Principal Product Manager Applied AI Strategy

San Francisco, CA - USA

Monthly Salary: Not Disclosed

Posted on: 4 days ago

Vacancies: 1 Vacancy

Job Summary

Job Title	Principal Product Manager - Applied AI Strategy
Location	4 days in office (M-Th) WFH Friday. No exceptions. Primarily location must be 100 North Point San Francisco CA Open to considering a candidate if they will do SF for 3 days with the 4th day to San Jose office: 1980 Zanker Rd San Jose CA 95112
Role/Project Description	is building one of the most sophisticated AI-enabled commerce platforms in the premium home and lifestyle segment. The Applied AI Strategy function sits at the center of that-owning the methodology the platforms and the capability programs that make it real across the business. Applied AI Strategy is forward-deployed AI function-embedded within the business not adjacent to it. We operate on a four-phase model: Embed Prove Enable Hand Off. We sit inside workflows identify where AI creates measurable commercial impact co-build with business teams and then transfer full ownership. Our mandate spans the companys largest revenue systems agentic commerce infrastructure enterprise AI platform governance and the capability programs that scale AI fluency across thousands of employees. This role operates at the intersection of agentic AI systems enterprise workflow design and commercial outcomes. You will partner with brand and functional teams to identify where AI-particularly agentic multi-step AI-creates durable value design the systems that deliver it and build the capability for teams to own and evolve it independently. You will work across the AI platform ecosystem WSI has deployed at scale: ChatGPT Enterprise Claude Gemini and Salesforce Agentforce. You will need to know when each is the right tool how to architect workflows across them and how to push back on platform vendors when their defaults dont serve our needs. This role is not about experimentation. It is about turning AI capability into durable commercial systems-agentic workflows that operate in production teams that own their AI-enabled processes and governance that scales without becoming a bottleneck.
Duties/Day to Day Overview	1. Design and Deploy Agentic AI Systems Architect multi-step tool-using agent workflows across AI platform ecosystem (ChatGPT Enterprise Claude Gemini Salesforce Agentforce) Design orchestration layers tool/MCP integrations and human-in-the-loop checkpoints for agentic commerce and operational workflows Evaluate when agentic approaches are warranted versus simpler prompt-based or rule-based solutions-and hold that line Establish reliability evaluation and failure-handling standards for production agentic systems 2. Redesign Workflows for AI-Native Operation Partner with brand and functional teams to map current-state workflows and identify meaningful AI transformation opportunities Apply structured decision-making to determine the right solution: agentic AI prompt-based automation traditional RPA or process redesign Co-design workflows that are scalable auditable and owned by the business-not dependent on the AI Strategy team to operate 3. Apply Cross-Platform AI Expertise Operate fluently across WSIs deployed AI platforms: ChatGPT Enterprise (OpenAI) Claude (Anthropic) Gemini (Google) and Salesforce Agentforce Evaluate platform strengths and limitations per use case-reasoning depth context handling tool use latency cost and governance constraints Contribute to platform vendor relationships as a practitioner peer not just a buyer: push back co-develop and represent WSIs enterprise requirements Stay current on MCP (Model Context Protocol) emerging agent frameworks and evolving platform capabilities 4. Enable Teams to Own Their AI Capabilities Co-build solutions with business stakeholders; never build in isolation Develop playbooks templates and repeatable patterns teams can evolve independently Establish clear ownership and transition models; avoid creating dependency by design Measure success by adoption independence and sustained operation-not by throughput of solutions delivered 5. Build Selectively and Ship to Production Develop lightweight automations agent workflows and prompt systems where required to unblock progress Ensure every solution has a defined path to production clear ownership monitoring and maintenance Balance accuracy cost latency reliability and auditability in every design decision 6. Drive Scalable AI Adoption and Governance Define and evolve enterprise AI tooling standards access controls and data governance practices Partner with Engineering Security Legal and Finance on AI governance frameworks and risk management Support WSIs enterprise AI skills program: lead workshops develop training and build AI fluency
Top Requirements (Must Haves)	8 years in product management including 5 years in Agentic AI Systems Generative AI AI/ML tooling or developer platform products. Agentic AI Fluency (Critical) Hands-on experience designing deploying or evaluating multi-step agentic AI systems in production or near-production environments Working knowledge of agent architectures: tool use ReAct patterns orchestration memory and multi-agent coordination Familiarity with MCP (Model Context Protocol) LangChain or comparable agent orchestration approaches Clear mental model of where agentic AI adds value versus where it introduces unnecessary complexity or risk Platform Expertise Direct experience with two or more of: ChatGPT Enterprise (OpenAI) Claude (Anthropic) Gemini (Google Workspace / Vertex) Salesforce Agentforce Ability to evaluate platform tradeoffs for specific use cases-not just benchmark comparisons but production design decisions Awareness of enterprise governance requirements: data residency access control audit logging model versioning Automation Judgment (Critical) Structured approach to deciding when AI is the right tool-and when it is not Understanding of deterministic vs. probabilistic systems and where each belongs in a production workflow Experience evaluating tradeoffs: prompt-based tools structured workflows rule-based systems and engineered solutions Systems Thinking Experience designing end-to-end workflows with attention to inputs outputs dependencies and failure modes Ability to reason about production readiness monitoring and long-term maintainability-not just initial deployment Enablement Mindset Proven ability to build team capability and transfer ownership-measured by independence not dependency Experience developing playbooks training programs or frameworks adopted at scale Comfort stepping away once teams are self-sufficient; no interest in owning operations indefinitely Execution in Ambiguity Ability to structure loosely defined problems and move without waiting for complete information Experience working cross-functionally without formal authority in a matrixed organization Accountability and Practicality Track record of shipping solutions that are adopted sustained and measurably effective Strong instincts around validation risk calibration and iteration Ownership mentality: you dont consider something done until it works reliably in the hands of the team that owns it

Job Title Principal Product Manager - Applied AI Strategy Location 4 days in office (M-Th) WFH Friday. No exceptions. Primarily location must be 100 North Point San Francisco CA Open to considering a candidate if they will do SF for 3 days with the 4th day to San Jose office: 1980 Za...

Job Title	Principal Product Manager - Applied AI Strategy
Location	4 days in office (M-Th) WFH Friday. No exceptions. Primarily location must be 100 North Point San Francisco CA Open to considering a candidate if they will do SF for 3 days with the 4th day to San Jose office: 1980 Zanker Rd San Jose CA 95112
Role/Project Description	is building one of the most sophisticated AI-enabled commerce platforms in the premium home and lifestyle segment. The Applied AI Strategy function sits at the center of that-owning the methodology the platforms and the capability programs that make it real across the business. Applied AI Strategy is forward-deployed AI function-embedded within the business not adjacent to it. We operate on a four-phase model: Embed Prove Enable Hand Off. We sit inside workflows identify where AI creates measurable commercial impact co-build with business teams and then transfer full ownership. Our mandate spans the companys largest revenue systems agentic commerce infrastructure enterprise AI platform governance and the capability programs that scale AI fluency across thousands of employees. This role operates at the intersection of agentic AI systems enterprise workflow design and commercial outcomes. You will partner with brand and functional teams to identify where AI-particularly agentic multi-step AI-creates durable value design the systems that deliver it and build the capability for teams to own and evolve it independently. You will work across the AI platform ecosystem WSI has deployed at scale: ChatGPT Enterprise Claude Gemini and Salesforce Agentforce. You will need to know when each is the right tool how to architect workflows across them and how to push back on platform vendors when their defaults dont serve our needs. This role is not about experimentation. It is about turning AI capability into durable commercial systems-agentic workflows that operate in production teams that own their AI-enabled processes and governance that scales without becoming a bottleneck.
Duties/Day to Day Overview	1. Design and Deploy Agentic AI Systems Architect multi-step tool-using agent workflows across AI platform ecosystem (ChatGPT Enterprise Claude Gemini Salesforce Agentforce) Design orchestration layers tool/MCP integrations and human-in-the-loop checkpoints for agentic commerce and operational workflows Evaluate when agentic approaches are warranted versus simpler prompt-based or rule-based solutions-and hold that line Establish reliability evaluation and failure-handling standards for production agentic systems 2. Redesign Workflows for AI-Native Operation Partner with brand and functional teams to map current-state workflows and identify meaningful AI transformation opportunities Apply structured decision-making to determine the right solution: agentic AI prompt-based automation traditional RPA or process redesign Co-design workflows that are scalable auditable and owned by the business-not dependent on the AI Strategy team to operate 3. Apply Cross-Platform AI Expertise Operate fluently across WSIs deployed AI platforms: ChatGPT Enterprise (OpenAI) Claude (Anthropic) Gemini (Google) and Salesforce Agentforce Evaluate platform strengths and limitations per use case-reasoning depth context handling tool use latency cost and governance constraints Contribute to platform vendor relationships as a practitioner peer not just a buyer: push back co-develop and represent WSIs enterprise requirements Stay current on MCP (Model Context Protocol) emerging agent frameworks and evolving platform capabilities 4. Enable Teams to Own Their AI Capabilities Co-build solutions with business stakeholders; never build in isolation Develop playbooks templates and repeatable patterns teams can evolve independently Establish clear ownership and transition models; avoid creating dependency by design Measure success by adoption independence and sustained operation-not by throughput of solutions delivered 5. Build Selectively and Ship to Production Develop lightweight automations agent workflows and prompt systems where required to unblock progress Ensure every solution has a defined path to production clear ownership monitoring and maintenance Balance accuracy cost latency reliability and auditability in every design decision 6. Drive Scalable AI Adoption and Governance Define and evolve enterprise AI tooling standards access controls and data governance practices Partner with Engineering Security Legal and Finance on AI governance frameworks and risk management Support WSIs enterprise AI skills program: lead workshops develop training and build AI fluency
Top Requirements (Must Haves)	8 years in product management including 5 years in Agentic AI Systems Generative AI AI/ML tooling or developer platform products. Agentic AI Fluency (Critical) Hands-on experience designing deploying or evaluating multi-step agentic AI systems in production or near-production environments Working knowledge of agent architectures: tool use ReAct patterns orchestration memory and multi-agent coordination Familiarity with MCP (Model Context Protocol) LangChain or comparable agent orchestration approaches Clear mental model of where agentic AI adds value versus where it introduces unnecessary complexity or risk Platform Expertise Direct experience with two or more of: ChatGPT Enterprise (OpenAI) Claude (Anthropic) Gemini (Google Workspace / Vertex) Salesforce Agentforce Ability to evaluate platform tradeoffs for specific use cases-not just benchmark comparisons but production design decisions Awareness of enterprise governance requirements: data residency access control audit logging model versioning Automation Judgment (Critical) Structured approach to deciding when AI is the right tool-and when it is not Understanding of deterministic vs. probabilistic systems and where each belongs in a production workflow Experience evaluating tradeoffs: prompt-based tools structured workflows rule-based systems and engineered solutions Systems Thinking Experience designing end-to-end workflows with attention to inputs outputs dependencies and failure modes Ability to reason about production readiness monitoring and long-term maintainability-not just initial deployment Enablement Mindset Proven ability to build team capability and transfer ownership-measured by independence not dependency Experience developing playbooks training programs or frameworks adopted at scale Comfort stepping away once teams are self-sufficient; no interest in owning operations indefinitely Execution in Ambiguity Ability to structure loosely defined problems and move without waiting for complete information Experience working cross-functionally without formal authority in a matrixed organization Accountability and Practicality Track record of shipping solutions that are adopted sustained and measurably effective Strong instincts around validation risk calibration and iteration Ownership mentality: you dont consider something done until it works reliably in the hands of the team that owns it