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Speaker 1: Hello everyone. This is Smart Talks with IBM, a podcast

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Speaker 1: from Pushkin Industries, I Heart Media and IBM about what

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Speaker 1: it means to look at today's most challenging problems in

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Speaker 1: a new way. I'm Malcolm Gladwell. Today I'm chatting with

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Speaker 1: Rob Thomas, the senior vice president of IBM Cloud and Data,

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Speaker 1: where his responsibility is bringing new ideas to life. But

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Speaker 1: despite being on the cutting edge of these technologies, he

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Speaker 1: still has an appreciation for age old problems. There's a

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Speaker 1: rabbit and a beaver and they're staring at the Hoover

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Speaker 1: dam and the beaver says the rabbit, No, I didn't

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Speaker 1: build it, but it's based on an idea of mind.

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Speaker 1: And the point of that story is there's ideas are

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Speaker 1: a dye that doesn't so great story. Everybody's got a

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Speaker 1: bunch of ideas. By the way, we're too quick to

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Speaker 1: dismiss the beaver. He's right, but I have you seen

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Speaker 1: beaver dams. I mean, he's right, it was his idea,

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Speaker 1: but he had nothing to do with the giants Cement

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Speaker 1: Hoover dow. In my interview with Rob will touch on

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Speaker 1: the importance of the cloud during the pandemic and how

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Speaker 1: IBM has been playing a part in vaccine distribution Stay tuned. I,

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Speaker 1: for one, had no idea about what it means to

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Speaker 1: be the senior vice president of IBM Cloud and Data,

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Speaker 1: so I asked Rob to break it down for me

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Speaker 1: in Layman's terms, we build software, and software is the

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Speaker 1: lingua franca of our time. Anything that will get done

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Speaker 1: in businesses and even interaction with consumers is going to

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Speaker 1: be done with software. It's really the language of everything

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Speaker 1: that's happening in the world. That's what we build. We

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Speaker 1: are focused on doing that for businesses. So how how

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Speaker 1: long have you been at IBAN twenty years or one?

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Speaker 1: I guess to be precise. And I started in consulting,

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Speaker 1: and then I moved into our semiconductor business, and I

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Speaker 1: was doing consulting. And the moment that really changed my

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Speaker 1: whole career was doing work with Nintendo where we were

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Speaker 1: designing the microprocessor for the Nintendo We and I realized,

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Speaker 1: we're going to do this one time, but then they're

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Speaker 1: going to be building software that we get copied billions

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Speaker 1: of times and used by people all over the world.

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Speaker 1: Maybe I'm not in the right business. And that really

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Speaker 1: piqued my curiosity around software, which then led me to

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Speaker 1: move into the IBM software business, where I've been for

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Speaker 1: most of my career at this point. So I've been

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Speaker 1: in software total twelve thirteen years. But you have seen

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Speaker 1: I'm guessing, so twelve years in software. Am I right

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Speaker 1: in thinking that Morris happened in those twelve years of

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Speaker 1: software than happened in the entire history of software before? That?

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Speaker 1: Is that a fair statement? Close? It's I'd say close.

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Speaker 1: It's certainly the rate and pace of innovation has increased.

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Speaker 1: Now has actually something hasn't read an outcome? Maybe that's

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Speaker 1: a different question. But if you think about you know,

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Speaker 1: software dates way back to even the first main frame

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Speaker 1: that we ever built in the fifties. So a lot

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Speaker 1: of good things have been happening in software for a

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Speaker 1: long time. But the rate and pace is a level

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Speaker 1: that we've never seen, and that's certainly been what is

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Speaker 1: accelerated in the last decade. I mean, I remember my

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Speaker 1: dad was a mathematician at the University of Waterloo. I

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Speaker 1: remember coming home as a kid, going into his office

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Speaker 1: and seeing stacks of computer cards. So in my lifetime

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Speaker 1: I have I have gone from looking at stacks of

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Speaker 1: computer cards to something far more so. I mean, I

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Speaker 1: am aware of just how fast this fast, this uh,

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Speaker 1: this pace is gone and it will be different a

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Speaker 1: year from now. Right, that's how fast this is moving.

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Speaker 1: Let's zero in on that a little bit. Um, What's

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Speaker 1: what's shifting right now? Imagine I'm a client and I

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Speaker 1: come to you and I say, you know, I want

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Speaker 1: to be prepared for next year and the year after next.

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Speaker 1: What should be at the top of my mind. Let

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Speaker 1: me give you a quick story, if you don't mind.

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Speaker 1: There was a time in the US where you could

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Speaker 1: not easily get from one city to another. And at

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Speaker 1: that point, back in the nine fifties, there was a

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Speaker 1: decision that said, let's actually build the infrastructure to connect

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Speaker 1: every city in America. And the result was fifty plus

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Speaker 1: years of work fo dollar ours and we now have

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Speaker 1: forty eight thousand miles of highways that connects all these cities.

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Speaker 1: But the real impact is more profound than that, because

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Speaker 1: you're able to eliminate traffic at intersections by building over passes.

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Speaker 1: There are all these second order businesses that were built. Hotels,

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Speaker 1: gas stations, the salty snacks that you buy in a

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Speaker 1: gas station, fast food rest areas, so an entire economy

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Speaker 1: was built around the idea that the first step was

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Speaker 1: just to connect all the cities in the US. And

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Speaker 1: that's what's happening right now with software. It is connecting

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Speaker 1: businesses and individuals in a way that we've never been

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Speaker 1: connected before, and we are just at the beginning of

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Speaker 1: all the second order effects that will come as a

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Speaker 1: result of them. And the biggest problem in software it's data.

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Speaker 1: Just like you had all these disparate cities and you

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Speaker 1: were building highways to connect those cities, every company has

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Speaker 1: all these different data sets all over the place and

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Speaker 1: it's a really hard problem. But AI is not going

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Speaker 1: to be a reality for businesses until the data problem

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Speaker 1: is solved. That's one thing that I spent a lot

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Speaker 1: of time on Right now, we're dig digging from it

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Speaker 1: that into that meaning of that phrase, the data problem.

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Speaker 1: I think every individual wants any company they interact with,

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Speaker 1: whether it's their local bank or restaurant, or the local cleaners,

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Speaker 1: whatever it may be, they want that business to know them.

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Speaker 1: It's the whole idea of when you had towns where

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Speaker 1: there was just one general store and the owner knew you,

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Speaker 1: they knew what you wanted. I think everybody wants that

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Speaker 1: level of engagement, and that is what software enables. And

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Speaker 1: the basis of that is data. And the biggest problem

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Speaker 1: every business faces today is how do I understand my data?

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Speaker 1: What it tells me about my customers, what it tells

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Speaker 1: me about my products. So this is fundamentally about how

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Speaker 1: do we live in a better way. You're talking about that.

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Speaker 1: I'm I'm a big company, and I have different sets

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Speaker 1: of data and they're all in different places, and they

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Speaker 1: don't speak to each other, and I can't combine them

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Speaker 1: and make sense. Is that what you mean by the

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Speaker 1: data problem? Correct? And even if I can combine them

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Speaker 1: and connect them, the data is not in a usable form.

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Speaker 1: You know, one one data, says m Gladwell, the other

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Speaker 1: one says Malcolm g Is that the same person? Maybe?

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Speaker 1: Maybe not. It's really hard because these systems have been

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Speaker 1: built up over time. We do work with a company

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Speaker 1: called Wonderman. Thompson story that they shared with me just

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Speaker 1: this month was doing work with Peloton. So Peloton collects

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Speaker 1: a lot of data what you call first party data

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Speaker 1: from a bike or the tread. I think you're a

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Speaker 1: runner if I recall and w P P. Wonderman Thompson

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Speaker 1: has all this third party data, which is what do

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Speaker 1: they know about consumers? So just to connect those two

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Speaker 1: data sets, build predictive models, and then to turn that

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Speaker 1: into an advertising campaign. The AI part is actually relatively easy.

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Speaker 1: It's actually connecting the data, rationalizing the data, cleaning the data.

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Speaker 1: That's the really hard part that nobody talks about because

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Speaker 1: all we ever see is you know the outcome, yeah,

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Speaker 1: which is so I understand this is super interesting. So

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Speaker 1: let's imagine you Robb or a Peloton user, and so

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Speaker 1: we have a data stream that comes from the bike

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Speaker 1: which says that you bike. Let's just say for them,

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Speaker 1: I'm gonna flatter you an hour and a half a

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Speaker 1: day at some insane pace and neither of which are true.

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Speaker 1: But keep going. I did do a half hour today,

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Speaker 1: but it was a very slow pace. I gotta tell you. So,

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Speaker 1: I'm and I'm looking at your via whatever it is

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Speaker 1: I'm collecting. I'm assuming Peloton collects a lot of sort

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Speaker 1: of physiological and you stat up on the bike, and

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Speaker 1: from that we can generate a rough sense of who

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Speaker 1: you are, how what your athletic interests are, how fit

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Speaker 1: you are, all those kinds of things, and Wonderman's Old

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Speaker 1: shop wants to know, how can I use that picture

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Speaker 1: of the kind of athlete you are to help bring

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Speaker 1: you the kinds of ad messages that you'll respond to.

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Speaker 1: Is that a fair? Is that the problem? It could

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Speaker 1: be bringing it to me? But it's more likely because

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Speaker 1: obviously they d anonymize all this data. It's more of all, right, so,

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Speaker 1: how do we find somebody else that's like rob? What

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Speaker 1: are the attributes of that person? And then how do

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Speaker 1: we relate to them in a way that makes it

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Speaker 1: feel like we're talking to them as opposed to talking

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Speaker 1: to a cohort or a group. The number one prediction

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Speaker 1: that most companies want to is what's going to happen

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Speaker 1: to my sales next month or the month after or

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Speaker 1: the month after. And what we found is that tends

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Speaker 1: to be a product of as many as fifty or

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Speaker 1: a hundred different inputs. How many people are visiting the website,

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Speaker 1: how many people are calling the call center, how many

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Speaker 1: sales calls if I have a face to face salesforce

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Speaker 1: are they making? How many marketing campaigns am I running?

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Speaker 1: If you take all of these different data points, which

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Speaker 1: is awful in fifty or a hundred. You feed those

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Speaker 1: into a model. Then the first month you see how

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Speaker 1: close with the model. Then you adjust, second month, you

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Speaker 1: see how close was the model? And these models get

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Speaker 1: really good over time. And we think we can help

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Speaker 1: companies predict their financial performance in a month and a

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Speaker 1: quarter in a year based on all these different data sources,

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Speaker 1: all these different inputs. That's pretty valuable to let's say

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Speaker 1: every company. So IBM, what's IBM's role in that you've

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Speaker 1: described that problem to me? What is you guys come

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Speaker 1: in and you say, we'll do what? A couple of

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Speaker 1: years ago I started. I was trying to think about

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Speaker 1: what is the right metaphor so that I can educate

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Speaker 1: our customers on this and built this concept that I

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Speaker 1: called the AI ladder. To think of it as steps

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Speaker 1: that you take up a ladder towards AI. The bottom

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Speaker 1: rung is collect data. So you have to be able

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Speaker 1: to collect all your data. I'll use a library analogy.

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Speaker 1: This is just you have to get books. You have

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Speaker 1: to get books into the library that's collecting. Next is

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Speaker 1: you have to organize that data and the now the

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Speaker 1: a lot back to library analogy. That's the card catalog.

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Speaker 1: So where are all the different data sets. I might

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Speaker 1: have five copies of the same data. How do I

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Speaker 1: know that's the same copy. Maybe one's checked out, maybe

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Speaker 1: one's on microfilm. These are actually all problems that existing businesses.

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Speaker 1: So you've got to collect data, you've got to organize data.

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Speaker 1: Then you have to analyze the data. So you're actually

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Speaker 1: starting to do data science machine learning in the library

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Speaker 1: metaphor that's where you're displaying your best seller list or

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Speaker 1: you're displaying, you know, popular magazine titles. And then the

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Speaker 1: top of the ladder is what I call infuse. So

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Speaker 1: then how do you take those models and infuse them

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Speaker 1: into a business process. So it's those four steps the ladder.

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Speaker 1: You have to collect, organized, analyze, and fuse. We build

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Speaker 1: software that helps customers with each of the wrongs of

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Speaker 1: that ladder, helps them do the collection. We actually build

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Speaker 1: what we call a data catalog to help you organize

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Speaker 1: your data. So we help them with all wrongs of

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Speaker 1: that ladder. Because ultimately, then you've probably heard of IBM Watson,

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Speaker 1: that is our AI platform. Once you've done those things,

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Speaker 1: you can use AI and get really great outcomes. Imagine

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Speaker 1: if someone from the White House came to you and

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Speaker 1: said we're about to do something we've never done in

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Speaker 1: this haven't done in this country for seventy years, which

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Speaker 1: is try and vaccinate everybody in the shortest possible time.

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Speaker 1: We have a multiple sets of three and eventually probably

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Speaker 1: four or five different kinds of vaccines being administered by

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Speaker 1: tens of thousands of local municipalities too, people who have

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Speaker 1: a wide ranging set of risk factors, urgency, pre existing

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Speaker 1: conditions going on, you know, on and on and on

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Speaker 1: and on on um. Can you help us do this

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Speaker 1: as efficiently and cost effectively and socially consciously as possible?

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Speaker 1: Is that a kind of task that you're talking about

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Speaker 1: now that is in part as much a logistics problem

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Speaker 1: as it is a data problem. Let me describe to

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Speaker 1: you one of the data problems though, that exist around

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Speaker 1: this because we're doing the work with CVS on the

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Speaker 1: COVID vaccine rollout. Yes, and so if you're CVS where

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Speaker 1: you're actually administering, their biggest problem is everybody has a question.

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Speaker 1: CVS can't hire enough people to answer the ten questions

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Speaker 1: you have, the ten questions I have, the twenty questions

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Speaker 1: your cousin has. They came to us and said, can

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Speaker 1: we use AI too respond to all the inquiries we're

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Speaker 1: getting and actually help route people to where they can

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Speaker 1: figure out they can get the vaccine when they're eligible.

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Speaker 1: So we built an AI agent for them that is

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Speaker 1: now dealing with the the vaccine rollout every day that

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Speaker 1: starts with data. They have a place that they store

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Speaker 1: data about different questions. We've got models that we have

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Speaker 1: trained on language, meaning we can understand different types of questions,

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Speaker 1: which really inferred versus implied versus what is stated. That's

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Speaker 1: a real data problem. That's where we've spent the majority

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Speaker 1: of our time looking at this, this current situation. So

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Speaker 1: you would that you when you say it's a data problem,

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Speaker 1: meaning that you started by trying to anticipate, by looking

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Speaker 1: at the data and using that to try and anticipate

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Speaker 1: all the possible questions that someone might ask, Is that

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Speaker 1: what you're correct? Yes, um, and then training a machine

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Speaker 1: learning model based on those inputs so that when the

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Speaker 1: question was asked, we had a high probability of giving

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Speaker 1: the right answer. How long did it take you to

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Speaker 1: build that system? Now this is the wonders of modern software.

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Speaker 1: To your question on acceleration, we did this in forty

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Speaker 1: five days. Are you serious. Yeah, it's insane. How how

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Speaker 1: many people worked on it? Thirty somewhere in that room.

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Speaker 1: It's not a huge group. Wow. Their thing about systems

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Speaker 1: like this is you hope it's really good on day one,

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Speaker 1: but you know for sure it's going to be better

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Speaker 1: on day ten. It's gonna be better again on day twenty.

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Speaker 1: These are learning systems. They do get better over time.

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Speaker 1: And the thing is with with the really difficult problems.

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Speaker 1: And this is this is why I like to talk

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Speaker 1: about AI is giving human superpowers. Most people want to

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Speaker 1: say it replaces humans. I actually think given superpowers because

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Speaker 1: in these cases you start to move the harder problems

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Speaker 1: to the humans, and so therefore your your customer satisfaction

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Speaker 1: goes up because people are getting their problems resolved. Would

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Speaker 1: you ever get to Probably not, because there's always going

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Speaker 1: to be something that's too difficult for the AI to handle,

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Speaker 1: But I think you can keep moving it up for sure.

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Speaker 1: Or maybe given what you've just said, would it be

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Speaker 1: more fair to say you don't want to get to

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Speaker 1: a hundred, that you want to rise nerve a certain

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Speaker 1: category of problem for a human human interaction, because that

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Speaker 1: might be ultimately more satisfying to the question. We have

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Speaker 1: that discussion a lot, and certainly in the ones that

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Speaker 1: I've worked on, that's that's typically the case, because let's

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Speaker 1: not forget these are businesses, and the goal of most

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Speaker 1: businesses is to sell something. So sometimes the best way

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Speaker 1: to sell something is to really help somebody with their

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Speaker 1: problem and then show them how your other product can

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Speaker 1: make their life even easier. When you think back in

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Speaker 1: the cases that you have kind of problems that that

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Speaker 1: your group has been asked to solve it, IBM of

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Speaker 1: last couple of years, what was the hardest I don't

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Speaker 1: know that I could name a single thing that's harder

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Speaker 1: than others. The ones that are the most time consuming,

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Speaker 1: things like regulatory compliance. If you're a bank, you've got

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Speaker 1: a lot of different regulations that you have to to

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Speaker 1: live up to. And it's easy to help build AI

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Speaker 1: that can make loan decisions yes or no, good idea,

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Speaker 1: bad idea, eliminate bias from that decision. That's very doable.

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Speaker 1: Am I Compliance with the regulations of where that individual

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Speaker 1: is based, because they're in a zip code, or they're

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Speaker 1: in a state, they're in a country, those problems get

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Speaker 1: really difficult because you're kind of connecting, you know, reams

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Speaker 1: of legality to a day to day business process. Those

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Speaker 1: get those those get really difficult. Has any customer ever

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Speaker 1: come to you with a problem that you, guys said,

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Speaker 1: we can't solve that. We are way too curious to

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Speaker 1: ever give up that easily. It's more of, you know,

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Speaker 1: it's the it's the cheap, fast, and good triangle. If

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Speaker 1: you've heard that, you know you only get two of those.

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Speaker 1: Do you want it cheap and fast, it's probably not good.

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Speaker 1: If you want it good and fast, it's probably not cheap.

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Speaker 1: If you want it cheap and good, it's probably not fast.

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Speaker 1: I think all of these situations come down to that triangle.

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Speaker 1: So you have a group of people who work on

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Speaker 1: these kinds of problems, and I'm curious, what do you

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Speaker 1: look for when you're bringing someone onto that team. Is

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Speaker 1: there a set of skills associated with dealing with this

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Speaker 1: area of the application of AI to these very complicated

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Speaker 1: data fields. Is there a specific set of skills that

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Speaker 1: are crucial and rare hard find? The skill that's easy

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Speaker 1: to test for is do you have the technical ability?

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Speaker 1: Do you understand Python? Do you understand machine learning? You

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Speaker 1: can kind of see from somebody's body of work and

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Speaker 1: what they've studied. Do they have that skill? Part where

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Speaker 1: it gets harder is the empathy question. Can you actually

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Speaker 1: understand a situation, understand a user, and empathize with what

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Speaker 1: they're trying to do such that you're not just building

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Speaker 1: a model for a robot, You're actually building this for

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Speaker 1: a human on some end, that one's hard, harder to

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Speaker 1: test for. And then the third one is I would

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Speaker 1: just call it curiosity, how widely read as somebody do

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Speaker 1: they understand business business problems because those kind of softer skills,

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Speaker 1: those make a huge difference when you're solving these kind

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Speaker 1: of problems. So it's easy to test for the first.

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Speaker 1: The other two are a little harder to test for,

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Speaker 1: and the best data scientists in the world have all

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Speaker 1: three of those. Let's talk about um the cloud. I

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Speaker 1: see this word hybrid cloud. I don't know what it means.

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Speaker 1: So can you start by telling me what it means

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Speaker 1: and then fit this into the conversation we've been having.

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Speaker 1: So any company that's been around for more than three years,

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Speaker 1: maybe five, they've got somewhere that they keep their data

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Speaker 1: and they keep the different technology that they have, and

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Speaker 1: in many cases that's in their office or that's in

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Speaker 1: a data center right right near their office. They've also

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Speaker 1: started over time to start to build new data sets

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Speaker 1: or new software in a public cloud, something from you know,

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Speaker 1: something inside of IBM Cloud or Amazon Web Services or

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Speaker 1: Microsoft Azure. The minute that you have more than one environment,

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Speaker 1: you have a hybrid cloud, whether whether you know it

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Speaker 1: or not. So think of it as I've got multiple

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Speaker 1: data sets and multiple places to kind of back to

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Speaker 1: the US Highway example, or I've got software applications and

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Speaker 1: multiple places. You have to get that to act like

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Speaker 1: a single technology environment. That is the essence of hybrid cloud,

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Speaker 1: which is I can manage that as a single environment.

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Speaker 1: The average company now has five different environments cloud wise,

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Speaker 1: it acts like one. I can connect the data sets

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Speaker 1: the average company has. Is that by by design because

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Speaker 1: they feel it's safe for Is that just because the

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Speaker 1: hodgepodge nature in which we grow our I team needs

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Speaker 1: means that we end up being all over the place.

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Speaker 1: It's because there's a lot of people that work in

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Speaker 1: every company, and everybody wants their own thing. That's how

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Speaker 1: it happens. So that this department started in their own

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Speaker 1: data center, This department started on IBM cloud. This department

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Speaker 1: wanted a CRM system from Salesforce, this department wanted to

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Speaker 1: use Azure. It's human nature. People just go do what

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Speaker 1: they want to do. And you wake up one day

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Speaker 1: and you realize, hey, we've got a lot of different

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Speaker 1: cloud elements. And so if you're storing your customer data

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Speaker 1: with Salesforce and you've got these three other environments, how

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Speaker 1: do you get the customer data to inform you know

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Speaker 1: what you're doing in the other parts of your business.

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Speaker 1: That's a hybrid cloud problem. And how how hard of

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Speaker 1: a problem is that? I mean, as that total naive

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Speaker 1: outside or I would have said, oh, surely all these

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Speaker 1: cloud businesses would have made it really easy to share

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Speaker 1: stuff in one place with stuff you've got in other place.

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Speaker 1: Is that not true? Unfortunately the opposite is true, because

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Speaker 1: for the pure play public cloud providers, the incentive was

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Speaker 1: actually the opposite. It's Hotel California. For them, you can

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Speaker 1: bring your stuff in, but you know you can. You

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Speaker 1: can check in, but you will never let you check out,

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Speaker 1: and they charge actually enormous fees if you want to

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Speaker 1: get your data out. So it's a bit of a

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Speaker 1: strategy tax for them to make it easy. It's also

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Speaker 1: a hard problem just because you're trying to connect different

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Speaker 1: data sets. Do you have in card cataloged that connects

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Speaker 1: all these different sources. It's actually not easy to do.

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Speaker 1: And what happens when you don't do that then you

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Speaker 1: end up rebuilding everything and so suddenly you're storing all

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Speaker 1: the same data five times. That gets very expensive. So

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Speaker 1: let's imagine what Having this conversation give me your sense

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Speaker 1: of where will be what would what would we be

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Speaker 1: talking about five years from now, we'll probably be having

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Speaker 1: very similar discussions as possible. Technology will be more advanced,

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Speaker 1: but a lot of them probably talked about. Let's be honest,

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Speaker 1: these have been around for for quite a while. There's

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Speaker 1: a story this this guy, Charles Towns, he was the

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Speaker 1: inventor of the laser, and he tells his story. There's

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Speaker 1: a rabbit and a beaver and they're staring at the

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Speaker 1: Hoover Dam and the beaver says the rabbit, no, I

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Speaker 1: didn't build it, but it's based on an idea of mine.

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Speaker 1: And the point of that story is there's ideas. Are

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Speaker 1: a dime? Doesn't it so? Great? Story? Everybody's got a

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Speaker 1: bunch of ideas. By the way, we're too quick to

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Speaker 1: dismiss the beaver. He's right, but have you seen beaver

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Speaker 1: Dam's I mean, I know he is right. It was

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Speaker 1: his idea, but he had nothing to do with the

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Speaker 1: giants Cement Hoover do. Yeah. The reason I I'd share

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Speaker 1: that story is a lot of people have ideas, now

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Speaker 1: what about what they can do? But what's going to

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Speaker 1: make a difference five years from now is what do

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Speaker 1: you go try and do? And I encourage companies that

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Speaker 1: you've got to be willing to have pretty high failure rate,

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Speaker 1: knowing that if you go try a bunch of things,

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Speaker 1: you know, maybe only half of them will work out.

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Speaker 1: I mean, if I look at AI today, so there's

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Speaker 1: five major things I see companies doing generally successfully. It's

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Speaker 1: customer service. We talked about that, It's financial budgeting, It's

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Speaker 1: regulatory compliance. We talked about that. That one's or harder.

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Speaker 1: It's employee experience hiring that type of thing. And it's

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Speaker 1: using AI to run their I T systems. So using

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Speaker 1: software to run the systems. Those are the five big

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Speaker 1: things today. I actually think those five things will still

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Speaker 1: be the topic in but will be a lot more

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Speaker 1: advanced on each of those because today it's a little

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Speaker 1: bit we're doing it for the first time, whereas will

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Speaker 1: be a much more advanced as we get out. I

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Speaker 1: do think quantum computing will be commercialized at that point.

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Speaker 1: That's pretty revolutionary. So more to come on that one.

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Speaker 1: Let's end on some more case studies. Tell me a

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Speaker 1: couple of examples of people you've worked with where the

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Speaker 1: outcome is it was really exciting or or unexpected. Or

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Speaker 1: we've worked with Sprint T Mobile. They have this classic

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Speaker 1: problem of they've got to do aftermarket service for all

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Speaker 1: the different telecom equipment that they sell and the data

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Speaker 1: that they have on those different systems, the warranty when

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Speaker 1: they were built, how they're running, it's spread across a

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Speaker 1: thousand different different data sources. We were able to build

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Speaker 1: an AI system for them that sits across those systems,

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Speaker 1: that was able to intelligently route how they do all

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Speaker 1: of their aftermarket service. So do you and I feel

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Speaker 1: that in our day to day life, Well, we we

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Speaker 1: feel that if they don't fix things, then it's obvious

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Speaker 1: because there's an outage or something that doesn't work. But

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Speaker 1: it was something that they had so much data on this.

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Speaker 1: They could have never done this by i'd say a

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Speaker 1: typical approach. So these are the kinds of things that

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Speaker 1: the average consumer doesn't see every day, but they do

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Speaker 1: make a difference in our life. And you're talking about

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Speaker 1: things like what like cell towers or yeah, it could

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Speaker 1: be cell towers, or it could be you know, the

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Speaker 1: power cable, not the power cable, the power boxes sitting

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Speaker 1: next to the cell tower. It could be any of

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Speaker 1: those things. Oh, I see. So you're so they have

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Speaker 1: all of these systems that might have been bought at

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Speaker 1: different times, made by different people, installed by different people,

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Speaker 1: And so what you want to do is to give

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Speaker 1: them a system that allows them to look at them

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Speaker 1: all in real time and figure out where there might

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Speaker 1: be an issue. Yes, we call it predictive maintenance. Right, Okay,

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Speaker 1: all the signs are that there's about to be a

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Speaker 1: problem on this one, they go out there, they check

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Speaker 1: it out. Yep, well and behold, there is a problem.

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Speaker 1: I have two cars. Can you build one for me?

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Speaker 1: So we haven't scaled down to that level quite yet,

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Speaker 1: but stay tuned. We're open to it. Why not, Why

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Speaker 1: are you neglecting I'm the ultimate end user. I'm I'm

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Speaker 1: one guy with two cars. That's a good question. Well

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Speaker 1: we'll bring it to you soon. We have any um,

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Speaker 1: have any professional sports teams work with you? Guys, Toronto

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Speaker 1: Raptors have been a publicity on that a few years ago.

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Speaker 1: You're You're I'm Canadian. This is that's my team. You're

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Speaker 1: warming my heart right now of course. Well this has

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Speaker 1: been really fun. Um, thank you so much. Yeah, I'm welcome,

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Speaker 1: appreciate it. I'd love to help out the Toronto Raptors

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Speaker 1: if I had the chance. Thanks again to Rob Thomas

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Speaker 1: for an intriguing conversation about data and the cloud. Smart

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Speaker 1: Talks with IBM is produced by Emily Rosteck with Carl Migliari,

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00:29:43,360 --> 00:29:48,520
Speaker 1: edited by Karen shakerge engineering by Martin Gonzalez, mixed and

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00:29:48,560 --> 00:29:54,600
Speaker 1: mastered by Jason Gambrel. Music by Granmascope. Special thanks to

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Speaker 1: Molly Sosha, Andy Kelly, Mia La Belle, Jacob Weisberg, Head

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00:29:59,040 --> 00:30:03,280
Speaker 1: Fane Aerk Sander, Maggie Taylor and the teams at eight

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00:30:03,320 --> 00:30:07,800
Speaker 1: Bar and IBM. Smart Talks with IBM is a production

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00:30:07,800 --> 00:30:11,840
Speaker 1: of Pushkin Industries and I Heart Media. You can find

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00:30:11,920 --> 00:30:15,800
Speaker 1: more Pushkin podcasts on the I Heart Radio app. Apple

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00:30:15,880 --> 00:30:21,000
Speaker 1: podcasts or wherever you like to listen. I'm Malcolm Gladwell,

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Speaker 1: See you next time.