Speaker 1: Hello everyone. This is Smart Talks with IBM, a podcast
Speaker 1: from Pushkin Industries, I Heart Media and IBM about what
Speaker 1: it means to look at today's most challenging problems in
Speaker 1: a new way. I'm Malcolm Gladwell. Today I'm chatting with
Speaker 1: Rob Thomas, the senior vice president of IBM Cloud and Data,
Speaker 1: where his responsibility is bringing new ideas to life. But
Speaker 1: despite being on the cutting edge of these technologies, he
Speaker 1: still has an appreciation for age old problems. There's a
Speaker 1: rabbit and a beaver and they're staring at the Hoover
Speaker 1: dam and the beaver says the rabbit, No, I didn't
Speaker 1: build it, but it's based on an idea of mind.
Speaker 1: And the point of that story is there's ideas are
Speaker 1: a dye that doesn't so great story. Everybody's got a
Speaker 1: bunch of ideas. By the way, we're too quick to
Speaker 1: dismiss the beaver. He's right, but I have you seen
Speaker 1: beaver dams. I mean, he's right, it was his idea,
Speaker 1: but he had nothing to do with the giants Cement
Speaker 1: Hoover dow. In my interview with Rob will touch on
Speaker 1: the importance of the cloud during the pandemic and how
Speaker 1: IBM has been playing a part in vaccine distribution Stay tuned. I,
Speaker 1: for one, had no idea about what it means to
Speaker 1: be the senior vice president of IBM Cloud and Data,
Speaker 1: so I asked Rob to break it down for me
Speaker 1: in Layman's terms, we build software, and software is the
Speaker 1: lingua franca of our time. Anything that will get done
Speaker 1: in businesses and even interaction with consumers is going to
Speaker 1: be done with software. It's really the language of everything
Speaker 1: that's happening in the world. That's what we build. We
Speaker 1: are focused on doing that for businesses. So how how
Speaker 1: long have you been at IBAN twenty years or one?
Speaker 1: I guess to be precise. And I started in consulting,
Speaker 1: and then I moved into our semiconductor business, and I
Speaker 1: was doing consulting. And the moment that really changed my
Speaker 1: whole career was doing work with Nintendo where we were
Speaker 1: designing the microprocessor for the Nintendo We and I realized,
Speaker 1: we're going to do this one time, but then they're
Speaker 1: going to be building software that we get copied billions
Speaker 1: of times and used by people all over the world.
Speaker 1: Maybe I'm not in the right business. And that really
Speaker 1: piqued my curiosity around software, which then led me to
Speaker 1: move into the IBM software business, where I've been for
Speaker 1: most of my career at this point. So I've been
Speaker 1: in software total twelve thirteen years. But you have seen
Speaker 1: I'm guessing, so twelve years in software. Am I right
Speaker 1: in thinking that Morris happened in those twelve years of
Speaker 1: software than happened in the entire history of software before? That?
Speaker 1: Is that a fair statement? Close? It's I'd say close.
Speaker 1: It's certainly the rate and pace of innovation has increased.
Speaker 1: Now has actually something hasn't read an outcome? Maybe that's
Speaker 1: a different question. But if you think about you know,
Speaker 1: software dates way back to even the first main frame
Speaker 1: that we ever built in the fifties. So a lot
Speaker 1: of good things have been happening in software for a
Speaker 1: long time. But the rate and pace is a level
Speaker 1: that we've never seen, and that's certainly been what is
Speaker 1: accelerated in the last decade. I mean, I remember my
Speaker 1: dad was a mathematician at the University of Waterloo. I
Speaker 1: remember coming home as a kid, going into his office
Speaker 1: and seeing stacks of computer cards. So in my lifetime
Speaker 1: I have I have gone from looking at stacks of
Speaker 1: computer cards to something far more so. I mean, I
Speaker 1: am aware of just how fast this fast, this uh,
Speaker 1: this pace is gone and it will be different a
Speaker 1: year from now. Right, that's how fast this is moving.
Speaker 1: Let's zero in on that a little bit. Um, What's
Speaker 1: what's shifting right now? Imagine I'm a client and I
Speaker 1: come to you and I say, you know, I want
Speaker 1: to be prepared for next year and the year after next.
Speaker 1: What should be at the top of my mind. Let
Speaker 1: me give you a quick story, if you don't mind.
Speaker 1: There was a time in the US where you could
Speaker 1: not easily get from one city to another. And at
Speaker 1: that point, back in the nine fifties, there was a
Speaker 1: decision that said, let's actually build the infrastructure to connect
Speaker 1: every city in America. And the result was fifty plus
Speaker 1: years of work fo dollar ours and we now have
Speaker 1: forty eight thousand miles of highways that connects all these cities.
Speaker 1: But the real impact is more profound than that, because
Speaker 1: you're able to eliminate traffic at intersections by building over passes.
Speaker 1: There are all these second order businesses that were built. Hotels,
Speaker 1: gas stations, the salty snacks that you buy in a
Speaker 1: gas station, fast food rest areas, so an entire economy
Speaker 1: was built around the idea that the first step was
Speaker 1: just to connect all the cities in the US. And
Speaker 1: that's what's happening right now with software. It is connecting
Speaker 1: businesses and individuals in a way that we've never been
Speaker 1: connected before, and we are just at the beginning of
Speaker 1: all the second order effects that will come as a
Speaker 1: result of them. And the biggest problem in software it's data.
Speaker 1: Just like you had all these disparate cities and you
Speaker 1: were building highways to connect those cities, every company has
Speaker 1: all these different data sets all over the place and
Speaker 1: it's a really hard problem. But AI is not going
Speaker 1: to be a reality for businesses until the data problem
Speaker 1: is solved. That's one thing that I spent a lot
Speaker 1: of time on Right now, we're dig digging from it
Speaker 1: that into that meaning of that phrase, the data problem.
Speaker 1: I think every individual wants any company they interact with,
Speaker 1: whether it's their local bank or restaurant, or the local cleaners,
Speaker 1: whatever it may be, they want that business to know them.
Speaker 1: It's the whole idea of when you had towns where
Speaker 1: there was just one general store and the owner knew you,
Speaker 1: they knew what you wanted. I think everybody wants that
Speaker 1: level of engagement, and that is what software enables. And
Speaker 1: the basis of that is data. And the biggest problem
Speaker 1: every business faces today is how do I understand my data?
Speaker 1: What it tells me about my customers, what it tells
Speaker 1: me about my products. So this is fundamentally about how
Speaker 1: do we live in a better way. You're talking about that.
Speaker 1: I'm I'm a big company, and I have different sets
Speaker 1: of data and they're all in different places, and they
Speaker 1: don't speak to each other, and I can't combine them
Speaker 1: and make sense. Is that what you mean by the
Speaker 1: data problem? Correct? And even if I can combine them
Speaker 1: and connect them, the data is not in a usable form.
Speaker 1: You know, one one data, says m Gladwell, the other
Speaker 1: one says Malcolm g Is that the same person? Maybe?
Speaker 1: Maybe not. It's really hard because these systems have been
Speaker 1: built up over time. We do work with a company
Speaker 1: called Wonderman. Thompson story that they shared with me just
Speaker 1: this month was doing work with Peloton. So Peloton collects
Speaker 1: a lot of data what you call first party data
Speaker 1: from a bike or the tread. I think you're a
Speaker 1: runner if I recall and w P P. Wonderman Thompson
Speaker 1: has all this third party data, which is what do
Speaker 1: they know about consumers? So just to connect those two
Speaker 1: data sets, build predictive models, and then to turn that
Speaker 1: into an advertising campaign. The AI part is actually relatively easy.
Speaker 1: It's actually connecting the data, rationalizing the data, cleaning the data.
Speaker 1: That's the really hard part that nobody talks about because
Speaker 1: all we ever see is you know the outcome, yeah,
Speaker 1: which is so I understand this is super interesting. So
Speaker 1: let's imagine you Robb or a Peloton user, and so
Speaker 1: we have a data stream that comes from the bike
Speaker 1: which says that you bike. Let's just say for them,
Speaker 1: I'm gonna flatter you an hour and a half a
Speaker 1: day at some insane pace and neither of which are true.
Speaker 1: But keep going. I did do a half hour today,
Speaker 1: but it was a very slow pace. I gotta tell you. So,
Speaker 1: I'm and I'm looking at your via whatever it is
Speaker 1: I'm collecting. I'm assuming Peloton collects a lot of sort
Speaker 1: of physiological and you stat up on the bike, and
Speaker 1: from that we can generate a rough sense of who
Speaker 1: you are, how what your athletic interests are, how fit
Speaker 1: you are, all those kinds of things, and Wonderman's Old
Speaker 1: shop wants to know, how can I use that picture
Speaker 1: of the kind of athlete you are to help bring
Speaker 1: you the kinds of ad messages that you'll respond to.
Speaker 1: Is that a fair? Is that the problem? It could
Speaker 1: be bringing it to me? But it's more likely because
Speaker 1: obviously they d anonymize all this data. It's more of all, right, so,
Speaker 1: how do we find somebody else that's like rob? What
Speaker 1: are the attributes of that person? And then how do
Speaker 1: we relate to them in a way that makes it
Speaker 1: feel like we're talking to them as opposed to talking
Speaker 1: to a cohort or a group. The number one prediction
Speaker 1: that most companies want to is what's going to happen
Speaker 1: to my sales next month or the month after or
Speaker 1: the month after. And what we found is that tends
Speaker 1: to be a product of as many as fifty or
Speaker 1: a hundred different inputs. How many people are visiting the website,
Speaker 1: how many people are calling the call center, how many
Speaker 1: sales calls if I have a face to face salesforce
Speaker 1: are they making? How many marketing campaigns am I running?
Speaker 1: If you take all of these different data points, which
Speaker 1: is awful in fifty or a hundred. You feed those
Speaker 1: into a model. Then the first month you see how
Speaker 1: close with the model. Then you adjust, second month, you
Speaker 1: see how close was the model? And these models get
Speaker 1: really good over time. And we think we can help
Speaker 1: companies predict their financial performance in a month and a
Speaker 1: quarter in a year based on all these different data sources,
Speaker 1: all these different inputs. That's pretty valuable to let's say
Speaker 1: every company. So IBM, what's IBM's role in that you've
Speaker 1: described that problem to me? What is you guys come
Speaker 1: in and you say, we'll do what? A couple of
Speaker 1: years ago I started. I was trying to think about
Speaker 1: what is the right metaphor so that I can educate
Speaker 1: our customers on this and built this concept that I
Speaker 1: called the AI ladder. To think of it as steps
Speaker 1: that you take up a ladder towards AI. The bottom
Speaker 1: rung is collect data. So you have to be able
Speaker 1: to collect all your data. I'll use a library analogy.
Speaker 1: This is just you have to get books. You have
Speaker 1: to get books into the library that's collecting. Next is
Speaker 1: you have to organize that data and the now the
Speaker 1: a lot back to library analogy. That's the card catalog.
Speaker 1: So where are all the different data sets. I might
Speaker 1: have five copies of the same data. How do I
Speaker 1: know that's the same copy. Maybe one's checked out, maybe
Speaker 1: one's on microfilm. These are actually all problems that existing businesses.
Speaker 1: So you've got to collect data, you've got to organize data.
Speaker 1: Then you have to analyze the data. So you're actually
Speaker 1: starting to do data science machine learning in the library
Speaker 1: metaphor that's where you're displaying your best seller list or
Speaker 1: you're displaying, you know, popular magazine titles. And then the
Speaker 1: top of the ladder is what I call infuse. So
Speaker 1: then how do you take those models and infuse them
Speaker 1: into a business process. So it's those four steps the ladder.
Speaker 1: You have to collect, organized, analyze, and fuse. We build
Speaker 1: software that helps customers with each of the wrongs of
Speaker 1: that ladder, helps them do the collection. We actually build
Speaker 1: what we call a data catalog to help you organize
Speaker 1: your data. So we help them with all wrongs of
Speaker 1: that ladder. Because ultimately, then you've probably heard of IBM Watson,
Speaker 1: that is our AI platform. Once you've done those things,
Speaker 1: you can use AI and get really great outcomes. Imagine
Speaker 1: if someone from the White House came to you and
Speaker 1: said we're about to do something we've never done in
Speaker 1: this haven't done in this country for seventy years, which
Speaker 1: is try and vaccinate everybody in the shortest possible time.
Speaker 1: We have a multiple sets of three and eventually probably
Speaker 1: four or five different kinds of vaccines being administered by
Speaker 1: tens of thousands of local municipalities too, people who have
Speaker 1: a wide ranging set of risk factors, urgency, pre existing
Speaker 1: conditions going on, you know, on and on and on
Speaker 1: and on on um. Can you help us do this
Speaker 1: as efficiently and cost effectively and socially consciously as possible?
Speaker 1: Is that a kind of task that you're talking about
Speaker 1: now that is in part as much a logistics problem
Speaker 1: as it is a data problem. Let me describe to
Speaker 1: you one of the data problems though, that exist around
Speaker 1: this because we're doing the work with CVS on the
Speaker 1: COVID vaccine rollout. Yes, and so if you're CVS where
Speaker 1: you're actually administering, their biggest problem is everybody has a question.
Speaker 1: CVS can't hire enough people to answer the ten questions
Speaker 1: you have, the ten questions I have, the twenty questions
Speaker 1: your cousin has. They came to us and said, can
Speaker 1: we use AI too respond to all the inquiries we're
Speaker 1: getting and actually help route people to where they can
Speaker 1: figure out they can get the vaccine when they're eligible.
Speaker 1: So we built an AI agent for them that is
Speaker 1: now dealing with the the vaccine rollout every day that
Speaker 1: starts with data. They have a place that they store
Speaker 1: data about different questions. We've got models that we have
Speaker 1: trained on language, meaning we can understand different types of questions,
Speaker 1: which really inferred versus implied versus what is stated. That's
Speaker 1: a real data problem. That's where we've spent the majority
Speaker 1: of our time looking at this, this current situation. So
Speaker 1: you would that you when you say it's a data problem,
Speaker 1: meaning that you started by trying to anticipate, by looking
Speaker 1: at the data and using that to try and anticipate
Speaker 1: all the possible questions that someone might ask, Is that
Speaker 1: what you're correct? Yes, um, and then training a machine
Speaker 1: learning model based on those inputs so that when the
Speaker 1: question was asked, we had a high probability of giving
Speaker 1: the right answer. How long did it take you to
Speaker 1: build that system? Now this is the wonders of modern software.
Speaker 1: To your question on acceleration, we did this in forty
Speaker 1: five days. Are you serious. Yeah, it's insane. How how
Speaker 1: many people worked on it? Thirty somewhere in that room.
Speaker 1: It's not a huge group. Wow. Their thing about systems
Speaker 1: like this is you hope it's really good on day one,
Speaker 1: but you know for sure it's going to be better
Speaker 1: on day ten. It's gonna be better again on day twenty.
Speaker 1: These are learning systems. They do get better over time.
Speaker 1: And the thing is with with the really difficult problems.
Speaker 1: And this is this is why I like to talk
Speaker 1: about AI is giving human superpowers. Most people want to
Speaker 1: say it replaces humans. I actually think given superpowers because
Speaker 1: in these cases you start to move the harder problems
Speaker 1: to the humans, and so therefore your your customer satisfaction
Speaker 1: goes up because people are getting their problems resolved. Would
Speaker 1: you ever get to Probably not, because there's always going
Speaker 1: to be something that's too difficult for the AI to handle,
Speaker 1: But I think you can keep moving it up for sure.
Speaker 1: Or maybe given what you've just said, would it be
Speaker 1: more fair to say you don't want to get to
Speaker 1: a hundred, that you want to rise nerve a certain
Speaker 1: category of problem for a human human interaction, because that
Speaker 1: might be ultimately more satisfying to the question. We have
Speaker 1: that discussion a lot, and certainly in the ones that
Speaker 1: I've worked on, that's that's typically the case, because let's
Speaker 1: not forget these are businesses, and the goal of most
Speaker 1: businesses is to sell something. So sometimes the best way
Speaker 1: to sell something is to really help somebody with their
Speaker 1: problem and then show them how your other product can
Speaker 1: make their life even easier. When you think back in
Speaker 1: the cases that you have kind of problems that that
Speaker 1: your group has been asked to solve it, IBM of
Speaker 1: last couple of years, what was the hardest I don't
Speaker 1: know that I could name a single thing that's harder
Speaker 1: than others. The ones that are the most time consuming,
Speaker 1: things like regulatory compliance. If you're a bank, you've got
Speaker 1: a lot of different regulations that you have to to
Speaker 1: live up to. And it's easy to help build AI
Speaker 1: that can make loan decisions yes or no, good idea,
Speaker 1: bad idea, eliminate bias from that decision. That's very doable.
Speaker 1: Am I Compliance with the regulations of where that individual
Speaker 1: is based, because they're in a zip code, or they're
Speaker 1: in a state, they're in a country, those problems get
Speaker 1: really difficult because you're kind of connecting, you know, reams
Speaker 1: of legality to a day to day business process. Those
Speaker 1: get those those get really difficult. Has any customer ever
Speaker 1: come to you with a problem that you, guys said,
Speaker 1: we can't solve that. We are way too curious to
Speaker 1: ever give up that easily. It's more of, you know,
Speaker 1: it's the it's the cheap, fast, and good triangle. If
Speaker 1: you've heard that, you know you only get two of those.
Speaker 1: Do you want it cheap and fast, it's probably not good.
Speaker 1: If you want it good and fast, it's probably not cheap.
Speaker 1: If you want it cheap and good, it's probably not fast.
Speaker 1: I think all of these situations come down to that triangle.
Speaker 1: So you have a group of people who work on
Speaker 1: these kinds of problems, and I'm curious, what do you
Speaker 1: look for when you're bringing someone onto that team. Is
Speaker 1: there a set of skills associated with dealing with this
Speaker 1: area of the application of AI to these very complicated
Speaker 1: data fields. Is there a specific set of skills that
Speaker 1: are crucial and rare hard find? The skill that's easy
Speaker 1: to test for is do you have the technical ability?
Speaker 1: Do you understand Python? Do you understand machine learning? You
Speaker 1: can kind of see from somebody's body of work and
Speaker 1: what they've studied. Do they have that skill? Part where
Speaker 1: it gets harder is the empathy question. Can you actually
Speaker 1: understand a situation, understand a user, and empathize with what
Speaker 1: they're trying to do such that you're not just building
Speaker 1: a model for a robot, You're actually building this for
Speaker 1: a human on some end, that one's hard, harder to
Speaker 1: test for. And then the third one is I would
Speaker 1: just call it curiosity, how widely read as somebody do
Speaker 1: they understand business business problems because those kind of softer skills,
Speaker 1: those make a huge difference when you're solving these kind
Speaker 1: of problems. So it's easy to test for the first.
Speaker 1: The other two are a little harder to test for,
Speaker 1: and the best data scientists in the world have all
Speaker 1: three of those. Let's talk about um the cloud. I
Speaker 1: see this word hybrid cloud. I don't know what it means.
Speaker 1: So can you start by telling me what it means
Speaker 1: and then fit this into the conversation we've been having.
Speaker 1: So any company that's been around for more than three years,
Speaker 1: maybe five, they've got somewhere that they keep their data
Speaker 1: and they keep the different technology that they have, and
Speaker 1: in many cases that's in their office or that's in
Speaker 1: a data center right right near their office. They've also
Speaker 1: started over time to start to build new data sets
Speaker 1: or new software in a public cloud, something from you know,
Speaker 1: something inside of IBM Cloud or Amazon Web Services or
Speaker 1: Microsoft Azure. The minute that you have more than one environment,
Speaker 1: you have a hybrid cloud, whether whether you know it
Speaker 1: or not. So think of it as I've got multiple
Speaker 1: data sets and multiple places to kind of back to
Speaker 1: the US Highway example, or I've got software applications and
Speaker 1: multiple places. You have to get that to act like
Speaker 1: a single technology environment. That is the essence of hybrid cloud,
Speaker 1: which is I can manage that as a single environment.
Speaker 1: The average company now has five different environments cloud wise,
Speaker 1: it acts like one. I can connect the data sets
Speaker 1: the average company has. Is that by by design because
Speaker 1: they feel it's safe for Is that just because the
Speaker 1: hodgepodge nature in which we grow our I team needs
Speaker 1: means that we end up being all over the place.
Speaker 1: It's because there's a lot of people that work in
Speaker 1: every company, and everybody wants their own thing. That's how
Speaker 1: it happens. So that this department started in their own
Speaker 1: data center, This department started on IBM cloud. This department
Speaker 1: wanted a CRM system from Salesforce, this department wanted to
Speaker 1: use Azure. It's human nature. People just go do what
Speaker 1: they want to do. And you wake up one day
Speaker 1: and you realize, hey, we've got a lot of different
Speaker 1: cloud elements. And so if you're storing your customer data
Speaker 1: with Salesforce and you've got these three other environments, how
Speaker 1: do you get the customer data to inform you know
Speaker 1: what you're doing in the other parts of your business.
Speaker 1: That's a hybrid cloud problem. And how how hard of
Speaker 1: a problem is that? I mean, as that total naive
Speaker 1: outside or I would have said, oh, surely all these
Speaker 1: cloud businesses would have made it really easy to share
Speaker 1: stuff in one place with stuff you've got in other place.
Speaker 1: Is that not true? Unfortunately the opposite is true, because
Speaker 1: for the pure play public cloud providers, the incentive was
Speaker 1: actually the opposite. It's Hotel California. For them, you can
Speaker 1: bring your stuff in, but you know you can. You
Speaker 1: can check in, but you will never let you check out,
Speaker 1: and they charge actually enormous fees if you want to
Speaker 1: get your data out. So it's a bit of a
Speaker 1: strategy tax for them to make it easy. It's also
Speaker 1: a hard problem just because you're trying to connect different
Speaker 1: data sets. Do you have in card cataloged that connects
Speaker 1: all these different sources. It's actually not easy to do.
Speaker 1: And what happens when you don't do that then you
Speaker 1: end up rebuilding everything and so suddenly you're storing all
Speaker 1: the same data five times. That gets very expensive. So
Speaker 1: let's imagine what Having this conversation give me your sense
Speaker 1: of where will be what would what would we be
Speaker 1: talking about five years from now, we'll probably be having
Speaker 1: very similar discussions as possible. Technology will be more advanced,
Speaker 1: but a lot of them probably talked about. Let's be honest,
Speaker 1: these have been around for for quite a while. There's
Speaker 1: a story this this guy, Charles Towns, he was the
Speaker 1: inventor of the laser, and he tells his story. There's
Speaker 1: a rabbit and a beaver and they're staring at the
Speaker 1: Hoover Dam and the beaver says the rabbit, no, I
Speaker 1: didn't build it, but it's based on an idea of mine.
Speaker 1: And the point of that story is there's ideas. Are
Speaker 1: a dime? Doesn't it so? Great? Story? Everybody's got a
Speaker 1: bunch of ideas. By the way, we're too quick to
Speaker 1: dismiss the beaver. He's right, but have you seen beaver
Speaker 1: Dam's I mean, I know he is right. It was
Speaker 1: his idea, but he had nothing to do with the
Speaker 1: giants Cement Hoover do. Yeah. The reason I I'd share
Speaker 1: that story is a lot of people have ideas, now
Speaker 1: what about what they can do? But what's going to
Speaker 1: make a difference five years from now is what do
Speaker 1: you go try and do? And I encourage companies that
Speaker 1: you've got to be willing to have pretty high failure rate,
Speaker 1: knowing that if you go try a bunch of things,
Speaker 1: you know, maybe only half of them will work out.
Speaker 1: I mean, if I look at AI today, so there's
Speaker 1: five major things I see companies doing generally successfully. It's
Speaker 1: customer service. We talked about that, It's financial budgeting, It's
Speaker 1: regulatory compliance. We talked about that. That one's or harder.
Speaker 1: It's employee experience hiring that type of thing. And it's
Speaker 1: using AI to run their I T systems. So using
Speaker 1: software to run the systems. Those are the five big
Speaker 1: things today. I actually think those five things will still
Speaker 1: be the topic in but will be a lot more
Speaker 1: advanced on each of those because today it's a little
Speaker 1: bit we're doing it for the first time, whereas will
Speaker 1: be a much more advanced as we get out. I
Speaker 1: do think quantum computing will be commercialized at that point.
Speaker 1: That's pretty revolutionary. So more to come on that one.
Speaker 1: Let's end on some more case studies. Tell me a
Speaker 1: couple of examples of people you've worked with where the
Speaker 1: outcome is it was really exciting or or unexpected. Or
Speaker 1: we've worked with Sprint T Mobile. They have this classic
Speaker 1: problem of they've got to do aftermarket service for all
Speaker 1: the different telecom equipment that they sell and the data
Speaker 1: that they have on those different systems, the warranty when
Speaker 1: they were built, how they're running, it's spread across a
Speaker 1: thousand different different data sources. We were able to build
Speaker 1: an AI system for them that sits across those systems,
Speaker 1: that was able to intelligently route how they do all
Speaker 1: of their aftermarket service. So do you and I feel
Speaker 1: that in our day to day life, Well, we we
Speaker 1: feel that if they don't fix things, then it's obvious
Speaker 1: because there's an outage or something that doesn't work. But
Speaker 1: it was something that they had so much data on this.
Speaker 1: They could have never done this by i'd say a
Speaker 1: typical approach. So these are the kinds of things that
Speaker 1: the average consumer doesn't see every day, but they do
Speaker 1: make a difference in our life. And you're talking about
Speaker 1: things like what like cell towers or yeah, it could
Speaker 1: be cell towers, or it could be you know, the
Speaker 1: power cable, not the power cable, the power boxes sitting
Speaker 1: next to the cell tower. It could be any of
Speaker 1: those things. Oh, I see. So you're so they have
Speaker 1: all of these systems that might have been bought at
Speaker 1: different times, made by different people, installed by different people,
Speaker 1: And so what you want to do is to give
Speaker 1: them a system that allows them to look at them
Speaker 1: all in real time and figure out where there might
Speaker 1: be an issue. Yes, we call it predictive maintenance. Right, Okay,
Speaker 1: all the signs are that there's about to be a
Speaker 1: problem on this one, they go out there, they check
Speaker 1: it out. Yep, well and behold, there is a problem.
Speaker 1: I have two cars. Can you build one for me?
Speaker 1: So we haven't scaled down to that level quite yet,
Speaker 1: but stay tuned. We're open to it. Why not, Why
Speaker 1: are you neglecting I'm the ultimate end user. I'm I'm
Speaker 1: one guy with two cars. That's a good question. Well
Speaker 1: we'll bring it to you soon. We have any um,
Speaker 1: have any professional sports teams work with you? Guys, Toronto
Speaker 1: Raptors have been a publicity on that a few years ago.
Speaker 1: You're You're I'm Canadian. This is that's my team. You're
Speaker 1: warming my heart right now of course. Well this has
Speaker 1: been really fun. Um, thank you so much. Yeah, I'm welcome,
Speaker 1: appreciate it. I'd love to help out the Toronto Raptors
Speaker 1: if I had the chance. Thanks again to Rob Thomas
Speaker 1: for an intriguing conversation about data and the cloud. Smart
Speaker 1: Talks with IBM is produced by Emily Rosteck with Carl Migliari,
Speaker 1: edited by Karen shakerge engineering by Martin Gonzalez, mixed and
Speaker 1: mastered by Jason Gambrel. Music by Granmascope. Special thanks to
Speaker 1: Molly Sosha, Andy Kelly, Mia La Belle, Jacob Weisberg, Head
Speaker 1: Fane Aerk Sander, Maggie Taylor and the teams at eight
Speaker 1: Bar and IBM. Smart Talks with IBM is a production
Speaker 1: of Pushkin Industries and I Heart Media. You can find
Speaker 1: more Pushkin podcasts on the I Heart Radio app. Apple
Speaker 1: podcasts or wherever you like to listen. I'm Malcolm Gladwell,
Speaker 1: See you next time.