Skip to main content
or read the full transcript

Live demos

Want to watch a live demo?

Atlan engineers bootstrap, test and deploy enterprise context on real data.

About this session

3 min2 speakers

From an Atlan masterclass with Forrester. Austin Kronz asks Raluca Alexandru which governance work AI will change most. Her answer is the manual work in stewardship, with people still checking the output. Austin adds how Atlan AI drafts documentation so stewards edit instead of starting from a blank page.

Full transcriptSearchable

Austin Kronz

0:00 Shifting from AI, GenAI for organizations, what are some of those applications within the governance space that are gonna be most impacted, or at least have the potential to be impacted? I know Atlan obviously is talking about Atlan AI. We're part of it as well, but I mean, what are the opportunities there within the governance space?

Raluca Alexandru

0:16 So, so I think the more, the most, what I see most actually happening right now with, like, with real investment and real changes and real applications that are not just in beta, but customers are actually using that, I think there's a lot of investment onto search and data discovery and the data quality, for example, or data remediation, if there's any. So there, there's a lot going into that, but also into sort of more administrative tasks, I would say, like documentation creation, for example, within the... Or business glossaries, right?

0:47 There's a lot of manual work going into that still for a lot of customers out there, for a lot of companies using different products or doing things themselves manually. And as you guys said, you know, there's... I think there's gonna be a revolution into the data govern— well, the stewardship roles in, in the data governance organization, because all of that manual work does not need to happen, and I think that's one of the main applications AI's gonna help with. And is it perfect? No, probably not. There still needs to be a human touch to it and, you know, different checks in place and different...

1:21 Well, there needs to be a time to, to train that model and have a bit of tweaking and refine it and understand what you actually need from it. But it's a very promising application in terms of reducing that amount of manual work and, you know, investing that time into yeah, different other things and making things a bit more intelligent and more automated, right?

Austin Kronz

1:43 Yeah.

Raluca Alexandru

1:43 So those are the places where I've seen actual change, I would say, in some of the technologies out there. And of course, the promise goes further away. There's always more, and hopefully that will also become reality. But for now, I think that those are the main areas.

Austin Kronz

1:56 Awesome. Yeah, that, that's really helpful. I mean, I think we're seeing it already with some of the Atlan customers. I, I always say, like, AI is great, especially with the documentation example, so with Atlan AI, right, it, it's not always about getting everything perfect the first round. It's kind of avoiding that blank slate, right? How do you start with something? People make better editors than they do authors, so how can I at least give them something to start with and probably keep them in the system to enrich that, add that human in the loop element? And I think we're seeing good results from that, at least from the Atlan perspective, from the Atlan AI components.

Back to the video