If you’ve ever shipped an AI feature that looked great in testing — only to watch it behave unpredictably in production — you’re not alone.

In this episode of IT Visionaries, host Chris Brandt talks with Lawrence Jones, Founding Engineer at incident.io, about the critical gap between AI that demos well and AI that works under pressure. Lawrence shares how his team designs tools that help engineers respond faster, learn from failure, and build systems that don’t crumble when it counts.

CHAPTERS / KEY MOMENTS

00:00 - AI Chaos & The Mike Tyson Rule

00:58 - Meet Lawrence Jones of Incident.io

03:14 - From FinTech Outages to Incident Response

06:22 - The Biggest Mistake in Incident Management

09:08 - Training for Chaos: Game Day Simulations

10:31 - Inside the AI SRE System

13:01 - What SRE Really Means

16:23 - From Prototype to Production AI

20:27 - Keeping Up with AI’s Rapid Evolution

22:50 - Understanding Vector Databases & Embeddings

28:34 - The Architecture Problem: Chaining Prompts at Scale

36:11 - Measuring AI Performance & Reliability

44:02 - The Future of SRE Meets AI

52:10 - Lessons from Real Incidents

56:42 - Final Thoughts: Building AI That Works

-- 

This episode of IT Visionaries is brought to you by Meter - the company building better networks. Businesses today are frustrated with outdated providers, rigid pricing, and fragmented tools. Meter changes that with a single integrated solution that covers everything wired, wireless, and even cellular networking. They design the hardware, write the firmware, build the software, and manage it all so your team doesn't have to.

That means you get fast, secure, and scalable connectivity without the complexity of juggling multiple providers. Thanks to meter for sponsoring. Go to meter.com/itv to book a demo.

---

IT Visionaries is made by the team at Mission.org. Learn more about our media studio and network of podcasts at mission.org.

Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.