AI Is Rewriting Professional Services: When Faster Implementation Moves the Bottleneck

Has that changed your ways of working, your project management processes, or the way you manage work in the services organization?

Yeah.

I think that looking at data sets, AI has been phenomenal for that.

So if you're looking at large data sets and say, okay, I want you to find for this data set top five customer problems or whatever it is, that's where all these AI tools can really dive in and take all these data and you know if you've been a software company for a while you have a lot of amazing data within your org and being able to layer ai solutions on top of that to get you know detailed findings and aha moments that you haven't had before that that's in an area where i think even even today we can still have more of those right with all the data that we have

and so so maybe to to ask the question a bit differently where do you manage this backlog of ai opportunities for improving the services experience?

Yeah, just like a Jira-type tool.

I mean, for that backlog.

And has the way you manage that backlog changed since you've started to use AI for the whole development and engineering processes for the services organization? I'm sure there are changes happening in the product organization, but I'm really curious about the services organizations.

Yeah, I would say previously a lot of product tickets would have went just to product, and now it's collaborative.

It's being reviewed, prioritized jointly with discussions.

So that's where we talk about the lines blurring.

right? And, you know, whatever that new kind of, you know, forward deployed engineers, whatever that's going to be.

But it's no longer product in silo, you know, it's product, you know, with all the folks that are customer facing, having that customer feedback, and all of their, you know, years of expertise together.

And once let's say a ticket has been prioritized, does it still go to the same product teams that work on it recently? Does Do services engineers sometimes pick it up and work on it? What's the lifecycle

of

those services improvements?

Yeah, depending on strategy, capacity, all of those things.

Cool.

Any challenges

that that raises that you're currently wrestling with? The fact that there are multiple teams touching...

The systems or is it?

No, I think it's just important to have detailed tracking.

And I would say that's one thing where I know there's a lot of pressure that people are on to move faster with AI.

I spoke with someone a couple of weeks ago, said he was doing four releases a day for his product.

and i i was like wow that's that's that's a lot that's very fast but i still think it's good to have some sort of documentation and rigor and you know following you know these processes yes keep them lean because we need to move very fast with ai but that doesn't mean no no documentation no controls no governance you still need to have those things