Out of the Lab and Into a Product: Microsoft’s Eric Boyd

**Sam Ransbotham** (0:01)
What exciting new AI-enabled tools are on the horizon? Find out on today's episode.

**Eric Boyd** (0:08)
I'm Eric Boyd from Microsoft, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:13)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:31)
And I'm Shervin Khodabandeh, Senior Partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (0:57)
Today, Shervin and I are excited to be joined by Eric Boyd, Corporate Vice President, AI Platform at Microsoft.
Eric, thanks for taking the time to talk with us. Welcome.

**Eric Boyd** (1:05)
Great to be with you both.

**Sam Ransbotham** (1:07)
Let's start with Corporate Vice President, AI Platform. Can you tell us what that job title entails and what the scope of that is? What do you do?

**Eric Boyd** (1:15)
Yeah, sure. AI obviously is such a heady buzzword these days. I lead the AI Platform team at Microsoft.
So the AI Platform team really has a couple of different things that we focus on doing. One of the things that we do is we bring the tools for people who are trying to build and train their own AI models to make them more productive. And so that's Azure Machine Learning and that's a set of tools that we make available externally. But we also use those same tools internally. So teams like Bing, like Office, like Azure, all across Microsoft, we're using those tools to really build all the models that we use across all the things that Microsoft does.
The other thing that we do is we build some of our own models ourselves. We call those cognitive services. So if you want the latest and greatest models in speech, in vision, in language, we've got a cognitive service model that does that that you can then call directly as a web service. And so with that, we're really working with the research departments that we've got at Microsoft Research and pushing this state of the art research that we have, pushing the state of art of AI really forward and then making that available both internally to our internal services at Microsoft as well as to our customers through Azure. So my job is building all those products and figuring out how we can best meet the needs of all of our customers in this rapidly expanding field of AI.

**Sam Ransbotham** (2:32)
What I really like about that is this idea that if everyone using these tools had to go invent them from scratch, obviously it would take forever and most businesses, their goal is not speech synthesis or speech generation.
That seems exactly the right sort of thing to be building these small components and delivering them. How do you know what to build? How do you tell people how to use them? How does this work? How does this infrastructure and ecosystem start to play out?

**Eric Boyd** (3:01)
We're pretty privileged at Microsoft to have a whole bunch of different businesses that we've been in for a while. We get to work and learn with all of them over time. Basically, everything that we've done in our AI field has grown out of something that we've needed internally at Microsoft. When we try and think about what are things that customers need, we've already proved out these services. If it's a tool for how to train models, we have thousands of developers and researchers across Bing and Office who are training models to do things that you'll experience every day as a user of Microsoft. When we think about speech recognition, we work with Microsoft Teams and so we can get a transcription of every call using the speech recognition software that we've already built. Then we take those exact same things and then make them available to our customers because we know that where we found value in them, our customers are also going to find value in them. That's been one of the major innovation engines for us. As the field continues to grow, obviously, Azure has thousands and thousands of enterprise customers all across the world, all across every industry that you could think of. We go and meet with them and talk with them. That also opens up a lot of insights on where are the places the companies are struggling with things.