Philippe Ensarguet, Orange (00:11):
If we want to target production-grade AI, we need three core components. The first one is about clean data. The second one is about programmable infrastructure and the last one is AI output connected with the real decision and to also the real owner. Most of the case, when I'm talking with my peers, I think that at least one ingredient related to the three that I introduced is missing.

Ohad Barnoy, Amdocs (00:41):
At Amdocs, we build the ontology, context, and semantic layer on top of BSS/OSS, because this is where we mainly play. Some of our customers are saying, "Can you expand this to a telco-grade ontology?" And we work with them to see whether it makes sense and there's any value in extrapolating it. But I think the most important thing is that people are realizing that as you start your talk to your data, whether it's to an agent, whether it's through a human, the ability to actually drive business outcomes is going to require that layer that Ahmed was talking about that has to be built. If I'm head of a network for a telco, I'm going to have to be able to share a lot more, not just the insight of what my network score is for this customer or for this tower or for this region, but the AI transformation is going to shift a cultural transformation, which I think is going to help evolve and change the operating model.

(01:36):
I think this is one of the unique capabilities that AI brings because Ahmed mentioned before the deterministic automation, which is what we had until now, which a lot of us are still using. And this is where we would take a process and automate it with RPA or whatever. And usually it would be the same process we would automate. This is exactly where with AI we can come and rewrite and rewire our business processes and our technical processes without having to determine the deterministic branch and say ahead of time what we think is going to be exactly the step-by-step process, both for us and our customers, by the way.