Scaling AI takes more than tools — it requires a clear plan and data. Join experts to learn how top companies move from vision to execution with practical, scalable steps for responsible AI adoption.
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Getting Started with AI: Use Cases, Data, and What’s First
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, VP Architecture, Salesforce
, Agentforce Engineer Director, Salesforce
, Global Lead AI Transformation Advisory, Salesforce
Role: Consultant, Architect, Data Analyst
Product: Einstein, Agentforce, Data 360
Topic: Agentic AI
Session Level: Intermediate
Session Type: Breakout
Session Summary: The session focused on Salesforce's advancements in generative AI, particularly Agentforce, and provided a roadmap for successful implementation within organizations. Attendees learned about the importance of defining clear and measurable use cases, starting with simpler projects that have high business value, and scaling AI initiatives across roles and processes. The speakers emphasized the critical role of data readiness, responsible AI practices, and iterative testing to ensure agents deliver accurate and compliant outcomes. They also highlighted the necessity of creating centralized governance structures, such as Centers of Excellence, to standardize and streamline AI deployment in a way that aligns with organizational goals and regulatory requirements.
Key Takeaways:
• Defining clear, actionable, and measurable use cases is the foundation for successful generative AI implementations, with a focus on high-value, low-complexity projects as starting points.
• Data readiness is crucial for effective agent performance, requiring careful mapping, ingestion, and ongoing refinement to support AI outcomes and scalability.
• Establishing centralized governance structures, such as Centers of Excellence, enhances compliance, fosters collaboration, and ensures alignment with organizational values and regulatory requirements.
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