Antonio Neri

Definition of use case is very important.

The return on invested capital has to be understood and validated before you jump into it.

And so that's why our factor for enterprise allows you to start small and then scale out in many ways as you grow, as you learn, because one success brings the next success.

So this mentality of fail fast and improve is very important because you're not going to succeed everywhere.

You're going to fail along the way.

But don't give up because if you give up too early, you're going to be left behind.

Well that's the core enabler by which we enable you to adopt AI technology because everything from the infrastructure all the way to the virtualization layer to the application layer comes tightly coupled inside our GreenLake Cloud and you decide where to put that infrastructure.

We understand from customers, and I spend more than 50% of my time with customers, that they want to have more control around AI and in particular around their data than just starting the public cloud.

They can do some experimentation in the public cloud, but when it comes down to really giving context, using their data to some of these models, they want to do it under their control.

When you deploy the models, you basically, what you're doing is inferencing these models with the data that you're generating.

And much of that data is created at the edge.

Remember, nine years ago, we talked about much of the data generated at the edge.

We talked about the concept of edge computing.

Now we're talking about bringing that AI, which is the inferencing to the data, where this model has been given context.

It's more logical.

It's more cost-effective.

It's, for sure, more secure because you don't have to move data back and forth.

And so that's why we see the edge as the next frontier where you actually enable people who have to make decisions real time.

Think about factory floors with robotics.

You cannot experience that latency in the models.