Martin Migoya: And the way — and the way, Diego, to think about them is, you go the full lifecycle of the product or a company and you start with the consultancy part which is the Reinvention Studios. Then if you want, you go to the Enterprise Studios where you work with the backend. Then you go to the Digital Studios to create your digital experience and AI, so on and so forth, and then you go with the Create Studio, that is about making it available for people, making it known by people, just digital marketing, digital sales, performance, advertising, so on and so forth. So, that for that full cycle is what we strive and we tackle that cycle with these four Studio Networks. Is that clear?
Surinder Thind: That’s helpful. And then in terms of — as a follow-up here, in terms of where we started the conversation on the call, which was around AI, right. There was the initial conversations that you were having with clients. You kind of started doing some proof-of-concept projects for them. What really comes next at this point, right? How you think about what the client adoption curve here is and the timeframes that are maybe involved when you think about it from a client perspective?
Martin Migoya: Yeah, still the projects are exploratory and now a little bit deeper, but it’s still exploratory. I believe that it will take a couple of more quarters to see that in full action. And let’s see how customers use it. There are hundreds of use cases that are extremely useful. They still need to prove that the quality of the answers and what they are getting from that is really — is really to the level that they need. So, I expect that given that corporations move much slower, it will take a little bit more time to see that evolution. I know, Diego, if you want to?
Diego Tartara: Yeah. I think as of today, things — we’ve seen a lot of activity with what we call Knowledge — Augmented Knowledge, which is managing large amount of information that the organization produces in a structural way and making it available to the organization under a different type of context. We’ve done a lot on that front. We’ve done a lot as well in — in something that’s called Converse AI, which is making all your business processes accessible to one single conversation and interface, which makes it super easy for anyone working at the company, be that during the ramp-up process or every type of a work. Those typically in terms of a project size are not huge, they are very good, they have very good impact.
But the thing is, the landscape within AI is changing a lot, new players, new capabilities. Microsoft just announced a bunch of new capabilities being exposed through Azure. We know that Google follows immediately, et cetera, et cetera. But the good thing is, we are all coming to this idea of what will be the AI application architecture in the future and what will be achievable. And we’re talking about agents. We’re talking about plug-ins, a much richer type of architecture, where you can control on which data you are based — basing your response, where you can actually interact with different systems within the company and make all that a seamless part of that conversation with the large language model. All of that together puts like big, large type of projects transformational where you actually change completely, as an example, the way a marketplace interacts with its clients or an OTS, as an example.
Those are the projects that are to come and this is what Martin was saying, it will take a couple of quarters for us to start seeing that.
Surinder Thind: Thank you.
Patricia Pomies: Thank you.
Martin Migoya: Thank you.
Arturo Langa: Thank you, Surinder. So, our next question comes from Thomas Blakey from KeyBanc. Thomas, your line is open. Please go ahead.
