The Data-Driven Bank: How AI is Reshaping CX in Banking

American Banker surveyed banking leaders to understand the unfolding role that data and artificial intelligence (AI) is expected to have on the customer experience in banking. The research looks at the use cases banks are prioritizing and the impact on customer journeys. The session explores the research findings and the characteristics and best practices that define data-driven banks to better understand their overall readiness for AI adoption.


Transcription:

Kerry Gross (00:11):

Good morning everyone. How's the day going so far? It was a great conversation from Nathan, right on a really current topic, digital asset strategy. Well, I'm so excited to introduce a panel that we'll be doing now on how banks are using AI and customer experience strategy, which I think is also a very timely topic for everyone. And I'd like to introduce my fellow panelists to the stage. First we have Harveer Singh, who's the Chief Data Officer of Consumer and Small Business Banking at Truist, and followed by Brianna Elsass, the VP and Head of US Digital Servicing and Technology at BMO. And joining us also is Erin Holloway, who's the Executive Industry Advisor at SAP America. Please give my fellow panelists a warm welcome. So as we start off this conversation, I just love everyone who's joining me to tell me a little bit about your background, what you're doing now, and kind of what brings you into doing customer experience stuff right now. So Brianna, if I'm going to start with you.

Brianna Elsass (01:27):

Yeah, thank you. Appreciate being here today with everybody. I am Brianna Elsass. I've been with BMO for about eight years. I head up our digital experience and technology teams for our retail bank, our small business segments and our wealth clients. And we are currently working through developing the next set of customer features that our customers will be leveraging as well as the open banking journey and stepping into AI.

Kerry Gross (01:58):

Cool. Thanks Brianna. Yeah, Erin,

Erin Holloway (02:01):

Good morning everyone. Welcome to Florida, my home state. I'm Erin Holloway. I am an Executive Advisor at SAP and I work with all of you. My customers are banks, credit unions, fintechs, wealth and asset management companies, anyone within the finance world. And I've been doing this for about 10 years now, but I didn't start out doing this. I started out doing what you do. I started out at JP Morgan straight out of undergrad, and the head recruiter was a former CIA interrogator, so that was an interesting way to start a career. Spent 20 years there. I've also been president and treasurer of a FinTech, so right from one end of the spectrum to the other and I'm thrilled to be here with you today.

Kerry Gross (02:45):

Awesome. Welcome Harveer.

Harveer Singh (02:47):

Hi, good morning, Harveer Singh. I am the Chief Data Officer at Consumer Small Business Banking for Truist. I've been here about a year and a half focusing on client data, how to use AI to forward our personalization experience with our clients, improving the customer experience, et cetera. Prior to that, I used to run global data for Western Union across 150 countries. And then prior to that I had a life in consulting with the big four. So I've been seeing the both side of spectrums of consulting, building, owning, and then delivering it to the clients as well.

Kerry Gross (03:24):

Great welcome. And I'm so excited for this conversation. And as we do this, we're going to be leveraging some data that we did maybe, yeah, a piece of research that we did as American Banker trying to understand how banks like folks who are in the audience are leveraging AI to push their CX forward. So we talked to 130 bank and credit leaders about their institutions tech journey. As you see up here on this slide, we have a nice split between the largest banks, national banks, midsize regional banks, community banks and credit unions. And you'll see a little bit of data splitting these organizations out because I know it's so useful for you all in the audience to see where does my bank stack up compared to my peers. But in this piece of research, all the folks who responded had to be involved in retail customer banking at their organization.

(04:13):

So they all have understanding on how AI is being used in CX at their organizations. And so as we go through, we'll be using this data as a jumping off point for a conversation between the three of us. And where I want to start that conversation is first with this open-ended question we asked folks asking them in their own words, how would you describe the customer experience that your financial institution is trying to provide its customers? And we see here on the left hand side of the slide, this is just a big word cloud of all the things that people responded. And the right hand side is coded response themes, and we see some clear themes coming through, which I'm sure folks in the audience know that their bank is trying to do as well. Easy experience, seamless experience, human touch and relationship focused. And if you look on the left hand side, keywords are popping out there, right? Personalized, simple problems, anticipation, and these are all things I'm sure folks in the audience are trying to solve for as well. And so just to open our conversation, I'd love to turn first to Brianna and Harveer to say what is the customer experience you're trying to deliver to your customers first? Maybe Brianna first.

Brianna Elsass (05:16):

Yeah, so for us, we are trying very hard. The seamless is critical and really looking at what's old is new again in some of these reals. Omnichannel had a fairly prevalent prominence in the past. It's coming up again and it's where we're able to service the customers in the channel of choice, make it easy for them, highlight the relevant topics to them at the moments that matter and really make things critical and in the moment for them, data plays a critical role in that where you're providing customers the use points for the technology, the features that they need at the moments that they need it, when they're leveraging that, and whether that is within your digital experience or it transfers over to the conversation with the banker or the contact center or any moment that the customer is engaging with you in the channel that they're engaging you.

Harveer Singh (06:16):

So what those customers really want from a bank like us, they want to be able to make sure that they know why they're there, they know their data is secured, they know their money is secured, and we provide a seamless service. It's not very hard to understand that need today, how do you deliver it? Last 25 years, there's been a constant change in the way technology has been delivered In 2010 or something, you started the big data journey and then within the next five, seven years, everybody jumped onto all these big hardware that they bought and then suddenly the cloud came in and started to wipe that out as well. So the banks and the fintechs and all the financial institution, and frankly everyone has been in a constant change, but one thing that has not changed is the need of the customer is actually becoming more and more prevalent, that they want more transparency, they want more authenticity, they want more trust from us, hence the name Truist. We are a bank that came together about five years ago, merger of BB and TN, SunTrust, to very heritage banks taking, and there's a lot of pride in where the customers are today. So we don't want to lose the pride that the people have in the region and want to basically build on it. One of the things that we started doing was is embedding our AI journeys or understanding of their customer needs in the digital journeys itself.

(07:52):

Some of the folks that will be on the awards tonight, we actually won an award for something that we are calling Truist client Pulse. It's essentially, if you think about a human body, you have a pulse and a healthy healthy pulse is about 72 to 80 heart BPM. That's essentially what we are trying. We are taking that exact same scenario, putting it back into how does the customer's health is by looking at their complaints, by looking at their surveys, by looking at how they're interacting with us, the engagement, and then really understanding what the next best actions should be proactively without they really explicitly telling us. I think that if you start to understand that need proactively and start to send these subtle interventions to ensure that they really feel that you are along the journey, it goes a long way. And that's essentially what Truist is trying to do.

Kerry Gross (08:47):

Thank you so much. And I think those specifics are so interesting. And Erin, I'd love to turn from you. You have a specific experience working in FinTech, but then also you have this 10,000 foot view from working at SAP. And I'm curious, what are you seeing and thinking about when you're thinking about the customer experience that folks are delivering to their banks?

Erin Holloway (09:08):

Well, especially my experience with the FinTech, I learned very quickly that there's three things customers really want. They want it to be simple, they want it to be fast, and they want it to be easy.

(09:19):

Once it gets hard, it's too easy to just click out and stop what you're doing or go somewhere else where it is easier. So simple, fast and easy. Those are the three things that I have most definitely seen from my customers, and that's why fintechs have been so successful. And it's really hard though to make the complex look simple and feel simple. It's not an easy task. And so the smaller fintechs are a lot more nimble versus the larger companies. The bigger the elephant, the harder it is to push up the stairs and to make it easy. And so I've been in the biggest and I've been in the smallers, and so the technology that fintechs and smaller companies have started with, they're born in the cloud. Cloud is so much easier than how the banks that are hundreds of years old have built their infrastructures. And that's what everything that customers do is based on, is based on the vaccine. They don't see it, but we see the backbone. We know how difficult those systems can be and the technology behind it. And so banks that can move into the cloud and become more nimble will do better. It's really easy for customers to change banks these days. It's so easy for them to change. So how do you marry that discipline of staying within regulatory requirements, having your data secure, but then also still being simple, fast and easy for your customer?

(10:45):

There's a balancing act in there that has to be taken into consideration. Brick and mortar is expensive, and we've got a lot of DE NoVo's and neobanks that don't have those expenses. So they can offer cheaper products to their customers and make it that experience that they want. But then at the same time, the pendulum swings back to wanting a personalized service, not wanting to talk to chatbots all the time, wanting to know that if I have a problem, I can really pick up the phone and talk to it. And there've been smaller fintechs that have gotten into trouble for not having accurate and responsive customer service elements to it. So I think we've seen it swing one way. It's kind of swinging back a little bit, but it's bringing that AI technology with IT to create a much better customer experience.

Kerry Gross (11:32):

And that's a great segue for us into where does AI sit in all of this? We heard from Brianna that we were how to focus on omnichannel and we're coming back around to it and thinking about, okay, so where do all of these customer needs? How does that fit in with the technology that organizations are using? And so we were asking organizations in this survey to understand first at a high level, how significant of an impact do you expect artificial intelligence will have on the overall customer experience at your organization in just the next two years? And we see here on the slide, left hand side is in total, and we see 57% of banks overall in the US expect AI will have at least a considerable impact on CX in the next two years. But as Erin was alluding to, we see some differences here by organization type, right? We see the largest banks, the national banks and global banks are expecting a much bigger impact in the near term than the mid-sized or community banks. And kind of taking that into mind, I'm curious from Harveer and Brianna first, what do you see happening at your organization? What do you expect to be happening? The next slide, we'll talk about what's happening right now, but what do you expect to be happening in the next couple of years with AI and CX?

Harveer Singh (12:39):

So I think two years is too long. It's going to happen the next maybe a year or so, how, like I said earlier, customers expect you to know why they're there, what they're there in for, whether you walk into a branch, whether you go on digital, whether you go on mobile, it's right now the data is available. If the data is available, it's so predictable that you know exactly what they're coming in for. There is a pattern to our behavior. We get a paycheck at a certain period of time. We pay our bills at a certain period of time. I think 80% of people log in the day you get your paycheck just to ensure that it's there. It's a very, very good predictable way to catch them when they're there. So I think even we had this data before, we never really had the ability to act on it.

(13:29):

I think the AI now can be embedded to start acting on it. What is that message that you want to give them right there? Because that is one of the most important steps events that have happened is receiving a paycheck, for example, when the mortgage goes out, they know that their hard earned money is going out. What is that message that you want to get? Because I frankly check every time there is a ding on my phone is like the mortgage was paid, right? Okay, but why do I want to go and check? It's just human nature. So tapping into that human nature, putting that AI on top of it to have a meaningful conversation, it's going to go a long way. And then there's going to be a pressure every other financial institution is looking into doing something like that. So it's going to be AI versus AI, and then the AI that is going to win is the one which is more nimble, which is more personalized, which doesn't look like AI. I think that's the key message. It should not look like AI. How can you make it personalized.

Kerry Gross (14:29):

Brianna, what are you thinking?

Brianna Elsass (14:30):

Yeah, I think it's a great point that you brought up with making it not look like it's ai. It's that element of trust. You're still out there, you're still getting used to the ai, the customers, they hear about it. They don't necessarily know when they're interacting with it at the moment. When we have it behind the scenes, it's when we're putting it in front of the customers that it needs to blend in a way that actually assists them, gives them the information, builds that layer of trust, but still makes it feel like we're actually dealing on the human side of things, not in a robotic or a impersonal perspective. So I think there's quite a bit that is actually going to happen in the next two years. A lot with setting a foundation, curating the data, curating the architecture on the backend in order to make sure that we are able to do what we need to do on the front end with the experiences.

(15:26):

And actually I think that's why it relates to what you see on the scale. You've got the bigger banks that have the finances to put behind resetting some of that architecture and the data, whereas the smaller community banks, the credit unions may not have that, but that's where to put a plug out there for all of the partners in the room. That's where you can look at leveraging your partners and where you look to bring in vendors and systems and platforms to actually help transition that journey a little bit for you as well. What you're going to need to do. AI is going to be everywhere, and if you're not in the game at a certain point in time, you're going to be out of the game in general.

Kerry Gross (16:09):

Definitely. And we're seeing that here in the data too, especially with the largest banks kind of headed there. Erin, again, you were talking in the prep call about these size splits and how it makes sense that national and global banks are kind of into it. And Brianna was just talking about that. So I'm curious, what else sticks to you about the organization's expectation about the impact of AI? What else is on your mind?

Erin Holloway (16:29):

Yeah, well, it is no surprise from the data that you see that it's more important to the largest, more global banks because one, they have deep pockets and deep pockets goes a long way to buying a lot of AI and technology. Two, the most complicated lines of business on a global basis with the currencies, et cetera. So that doesn't surprise me at all in there that that's the data that we're seeing. But then the fintechs, they're not even in our study because they're born in the cloud. They are ai, they're already there. It's easy for them. So it's the bookends I think that are doing the most. And then I think it's the banks in the middle that are struggling a little bit. They may not have as deep of pockets. Their systems might be a little bit more antiquated. Maybe they didn't keep up quite as well because the money that they've put into their solutions, it's not a lift and shift.

(17:27):

That's what we help customers do. We help them move into the cloud with their back offices, which then supports their customers. And that's a process. That's not an easy thing to do. I mean, if it was easy, it would've been done by now. Everybody be finished with it. But we're embedding AI in pretty much every product we have that we use to support our banking clients. And that's the customer facing elements, that's the back office facing elements that they're using themselves. And then it's that middle kind of area, the middle office, which helps sales and marketing target customers with AI so that people feel like they're banking in a bank of one to give it that more personalized experience and take away that kind of robotic feeling that they're looking at. But it's an expensive undertaking. The more technology you have, the more you have to upgrade, the more you have to support.

Kerry Gross (18:23):

And that's a great segue into the current status of AI at organizations. That last slide was where do we expect to be in the next two years? This slide is where are we right now in terms of applying AI for the purposes of improving the customer experience? And again, left hand side is banks in total, we see 45% say they have small scale implementations or enterprise wide AI initiatives for the CX journey right now. And we'll be a little bit brief here and just talking high level because the next slide we're actually going to talk about specific use cases. But first going to you Erin, I think you were starting to talk about this now, where is AI already being implemented? And then we'll hear from Harveer and Brianna what they're doing.

Erin Holloway (19:03):

We're seeing it implemented so many places that you don't even know it's there. For example, we just made a big announcement with JP Morgan. We're embedding some of our technology into their Morgan money solution, which is one of their asset management platforms for their asset management customers. So that's more of a commercial type of application. But all of our solutions that support our financial institutions have it in there. We all have copilots that help you do things, but they're becoming more agentic. So instead of it being like, how can I help you today, that's not very helpful because you have to know the right question to ask. And the answer is only going to be as good as the AI that copilot or that chatbot can get to. And right there have to be firewalls and safety rules put in place because of regulation. Maybe they can't get to it.

(19:54):

I mean, let's be real. Chatbots are the least favorite way to interact with your bank. I mean, they really are. They're getting better. They're absolutely getting better, but it's the least favorite way because half the time you can't get the information that you need. And it's not the chat bot's fault, it doesn't have access to the data. So AI is only as good as the data it has access to and it's built on it, and it has to be real, it has to be reliable and it has to be relevant. So it's got to be fresh. Once it goes stale, it's no good anymore. So I think that that's one of those things that we're seeing and we're embedding it. So back to the JP Morgan example, we're putting ag agent AI in. So instead of it being How can I help you? Hey, I see that your balances in your Norwegian account have gone low, the most efficient way for you to bring that up to where it needs to be is to make a debit of $10 million from your multicurrency account in London. Would you like me to go ahead and start that transaction? So doing half that work for you, and you just have to say yes or no.

(20:58):

And you can train AI agents to do all kinds of things. You can put rules and regulations around what each of them does, and then you have to track it. So behind the scenes, you also have to have tracking mechanism and keep all those guidelines in place. And we help our customers do that too because somebody's got to be in charge of what are the rules that we set up in terms of what the AI agents can do and what they can't do. So I think it's pretty interesting. Now, big banks obviously see that they've got a lot aggressively they're moving toward because they have the most to lose because they have the largest customer bases. They have the most complicated products they have the most to lose. For example, there's a UK Neobank called Monzo. They're like eight years old. They have 14 million retail customers now, 14 million. They stole them from somewhere. They stole 'em from other UK banks. So the more nimble you can be as we keep using that word and giving customers what they want, giving them all of those access to their funds in that simple, fast and easy way, I think is a great way that we can all learn from some of the neo banks how to make our customer experience better.

Kerry Gross (22:14):

Thank you. And Harveer, maybe you first, where are you at with AI?

Harveer Singh (22:20):

When you have to measure something, you need to know what the end state is going to look like. We don't know, to be honest, we don't know what the end state of AI is today, the speed at which it's developing, the speed at which things are being rolled out. If you asked me this question last year, I will be like, we are doing really well. And then suddenly this whole agent AI is brought up and like, oh shit, we have to start again. So that's the problem that everyone will be dealing in. So I'm not putting too much thought into where we are. What we are trying to do is how does it improve the lives of our customers? If it's you have to choose your battles wisely, there is going to be cost to every single thing. So not every shiny object needs to be chased.

(23:02):

That's a very important lesson that I've learned in the last few years of my life is don't chase that shiny object. Agentech may be fantastic for certain use cases might not be fantastic for every single use case. Building LLMs for every single thing may not work out. So the one thing that AI is allowing us to do is test and learn very, very quickly. You test, you mimic, you try to test a scenario out, you try to test a model out, you try to test it with a group of customers. If it doesn't work, you can very quickly pivot. That's what AI is allowing us to do. In the past, it was very difficult just to get one thing through production and one thing through test and environments, it was months and months of work, procuring hardware, going down the path of procuring data. Right now, the way we are looking at doing things is we want to centralize our data and that's what my team is powering, is centralizing the data so that AI can have access to as much data as possible so we can test and learn these scenarios.

(24:04):

I think that's going to be the key here, not chasing that shiny object. So honestly, I don't know what stage are we in? We are in some stage we have something. And I think we are looking to grow as a bank. We are looking to retain our customers. Our customers are very precious with the history that we have. And again, if you look around, just a quick show of hands, how many folks have changed banks in the last five years, like handful? That's a very simple answer. If you do something well and if the institution that you're with is giving you the right service, there is no reason to change. We take pride in keeping those customers. The way to acquire the new ones is get them early, get them fresh. The folks in this room, you take your kids down to the bank and say, Hey, have a bank account.

(24:59):

Why? Because you are happy with that. I think if you look at AI from that perspective, I think it's a long way to go. The market is so big, there's space for everyone. You talked about one bank trying to take customers from another, it'll happen, it'll happen. But what is important is banking is an institution where one account might not be enough for you and that is what is important to understand. It's okay for your customer to be a customer somewhere else, but what are they doing with them that you cannot provide them is key. I think use AI for that.

Kerry Gross (25:42):

So yes, on all of us, I'm sure most of us have accounts with more than one bank and for reasons, everyone has reasons. And so understanding what your reason to exist for it is the goal and using AI to leverage that. Brianna, I'd love to hear from you. Where do you think you're at right now with AI?

Brianna Elsass (26:01):

Yeah, so I will agree. It's kind of hard to say where on the continuum you are. I'll actually frame it up a little bit different, and I'll go back to a bit of an old adage. When digital was becoming more and more pervasive, the who in the company has the mindset around digital and are you going digital first? Is digital part of the strategy? It's becoming that way with ai. So how many folks, title wise have AI in their title? How many job descriptions within your company have AI? And then when you start to reach a tipping point in my mind is how many people that don't have AI as part of their day-to-day job are interacting and thinking about how AI is going to impact their job or impact your customers? And where are they pulling that information in? How much individual research and pure curiosity are they injecting into learning about AI, learning about the impacts to your customers and how are they injecting it into what they're doing or experience wise?

(27:03):

And I think that's for me, the continuum at this moment that really matters because I mean if you look at that, depending on where you frame it up, it's like, well, we were in the light blue earlier, now we're kind of in the dark blue. We're aiming for that really dark blue, but that's going to be a lot more investment. And where are we at in the continuum? What do we want to focus on first? And so I think it's really about just how pervasive is it in our company in terms of the mindsets, in terms of how we're structuring data and the functionality and the features and the experiences so that it can all be layered across.

Kerry Gross (27:39):

And that's a great segue I think into the next piece of data we have to share with everyone. You started really high level and now we're thinking about specific technologies and where are we seeing in terms of implementation specific AI based technologies within the organization. That dark blue, as Brianna was just talking about again, is that maybe goal place for organizations is fully implemented and the light blue is in pilot or rollout. So we see if we combine those two together, the most common area, as Erin was talking about earlier, chatbots, nearly 80% of organizations have chatbots either fully implemented or in pilot or rollout. And as we look down that list, the next place where we see pretty full implementation is around biometrics. But another couple of things up there at the top, virtual assistance and threat detection, and I've heard in lots of the research we've been doing that risk and fraud are target areas for a lot of organizations for ai because you can take the massive amounts of data, know what unquote normal is, and then figure out how to flag those challenges. But I'm curious, just maybe if you can share one use case Brianna and one use case Harveer for where you're at and then Erin maybe the 10,000 foot view on where we are. So Brianna, you first.

Brianna Elsass (28:50):

Yeah, so the easy one is the chatbots, but I think distinguishing between a chatbot for a pure decision tree type of chatbot versus something that is actually intelligently leveraging AI, knowing the customer and making smart decisions. If you make that distinction, I don't know if 52% are actually there yet. I think there's a very big value there. And I think that starts to blend the contact center as a service, which I think is going to be massively critical and important. That's the one that really excites me is how am I blending the digital experience with the in-person experience that you have with the customer as well, really going across the channels. And there's really no difference when you are leveraging a digital feature, you're interacting with a customer in the contact center or in a bank and you're able to blend that across. And I think that's where AI will help transition the conversation, put up the right information, prep the agent in the background so that they know what they're doing, they know what the experience was, they know what they're looking for and sort of help prompt the here's the next best conversation.

(30:03):

Here's what they were looking at in the past, here's the sales pitch, you should really be putting in front of them and here's what they don't want to talk about. You've tried it three times and they are so not interested. So stop mentioning whatever those are going to be where the world's collide and really cool and unique customer experience situations happen that I think will be game changing for us.

Kerry Gross (30:25):

And I love that I hadn't thought about it, the negative case of like, stop doing this.

Brianna Elsass (30:28):

Yeah,

Kerry Gross (30:29):

That's,

Brianna Elsass (30:30):

It annoys people so quick. If you can't get off of you've tried to sell me alone 14 times, I don't care, and move on to something else, that's actually a better value for the customer.

Kerry Gross (30:43):

Totally, Harveer.

Harveer Singh (30:43):

Yeah, so I'm going to quote our head of marketing. She's here as well. I don't know if she's in the room, but she's going to be presenting as well. Sherry Graz, she made a comment in one of the conversations, which is how do you move clients from connections to conversations? I think using AI to do that is going to be critical. I think a combination of the ones that you see here are involved in that. Chatbot is just one of those twist client pulse that we are looking at, looking at how do you really understand what the client is going through on a day-to-day basis? How do you connect those connections and then take them to conversations are going to be absolutely key here. So we want to enable the one voice across all channels because we want to be able to make sure that they hear a consistent message, whether they go into the branch, whether they're going onto the digital, whether they are calling into the call center. We want all our teammates to be aware of their situation, their data, and their personalized experience that we want to provide them. So taking a connection into a conversation using AI is going to be key.

Kerry Gross (31:56):

And Erin, to you thinking about these technologies that you see up here, what's sticking out to you?

Erin Holloway (32:03):

When I look at it, I kind of categorize it into three areas. One is the truly customer facing elements. The second is kind of the middle office within the bank, the elements that help our employees service our customers. And then the third is actually the behind the scenes, the back office, getting the financials, getting the analytics, getting the predictive analytics correct, but that all moves forward to the customer facing element to the front of the house. How do I get the right information for marketing and sales so that they can target customers and know exactly where they are in their life cycle? When are they ready for a new car loan? When are they ready for a 401k? When are they ready for a 529? They just had a new baby. Making sure that you can sharp shoot those marketing elements so they're not saying enough, I don't have any kids, I don't need a 429 or 529. Making sure that you're getting the right targeted product and right marketing message to the right customer at the right time. I mean timing is everything with that. And so sharpshooting, I think AI helps to do that so much better and having those suggestive copilots again, but that learn because they have to have information to learn on and they just get smarter and smarter and smarter and they get sharper and sharper and sharper as they get those reactions. So I think that's really the exciting part is to see it's only getting better.

Kerry Gross (33:33):

And to me these are all the where we're going, where we're headed, but let's acknowledge everyone in the room. Everyone is having challenges too. It's not all pie in the sky. Great areas. What I see in this is we have a few things that are at the top here. Legacy infrastructure can't support new processes, regulatory concerns, no demonstrated, ROI. Those are all things that aren't saying AI is not useful. And we've just been talking about what is useful about it. And so I'm curious, let's go specific first, Brianna, what's the number one challenge that you're facing in trying to implement it? And then Harveer, and then we'll come into Erin.

Brianna Elsass (34:10):

What's the one that's very hard? So for me, I'm looking at blending across different backend systems and architectures that wasn't built to span in the way that AI really needs it to. So how am I able to bridge that on behalf of what our customers need.

Kerry Gross (34:32):

And that target thing top right for folks. Exactly. Harveer, are you having the same challenge?

Harveer Singh (34:37):

Yeah, I think I may classify into maybe two or three. One is obviously the regulatory aspect. Want to make sure that everything that we do is under the realms and the guidelines of the regulatory exposure that we have. Very important. The second one is very interesting. When I arrived to the US I think 25 years ago, 20 years ago or something, first time I went to a grocery store and I looked at the aisle which has ice cream, I was like, oh crap, how do I choose? I think that's exactly what AI is right now. How do you choose all these tools that are out there, all these models out there, all these vendors out there trying to sell you something, you will end up trying a few and discarding some, you will end up having a preference. The cloud platform will obviously dictate some of that.

(35:27):

Your consulting partners will dictate your ease of implementation will dictate. So make sure you have a little bit of appetite for the trial. Otherwise it's going to be very tough because you go down the path of implementing something and within after a few months you realize it's not working out, it might be too late. So get that trial out of the way quickly. Look at the right advice, find the right partners and then get it done. And obviously with the regulatory impact that angle has to be looked at every time you choose a technology in the AI.

Kerry Gross (36:04):

Erin, are these challenges that Brianna and Harveer are talking about resonating with you and what you're hearing?

Erin Holloway (36:09):

I see all of these. I see all them with my customers. The legacy infrastructure, that's one of the things that we help with all the time is you'd be surprised how many people have tons of on-premise solutions behind the office running their businesses and moving to the cloud. Even though it feels like a big leap for a lot of companies who've been doing on-prem for so long. I mean you spend a lot of money on these on-prem solutions. And the partners, they like it that way. They like it that way. Moving to the cloud is definitely not easy for them to do because they're losing all of that business that they've been supporting all this time. But once you move into the cloud, you have so much more AI you can take advantage of. All of these things are going to become just automatic. They're right at your fingertips.

(36:58):

You can take advantage of them. But everything else, we see all these things. But the one thing that jumps out at me is when you look at a lot of these themes, most of them are internal. It's the people. And when we sell solutions to our customers, when they make the leap into cloud and they make the leap so that they can utilize AI and take advantage of all of those things, I'd say only about 40% of that cost is the actual software and 60% is change management. And it needs to be there because when you look at all of these, these are all internal issues with people. The technology is not the issue. The people are the issue because change is hard and they want to change and they want something better. But oh, it's different. And we get a lot of that with our customers. And learning is hard and we've always done it this way. How many times have the banks, that's where you get that resistance, that digging of the heels in. I want it to be different and I want it to be new, but I don't want to learn something that I'm not comfortable with. So you see a lot of that. So in essence, sometimes we're our own worst enemies in this and the technology is trying to be our best friend.

Kerry Gross (38:02):

Exactly. And I think that points to United kind of where we're headed and where we all want to be headed. And so as we close out this conversation, I want to leave us with some data here and we'll have Unica our last thoughts about where we think is headed. But to set up this conversation, this question is to what extent has your organization successfully leveraged AI to support the following aspects of the customer journey? And that dark blue is folks saying all possible AI gains have been realized. And as you look up there, fewer than 30% say that for any of these line items. And these are going back to the goals of CX that we started the conversation with. And so what I see in here is there's still so much opportunity out here. And so instead of talking about any of these specific line items, I think we'd all agree here, there's room to go. And so I think I'm curious from each of you, what's next? Where are we headed? And Erin, if we want to start with you and then we'll have Harveer end with Brianna, what do we see is next? Where are we going?

Erin Holloway (38:58):

Well, I think we've just barely scratched the surface on what can be. And we are starting to get there. We absolutely are. But banks, we're not tip of the spear. We're not even fast followers a lot of times. And it's because we're regulated. We are digital, but we are regulated more than any other industry. So we have to be cautious. We have to take care a lot is at stake if we get it wrong. So I think that we are getting there, regulators have to be more supportive of embracing it with us and being our partners in this and knowing that it's the best thing for our customers. But I think there's so much more to come. It's very exciting. But again, it all comes back to simple, fast, and easy.

Harveer Singh (39:48):

So I think to my previous comment, the successful organizations who can take connections to conversations is going to be key here. And to enable that, our methodology or our moment is do we not have the data in the say all of the data in the same place? Why can't we connect all of that and build a journey? And the journey doesn't have to be just a digital journey omni. It is a journey of a client. It is a lifecycle of that client with us, whether they choose to do business with us today, whether they not choose to do business with us, but how do you make sure that you are part of their life in some way or some form? And I think that's where if leveraged properly, AI can really be a game changer in how do you deliver services to the client.

Brianna Elsass (40:37):

Awesome. Yeah, and I would agree, I'll go back to I think the customer conversation, the melding across channels, the enabling your agents, whether it be in branch or in the contact center, is going to be critical and key. I'd actually even poke a little bit at all of those folks that say all of the possible gains of AI have been already achieved. I think that's probably skewed way too high because right now with the regulations, with the technology, with the data, I think we're going to get better at it. And as we get more practice, all of the current barriers and stopgaps that we have will become squishier and we'll be able to push those and we'll be able to make additional gains. And so it's back to this iterative constant learning, constant fine tuning ourselves as well as the AI models to make more and more progress each time.

Kerry Gross (41:32):

And I agree, and if anyone has any interest in talking to any of us, we'll be around the conference. Please come find us afterwards. I know there's so much more conversation that we could have had here on the stage about all of these things, but I'm looking forward to the rest of the conference and I hope folks will come and find us as we go. Thank you everyone.