Bobby Molu: Yes, let me do the first question on the margins and the expansion and the gross margins. So, that’s right. So, we talked about the fact that we are taking on a lot of cost reduction this year as a result of the macro headwind. And we’ve seen that play out. We’ve talked about the fact that we’ll see operating profit improvement on the back of cost reductions and that’s exactly what we’ve executed. Just to give you a little color on Public Cloud we also have mentioned that we had staffed up in our delivery organization for an expected demand at the beginning of the year. But given the cyclical headwinds given the macro environment that wasn’t coming through and we were experiencing an underutilization. So, as part of our cost reduction we took some actions there.
We’ve got utilization. As a result we’re seeing that flow through in the gross margins. Also in the OpEx you’ll see improvements there as well. This quarter flowing through from the cost actions we’ve taken. So, a lot of it is on the back of cost reduction and you’ll see that as well in Q4.
Amar Maletira: So, let me take the Gen AI question. First of all thanks for the question. This is a very exciting space for Rackspace. As you know we are a very workload-centric company. And for us AI and Gen AI is the net new workload that didn’t exist before. So, it’s a massive TAM expansion for us. And what we did to capitalize on this trend, which we believe is a secular trend that will accelerate very rapidly. We launched Foundry for AI by Rackspace or FAIR back in June. And since as we launched FAIR, we refenced a part of the organization, we spun up the organization with resources within the company. We had a lot of resources who very well versed with AI and we saw good traction in the last six months. Let me give you a little bit of color on what from a bookings perspective where we landed.
The interest in Gen AI has increased. We roughly have about 900 active leads in our pipeline. And this is 2x what we saw when we reported last quarter. We were close to about 100 qualified opportunities, probably across all three regions Americas, Europe and Asia Pac. We won about 10 deals. Now these are pure Gen AI deals, which are paid engagement after we launched FAIR. And most of these deals are in what we call it the ideation, which is a discovery and incubation phases. And the deal sizes tend to be smaller as expected since we are in early phases of Gen AI adoption. So when we start moving into what we call as an industrialization or production space which will involve both fine-tuning and inferencing the models we expect this to scale.
So as I mentioned in my prepared remarks, we work with all three hyperscalers to launch joint AI solutions on Public Cloud and we have won deals across all the hyperscalers. We are also working on implementing the private AI cloud reference architecture collaborating with both Dell as well as NVIDIA. So early stages early phases so to speak probably it will take time but most important thing for us is to go get the thought leadership, which will lead to more mind share which will lead then finally to volunteer. And we can help customers wherever they are in the AI journey. Today, it’s all about consulting services. It’s all about building and helping them building those applications. Ultimately, it has to work on certain infrastructure and the landing zones can be either Public Cloud or Private Cloud and that’s where the real monetization happens.
And now we have really started driving partly leadership in this place. So I would say still more to come. And we are just in the early innings so to speak.
Operator: One moment for our next question. Our next question comes from Matthew Roswell with RBC. Your line is open.
Matthew Roswell: Yes. Good evening. Two questions. I guess first pricing and competition for both Public and Cloud where you’re seeing this quarter and how has it changed over the last couple of quarters? And then following up on the AI conversation, are you finding as you go in and do these ideation deals that clients often need to sort of do preliminary work, for example, move market that workflow to the cloud, things like that before they can even take advantage of Gen AI? Thank you.
Amar Maletira: Yes. Let me — so what was the first question around pricing?
Matthew Roswell: Correct, pricing and competition.
Amar Maletira: Yes, pricing competition. So listen I think in Public Cloud we play in markets. So let me give you a little bit of color on the markets we play in so that you get an understanding of who we compete against, right? So we address all three market segments: enterprise, mid-market and commercial. In enterprise, we are very focused, because enterprise is where you find a big GSI global system integrators. And we are very focused. We think about 50 accounts and we have a selective penetration strategy in enterprise. So we don’t go head on with the GSIs. When it comes to mid-market, that’s a sweet spot. Mid-market is customers with between $300 million to about $3 billion in revenue you don’t have large GSI call on these customers.
These customers have the same complexity and challenges that an enterprise CIO will have. This is a sweet spot for Rackspace. This is where we want to expand and that’s where we have built a big business. And commercial is $300 million and lower. It’s also a sweet spot for us. So when you think to look at competition we see — depending on which markets we play we see competition in that market. Obviously, the services business right now has a cyclical headwind and everybody is chasing the same business. But it’s more important for us to remain engaged with the customers so that when the demand returns we’ll go and capture the demand. That’s how we are approaching this, okay? Now, talking about AI and your question around ideation and whether customers are ready for incubation and that’s a great question by the way because right now I think a lot of customers are looking for use cases.
In fact, we are also very surprised that some of the customers already have use cases. So we actually go into the incubation pace with this customer, where we help them to pick what we call as the large language models but it is an open model or it is a proprietary model will help them to architect the data and then make sure that we’ve trained these models on the data. So instead of data coming to AI, AI applications are going to the data so that we can go — but the real monetization will happen with industrialization and the production phase as I mentioned earlier. That’s when I think you have to create massive data lakes. But what we are seeing is currently customers are using the existing data and running what we call as co-pilots. So we have created many intelligent co-pilots for enterprise.
