enterprise AI Archives - Scrum Master Toolbox Podcast

BONUS: How AI Is Reshaping Software Teams From the Inside — Lessons From Google, Meta, and Snowflake

In this episode, Dwarak Rajagopal — VP of AI Engineering and Research at Snowflake — shares what he’s seeing firsthand as AI agents become part of the software development process. From compressed sprint cycles to automated standups across time zones, Dwarak draws on two decades of building AI infrastructure at Google, Meta, Uber, and Apple to show what’s actually changing inside engineering organizations today.

From Compiler Engineer to AI Leader — The Thread That Connects Two Decades

“In AI, the hardest part isn’t just the models itself, it’s making them work in real environments where data is messy, fragmented, and governed.”

Dwarak started his career as an open-source GCC compiler engineer over two decades ago, optimizing hardware performance. He moved into graphics at Apple, then pivoted to AI when AlexNet started running on GPUs around 2011-2012. From there, he built autonomous driving software at Uber, led Meta’s PyTorch core framework team bridging research and production, and at Google led AI Frameworks including getting Gemini training on TPUs. The common thread: always working at the intersection of research and production, making powerful technology work in the real world. That focus on real-world application is what drew him to Snowflake — where enterprise data meets AI at scale.

AI Is Changing What Engineers Actually Do All Day

“Engineers are spending more time on system design, validation, production reliability — and less time doing the implementation itself, because AI is helping that.” Continue reading BONUS: How AI Is Reshaping Software Teams From the Inside — Lessons From Google, Meta, and Snowflake

BONUS: AI Won’t Just Change How You Work — It Will Reshape Your Organization

The Global Agile Summit is around the corner, and the AI in Organizations track is one you don’t want to miss. In this episode, track co-hosts Michael Dougherty and Michał Parkoła walk us through what they’ve built — from the thinking behind the track name to the sessions that stood out, and why this isn’t just another AI conference lineup.

Why “AI in Organizations” — Not Just “AI”

“AI will not only be useful to existing organizations, but it will reshape organizations in a very significant way, the same way cars reshaped cities.”

Michael and Michał drew a deliberate line with the track name. Michael points out that AI has been around for decades — it didn’t start with ChatGPT. The real shift now is AI agents scaling to enterprise level, replacing automation that used to require specialized tools.

Claude Enterprise holds about 29% of the enterprise AI market, Gemini around 15%. But Michał pushes the framing further: the first-order effect is applying AI to existing work. The second-order effect — the one he’s most interested in — is how AI will reshape organizations themselves. New species of companies will emerge, smaller teams will achieve what used to require hundreds of people, and some existing organizations won’t survive the transition. That’s the conversation this track is designed to start.

Filtering the Signal From the Slop

“There was a bit of AI slop in the submissions. There was a lot of talk that, unfortunately, was meta-talk — there was no real value that I could glean.” Continue reading BONUS: AI Won’t Just Change How You Work — It Will Reshape Your Organization

BONUS: Why Observability, Not Capability, Is the Next AI Frontier

We’ve spent years asking what AI can do. But the next frontier isn’t more capability—it’s something far less glamorous and far more dangerous if we get it wrong. In this episode, Ran Aroussi shares why observability, transparency, and governance may be the difference between AI that empowers humans and AI that quietly drifts out of alignment.

The Gap Between Demos and Deployable Systems

“I’ve noticed that I watched well-designed agents make perfectly reasonable decisions based on their training, but in a context where the decision was catastrophically wrong. And there was really no way of knowing what had happened until the damage was already there.”

Ran’s journey from building algorithmic trading systems to creating MUXI, an open framework for production-ready AI agents, revealed a fundamental truth: the skills needed to build impressive AI demos are completely different from those needed to deploy reliable systems at scale. Coming from the EdTech space where he handled billions of ad impressions daily and over a million concurrent users, Ran brings a perspective shaped by real-world production demands.

The moment of realization came when he saw that the non-deterministic nature of AI meant that traditional software engineering approaches simply don’t apply. While traditional bugs are reproducible, AI systems can produce different results from identical inputs—and that changes everything about how we need to approach deployment.

Why Leaders Misunderstand Production AI

“When you chat with ChatGPT, you go there and it pretty much works all the time for you. But when you deploy a system in production, you have users with unimaginable different use cases, different problems, and different ways of phrasing themselves.” Continue reading BONUS: Why Observability, Not Capability, Is the Next AI Frontier