AI projects fail far more often due to poor architectural and delivery decisions than flawed code. In modern enterprises, success depends on how well data platforms, agents, integrations, security, governance, and operations are designed to work together—not just on model selection or prompt engineering.
This full-day workshop is for developers, architects, and technical leaders responsible for delivering AI solutions that create real business value. Drawing on lessons learned from delivering enterprise AI systems across government, education, finance, and healthcare, the session explores what consistently works, what commonly fails, and why.
Attendees will examine real-world AI architectures, learn how to prioritise use cases, choose between pro-code, low-code, and third-party AI tools, and understand how emerging platforms such as Azure AI Foundry and Microsoft Fabric fit into today’s delivery landscape. The focus is practical and experience-driven, equipping participants with the decision frameworks needed to build AI systems that scale, comply, and deliver measurable ROI.

Agenda:
AI Ecosystem & Capability Map
Use-Case Selection & Value Realisation
Architecture Patterns That Work
Failures, Crashes, and Hard Lessons
Azure AI Ecosystem: What to Use When
Security, Ops, and Scaling Reality
Building the Next 12–24 Month Roadmap