Building an AI solution today is a bit like building an Iron Man suit. The hard engineering gets you something powerful. Flying it is a different problem entirely.

That gap is where most enterprise AI stalls. The demo impresses the room, and then the questions change. Who is watching it six months from now? What happens the first time it is wrong in front of a customer? Can you show an auditor how it reached that decision?

Shaylen has spent years closing that gap, building responsible AI at enterprise scale across multiple industries in South Africa and inside US enterprises. In this session he takes on the most expensive sentence in enterprise AI, which is some version of “our data isn’t ready yet,” and shows why you do not have to wait. He covers the four ways AI crashes after take-off, why authority has to be earned in steps from assist to augment to automate, what governance looks like when it lives inside the system rather than in a spreadsheet, and why a small team that owns a use case end to end beats a twenty-person project every time.

Real use cases will get discussed, including what worked, what did not, and what only became obvious once the systems had been live for a while.

Tony Stark was a hero because of what he did with the suit, not because he built one. But he never flew alone. Jarvis watched every system and a workshop stood behind him. You are the one who must fly, and this session is about what has to stand behind you when you do.

You will leave knowing which use case to fly first, and what a sensible first flight actually needs.

Learning 1. What you can do with AI even when you feel your data is not ready.

Learning 2. Why AI can fail, what to do to move you from using AI to assist you to automation and governance required.

Learning 3. The support you require when embarking on an AI journey