Applied AI Product Manager

Job Description

Overview

An 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 with high 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 Applied AI Product Manager harnesses AI and agentic tools to compress the concept-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.

Key Responsibilities

1. Product Accountability

• Responsible and accountable for the product's value and viability showcasing a measurable Return on Investments (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 early and often, lowering total cost of ownership (TCO).

• 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 yet seen.
• Align product objectives with the product line and 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 and innovative visions backed by compelling strategic rationale.

• Conduct user research and competitive analysis, using AI agents to synthesize research at speed—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 toward solutions that address priority user and business needs.
• Apply analytical skills to analyze data and derive 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 that validate solutions quickly and retire bad ideas just as fast.

• Work side-by-side with 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.

• 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 a force-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.

• Amplify innovation: use AI to rapidly deepen domain knowledge, surface untapped market and user 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 time by 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 Secure Software Development Life Cycle that paves a clear path to productionize early and often.

Required Qualifications

1. Education: Bachelor’s degree in business, Marketing, Engineering, or a related field.
2. 10+ years of proven experience in lean product management or related roles.
3. 3+ years enterprise scale experience across multiple business areas.
4. 1+ years of building AI based intelligent products
5. 1+ year’s experience in using GenAI tools to perform product management tasks like conduct idea research, shaping, synthesis, roadmaps, requirements, prototyping, testing

Preferred Qualifications

• Preferred Education: An MBA or related advanced degree is preferred.
• Preferred Experience:

• Demonstrated experience in modern product craft of delivering the right thing, in the right way, at the right time. Significant experience in lean product management craft and domain (tools, methods, and practices). Seen as a leader in this space.
• Proven accountability for value, viability and P&L objectives for a product and for an empowered product team.

• Preferred Skills

• Communication: Clear and effective communication with team members, stakeholders, and customers. Excellent communication and collaboration abilities.
• Leadership: Ability to lead and inspire cross-functional teams, fostering collaboration and collective movement toward product goals. Ability to influence at all organizational levels through inclusion and leadership.
• Customer-Centricity: Deep understanding of customer needs and engagement patterns, driving teams to deliver solutions that customers love and that work for the business. Expertise in applying customer-centric methods and practices.
• Strategic Thinking: Ability to develop and execute a strategic vision for the product, aligning it with broader business objectives.
• Exceptional analytical and problem-solving skills.
• Detail-oriented, organized, and visionary.
• Learning-forward, experimental, and value-oriented mindset.
• Ability to navigate complexity and uncertainty.
• Quick to reach expert-level knowledge within the product domain being served.
• AI Agentic Fluency: Comfortable orchestrating multiple AI agents across the concept-to-cash flow (research, insight, prototyping, specification, coding, and compliance), with guardrails at each hand-off—assumptions, confidence levels, and links to sources of truth.
• AI Realism and Eval Fluency: Understands the difference between deterministic logic and probabilistic generation; designs guardrails for hallucination, bias, and drift; uses evaluation harnesses before launch and monitors drift after, with a kill-switch mentality—and knows when not to use AI.
• Experience with modern agentic AI tools such as Claude Code, Claude Co-work, Open AI Codex Cursor, and Visual Studio Code.

• Preferred Personal Traits

• Strong leadership capabilities.
• Customer-centric mindset.
• Ability to work as an individual contributor in a collaborative, cross-functional team.
• Humble, curious, and learning-forward mindset.
• Favor small step action and evidence over detailed upfront planning and precision aiming.
• Experience with lean solutions and rapid, inexpensive experimentation to emerge the right thing, in the right way, at the right time.
• High levels of continuous customer and user engagement.
• Treats taste and judgment as the un-automatable moat—using AI to amplify, never replace, product sense-making.

Conclusion

The Applied AI Product Manager plays a crucial role in ensuring the success of our high value, moderately complex products by balancing customer needs with business objectives. This role requires a blend of strategic vision, analytical skills, and collaborative teamwork to deliver valuable, viable, usable, and feasible solutions. It demands significant experience in the modern product management craft and a drive for continuous improvement—amplified by the fluent, responsible use of AI to learn faster and earn faster.

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