Most organizations experimenting with large language models (LLMs) are stuck in pilot mode, running isolated use cases, generating excitement, but struggling to scale real business value. The gap between experimentation and enterprise-wide adoption isn’t technical alone; it’s operational, cultural, and strategic.

This session delivers a practical, enterprise-ready playbook for moving LLM initiatives from proof-of-concept to production. Drawing on real-world experience across regulated and data-intensive environments, we’ll break down what actually works: aligning use cases to business outcomes, establishing governance and security guardrails, preparing your data estate for AI, and designing scalable architectures that integrate with existing systems.

Attendees will gain a clear framework to evaluate readiness, prioritize high-impact opportunities, and avoid the common pitfalls that derail AI programs. We’ll also explore how to operationalize LLMs responsibly, balancing speed with control, while enabling teams to confidently adopt AI in their daily workflows.

Whether you’re leading AI strategy, modernizing your data platform, or enabling tools like Microsoft Copilot, this session will equip you with the structure, language, and execution model needed to turn early momentum into sustained enterprise value.

Key Takeaways:
• A step-by-step framework for moving from LLM pilots to production at scale
• How to align AI use cases with measurable business outcomes
• Governance, security, and compliance considerations for enterprise AI
• Data readiness and architecture patterns that enable scalable adoption
• Practical lessons learned from real-world enterprise deployments