Transforming Data and AI for Seamless Customer Experiences with Lori Bieda of BMO

[00:00:00] Lori: so you have to learn, game you're playing and the parameters you're playing within so that you can continue to innovate and keep analysts engaged.

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[00:00:07] Lori: And then the other issue, keep them interested and keep them engaged because there's a global analytics talent drought and they can get jobs anywhere in the world. 

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[00:00:41] Anthony: Welcome to another episode of Data Masters. Today, I'm thrilled to welcome Lori Beda, a true leader in the field of data and analytics. Lori currently serves as the Chief Data and Analytics Officer for BMO North America Personal and Business Bank, where she oversees the strategic use of data to drive revenue, enhance efficiency, and improve Customer experiences across North America with over 20 years of experience in analytics, technology and marketing.

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[00:01:10] Anthony: Lori is not just a seasoned executive, but also a thought leader, speaker and published writer. She's a track record of success across global industries and is passionate about leveraging data to unlock business growth. We're excited to dig into her expertise and insights today. Welcome to the show, Lori.

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[00:01:30] Lori: Thank you so much for having me.

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[00:01:33] Anthony: So I wanted to start off, you know, BMO is not a small organization, truly one of the big banks in the world. and you are really running an enormous team, across data and analytics. This is really, you know, data and analytics at scale. Maybe you could start, and share, some, maybe some sort of facts and figures to ground folks just in terms of how big these teams are.

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[00:01:56] Anthony: but maybe we could dig in a little bit, for the listeners about how these teams are organized and how you can help such a big team be effective.

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[00:02:05] Lori: Yeah, it's a great question. Thank you. You know, at BeMo, data analytics and A. I., I would say, are central to everything that we do. And we believe that really that the answers are in the data. we've organized ourselves in a way that allows us to get at it rather rapidly and pervasively across our businesses and countries in which we operate. So we believe that there is an efficiency and an expertise, most definitely, that can be gained when things are at the center, So to speak. when we centralize tasks or we want to compete analytically, we need to make insights available, though, at every touch point across our businesses, whether that's channels, products, services, et cetera.

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[00:02:44] Lori: So at the spokes, there's a hub and there's a spoke. So we have a model, I would say, where there are key functions like data and analytics. . S. T. A. R. S. Governance, for example, and Technology Enablement are purposefully at the center. And they're deployed across every business unit from there and every segment in every country that we do business in with the premise that data is an enabler to absolutely everything that we do. And so our center function reports up into a technology and operations organization and they help govern our data and keep it safe in and outside of the cloud and on target with our tech roadmaps. Now, when you think about all the spokes, they're nested in the lines of business, and they're partnering with those individuals within the business who report up into the folks who run them. And they sit shoulder to shoulder with our business colleagues every day. They're sharing the same goals, actually, and they're empowering partners with fast facts. So there's no one org model, I would say, for any one company, but you have to look at where you are. I'd say, Uh. C. I., And your journey and perhaps your maturity and with respect to your strategy. Because In my experience, smaller firms have tended to centralize just to get their mojo. sized firms sort it out whether they need a hub or a spoke or some combo. but most large firms in my experience who compete analytically, want some level of hub and spoke.

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[00:04:01] Anthony: So 

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[00:04:02] Anthony: that's a good framework. So you're anchoring on, the size of the organization. but also the maturity. So even a big company that was just getting started might choose to keep things centralized. Is that fair?

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[00:04:16] Lori: Yes, often they do, right? For funding purposes, for expertise, for training of the analyst, to ensure that technically the roadmap makes sense, that they can govern data appropriately. So I think it's both the maturity of the organization as well as their strategy and size, that kind of combination. You tend to apply a model based on what fits.

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[00:04:36] Lori: where you're at in your journey.

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[00:04:37] Anthony: And then also in terms of what part of the data, landscape, you make this point about, security and governance, are there certain things you would say always should be centralized versus otherwise?

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[00:04:50] Lori: I think you tend to find that governance of data and the policy around it, now A. I. governance, analytics governance, data governance, the guardrails on which all businesses and all parts of the spokes have to operate, in my experience is best left in a center function because it needs to be administered fairly but you also need appropriate oversight to ensure that it's done that way. So, You need that kind of Lakewood level. Lines of defense, if you will. And so I've found that to be very effective. And when you look at roadmaps, technology roadmaps, and the manner in which data flows, you do need some coming together of those, else you end up with data islands, which leads to data governance issue, regulatory problems.

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[00:05:29] Lori: And then you just propagate data all over the place and it ends up costing you a lot of money. So my experience, I found that those two things at a center function are really smart.

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[00:05:38] Anthony: Yeah, I think one framework to think about is, there's this concept in economics of externalities, where a transaction between two people has an effect on a third party, that third party effect being the externality, and governance is a good example, of, a case where, you know, local team might be making decisions about moving quickly, and oh, we don't need to worry about encrypting the data because we just need to move quickly, without recognizing that when that data is breached,and leaked, that actually has an impact on the entire company, not just that business, to use a very dark example.

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[00:06:13] Lori: Oh, absolutely. And I think in a world of A. I. where things are automated and automated at scale, that problem becomes even,more amplified, right? There's this catalytic effect or downstream effect of where data flows. And then not only that, if there's data analytics and action in my brain, I have those three kind of frameworks. you can put data in one place, you can start to do analysis in it in multiple places, but then you can take action in multiple places. And maybe that's across multiple bots. So this idea of governance and that model becomes rather nascent and really difficult to do if you don't have some central teams with guardrails around that protecting customer information.

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[00:06:51] Anthony: would you say the same thing true for infrastructure questions, or are there other things that, organizations should keep centralized?

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[00:06:59] Lori: I would say pertaining to the data infrastructure and where the kind of entities sit and your definitions of those sorts of things. I know that's right in your wheelhouse, but the ability to be able to understand and detect down to a person level Who is the person? How are we identifying them? Where it's at?

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[00:07:14] Lori: What's our metadata management solution to help understand that irrespective of where data flows? There's elements like that, that in my experience make a ton of sense to have at a center, but you can't centralize everything, right? Else you'll choke innovation at the spoke level. So So a lot of the businesses are going to go off and they're going to innovate, and you're going to want them to do so.

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[00:07:33] Lori: you're just going to want them to do so within the parameters of what keeps data safe. And I think there are ways to do that through good communication, through good kind of underlying data structure tools, and metadata management through good policy, through good oversight. There's ways to do it, to be both innovative and nimble, while at the same time protected.

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[00:07:51] Lori: And that's the balance we strike at BMO every day.

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[00:07:55] Anthony: Yeah. Well, we'll come back to this question of the entity, because I think it's an important one. but I wanted to, maybe quickly get your thoughts on the other side of the center versus decentralized conversation. Are there Pieces of the data analytics and A. I. strategy that you think always should be really close to the business.

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[00:08:15] Anthony: And I'm thinking here about things like, well, the extreme example would be spreadsheets, but, heaven forbid. But, like, are there examples where you really feel like the only model is a decentralized model? The only place that's,valuable is decentralized?

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[00:08:28] Lori: You need, if, depending upon the maturity of the organization, if you're very mature and your data is in reasonable shape and you have a very complex business, you have all these nodes, and those folks are craving information, and the closer you are to the business, you understand the leaguing and lagging indicators of that business, and you're best positioned as an analyst in those nodes to deploy B.

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[00:08:49] Lori: I., Business Intelligence, B. I., Business Intelligence. to those various folks, and you want that, right? is a currency more valuable than water. It is the most valuable thing an organization can have, and so you want it transformed and readily available, and you want to be able to anticipate. And sometimes the further you draw a data and analytics or technical team away from those nodes, the less they understand about what's happening in the trenches, because they can't be there. They can't be at all the meetings and understand that there's an interplay between bringing on a customer effectively and thwarting off downstream attrition. They don't know because they're not living it every day. But if you're sitting at the table of those businesses, you know that. So then you're serving up dashboards, you're serving up metrics.

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[00:09:32] Lori: That are helping them get ahead of that problem. And you need that. You need data driven to a desktop with rapid pace to compete. And so I've tended to, empower those nodes and those people in that way to make sure that they get the information they need to make smart business decisions all the time as it changes. And, and you don't have time. Sometimes to rely on a center function in order to pump that out and jump in a queue and all the rest, you just, you need to run your business effectively. And I think our jobs as data and analytics professionals is to make that happen, democratize the data, help it be visually consumable so that they understand.

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[00:10:08] Lori: Uh. If you understand.

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[00:10:08] Lori: it. Make sure it's available to all the nodes when they need it. And then use your intelligence layer on the analytics side to anticipate what more things are to come. because the world's evolved, right? The trends that are gonna face us in the future are not the ones we've seen, In time periods like that. And you need your analysts that they're best getting ahead of those problems. empowered the business with fast facts. 

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[00:10:31] Anthony: Yeah, so I think that's a, great model. So I think about pushing analytical decision making analysis, dashboards, that sort of work out to the edges. And in the center are things like governance and managing, data and thinking about, as we'll talk about in a second, entities. So let's turn to this topic of entities.

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[00:10:48] Anthony: And as you're well aware, I'm a tamer. We,tend to think about everything in the context of an entity. B. M. O. For you, the key entity is people. Let's start with that. I assume that's right, but tell me if I get it wrong.

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[00:11:03] Lori: Yeah, it's people as well as businesses, which is sort of a collection of people, but I would say entity management or entity resolution is at the heart of any business, not only banking. And without it, you don't really know who you're dealing with, do you? You don't know who the actual person, let's say, is or the company is.

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[00:11:19] Lori: And so to compete, you need this supreme level of customer understanding. Standing. and then at the at the corner of that nugget is the entity itself. And so this issue is further magnified when we think about things like threat actors or folks who are impersonating others in the fraud world or cyber world.

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[00:11:35] Lori: And in fraud and cybersecurity, they're very real. I mean, in today's world, they're just experiencing significant growth. so you need to know who it is you're actually dealing with. And leveraging some of the solutions in order to get perfection around that understanding so you can re inference what looks real. and that is really, if you don't do that, you don't have the privilege of doing business.

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[00:11:56] Anthony: you think about this from the perspective of both on the positive side of the ledger, the customer, and presumably thinking about how we serve customers more effectively and then you also brought up the, maybe the darker perspective of fraud and risk. But let's talk about the customer side for a second.

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[00:12:13] Anthony: How are you thinking about, you know, the value of that kind of customer understanding? I gotta think that's really at the center of the analytic requirements.

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[00:12:22] Lori: Oh, it sure is. Yeah, first you know the who, and then you have the adjacencies to the who. So some will call it householding, or relationships, or sort of the, affiliated set of individuals who are attached to that customer information sort of file or record, if you will. And so we live or die based on the accuracy of that information, because if you lose track of the who, and I'm at the center, and I've got my family around me, You're in a really difficult spot because you don't even know who you're talking to and you don't understand how to influence who you're talking to or how to cater to the needs of the full family in order to help them make real financial progress.

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[00:12:57] Lori: You don't know, you don't understand where I am on my journey, who I am, number one, and then where I am on my journey based on those things. Suite of of Folks That Surround Me. So it's very central to to what it is that we do and then those issues, of course, as you automate and you put into AI, they just become amplified. So whatever kernel of issue you have becomes magnified and, and then you end up either Wasting dollars, ruining a customer experience and losing the privilege of having that customer. on the other end, you end up just costing your organization a ton and customers hassle through fraud issues and cyber issues and things that else could be avoided had you, done some discipline on the data. 

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[00:13:37] Anthony: so this idea of the sort of ever expanding, almost exponential, uh, relationship here, I think is actually a really important one. this is something I've often talked about, but really what we're talking about are sort of network, or even graph relationships between entities. So we're talking about like a person at the center, the relationship together as a household, potentially relationships across households, relationships between households and say a location, maybe a house that they live in or et cetera.

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[00:14:06] Anthony: and one of the things we've seen is that In these graph relationships, there's this kind of exponential relationship when you can improve the quality of nodes in the graph. When you bring things together and you resolve to, using your example, we know that Lori is Lori and that you're not two people and, you know, it's actually the same person.

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[00:14:25] 

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[00:14:25] Anthony: the quality improvements in the graph relationships go up exponentially. I don't know if that resonates with you.

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[00:14:32] Lori: It sure does. Yeah, it's like a neural network of people surrounding you. And I would say it's amplified even more in this day and age because of the dependency on multi generational wealth. So if you think about what's happening now where kids are staying home for longer periods of time, they're reliant on their parents, they're unable on account of affordability indices to purchase their own home as our generation perhaps was. And so their next tranche of inflow of cash is going to come from inheriting it from parents. In any significant way. And so you end up in this situation where if you don't understand and you can't detect that early, it's an impediment for you to grow the customer relationship now, but it's A., massive impediment in future.

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[00:15:12] Lori: If you either mistreat or you're Not aware that I have a dependent, I have to have a cat. That's a dependent. But if I had a child that was a dependent, you can bet your dollars to donuts. That we would be in an interesting spot if the organization that I banked with, if BMO didn't understand that. And so we put a lot into the householding and the ability to understand who's adjacent to whom.

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[00:15:32] Lori: So we make sure that their experiences are pure and good, across how they bank for us. And we take a one client mindset so that we understand also how that client consumes our bank as they navigate through. If they're an individual but they happen to have a business or maybe they've got a commercial entity or they're engaging with wealth.

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[00:15:51] Lori: We're looking at all of those, or they fly south for the winter and they engage with us in the States. We're looking at what that person, to your analogy, that entity does, so that we can best serve their needs wherever they travel and whomever they affiliate with. 

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[00:16:44] Anthony: Yeah, that makes a ton of sense. And I think there's a kind of intuitive, understanding and, the value of understanding those relationships more deeply, so that we can serve the customer better, to your point and have them use the bank better. but there is a dark side here as well.

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[00:16:59] Anthony: And,I sort of brought this up before, but maybe it's worth digging into a little bit around fraud and risk. And is this something I guess it's a two part question. One is, is this getting worse? other words, is fraud and risk more challenging problem today, than it's been and then related, but a bit different, which is my sense is people.

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[00:17:20] Anthony: They want to invest in understanding customers better to sell better. They don't really want to invest to reduce risk and fraud. It's hard to sell the value of that. it's like insurance, right? It's like, well, why do I need insurance if I'm not going to get into a car crash? I don't know. But let's talk about the first one first.

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[00:17:38] Anthony: Like, is this getting worse?

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[00:17:41] Lori: Yeah, it sure is. and I would say that when you look at even pre COVID to post COVID, and you look globally what's happened around the world, times are tough for folks, right? and often, those times you see all kinds of different scams cropping up and different flavors, let's say, of around the world. And so no question, we're using data and analytics and A. I. combined to get ahead of that. So atthe kernel of it, it's understanding who you're dealing with, like is that threat actor? Who are they affiliated with? What's their network look like? And then you're using machine learning, as well as different A.

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[00:18:14] Lori: I. uh,applications to figure out the patterns of that so you can get ahead of it. And so on all fronts, Those things are very real, concerns for all organizations like ours. And we're leveraging data and analytics as that tipping point to get to betterment. So that not only the customer is protected, but we keep Bad activity at bay.

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[00:18:36] Lori: there's a lot of clever, actors out there. And, you gotta use good data to stay ahead of them. And, andit's like a battle of the bots, often. You know, they're fighting our bot and we're fighting their bot. And you're, you know, you're up against some steep curves. And so, he or she who learns how to master their data with greatest proficiency wins.

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[00:18:54] Anthony: Yeah. that couldn't agree more. And then, you know, Turning to the question of selling the value of an intangible. Again, I think people can often intuit the feeling that, well, if I understood my customer better, I could sell them more. but the downside risk of a fraud event, may be hard, a harder sell, but clearly not a challenge for you.

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[00:19:12] Anthony: So share the secret. How,do you get people to understand the value?

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[00:19:16] Lori: spent, oh my goodness, better part of 20 years packaging up the investment that one needs to make in all kinds of solutions. Many years ago, it used to be the database itself. You know, we need a relational database and good Lord, and we need to keep that thing clean. And then it was, we need to invest in modelers.

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[00:19:32] Lori: And then now it's like an AI environment and infrastructure. Now we're moving data to cloud. So you're always selling something. I would say in the technical And sometimes you're selling smoke or air before people realize that they need to take a breath. Like you, so you're selling an idea of something. And so in my experience, you sell ideas in two ways. You sell the dark side of that movie that's like, well, if you don't take a breath, you're going to die or things are not going to look good. You sell the dark side of that movie or you sell the uplift. It's like, you know what? You're going to be able to breathe and the capacity created through that breath and that oxygen looks like this. two million dollars. It's X, but they often start like anything, it with your attaching it to an initiative. You're finding a dance partner. Often I'll find the line of business that is very interested, let's say, in data curation or the ability to be able to do and build an A. I., a bot, and I'll court that partner and say, all right, we're going to package this up.

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[00:20:29] Lori: And as a consequence or attached to that, I'm going to touch my little kite to your project and it's going to come. I'm going to move data to the cloud in the process. And then I'm going to do that with the next initiative and the next initiative. And before you know it, look, everything's in the cloud.

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[00:20:42] Lori: That kind of concept. It's whatever the organization has the appetite for that's in line with the roadmap that you're building. And it's not contrived. You're not trying to tell Trick someone into something they don't want. People wanna move data to the cloud, they wanna ai fify the world and they wanna become more efficient. They just don't always wanna make the investments to get them there or to clean the data along the way because they see it as just, you know, there's a bunch of analysts in the background and dark rooms that don't come out, until, you know, spring. just keep 'em there. Just pay, you know, buy more of them and just get them to do some stuff without the realization that. When you're gonna innovate and build and automate, you're gonna bloat in FTE and cost before you thin. And I have that argument all the time, organizationally. And I, play within the fiscal to say, all right, at this period I'm going to bloat when I know the organization is going to be okay with it, and I'm going to thin out by the end of the fiscal. But in the interim, I'm going to build some stuff. I'm going to run over, I'm going to have some variances, but I'm going to get real skinny and I'm going to get fit for purpose by the end of the fiscal, so we can all carry on and not have a collective gasp. so you have to learn, game you're playing and the parameters you're playing within so that you can continue to innovate and keep analysts engaged.

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[00:21:53] Lori: And then the other issue, keep them interested and keep them engaged because there's a global analytics talent drought and they can get jobs anywhere in the world. So if you want to stay ahead of the game and ahead of the curve and be innovative, you got to keep brilliant analysts with you and you got to give them interesting work to do that.

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[00:22:11] Lori: And interesting work requires some funding. and else you see the whole thing crumble. You got to hang on to great people and afford some interesting technologies in order to keep the organization humming. Yeah,

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[00:22:23] Anthony: a great point. And to this point of innovation and where the puck is going, to use a Canadian analogy, so let's talk a little bit about A. I. And one of the things you've shared with me is, that A. I. applications at, BMO are, to use your words, on fire. and maybe share a little bit about how you're investing in this space, this innovation to keep people engaged and interested.

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[00:22:47] Anthony: Hopefully they love working on A. I. projects. So what are you guys doing at a high level on A. I.?

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[00:22:53] Lori: that. Well, I would say we're a digital A. I. and cloud first bank, so we go into this phase of our growth with that kind of litmus test in the back of our minds to say, all right, if we have some data, could it be put in the cloud? Great. Can we set ourselves up for the future? Can we digitize it out of the gate? And good Lord, can we A. I. ify the thing rather than launch it in a manual fashion? might that be better for us and better for customers? So we think that way and we align our investment to those thought lines. So we have, A. I. Innovation Funds sitting at the center to make sure that we enable and encourage that growth and innovation across our company.

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[00:23:30] Lori: We compete to get those dollars. So it's actually good. You get analysts coming in. Coming outta their corners and going, I got an idea, like I got a bot idea for this. I've got, which is amazing, right? You get some interesting innovation and we attach to problems. We own those problems, and you get agile teams who come together from different walks of life to be able to create things that will help us streamline, for example, policies retrieval. And I think A. I. gets scary when you think about frontward facing employee or customer facing applications versus in the background, but the reality is an employee often is helping a customer, but the employee has to do a lot of heavy lifting right in order to get stuff across the line. So one of the things that we've been working really hard on is to transform our home buying transformation process to make sure that whatever information one needs to get your house in order, literally and figuratively, that you have access to those.

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[00:24:23] Lori: You can draw on those policy libraries. You can draw on the paperwork from the kind of caverns of the organization and experience. make that connection of company to customer easier. we're looking at that across the board for all of our policies and procedures and things that our employees regularly draw on. So we're leveraging A. I. to make it easier for employees to do what we know customers need. So it's creating ease in the system, and we know when you create ease in the system and it's easier to get out of this Out of the bank, what it is that you're looking for? NPS goes through the roof. Customers are looking for effortless experiences. And when they get them in our contact centers alone, we see uplifts of almost 30 percent in our NPS scores alone.

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[00:25:05] Lori: They want ease. We all want ease. And I see technology a capability that allows you to give customers ease.

[00:25:13] Anthony: So, I think that's a really interesting idea and something listeners can take away almost immediately. So just to put words in your mouth, if we're starting with A. I. applications, think of the customer as the internal user first that's serving the customer rather than, which I think is the intuition, to dump it in front of the actual customer, which has a bit more risk and potentially no added benefit.

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[00:25:38] Anthony: Is that a fair way of saying that?

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[00:25:40] Lori: think there's benefit in both, but I think that the as the world matures and we become comfortable with, A. I. governance with bias in the water, so to speak, with how to integrate human in the loop we learn, to live in a world that's A. I. ified and establish trust around that. it's smart to start inside and work through some of the kinks and practice it and learn what happens from a change management perspective. And then you start to expose increasingly things to customers. I also think it's just good change management practice. You get your employees used to experiencing something before they then have to quote, train or assist a customer, then you have the ability to go, you know what, this we should intervene and have the option to bounce to chat so that they have some sort of live interplay. So I there's this elegant interplay I would say between. What is automated and technology does versus what talent does. And it depends on the journey.

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[00:26:45] Lori: It's not like it's one, you're selling one product and one product is all digital or another thing is all branch based. For example, there's this omni channel sort of. Play, where it's dictated by the customer. It's how they want to experience things. And sometimes they don't know what it could be like if it were fully digitized or if it were fully enabled through a bot.

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[00:27:05] Lori: It's the fear of it that is really alarming versus the reality of it. So sometimes you're co creating that with customers to be able to figure out where it is that we think we can go next, the Royal We. And if the world's new, right, with A. I., especially generative A. I. and co pilots and the usage of natural language processing with machine learning in some of those.

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[00:27:26] Lori: We've been at that for, you know, over a decade. We have, upwards of 300 models running at any one time that are machine learning based. So A. I. is not new to Beemo, and, we've been at the helmwith some digital innovation awards globally. So we've figured out some things, but by no means are we done. we have a world ahead of us.

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[00:27:44] Anthony: maybe II find it sometimes helpful in these A. I. conversations. a lot of, a lot of ideas, 

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[00:27:51] Anthony: not a lot of practical examples. And to your point, your organization and you in particular, you're not a practical example that a listener could be like, Okay, I get it. Like, I see what, you know, the value there, and I understand.

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[00:28:05] Anthony: you know, and maybe they could, take advantage of. But, the practical side of this, I feel like, is missing. Like, sometimes I feel like, there's too many obvious examples, I'm just curious what the bank's done that you've been particularly proud of.

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[00:28:17] Anthony: You

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[00:28:18] Lori: Yeah, you got it. Well, I will say that we have an example that in October you're going to see come to full fruition and be exposed at the customer level that transforms our home buying transformation process. And as you can well imagine, a home is the single largest purchase that any of us make in a lifetime, typically. And it comes with a lot of paperwork, a lot of policy, a lot of staff. sequenced elegant events. Time matters in that process. And we've been busy tearing apart what that journey looks like from a customer's perspective. And how to make it easier. How to make it easier for the people, the bankers, who are actually having those conversations, cause they need to draw on information and have it at the ready. And also for customers to follow along like through, chat bot based feature and and capability that allows them to see how this whole process goes. Because absent that, it can feel a little opaque. It could feel like there's a banker behind the curtain and they're doing their thing. And then eventually you get these pennies from heaven.

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[00:29:17] Lori: but we're trying to empower with information that allows them to make decisions for themselves and see things transparently and technology is their friend. It's attaching all those. C. H. A. So it's that good stuff and information that we have and making it exposable and available for customers, as well as the bankers who serve them. So that's where I see technology and talent. I. keep using those terms, come together to be able to power of buying a home back into the hands of the customer. So that it feels empowering and it feels like, wow, I just bought a new home and boy, I have it.

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[00:29:52] Lori: I have everything I need all set up and BMO helped me do that. And so we've got some new changes coming in October where we are putting that on steroids and it's going to be great. And we're really proud of it.

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[00:30:05] Anthony: Yeah, no, that's wonderful. And it strikes me that home buying is an extreme example ofOf the asymmetry of information that exists between, in this example, the bank and the,the customer. I'd be lucky if I bought two or three homes in my life. it's very likely that BMO's working on.

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[00:30:26] Anthony: Two or three hundred at any given time or a thousand perhaps, I'm not even sure, like it's an enormous number. So, I always make this point to real estate friends of mine, is like, they're much better at buying homes than I am. Like they, they do it a lot. I do it infrequently. And to your point, it's a perfect example of where, guiding or empowering the user, you know, the customer, in a way that makes them feel like an expert, could be a really wonderful application of A.

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[00:30:50] Anthony: I.

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[00:30:51] Lori: It sure is. And I think that when I, from my data side of the things, I think about all those pieces and tasks and information as pieces of data,

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[00:31:01] Lori: So

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[00:31:01] Lori: I need to organize them in a way to make them available. Because you're right, it's hundreds of thousands of people who want information, but their circumstances are slightly different, right?

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[00:31:10] Lori: It's a different home. It's a different capital outlay. It's different payment terms. It's different timeframes. Renting versus buying. Commercial versus business, except there's all these subtleties, but I guess what I'm really excited about when I think about A. I. It's the ability to bring that personalized experience at scale to customers in a way that you just cannot do. Like absent technology, you cannot do it. and that to me is cool. it's the desired experience we all wanted when you go online and you're shopping for retail and things are all displayed and you've got these customized outfits that are personalized just for you. It's like bringing the customized bank to the individual using the power of data. think about that all the time, like how am I going to do that? How am I going to do it better? How am I going to anticipate the call that you have? look to see where you've come from on our digital channels to know that when you arrive and you call us, what's the most likely thing you're calling about? So then we grab all that data and experience and as we've looked at your journey and analyzed it and said, you know what, she's probably calling about that failed bill payment. her out. This is the script. Here's what we need to do for Lori. Debug the system. Make it effortless. Make it effortless. and so. that's what we get up every day trying to do.

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[00:32:28] Anthony: Yeah, I love that you said I think this is a really important idea. often we think about A. I. as a tool for automation. Like it's like, well, we can just now I could have the computer do it. And it's true. It does that. It's great. But what it really does is it does that at scale.

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[00:32:42] Anthony: And all of a sudden you can have a thousand or ten thousand or a million personalized experiences that really feel like they're unique to you. and that's. Literally impossible to do until you have an A. I. application.

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[00:32:56] Lori: It sure is. and you need a digital mindset, like you need the discipline to think that way, to think that it's a segment of one, versus us to many, and I think when you get up thinking that way and how to curate an experience for a person in a very personalized way, cause they're on their way.

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[00:33:15] Lori: and they need financial help to get there, then your job is to bring the complexity of that company down to a zone where it's curated for Laurie,

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[00:33:25] Anthony: Yeah.

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[00:33:26] Anthony: So like, imagine that, we make a big bank, feel like bank for one. that's a wonderful visionfor how to think about using A. I. Uh, in really modern ways. well, Lori, thank you. There's a lot of fantastic insights there from, You know, how to manage and run and, you know, the data analytics teams at scale, how we think about managing data at the entity level and making sure we organize around that.

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[00:33:49] Anthony: And it really sounds like, you're driving some innovative A. I. applications at BMO and, I'm excited to see, I'm, I'm going to have to go buy a house in Canada just to experience the new stuff you've been working on. so 

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[00:34:01] Lori: Well, if you're going to do that, I go to a bank that can help you.

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[00:34:05] Anthony: Thanks for joining us on Data Masters.

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[00:34:08] Lori: My pleasure.

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[00:34:08]