyping, and compliance detail-work so the team can focus on the human judgment AI cannot replace:product sense-making. The role plays a crucial part in ensuring the success of high value, moderately complex products by balancing customer needs with business objectives, requiring a blend of strategic vision, analytical skills, and collaborative teamwork, amplified by the fluent, responsible use of AI to learn faster and earn faster.

• Product Accountability:
  Responsible and accountable for the product's value and viability showcasing a measurable
  
  Return on Investment (ROI).
• Drive strategy-aligned solutions to achieve product value objectives.
• Formulate and achieve Key Performance Indicators (KPIs) for identified problems to solve.
• Measure KPIs and analyze outcomes to inform future strategies.
• Leverage AI to harvest outcome evidence earlyand often, lowering total cost of ownership (TCO).
• Vision and Strategy:
  Co-create, own, and evangelize the product vision, strategy, and roadmap, using AI to deepen domain knowledge and simulate future scenarios to chart pathways others have not yetseen.
• Align product objectives with the product lineand business goals.
• Co-create in collaboration with business stakeholders, engineering, experience, and delivery.
• Use AI to expedite research, gather evidence,bolster domain knowledge, and craft innovative visions backed by compelling strategic rationale.
• Market and User Engagement:
  Conduct user research and competitive analysis, using AI agents to synthesize research atspeed-accelerating the data crunching, ensuring the human connection.
• Engage the team with users and stakeholders through continuous research and direct interactions.
• Collaborate and guide the team towardsolutions that address priority user and business needs.
• Apply analytical skills to analyze data andderive actionable insights, shifting from waiting on analysis to working on insights.
• Adopt innovative and experimental approaches to solving complex problems, including AI-built, disposable prototypes thatvalidate solutions quickly and retire bad ideas just as fast.
• Collaboration and Teamwork:
  Work side-by-sidewith cross-functional (business, engineering, experience, and delivery) team members to achieve KPI outcomes.
• Promote a product operating model that emphasizes outcomes over output (minimize overproduction while maximizing value).
• Build empowered teams and product communities who exhibit collective product ownership and level-up their outcome potential through AI and agentic tools.
• Continuous Improvement:
  Promote and drive rapid, emergent, and ongoing learning and adaptation to meet objectives.
• Drive innovation and improvement of the process to drive out waste and accelerate value achievement, using AI as aforce-multiplier to offload the repetitive, speed up the sluggish, and automate the mundane.
• Remove obstacles for the team and ensure smooth flow of continuous value achievement.
• Spread knowledge and best practices within the product vertical community.
• Applied AI Ways of Working:
  Amplify innovation by using AI to rapidly deepen domain knowledge, surface untapped market anduser potential, and simulate future scenarios-charting new pathways for the business.
• Amplify learning: use AI agents to synthesize research and validate ideas before they enter the backlog-compressing lead timeby accelerating the data crunching, ensuring the human connection.
• Amplify focus: act as Editor-in-Chief-using AI to rigorously test assumptions and retire ideas that do not genuinely serve the user's workflow in a way that works for the business.
• Amplify experimentation: use AI to build early, functional, disposable prototypes that validate the architecture and the solution, playing a key role in the Agentic…