Microsoft Shifts Strategy on Enterprise AI

Well, thanks for taking the time today, I appreciate it. We're really excited about the launch of Microsoft Frontier Company. The aim of the business is really to help customers drive frontier transformation across their businesses. It's about their outcomes, about them getting value out of AI while having intelligence compound within their organization so that they're taking control of the outcomes that AI drives versus the other way around. So we felt it was necessary to assemble a world class team with the right skill, the right scale, and the right platform to drive these outcomes.

I think the thing to really grasp here is that AI has to serve the business. It has to serve business outcomes. It's less about deploying PhDs to just simply drive AI adoption, but rather infusing the right level of skill around industry expertise, around change management and continuous improvement. And then, of course, world class AI engineering. That's what's really different about what we're doing with Microsoft Frontier Company. Sure, we're going to put a lot of great engineers on the ground at customers to help them with AI, but we're first going to be methodical about making sure that the AI solutions that they're building are really driving business outcomes and that they're infused into the way they work. AI has to empower human ambition, and it has to empower AI outcomes for customers. And that's where we're really focused here, and that's differentiated from how others are approaching this.

It's a great question. I'm really glad that you asked that, because it's super important to understand how customers get value out of these types of investments and, frankly, what's left behind. We're really, really focused on our customers' intelligence and their outcomes. So every bit of work that we do at the face of the customer is going to be about compounding their intelligence and their unique value. So any IP that's built, any data and semantic context that's derived, the evaluation thinking, all of that belongs to the customer at the end of the engagement, which is fairly differentiated here. Again, the skill sets required to do this, I think, are quite unique. We've got folks that have been in banking for 20 years, in retail for 20 years, energy, life sciences, getting to the meat of what customers actually need to achieve with their business. Putting the right folks on the ground that actually understand how to evolve the business process, getting the right AI capabilities in place, and then establishing a continuous loop of improvement so that, whether it's the supply chain or the finance organization or the HR organization, the business flows that we're establishing continuously get better through model diversity and openness and optimization. And then what's left behind is really unique intelligence for the customer. Of course, if there are things that we have to do in the Microsoft platform to make our assets better to serve the customer, we're going to do that too. That's the unique linkage of the forward deployed engineering into the Microsoft engineering teams to better serve the customer. But really, at the end of the day, the outcomes that are delivered belong to the customer. And that's what's really unique here.

I think the thing to really latch on to here is the notion of model diversity. Customers understand the business processes that need to be involved. And when I talk to CEOs and when I talk to their boards, it's super clear that they want to shift left and shift right, but put the bulk of their employees scale and working on engaging with customers and developing new products, and then try to automate everything in between through use of model diversity and having the right talent that knows how to select the right model for the right task, for the right outcome at the right price point. That is a super important skill. And so we're going to hire a ton of world class AI engineers to add to the skill that we already have, to meet the customer where they are in terms of adopting all of this. So it's not about any one model. It's about choosing the right model for the right outcomes, at the right price point. And the types of optimization that we can do with customers is really driving impact. Customers are excited all the way up to the C-suite and into the boardroom around the opportunity here. And this is about putting scale in delivering those outcomes, of course working with our partner ecosystem as well, but really driving the tip of the spear engagement with our customers.

Look, I think Palantir is to be applauded for the work that they've done with FTE. I think for us, it's more than FTE. It's about having the right skills that actually understand the business and the business outcomes. It's about having the right platform. Only Microsoft has a platform that has world class AI assistance at the face of human ambition with our copilot portfolio. And even with assets like Teams where agents can come to life and collaborate with people around the business, we have a unique AI platform that's model diverse. We support over 11,000 models so that you can start with a frontier model, optimize it, maybe use an open source model, fine tune it, get it down to the right outcome at the right price point to avoid the cost explosion around token yield. And then we have an observability platform that allows you to look closed loop and all of that. See every agent that's running in your environment, make sure that it's driving the outcomes that you want, make sure it's cyber secure, and make sure that the financial operations around the totality of the business flows are intact. So we've done a great job with FTE, but again, that's only one part of the equation. You have to have this left to right platform that allows AI to empower human ambition and do it in a model diverse, open and heterogeneous way across every layer of the stack.