- Seeks an accomplished product leader with a track record of turning business demand from multiple commercial functions into a well-managed data product backlog.
- Strong prioritization judgment, stakeholder partnership, and hands-on data fluency are essential to success in this role.
- Reporting to Director, PO Audience Activation & Marketing Intelligence, Agentforce Product Manager owns the enterprise Agentforce capability roadmap and backlog for governed AI agents that use Data 360 as a trusted context layer.
- The role manages intake and prioritization, defines reusable agent patterns and guardrails, coordinates Data Cloud and AI dependencies, and works with enterprise AI governance, the DDIT Center of Excellence, and architects to build, validate, release, and operate Agentforce capabilities responsibly across marketing, sales, and future functional areas.
Major Accountabilities:
- Manage Agentforce intake: Receive and qualify requests for Agentforce capabilities and features that leverage Data 360, documenting the user need, intended outcome, data context, risk, and enterprise reuse potential.
- Set capability strategy and roadmap: Define the Agentforce roadmap, reusable capability model, and sequencing across initial marketing and sales use cases and future functional areas.
- Prioritize enterprise demand: Prioritize support and platform capabilities using strategic value, readiness, governance risk, shared demand, data availability, and delivery capacity.
- Own and refine the backlog: Translate prioritized use cases into epics, features, user stories, evaluation criteria, guardrails, and release plans for Agentforce capabilities.
- Define governed context patterns: Partner with the Data Cloud Platform Product Owner to specify how unified profiles, Data Lake Objects, calculated insights, permissions, and other governed data context will support agents.
- Coordinate PI planning: Plan Agentforce demand and dependencies with Data Cloud, architecture, engineering, security, privacy, AI governance, and DDIT delivery teams.
- Embed responsible AI governance: Liaise with Nova OS and DDIT AI Governance to apply required reviews, documentation, data policies, model and agent guardrails, human oversight, and release conditions.
- Lead delivery with the Center of Excellence: Work with DDIT Center of Excellence teams and architects to design, build, test, release, and support reusable Agentforce capabilities.
- Validate quality and safety: Define acceptance and evaluation criteria for functional performance, grounding, access control, reliability, traceability, user experience, and appropriate escalation to humans.
- Drive adoption and learning: Partner with change, training, operations, and business leads to support adoption; capture feedback and operational evidence for iterative improvement.
- Measure and communicate impact: Track business value, adoption, quality, risk, and delivery health; communicate decisions, limitations, and outcomes transparently to stakeholders and governance bodies.
KPI/What good looks like:
- Business value: Agentforce releases demonstrate outcomes against agreed use-case KPIs and user needs.
- Responsible AI compliance: Required governance reviews, guardrails, evidence, and release conditions are complete and traceable.
- Agent quality: Capabilities meet agreed evaluation criteria for grounded outputs, access control, reliability, and human escalation.
- Roadmap and backlog health: Enterprise Agentforce demand is prioritized, clearly specified, and aligned to Data Cloud and AI dependencies.
- Adoption and trust: Target users adopt released capabilities and provide actionable feedback on usefulness, clarity, and control.
- Reuse and scalability: Shared agent patterns, data context, and controls support multiple use cases without unnecessary duplication.
Work Schedule: Hybrid (3 days (Mon-Wed), 2 remote.