AI Inside the Enterprise

April 25, 2026

1:10

Here's the thing — AI inside big companies isn't living up to the hype, and it’s not for lack of effort. According to Steven Sinofsky and Aaron Levie, most AI projects fail because they overlook the complex workflows and infrastructure that actually make AI useful at scale. Instead of shiny new tools, what’s really driving change is how agents and workflows are evolving beyond the buzzwords. The gap between Silicon Valley’s innovation and what’s practical in the enterprise is still huge, and many initiatives stumble because they ignore the real-world constraints. But here’s where it gets interesting — building AI that sticks requires a shift from just deploying tech to embedding it into everyday workflows, making it actually work for the people using it. As Martin Casado from a16z points out, success depends on how quickly organizations can adapt and learn from their AI experiments. So, the big takeaway? The real advantage now isn’t just about new AI tools — it’s about faster, smarter learning loops that turn AI into real, lasting value.

Steven Sinofsky, board partner at a16z, Aaron Levie, CEO of Box, and Martin Casado, general partner at a16z, discuss the reality of AI inside enterprises. They cover the gap between Silicon Valley and the rest of the world, why most AI initiatives fail in large organizations, and how agents, infrastructure, and workflows are evolving beyond the hype.

Resources:

Follow Aaron Levie on X: https://twitter.com/levie

Follow Steve Sinofsky on X: https://twitter.com/stevesi

Follow Martin Casado on X: https://twitter.com/martin_casado

Follow Erik Torenberg on X: https://twitter.com/eriktorenberg

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Audio Transcript