Smart Talks with IBM and Malcolm Gladwell: Using AI to Rethink the Way Work Gets Done

still has an appreciation for age old problems. There's a rabbit and a beaver and they're staring at the Hoover dam and the beaver says the rabbit, No, I didn't build it, but it's based on an idea of mind. And the point of that story is there's ideas are a dye that doesn't so great story. Everybody's got a bunch of ideas. By the way, we're too quick to dismiss the beaver. He's right, but I have you seen

beaver dams. I mean, he's right, it was his idea, but he had nothing to do with the giants Cement Hoover dow. In my interview with Rob will touch on the importance of the cloud during the pandemic and how IBM has been playing a part in vaccine distribution Stay tuned. I, for one, had no idea about what it means to be the senior vice president of IBM Cloud and Data, so I asked Rob to break it down for me in Layman's terms, we build software, and software is the

lingua franca of our time. Anything that will get done in businesses and even interaction with consumers is going to be done with software. It's really the language of everything that's happening in the world. That's what we build. We are focused on doing that for businesses. So how how long have you been at IBAN twenty years or one? I guess to be precise. And I started in consulting, and then I moved into our semiconductor business, and I

was doing consulting. And the moment that really changed my whole career was doing work with Nintendo where we were designing the microprocessor for the Nintendo We and I realized, we're going to do this one time, but then they're going to be building software that we get copied billions of times and used by people all over the world.

Maybe I'm not in the right business. And that really piqued my curiosity around software, which then led me to move into the IBM software business, where I've been for most of my career at this point. So I've been in software total twelve thirteen years. But you have seen I'm guessing, so twelve years in software. Am I right in thinking that Morris happened in those twelve years of software than happened in the entire history of software before? That?

Is that a fair statement? Close? It's I'd say close. It's certainly the rate and pace of innovation has increased. Now has actually something hasn't read an outcome? Maybe that's a different question. But if you think about you know, software dates way back to even the first main frame that we ever built in the fifties. So a lot of good things have been happening in software for a long time. But the rate and pace is a level that we've never seen, and that's certainly been what is

accelerated in the last decade. I mean, I remember my dad was a mathematician at the University of Waterloo. I remember coming home as a kid, going into his office and seeing stacks of computer cards. So in my lifetime I have I have gone from looking at stacks of computer cards to something far more so. I mean, I am aware of just how fast this fast, this uh, this pace is gone and it will be different a year from now. Right, that's how fast this is moving.

Let's zero in on that a little bit. Um, What's what's shifting right now? Imagine I'm a client and I come to you and I say, you know, I want to be prepared for next year and the year after next. What should be at the top of my mind. Let me give you a quick story, if you don't mind. There was a time in the US where you could not easily get from one city to another. And at that point, back in the nine fifties, there was a decision that said, let's actually build the infrastructure to connect

every city in America. And the result was fifty plus years of work fo dollar ours and we now have forty eight thousand miles of highways that connects all these cities. But the real impact is more profound than that, because you're able to eliminate traffic at intersections by building over passes.

There are all these second order businesses that were built. Hotels, gas stations, the salty snacks that you buy in a gas station, fast food rest areas, so an entire economy was built around the idea that the first step was just to connect all the cities in the US. And that's what's happening right now with software. It is connecting businesses and individuals in a way that we've never been connected before, and we are just at the beginning of all the second order effects that will come as a

result of them. And the biggest problem in software it's data. Just like you had all these disparate cities and you were building highways to connect those cities, every company has all these different data sets all over the place and it's a really hard problem. But AI is not going to be a reality for businesses until the data problem is solved. That's one thing that I spent a lot of time on Right now, we're dig digging from it that into that meaning of that phrase, the data problem.

I think every individual wants any company they interact with, whether it's their local bank or restaurant, or the local cleaners, whatever it may be, they want that business to know them. It's the whole idea of when you had towns where there was just one general store and the owner knew you, they knew what you wanted. I think everybody wants that level of engagement, and that is what software enables. And

the basis of that is data. And the biggest problem every business faces today is how do I understand my data? What it tells me about my customers, what it tells me about my products. So this is fundamentally about how do we live in a better way. You're talking about that. I'm I'm a big company, and I have different sets of data and they're all in different places, and they don't speak to each other, and I can't combine them and make sense. Is that what you mean by the

data problem? Correct? And even if I can combine them and connect them, the data is not in a usable form. You know, one one data, says m Gladwell, the other one says Malcolm g Is that the same person? Maybe? Maybe not. It's really hard because these systems have been built up over time. We do work with a company called Wonderman. Thompson story that they shared with me just this month was doing work with Peloton. So Peloton collects a lot of data what you call first party data

from a bike or the tread. I think you're a runner if I recall and w P P. Wonderman Thompson has all this third party data, which is what do they know about consumers? So just to connect those two data sets, build predictive models, and then to turn that into an advertising campaign. The AI part is actually relatively easy. It's actually connecting the data, rationalizing the data, cleaning the data.

That's the really hard part that nobody talks about because all we ever see is you know the outcome, yeah, which is so I understand this is super interesting. So let's imagine you Robb or a Peloton user, and so we have a data stream that comes from the bike which says that you bike. Let's just say for them, I'm gonna flatter you an hour and a half a day at some insane pace and neither of which are true. But keep going. I did do a half hour today,

but it was a very slow pace. I gotta tell you. So, I'm and I'm looking at your via whatever it is

I'm collecting. I'm assuming Peloton collects a lot of sort of physiological and you stat up on the bike, and from that we can generate a rough sense of who you are, how what your athletic interests are, how fit you are, all those kinds of things, and Wonderman's Old shop wants to know, how can I use that picture of the kind of athlete you are to help bring you the kinds of ad messages that you'll respond to. Is that a fair? Is that the problem? It could

be bringing it to me? But it's more likely because obviously they d anonymize all this data. It's more of all, right, so, how do we find somebody else that's like rob? What are the attributes of that person? And then how do we relate to them in a way that makes it feel like we're talking to them as opposed to talking to a cohort or a group. The number one prediction that most companies want to is what's going to happen to my sales next month or the month after or

the month after. And what we found is that tends to be a product of as many as fifty or a hundred different inputs. How many people are visiting the website, how many people are calling the call center, how many sales calls if I have a face to face salesforce are they making? How many marketing campaigns am I running? If you take all of these different data points, which is awful in fifty or a hundred. You feed those into a model. Then the first month you see how

close with the model. Then you adjust, second month, you see how close was the model? And these models get really good over time. And we think we can help companies predict their financial performance in a month and a quarter in a year based on all these different data sources, all these different inputs. That's pretty valuable to let's say every company. So IBM, what's IBM's role in that you've described that problem to me? What is you guys come in and you say, we'll do what? A couple of

years ago I started. I was trying to think about what is the right metaphor so that I can educate our customers on this and built this concept that I called the AI ladder. To think of it as steps that you take up a ladder towards AI. The bottom rung is collect data. So you have to be able to collect all your data. I'll use a library analogy. This is just you have to get books. You have to get books into the library that's collecting. Next is you have to organize that data and the now the

a lot back to library analogy. That's the card catalog. So where are all the different data sets. I might have five copies of the same data. How do I know that's the same copy. Maybe one's checked out, maybe one's on microfilm. These are actually all problems that existing businesses. So you've got to collect data, you've got to organize data.

Then you have to analyze the data. So you're actually starting to do data science machine learning in the library metaphor that's where you're displaying your best seller list or you're displaying, you know, popular magazine titles. And then the top of the ladder is what I call infuse. So then how do you take those models and infuse them into a business process. So it's those four steps the ladder.

You have to collect, organized, analyze, and fuse. We build software that helps customers with each of the wrongs of that ladder, helps them do the collection. We actually build what we call a data catalog to help you organize your data. So we help them with all wrongs of that ladder. Because ultimately, then you've probably heard of IBM Watson, that is our AI platform. Once you've done those things,

you can use AI and get really great outcomes. Imagine if someone from the White House came to you and said we're about to do something we've never done in this haven't done in this country for seventy years, which is try and vaccinate everybody in the shortest possible time.

We have a multiple sets of three and eventually probably four or five different kinds of vaccines being administered by tens of thousands of local municipalities too, people who have a wide ranging set of risk factors, urgency, pre existing conditions going on, you know, on and on and on and on on um. Can you help us do this as efficiently and cost effectively and socially consciously as possible? Is that a kind of task that you're talking about now that is in part as much a logistics problem

as it is a data problem. Let me describe to you one of the data problems though, that exist around this because we're doing the work with CVS on the COVID vaccine rollout. Yes, and so if you're CVS where you're actually administering, their biggest problem is everybody has a question. CVS can't hire enough people to answer the ten questions you have, the ten questions I have, the twenty questions

your cousin has. They came to us and said, can we use AI too respond to all the inquiries we're getting and actually help route people to where they can figure out they can get the vaccine when they're eligible. So we built an AI agent for them that is now dealing with the the vaccine rollout every day that starts with data. They have a place that they store data about different questions. We've got models that we have trained on language, meaning we can understand different types of questions,

which really inferred versus implied versus what is stated. That's a real data problem. That's where we've spent the majority of our time looking at this, this current situation. So you would that you when you say it's a data problem, meaning that you started by trying to anticipate, by looking at the data and using that to try and anticipate all the possible questions that someone might ask, Is that

what you're correct? Yes, um, and then training a machine learning model based on those inputs so that when the question was asked, we had a high probability of giving the right answer. How long did it take you to build that system? Now this is the wonders of modern software. To your question on acceleration, we did this in forty five days. Are you serious. Yeah, it's insane. How how many people worked on it? Thirty somewhere in that room.

It's not a huge group. Wow. Their thing about systems like this is you hope it's really good on day one, but you know for sure it's going to be better on day ten. It's gonna be better again on day twenty. These are learning systems. They do get better over time. And the thing is with with the really difficult problems. And this is this is why I like to talk about AI is giving human superpowers. Most people want to

say it replaces humans. I actually think given superpowers because in these cases you start to move the harder problems to the humans, and so therefore your your customer satisfaction goes up because people are getting their problems resolved. Would you ever get to Probably not, because there's always going to be something that's too difficult for the AI to handle, But I think you can keep moving it up for sure.

Or maybe given what you've just said, would it be more fair to say you don't want to get to a hundred, that you want to rise nerve a certain category of problem for a human human interaction, because that might be ultimately more satisfying to the question. We have that discussion a lot, and certainly in the ones that I've worked on, that's that's typically the case, because let's not forget these are businesses, and the goal of most

businesses is to sell something. So sometimes the best way to sell something is to really help somebody with their problem and then show them how your other product can make their life even easier. When you think back in the cases that you have kind of problems that that your group has been asked to solve it, IBM of last couple of years, what was the hardest I don't know that I could name a single thing that's harder than others. The ones that are the most time consuming,

things like regulatory compliance. If you're a bank, you've got a lot of different regulations that you have to to live up to. And it's easy to help build AI that can make loan decisions yes or no, good idea, bad idea, eliminate bias from that decision. That's very doable.

Am I Compliance with the regulations of where that individual is based, because they're in a zip code, or they're in a state, they're in a country, those problems get really difficult because you're kind of connecting, you know, reams of legality to a day to day business process. Those get those those get really difficult. Has any customer ever come to you with a problem that you, guys said, we can't solve that. We are way too curious to ever give up that easily. It's more of, you know,

it's the it's the cheap, fast, and good triangle. If you've heard that, you know you only get two of those. Do you want it cheap and fast, it's probably not good. If you want it good and fast, it's probably not cheap. If you want it cheap and good, it's probably not fast. I think all of these situations come down to that triangle. So you have a group of people who work on these kinds of problems, and I'm curious, what do you

look for when you're bringing someone onto that team. Is there a set of skills associated with dealing with this area of the application of AI to these very complicated data fields. Is there a specific set of skills that are crucial and rare hard find? The skill that's easy to test for is do you have the technical ability? Do you understand Python? Do you understand machine learning? You can kind of see from somebody's body of work and what they've studied. Do they have that skill? Part where

it gets harder is the empathy question. Can you actually understand a situation, understand a user, and empathize with what they're trying to do such that you're not just building a model for a robot, You're actually building this for a human on some end, that one's hard, harder to

test for. And then the third one is I would just call it curiosity, how widely read as somebody do they understand business business problems because those kind of softer skills, those make a huge difference when you're solving these kind of problems. So it's easy to test for the first. The other two are a little harder to test for, and the best data scientists in the world have all three of those. Let's talk about um the cloud. I see this word hybrid cloud. I don't know what it means.

So can you start by telling me what it means and then fit this into the conversation we've been having. So any company that's been around for more than three years, maybe five, they've got somewhere that they keep their data and they keep the different technology that they have, and in many cases that's in their office or that's in

a data center right right near their office. They've also started over time to start to build new data sets or new software in a public cloud, something from you know, something inside of IBM Cloud or Amazon Web Services or Microsoft Azure. The minute that you have more than one environment, you have a hybrid cloud, whether whether you know it or not. So think of it as I've got multiple data sets and multiple places to kind of back to the US Highway example, or I've got software applications and

multiple places. You have to get that to act like a single technology environment. That is the essence of hybrid cloud, which is I can manage that as a single environment. The average company now has five different environments cloud wise, it acts like one. I can connect the data sets the average company has. Is that by by design because they feel it's safe for Is that just because the hodgepodge nature in which we grow our I team needs means that we end up being all over the place.

It's because there's a lot of people that work in every company, and everybody wants their own thing. That's how it happens. So that this department started in their own data center, This department started on IBM cloud. This department wanted a CRM system from Salesforce, this department wanted to use Azure. It's human nature. People just go do what they want to do. And you wake up one day and you realize, hey, we've got a lot of different

cloud elements. And so if you're storing your customer data with Salesforce and you've got these three other environments, how do you get the customer data to inform you know what you're doing in the other parts of your business. That's a hybrid cloud problem. And how how hard of a problem is that? I mean, as that total naive outside or I would have said, oh, surely all these cloud businesses would have made it really easy to share stuff in one place with stuff you've got in other place.

Is that not true? Unfortunately the opposite is true, because for the pure play public cloud providers, the incentive was actually the opposite. It's Hotel California. For them, you can bring your stuff in, but you know you can. You can check in, but you will never let you check out, and they charge actually enormous fees if you want to get your data out. So it's a bit of a strategy tax for them to make it easy. It's also a hard problem just because you're trying to connect different

data sets. Do you have in card cataloged that connects all these different sources. It's actually not easy to do. And what happens when you don't do that then you end up rebuilding everything and so suddenly you're storing all the same data five times. That gets very expensive. So let's imagine what Having this conversation give me your sense of where will be what would what would we be talking about five years from now, we'll probably be having

very similar discussions as possible. Technology will be more advanced, but a lot of them probably talked about. Let's be honest, these have been around for for quite a while. There's a story this this guy, Charles Towns, he was the inventor of the laser, and he tells his story. There's a rabbit and a beaver and they're staring at the Hoover Dam and the beaver says the rabbit, no, I didn't build it, but it's based on an idea of mine. And the point of that story is there's ideas. Are

a dime? Doesn't it so? Great? Story? Everybody's got a bunch of ideas. By the way, we're too quick to dismiss the beaver. He's right, but have you seen beaver Dam's I mean, I know he is right. It was his idea, but he had nothing to do with the giants Cement Hoover do. Yeah. The reason I I'd share that story is a lot of people have ideas, now what about what they can do? But what's going to make a difference five years from now is what do

you go try and do? And I encourage companies that you've got to be willing to have pretty high failure rate, knowing that if you go try a bunch of things, you know, maybe only half of them will work out. I mean, if I look at AI today, so there's five major things I see companies doing generally successfully. It's customer service. We talked about that, It's financial budgeting, It's regulatory compliance. We talked about that. That one's or harder.

It's employee experience hiring that type of thing. And it's using AI to run their I T systems. So using software to run the systems. Those are the five big things today. I actually think those five things will still be the topic in but will be a lot more advanced on each of those because today it's a little bit we're doing it for the first time, whereas will be a much more advanced as we get out. I do think quantum computing will be commercialized at that point.

That's pretty revolutionary. So more to come on that one. Let's end on some more case studies. Tell me a couple of examples of people you've worked with where the outcome is it was really exciting or or unexpected. Or

we've worked with Sprint T Mobile. They have this classic problem of they've got to do aftermarket service for all the different telecom equipment that they sell and the data that they have on those different systems, the warranty when they were built, how they're running, it's spread across a thousand different different data sources. We were able to build an AI system for them that sits across those systems, that was able to intelligently route how they do all

of their aftermarket service. So do you and I feel that in our day to day life, Well, we we feel that if they don't fix things, then it's obvious because there's an outage or something that doesn't work. But it was something that they had so much data on this. They could have never done this by i'd say a typical approach. So these are the kinds of things that the average consumer doesn't see every day, but they do

make a difference in our life. And you're talking about things like what like cell towers or yeah, it could be cell towers, or it could be you know, the power cable, not the power cable, the power boxes sitting next to the cell tower. It could be any of

those things. Oh, I see. So you're so they have all of these systems that might have been bought at different times, made by different people, installed by different people, And so what you want to do is to give them a system that allows them to look at them all in real time and figure out where there might be an issue. Yes, we call it predictive maintenance. Right, Okay, all the signs are that there's about to be a problem on this one, they go out there, they check

it out. Yep, well and behold, there is a problem. I have two cars. Can you build one for me? So we haven't scaled down to that level quite yet, but stay tuned. We're open to it. Why not, Why are you neglecting I'm the ultimate end user. I'm I'm one guy with two cars. That's a good question. Well we'll bring it to you soon. We have any um, have any professional sports teams work with you? Guys, Toronto Raptors have been a publicity on that a few years ago.

You're You're I'm Canadian. This is that's my team. You're warming my heart right now of course. Well this has been really fun. Um, thank you so much. Yeah, I'm welcome, appreciate it. I'd love to help out the Toronto Raptors if I had the chance. Thanks again to Rob Thomas for an intriguing conversation about data and the cloud. Smart Talks with IBM is produced by Emily Rosteck with Carl Migliari, edited by Karen shakerge engineering by Martin Gonzalez, mixed and

mastered by Jason Gambrel. Music by Granmascope. Special thanks to Molly Sosha, Andy Kelly, Mia La Belle, Jacob Weisberg, Head Fane Aerk Sander, Maggie Taylor and the teams at eight Bar and IBM. Smart Talks with IBM is a production of Pushkin Industries and I Heart Media. You can find more Pushkin podcasts on the I Heart Radio app. Apple podcasts or wherever you like to listen. I'm Malcolm Gladwell, See you next time.