Reengineering Work with Intelligent Agents - with Debanjan Saha of DataRobot

**Daniel Faggella** (0:12)
Hi, this is Daniel Faggella. You're listening to an episode from the AI Infrastructure Podcast. I know you're thinking, isn't this the AI in Business Podcast? And you'd be correct, but we've just launched a new show called the AI Infrastructure Podcast. We have had such a massive audience growth around everything related to infrastructure. We're talking about hardware, data platforms, governance and compliance, enterprise-wide concerns that go beyond any one industry that we've launched a whole new show called the AI Infrastructure Podcast. Our guest this week, and again, this episode is from the AI Infrastructure Podcast, which we'll talk about in a moment, is the CEO of DataRobot. DataRobot was one of the early AI unicorns more than a decade ago now. Based originally in Boston, they have team members around the world. Their CEO Debanjan is in the Bay Area. We speak this week about reengineering work in the era of intelligent agents. Basically, we talk with Debanjan directly about what the mix of man and machine is going to be in the future of the enterprise. What is it going to look like to have digital employees who are working side by side with humans? And how should departmental leaders think about building departments, not just with people, but with kind of virtual people? The analogy is actually quite apt. He talks about all kinds of analogies between human and AI workers in terms of training time and HR related considerations. Weirdly enough, there's a lot of overlap with how people think now, but it definitely needs to be leveled up. And Debanjan gives us some interesting perspective. Again, this episode is from the AI Infrastructure Podcast. If you are interested in hardware, governance, compliance and the broader infrastructure implications of AI in the enterprise, and you want to see what other infra and AI leaders in the Fortune 500 are tuned into, be sure to check out the AI Infrastructure Podcast. You can go to emerj.com/inf1.
That's INF like infrastructure. emerj.com/i-n-f, and then the number one, emerj.com/i-n-f-1, and you can go directly, it'll take you directly to the Apple page for the AI Infrastructure Podcast. We've got amazingly big name guests, a ton of C-suite from Google Cloud to Nokia to ARM, you name it. I mean, a lot of major names speaking specifically on infra, so be sure to check out that show. Without further ado, let's fly into this episode with Debanjan, CEO of DataRobot here on the AI in Business Podcast.
Debanjan, welcome to the show.

**Debanjan Saha** (2:47)
Thank you, Dan. Glad to be here, looking forward to it.

**Daniel Faggella** (2:50)
Absolutely. Cool to catch up with you folks again. Lots of evolution, AI ecosystem, DataRobot, no exception. Today, I want to knuckle down on the topic of kind of digital employee. Seen it in some of your folks' work, definitely a phrase going around the enterprise world. We'll talk about examples, but I want to begin maybe with definitions. When you're speaking to the C-suite and explaining what this paradigm is about and how people should be thinking about it, how do you like to break it?

**Debanjan Saha** (3:15)
There is of course a wide variety of definitions of what an agent is and what a digital employee is. Some people call it the silicon agent versus the carbon agent, which is the human employees. I put them based on the functionalities and what they do in three buckets. Number one, which we see a lot of it is personal productivity agents. These are people who are your co-pilots and they help you with your personal productivity, whether it is email or whether it is running some script, doing some odd job that you need to do every day, etc. And there are plenty of examples of that. I mean, co-pilot being probably the most prominent one. The second one, I would call agents which focus on, I would call line of business specific activities. These are agents which are in Salesforce, in ServiceNow, in Workday. And in many ways, they are essentially making those application a lot more user-friendly.
I mean, you have a chat interface, you can talk to your agent rather than point and click or open a screen, et cetera, et cetera. And what we focus on, what I call more serious enterprise agent. These are highly valuable agents which are doing sometime the back office work, and sometime they're doing work that we cannot do without them. I'll give you a couple of examples of that. So one of the problems that we see in enterprises, what I call the coordination tax. A lot of enterprises have these applications in various different silos, whether it is HR or operations or finance or engineering or logistics, et cetera. And oftentimes, the business processes cut across these silos.