While data is the lifeblood of both Generative Artificial Intelligence (GenAI) and traditional organizations, the way we model that data differs significantly for each respective purpose. This session will explore these distinctions, guiding you through the specific data needs for successful GenAI implementation compared to organizational data modeling practices.

Key Takeaways:

• Understand the vast data volumes and diverse formats required for GenAI compared to the structured data typical in organizations.
• Explore the trade-off between model explainability and predictive power in GenAI applications.
• Discover the contrasting needs for real-time vs. batch data processing in GenAI and organizational settings.

Explore the essentials of responsible GenAI adoption, emphasizing collaboration with BPO providers. This session highlights critical aspects, focusing on data privacy, guardrails, and ethical considerations. Receive examples of a robust framework, building upon existing governance models for transparency and trust. Learn how Shared Services serve as a central coordination point, navigating the complexities of the GenAI “black box” and fostering enterprise-wide responsible practices.  

• Establish practices for data collection, storage, privacy, and ethical data use  
• Learn how to make deliberate choices in architecture and development 
• Consider alignment with internal and external stakeholders