6 in 10 Enterprises Can't Find the Root Cause When Their AI Workloads Fail | Paul Appleby, Virtana
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Why can’t 6 in 10 enterprises automatically identify the root cause of AI workload failures?
Six in ten enterprises cannot automatically identify the root cross across AI infrastructure domains when AI workloads fail. Why a root cause is so much harder in an AI factory than in traditional enterprise IT to discover?
what you're saying we're entering our phase where ai roi will be judged for ai will be judged less by model performance and more by operational efficiency ergo cost and business impact you know it might not just be a cost dynamic the kind of gold rush to be in the ai investment cycle is overriding some of the good governance principles that companies have we'll see the equilibrium come back
So let's start by having you introduce yourself to listeners.
Thanks, Craig. Yeah, I'm Paul Appleby. I'm the CEO of Vertana. And we're a company that exists really for one really important purpose. where companies across many industries, whether it's banking, telecommunications, healthcare, retail, airlines, whatever, are so reliant on their technology. In fact, a lot of chief risk officers say that the single biggest point of catastrophic risk of failure for a business now is their technology and infrastructure. But Tana's in the world of trying to protect that. We live in this world called observability, and that's all about business resilience. and operational efficiency. How do we make sure those services that are critical to your business and your customers stay performant and available?
And even more relevant in this era of accelerated AI adoption, of course.
Yeah. And how long have you been with Vertana and what was your background before coming?
I've been here now for a couple of years. Inherited an amazing business that in the past, in fact, under its prior name of Virtual Instruments, was run by John Thompson, the former chairman of Microsoft. So the company's been around for some time and has an amazing history with its core technology. But I came to the company two years ago with a charter from the board to really lean into this world of the broad digitization of services and the scale out adoption of AI technology. Prior to that, I've been working in technology for years, sometimes in startups, because I love that whole idea of the scale up. But sometimes in much larger companies, through phases of transformation and growth, like Salesforce, where I was for a number of years, and also companies like Elasticsearch more recently as their president.
So I've got a long background in enterprise software and enterprise technology and growth and scale of businesses.
Yeah, in Vertana, we were talking before we started recording, is an observability platform for large-scale operations or technology operations, not necessarily AI. I mean, it existed before the generative AI boom, is that right?
Yeah, I think it's a couple of really important things to say. I mean, this class of software, Craig's been around for a long time. For as long as technology's been supporting critical business services, there's been a need to monitor that infrastructure to make sure it stays available and performant. But interestingly, if you think about... observability what really is its purpose its purpose is to identify threats and risks in real time um you know identify what the cause of those was and remedy those so as a consequence we've been building deep ai and ml capabilities for over a decade. So AI is not new to us. It's really part of our core reason for being streaming in all of that event data in real time and doing the mapping and correlation needed to identify risks and threats.
What we've done more recently, of course, is lean in heavily to a lot of agentic capabilities to support automating a lot of these IT operations. But the other thing that we've done is recognize that as companies scale out, you know, Yes, the true industrialization of AI with massive AI data center investments that the market loves to call AI factories is build full end-to-end observability for the AI. So what we've essentially done is taken an incredible legacy of observability and
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Chapters
8 chapters
1
Why can’t 6 in 10 enterprises automatically identify the root cause of AI workload failures?
0:00–5:24
2
What is Virtana’s observability platform and how does it capture 20,000 metrics per second?
5:24–10:49
3
Why is root‑cause detection harder in AI factories than in traditional IT environments?
10:49–15:22
4
How do token price drops and exploding token consumption affect AI‑related costs?
15:22–21:12
5
What governance gaps did the AI Factory Reality Check study reveal?
21:12–26:15
6
How does Vertana use agentic capabilities to automate remediation of AI failures?
26:15–31:43
7
Which metrics matter most for measuring AI ROI and how does Vertana track them?
31:43–37:47
8
How are hybrid on‑prem and cloud AI factories observed and managed at scale?
37:47–44:25
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