Moving from simple LLM integration to building robust AI systems with clarity on infrastructure readiness and cost allocation.

While the industry is saturated with the "what" of Generative AI, engineering teams are still struggling with the "how." Moving from a successful prototype to a production-grade AI system requires more than a fine-tuned model; it requires a fundamental shift in software architecture and operational strategy.

In this session, I will draw from my experience of building large-scale systems at Microsoft and other top product companies to demystify the transition from AI experimentation to engineering rigor. We will move past "prompt magic" to explore the 2026 AI Engineering Roadmap, focusing on the infrastructure, observability, and serviceability required to sustain AI at scale.