Senior Product Manager, Enterprise Ai Jp240
Pathlion
The Senior Product Manager, Enterprise AI will provide end-to-end product leadership for priority AI initiatives, from opportunity identification and intake through prioritization, delivery, adoption, and benefits realization.Working across business, Data & AI, Technology, and governance teams, the role will translate business needs into high-value AI opportunities, establish product roadmaps and priorities, and ensure AI investments deliver measurable organizational value.Key Responsibilities
Lead the intake, assessment, and prioritization of enterprise AI opportunities and use cases.Develop and maintain the enterprise AI roadmap, balancing business value, strategic alignment, feasibility, data readiness, risk, and delivery capacity.Identify business needs, enterprise data priorities, and capability gaps through engagement with stakeholders across the organization.Translate business challenges into clearly defined AI products, use cases, requirements, outcomes, and success measures.Provide product leadership across the lifecycle, from discovery and experimentation through implementation, adoption, and continuous improvement.Coordinate across business, data science, engineering, architecture, technology, project delivery, and governance teams.Engage Privacy, Cybersecurity, Legal, Data Governance, Responsible AI, and other control functions as appropriate.Develop business cases, value propositions, KPIs, and benefits-realization measures for priority initiatives.Track adoption and business outcomes and recommend whether AI solutions should be scaled, enhanced, sustained, or retired.Identify opportunities to develop reusable enterprise AI and data capabilities that can support multiple business areas.
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Lead the intake, assessment, and prioritization of enterprise AI opportunities and use cases.Develop and maintain the enterprise AI roadmap, balancing business value, strategic alignment, feasibility, data readiness, risk, and delivery capacity.Identify business needs, enterprise data priorities, and capability gaps through engagement with stakeholders across the organization.Translate business challenges into clearly defined AI products, use cases, requirements, outcomes, and success measures.Provide product leadership across the lifecycle, from discovery and experimentation through implementation, adoption, and continuous improvement.Coordinate across business, data science, engineering, architecture, technology, project delivery, and governance teams.Engage Privacy, Cybersecurity, Legal, Data Governance, Responsible AI, and other control functions as appropriate.Develop business cases, value propositions, KPIs, and benefits-realization measures for priority initiatives.Track adoption and business outcomes and recommend whether AI solutions should be scaled, enhanced, sustained, or retired.Identify opportunities to develop reusable enterprise AI and data capabilities that can support multiple business areas.
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