Agentforce Product Manager

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.

Key Performance Indicators

KPI area  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.

Ideal Background

•  Bachelor's degree in data, technology, business, engineering, computer science, or a related field required; advanced degree preferred.

•  Fluent English; other languages are desirable.

•  5+ years of product management, AI product, CRM platform, automation, data platform, or enterprise SaaS experience.

•  Strong understanding of AI-enabled product delivery, agent workflows, grounding and context, access controls, evaluation, human oversight, and responsible AI governance.

•  Hands-on knowledge of Salesforce Data Cloud and Agentforce or comparable enterprise AI and customer data platforms.

•  Demonstrated ability to manage an agile roadmap and backlog across business, Data Cloud, architecture, engineering, security, privacy, legal, risk, and AI governance stakeholders.

•  Excellent communication skills and the ability to translate AI opportunities, constraints, risks, and technical dependencies for business and leadership audiences.

Preferred

•  Experience with Salesforce CRM, Data Cloud, Agentforce, APIs, workflow automation, and enterprise integration patterns.

•  Familiarity with AI governance frameworks, model or agent evaluation, prompt and grounding design, monitoring, and incident management.

•  Background in pharma, life sciences, healthcare, or another regulated industry where privacy, trust, and controlled technology use are essential.

Leadership Competencies

Navigate complexity

•  Enable impactful and timely decision-making across business, data, technology, privacy, and compliance stakeholders.

•  Identify the critical issues in complex situations, maintain focus on enterprise outcomes, and adapt priorities as conditions change.

•  Take a long-term view of platform sustainability, downstream impacts, and reusable enterprise capabilities.

Deliver collective impact

•  Integrate diverse perspectives to achieve the best outcome for the enterprise.

•  Influence without authority and collaborate effectively across organizational boundaries.

•  Challenge assumptions constructively and make decisions grounded in evidence.