Divya Goyal: Good afternoon, everyone. So — good quarter here. Thanks a lot for taking my question here. I wanted to get some color on the scale of automation that Chris you talked about. So it’s interesting obviously and something that we’ve been doing about more often nowadays. Could you help us understand from a CX standpoint, how do you see a shift in revenue model going forward with the scale of automation now coming in and with obviously AI playing a bigger role going forward?
Chris Caldwell: Yeah. So let me talk about two dimensions. The first thing is that what do you have to look at is the complexity of the transaction. It doesn’t matter how the transaction happens, voice non-voice that’s immaterial. It’s really about the complexity of the transaction. And what we’re seeing is that clients are clearly focused on the most cost-effective way as they’ve always been, this is not new, most effective way of automating work. AI is just another tool or generative AI is just another tool to make that happen. What we’re seeing and what we want to demonstrate with the example of the large regional airline that we’ve done is that as we have automated work, we’ve seen the client be able to be more efficient be able to maybe grow faster in the marketplace or look at additional work that they’ve done in-house and outsource it to us because we’re more effective in dealing with their work.
And so we continue to see as a benefit and continue to want to make sure that we automate it, plus we get stickier with the clients if we’re driving the automation within their account base. And so we see it as frankly a strong positive. With generative AI specifically, look really hundreds and hundreds and hundreds of combinations or conversations with clients. Clients primarily are still focused on staff augmentation and delivering a better service with better productivity than complete labor replacement. That will come for sure. We’re doing that now in some places like again, a regional airline as an example and a large retailer in Europe that we’re doing it. That will definitely remove some work, but they’re giving us more work than historically they have outsourced to offset that because we’re far more efficient at being able to deliver these solutions.
Divya Goyal: That’s helpful. So along the same lines like you did talk about your Catalyst business that’s growing very, very well. And Catalyst from what I understand is obviously predominantly digital engineering. You did provide the breakdown of what exactly does it comprise. But given the competitive pressure out there, given the lack of discretionary spending and the recent results by some of the larger companies out there, do you see pricing pressure? Or what are some of the drivers for that growth in the Catalyst business?
Chris Caldwell: Yeah. Good question. So I would agree the sentiment out in the marketplace is people are not signing off on multi-multiyear transformational deals or replatforming deals, et cetera. And that was a big part of our Catalyst business 2.5 years ago. Now people are looking for much faster return on investment within the fiscal year, within a couple of quarters. And so that’s changed the type of engagement that we’ve seen, but we also do very, very well of that because we understand the domain knowledge of our clients so well that we can see where there’s low-hanging fruit to be able to put in our technology. And catalyst is not only aligned with sort of the platforms that are out there, but also we can build bespoke.
We also are very good at sort of journey mapping consulting. So we can actually do the whole design build run within that business specific to that client. And because we’re servicing that client generally, for the vast majority of those clients, we have the domain knowledge to understand where the best benefit is for that client. And that is really resonating in the marketplace and certainly has resonated more in the last couple of quarters where we’re actually seeing that consumption of services in an integrated fashion better than what we’ve seen prior.
Divya Goyal: That’s helpful. Thanks a lot for all the color.
Operator: Thank you. [Operator Instructions] Our next question comes from Vincent Colicchio from Barrington Research. Your may proceed.
Vincent Colicchio: Yes. Chris, curious, are the productivity benefits from some of the new tools you’ve developed meeting your expectations? And is there any way to quantify the margin benefit for ’24?
Chris Caldwell: Yes. So, the vast majority are, I think meeting our expectations. There’s a few that we think we can do a lot better with. And some of this requires a lot of changes within our client data structures and cleaning our clients’ data to get the best benefit from it. That’s one of the big things that you need to do when you’re driving better efficiencies with either machine learning AI or generative AI. And we see there being a lot more benefits as we roll it out further, both from a margin perspective and really value to our client perspective. We haven’t really quantified it in terms of a margin perspective because we’re taking a lot of those savings and reinvesting it in more deployments and more features in the tools.
So, I’d hesitate to kind of give you a number, but I’ll tell you like it’s thousands of people we’ve been able to — I don’t know what’s the best way of putting it. It’s basically thousands of people we haven’t needed to hire, while we’ve been able to grow the business, based on the productivity and we just see that increasing.
