Delivering Enterprise AI: From Architecture to Real-World ROI
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
What should I know before attending?
You don’t need prior AI experience — but familiarity with modern cloud architecture will help you apply the concepts more effectively. Is this hands-on / will I write code? No — and that’s the point. This workshop focuses on everything beyond the code that determines success: architecture, use cases, governance, and the pitfalls that derail AI projects.
How deep does it go technically?
Deep enough to design and evaluate real enterprise AI systems — but focused on architecture, patterns, and decisions rather than code.
What will I leave with?
A clear, practical framework for delivering enterprise AI — including how to choose use cases, design architectures, and avoid the mistakes that kill projects.
Adam is the AI Practice Lead at Agile Insights. He works with customers to help them understand where AI can empower their organisations, and then makes that vision come to life. Adam loves adopting emerging technologies early, and then sharing best practices and lessons learned with the broader development community. In his downtime, you can find him outdoors camping, kayaking, cycling, 4WDing, or at the beach.