Leadership Under Pressure: Governing Risk in Modern Financial Services

Speaker A: Welcome to Financial Forward, where we focus on the real forces shaping consumer finance, regulation, risk innovation, and the responsibility that comes with serving millions of Americans. I'm Jim McCarthy, founder of McCarthy Hatch, former founding member of the Consumer Financial Protection Bureau, and someone who has spent more than three decades working at the intersection of supervision, enforcement, compliance, and market innovation. In this season four, we're starting exactly where we should with leadership in this industry. Change is constant, regulatory expectations evolve, technology accelerates, consumer expectations shift, and institutions are asked to be safer, faster, more transparent, and more innovative all at the same time. That tension is not theoretical, it's operational. Today's guest understands that tension deeply. Betsy Kaufman has spent her career navigating financial services from the inside, helping institutions balance strategy, risk management, governance, and execution. She brings a perspective that blends business leadership with regulatory awareness. And that combination is exactly what this moment in banking demands. In this episode, we're going to talk about how financial institutions can lead through regulatory uncertainty, the difference between reactive compliance and proactive risk intelligence, the role of governance in protecting both consumers and profitability, and what real leadership looks like in an industry that is under constant public scrutiny. Season four is about clarification. It's about elevating the conversation beyond headlines and into execution. And there is no better way to begin than with Betsy Kaufman. Let's get started.

Speaker B: All right, Betsy, how are you?

Speaker C: Hi, Jim. Good morning. How are you? Good to see you.

Speaker B: Good to see you as well. Hey, tell everyone who you are and how you got to where you're at today.

Speaker C: Awesome. Yeah, sure. So I'm Betsy Kaufman. I'm, um, a leadership coach and organizational consultant. Um, and I work with all kinds of interesting firms, um, really helping leaders align, um, as a team and how to come together as a team. So, um, I started this work, gosh, I started out actually as like a project manager, program manager on the technical side. Um, really kind of got a nice break in my early 20s. Um, and as I was working through my career, I would come in and be doing all kinds of large scale programs and projects, working, uh, with teams. And I found that they always kind of started to put me on these leadership and executive, uh, programs. Um, highly visible. Um, and so I have literally been working with executives since I was probably in my early 20s. Um, but I've been that arm between the strategy and the execution side of it. Um, and so back in 2014, I was working at Bank AH of America, um, loving the work, uh, working with, uh, product, technology, business, um, helping to really bring all sides of that together. I Went and spent, spoke, um, at a conference and it was a topic about, um, building high performing global teams. Um, and the room was packed. It was one of these kind of like, okay, it was just a local meetup but the room was packed. Um, at the end of it I had a really long line of people that wanted to speak to me and talk about, you know, whatever it was I was talking about. And the very last person in line, um, after we had finished the conversation and we had all the people coming through the line, said to me, you better take the show on the road because you've got a lot to say and you've got a lot to do. You've got a lot of work that you need to do here. So I did, I actually quit my very secure job at bank of America. I'm um, based in Charlotte. Um, loved it, love the company, love the people, but decided it was maybe time to uh, to take it and take the message and take the voice outward. Um, so I did, so I started my own consulting firm. Um, and we really started off doing more like agile training and agile coaching, working with high performing teams, trying to build high performing teams in the fintech and technology spaces. Uh, fintech, health tech, insurance tech, you name it. Um, and then as we kind of got in and worked with the teams, the leaders would come again and tap me on the shoulder like, hey, can you come work with us? Can you come spend some time with us? Um, and so the company really evolved into more of a leadership development, um, company. So you know, we'd come in, work with the teams and the leaders were like we're needing some help too. Um, and so we offer all kinds of things from leadership coaching to leadership alignment, strategy sessions, executive retreats, um, really facilitating hard, tough conversations and trying to help leadership teams come together, um, and help to make team help to make decisions as a team and then cascade them down into the organization so they execute them. Um, so that's really where we are today. Um, it's been a fun, amazing, crazy journey. I work with all kinds of different customers, um, you know, credit bureaus and technology, health tech, fintech. So it's just been a really, really fun journey. Um, and there's a lot happening in this world right now with I think we're going to talk a lot about AI and what we're seeing and leaders and how to start to implement that successfully. Um, and you know it's another big transformation type of thing. But maybe it's not a transformation, it's actually a new way of A new operating model. Um, and so I think there's some things that we're going to pick on there, Jim, as we get into this conversation.

Speaker B: Yeah. Oh, that's just great. Uh, what a great map to where you're at now. What an exciting roadmap. And I can see why you have so much energy and passion for what you do and why you get the results that you do. So it, it comes from a great place. So that's terrific. Yeah. We're going to talk a little bit about and I'll, and I'll tell you what it is. This is the, this is what's got the curiosity bug in, in my brain.

Speaker C: Okay.

Speaker B: So do you remember when we went through the.com.

Speaker C: mhm.

Speaker B: Uh, era?

Speaker C: Yes.

Speaker B: And I was inside the banking industry in the private sector at the time and um, I think you were, you, you were. I don't know where you were pre bank of America.

Speaker C: But um, yeah, all over the place. But.

Speaker B: Yeah, but different, different places but still in the, in that line of work. And, and it, it was um, it was really interesting to see how the leadership of the, in my industry, banks, it could be any company managed that cycle.

Speaker C: Yeah.

Speaker B: Um, because there was, there were just as many unknowns in 1998.

Speaker C: Yes.

Speaker B: As there are today about AI. And when we look back at the dot com boom, it's almost silly, right? It almost looks silly to us.

Speaker C: Yeah.

Speaker B: So, uh, I'm really curious about people in your profession and that's why I wanted to have you on here is you're at the top of that game and I kind of want to know from you, is this being talked about? I mean, how we're going to handle AI And I have a whole series of questions for you, but maybe you can even talk whether it's part of your market or not.

Speaker C: Yeah. I mean obviously everybody's talking about it. Right. And so what we're seeing out there is everybody wants it. And this is the thing. There's a lot of ambiguity around it. What is it? We all want AI. We all need to keep up, we need to figure out how to implement it. It, uh, what does that look like and what's the best use of it and how do we deploy it out to the company and to our customers and all of that. So I think it's this, this big nebulous like ambiguous thing that everybody needs. Um, and they think they need and they probably do need. But then how do we use it? And that's what we're seeing is the, the okay, we're all going AI and they're like, but what does that mean? And how's it gonna, and how's it going to impact me and how's it going to impact my teams and how's it going to impact my company and my customers? And I think that's been, that's really the, the what I'm hearing and seeing as I work with my companies, um, in this day and age and whenever we get into our questions, I actually just had something really interesting come across my feed, so I grabbed a screenshot of it and so maybe I'll be able to read it and we can talk about that as well.

Speaker B: Sure, absolutely. And we can weave it in. But you describe yourself out there as an integrator, right?

Speaker C: Mhm.

Speaker B: So you're, you connect strategy and execution. So when leadership decides to invest in AI, what are the, the key in your mind? Um, decision rights that need to be cleared up or defined up front. Like what are those decision rights?

Speaker C: Right. Uh, yeah. So I think there's a few things here there. One is, you know, what is AI and do we all understand what it is? Right. And how are we going to use AI? What is, what is our philosophy when it comes to it? Um, you've got, when, when you think about all the different components or facets of a company, you have business, you have technology, you may have product, you have hr. Right. Everybody has to play a part in it. And I think they really have to understand what is their part. What is this going to look like for us? Um, and what are the goals that we want to accomplish or the outcomes that we're hoping to accomplish by implementing AI and leveraging it for, on behalf of our company. So those are the few things that, that we're going to do. And then where's the data coming from? Right.

Speaker B: And who owns, who owns the data?

Speaker C: And coming into companies, even pre AI, I mean, AI has been out there for a while, but it's obviously gotten a lot. You know, it's become more mainstream with everybody using all the mainstream tools out there. Um, but data has actually been a bigger challenge even like I'm saying pre AI, because having good clean data that we can leverage and make informed decisions off of has always been somewhat of a challenge. Um, at least in my experience and my work and the ownership of it. You know, is it an IT function? Is it a business function like who owns that data? I think is the big shift now that companies are going to have to figure out because AI is driven off of data. Right. IT Is very, very reliant on data and making decisions off of data. Um, as long as we're using it right in all facets, hr, finance, tech, business, product, whatever, whatever, you know, area that you're in within your organization.

Speaker B: So what are some of the early failure signals? So through your Evolve 360 lens, aligning talent, technology and strategy, um, where do you most often see AI initiatives kind of stumble out of the gate?

Speaker C: Yeah, um, you know, I would say we don't have a clear. We aren't being very clear with our leaders as to what is the plan that we have for AI and what do we want to do with it. And so we're saying, okay, leaders, use AI, figure out how to use it, use it to make yourself more nimble and more agile and more adaptable. Um, we're giving you all these tools. Go. Um, and so I think a lot like, okay, you know, leaders are saying, great, how, what, when, where can I use it, what can I use? Um, there's also a whole data privacy thing that we have to worry about. So if we give these folks tools, have we given them the guardrails around? Here's what you can share within the AI tools. We have our own proprietary instance of it. Um, this is what we're leveraging. So I'm seeing that they're struggling with giving, with setting out some boundaries. And I know boundaries don't have to be bad, right, but some guardrails and some governance to say, here's the tooling, here's how we want you to use it. And then as you use it and as you learn, how do you share that back to us and how do we also enable it within our company? Um, you're going to have lots of. There's also a fear. So is this going to replace my role within a company? Right. So, yeah, we love AI, we love technology, we don't. You're going to have that whole, you know, the whole spectrum of people out there. And so I think there's also a fear of if I use it too much or if I'm too successful with it, will it actually eliminate me and eliminate my role and eliminate my team? So again, the transparency coming from the leadership team as to what are our goals. Yeah, we might actually be eliminating roles with it. That's part of the human nature of what's happening. And today I'm not going to sugarcoat it. Um, but why, what's the importance of it? What's important for us to use AI and how do we make sure that we're using it for the right things and for the right outcomes, not just because it's the exciting, sexy new thing out there and we all have to do it.

Speaker B: Right. So those early failure signals, you just described them perfectly. It's, it's, it's all of those, it's all of that friction at the beginning that really causes that. So let's talk about decision grade data.

Speaker C: Right.

Speaker B: Uh, so we talked a little bit about data before, but a lot of executives still think that, um, data discipline, is it right? Right. Or compliance issues.

Speaker C: Yeah.

Speaker B: But from a CEO or a board perspective, what actually makes data decision grade for AI and kind of expand on that a little bit, if you could.

Speaker C: Yeah. So I don't think that it actually owns the data. Right. This might be a little bit challenging for folks. I think they enable the data and they're there to enable the tooling. However, I do believe that, like I said before, every area needs to own the data. And as a company, we need to be very clear on what data we're leveraging. Do we have common definitions, common sources, is it clean? Uh, do we understand the actual output that we're trying to achieve with it? And then it needs to be there to enable the data and to provide access to it so that we can leverage it, but not necessarily be the owners and the stewards of it. And so that might be a little bit different as to what's happened traditionally in the past where we always blame technology on we don't have clean data, we don't have clean data stores, we don't have clean data warehouses. Well, garbage in, garbage out. Right. So if we don't take data and own it across the company holistically, then, you know, it's easy to blame the one group that's actually the enabler of it, as opposed to. No, this is everybody's responsibility. Because I don't know how you make decisions or good informed decisions without having clean data and helping to get the clean data. Um, and I think, you know, AI right now to me feels very mushy. So whatever you feed it, it's going to build something off of and it's going to keep learning off of that same type of data. So if you don't put in good clean data and have good clean terminology, um, then you're going to actually get garbage out again and you're going to get mushier and mushy. I know people have seen that. I mean, I'm sure, Jim, and the work that you do, like we throw something in There it keeps learning off of itself. It keeps learning off of itself. But if it's not actually learning off of the right models or the right data, it's going to, to be trash. And so, you know, that then becomes, if we're making decisions as a company based on what we're feeding these AI agents and these models, you know, what are we actually, what are we actually looking at? And is it the real source of truth? And I think that's what's scary right now with AI is that we don't really know. Are we actually working off a good clean sources of truth? Are we just working off of whatever we've had? And we're gonna, we're gonna rely on it because we aren't being smart about it. So there's some differences that you gotta think about, like who owns the data, how do we keep the data clean, how do we make sure, how are we doing, like audits and govern governance on the data and that is still producing what we want it to. Um, or is it, or is it just creating mush after mush? We're making bad decisions based on whatever it's been being fed.

Speaker B: Right. And I just, I go back to a lot of the other episodes that I've done where I talk about data governance. Right. And so you're, you're, you're saying it. Exactly. So we have those early failure signals which is, oh my gosh, I'm going to lose my job. And all the speculation and what's happening around it and the disorganization of the data ownership and governance structure and all of that just causes model failure. In the long run. We think we're making some ground, but we're really not. And then you have the decision grade data that um, is becoming more and more valuable as you've just described, as it becomes, banks are really decision machines with data. That's all they do is decision things with data.

Speaker C: Right.

Speaker B: And um, so their data has to be the most important thing that they have. And you're describing these um, decision grade data and early, early failure signals and it just, to me, it makes me want to be a fly on the wall in the boardrooms talking about this risk that is just coming straight at us like a train at 100 miles an hour. And when is the industry going to wake up and start placing chief data officers at the table at the top, um, and start to work on data governance before they mature too far into AI modeling?

Speaker C: Yeah, it's interesting, I would be curious is as we're sitting as A fly on the wall in the boardrooms. Are they actually even talking about this? Right. So are they even talking about the importance of what's happening, or are we just saying, we need AI, we need to get, we need to get in the AI game. What are we doing here? Let's, you know, let's put in a chief innovation AI officer. Um, and, you know, no, actually, let's back it up a little bit. How do we, you know, how do we make sure we've got the right data and governance and people and stewards in place, as well as guardrails in order to enable the AI? So I don't even know that the boardrooms are at that point yet to say, you know, oh, we need a chief data officer. They're probably saying, hey, cio, hey, cto, what are you going to do? AI so that we can stay out of the game and how do we get leaner and, you know, continue to start to, to, you know, get the right, the right tooling in place in order to, to enable our company and our customers?

Speaker B: Right. Just like we did in the dot com era, we started pointing at the CMO saying, hey, this is a marketing issue. That's a web page. You have to, what do you. Come on, get on top of this. And then all of a sudden you saw the birth of the, of the cto. Um, and then the CTO took over the technology portion risk at the table and was well represented. And I think that is fine, but I think the data needs representation at the table through a cdo. And, um, I just am always curious to know if that conversation is starting to happen. So I'm curious to know if you had some CEOs in the room and you suspected what you just said, that they're not having this conversation, they're more, let's get AI.

Speaker C: Yeah.

Speaker B: Um, what would be your elevator pitch to them to pay attention to this? Like, how would you say that to them?

Speaker C: Yeah, I mean, I. It's a great question, really. It's about, what are you going to use to feed the AI? Right. So again, what are the inputs to get the outputs? And, and, and I think the bigger thing is taking a step back. What outcomes are we hoping to achieve with it? So if we want to get on the AI train, what is the outcome? Why? Is it because you want to reduce headcount? Is it because you want to do it because everybody else is doing it? I know it wouldn't be that obviously elementary, but there'd be similar conversations. Um, and then, okay, so here's your outcomes now. What do you need to have in place in order to enable it? And you will hear, I mean most of the. Hopefully the peers will say we need good data, we need quality data if we're going to use it to start to make human decisions and uh, drive where we're trying to go as a company, that we have to feed it that good data and that good processes and those good procedures in order to implement it. Well. Right. Uh, I think that's where we might not be having the conversation is the yes, we want it. Okay. Now why? What's the goal of it? And then how do we get there and do it successfully so that we aren't just putting a bunch of mush out there and eliminating everybody and trying to start over right from the beginning?

Speaker B: Yeah. And I look at that table as the risk tonsils of the organization. Right. So it's supposed to catch all of the bad risk. And so each person representing a different part of the company is there because that part of the company represents a certain amount of risk. My argument to them is, is I can I. The things you're hearing from your teams that they can improve 10x100x,000x and you can start reducing costs on operating your company are true?

Speaker C: Mhm.

Speaker B: Those are true. But what I tell them to get them to listen is a mistake also happens at 10x100x,000x and you can't stop it.

Speaker C: Right.

Speaker B: And so without proper data governance, your risk is too high to turn these models loose on your data. And, and it usually gets them to sit down and say, well, wait, wait, what, what are you talking about? So yeah, AI is great. It speeds things up, it makes processes much more efficient and less human centric.

Speaker C: Mhm.

Speaker B: But it also accelerates errors, failures, risks.

Speaker C: Right. You know, and I think the other thing that I'm afraid that we're going to start to see is, is these departments going rogue a little bit too. Right. So everybody's going to start to pick their different tooling, um, and start to leverage it in different ways. And we're not going to have consistent standards and playbooks as to how we want to leverage it. So you're going to have HR possibly using some of these tools and finance using these tools. Here's the ones that we're recommending, or here's the ones that we're going to push out. Um, and you could also then have data leaks. Right. And so that's the whole nother piece. How do you keep data secure and safe? Um, and you know, how do I know that somebody's not going home every night and uploading every employee's information to try to crunch? How many employees do I have, right, that are doing this X, Y and Z, or A, B and C? So there's a lot of facets to it, not just the we need to implement it to speed up or to reduce error or whatever they're trying to put in place. And I think, I don't know that those conversations are happening either in the boardroom yet, but they need to be. Because back to your point, the risks are out there where now as soon as you have all these different agents out there and people have free reign to upload data, it's out there and we're crunching off of it and we're now learning from it. So I think there's also some security risks that folks need to really think about, um, when they are thinking about where we're going with the data. And with the AI, just like in healthcare, with having to be private and secure it, it's now kind of becoming a more mainstream thing.

Speaker B: So you've stewarded, uh, major transitions in your career, including M and A. Um, so when AI is introduced during a period of significant change, which we're in right now.

Speaker C: Yeah.

Speaker B: What are the highest stakes? Governance decisions. The, the governance decisions leaders need to make in the next or the first 60 to 90 days. So what's important right out of the gate?

Speaker C: Yeah. So, uh, you are, you know, implementing any type of change is always risky. Right. And so when you are, if you are a, uh, going through an acquisition or a merger, and you're trying to also implement AI, um, in that nature, you're basically going through a whole new operating model change as well. Right. Super important to be thinking about it from that perspective. Not necessarily a transformation, but a whole change in what we're doing, um, it's important that we have clarity. We understand again, what we're trying to achieve. Uh, we've taken into account here are the outcomes, um, that we want to achieve by going through these transformations and these transitions and these operating model changes. And then how do we communicate to all of our folks what the expectation is? Where are we going? Right. So as long as you have that transparency, as with any transformation, as with any acquisition, as with anything, where are we trying to go? What's the goal? How do we make sure that we stay on the same page? And, and, you know, I think it's really being clear and giving guardrails, um, out to anybody that's involved in that type of transformation and, or acquisition when it happens.

Speaker B: Absolutely fantastic. So you know, and it, and it's. How would you describe the work that you're doing today? Like where, where are your clients? Where are they?

Speaker C: Yeah, yeah, they're kind of all over. I've been doing a lot of work. I um, am working with everything from an 8 billion dollar fintech company, um, helping to, to elevate the human standard. But they're really trying to push AI right to a smaller regional farm credit bureau, helping them to think about how they work better together. Strategy, technology, business data is a big part of that. It's a big challenge for them. AI. I'm working with an insurance uh, company and really trying to help them, you know, become better as leaders and aligned as a team. And one of the initiatives that they've identified for the year is implementing AI and as along with a few other things, right, like how do we get better, how do we get more nimble, how do we reduce, how do we enable more capacity in our team and how do we start to automate things? So there's some common themes there. Um, but it's really about you know one getting all the leadership talking together and on the same page with whatever we're trying to achieve as a company or as a department, um, and helping to just bring that leadership team together. So while it's kind of lots of different, you know, sizes and sectors and everything of that nature, it really is about getting people to talk and getting people on the same page.

Speaker B: So talk to me about um, like who, who is your engagement with in the, in the organization? Like who typically hires you?

Speaker C: Yeah, so my sweet spot is working in mid market firms. Um, I do like to come in and work with that C suite, you know, anywhere from 20 million to 100 million, 300 million. I'm kind of, I kind of play in this, this interesting mid market space as my sweet spot. That's my ideal client. Um, and really coming in, working with that C suite team, helping them to one come together as a leadership team to talk about what it is that they want to work together, what they want to focus on for the year, um, as well as doing one on one coaching. So what typically happens is a CEO will hear me or see me or come across me and they'll be like can you come in, can you come help me coach this one leader? Because they've got something that, that's not right, something that's maybe broken as a leadership trait. Um, and as we get in and we Start to really understand the company. We end up coaching the entire C suite team one on one. And then we also come in and help them come together to work to tackle their bigger problems. So that's, that's really the sweet spot where we're fitting right now. Um, building out the strategy and then helping them to keep accountable, to get the execution done within the organization. So that's been, I would say, you know, as we've worked in all these massive companies and smaller companies, really we're playing well in that space, um, within that C suite team, helping them to figure out the AI, the transformations, the digital piece of it, getting business and technology to talk together at a leadership team level first. Because when you don't have alignment there, the rest of your company is going to falter. Um, and I think that's the reason why as I got into companies and started working with teams, the leaders would tap me on the shoulders and say, come work with us. And is they realize that the teams are struggling to execute and they think it's a team's fault, but I would say it's not the team's fault. The teams have no clear direction. They're all kind of working on their own. How do we get you guys all on the same page and singing off the same sheet so that we can then provide that direction back down to the teams so that they can all execute together? Teams, um, want to do. They want to work on the, on the right things. Right. They want to have clarity, they want to have, um, clear paths of what we're trying to accomplish as a company. But when the leadership team can't agree, then you're going to see that, you know, when it comes to execution within the ranks. So that's kind of the, the where this transition that has been made with me personally and the work that we do, um, is being able to really get that team first aligned so that it can trickle down into the execution side of it.

Speaker B: That seems so important. And you're going to carry the water for us too, because it's so important when you're in, when you're in with these CEOs or the bank executives and they're all talking about, we've got this new AI or we're going to do AI. It's kind of like if we owned a major factory and everything from a retail shop out front to where we make our stuff in the back and a big, huge compound where we make some product. Right?

Speaker C: Right.

Speaker B: Well, the paint department invents a new color of paint and it's really popular and people really like it.

Speaker C: Yeah.

Speaker B: And that paint means something in that paint department. Right. They invented it for painting whatever we're making.

Speaker C: Yeah.

Speaker B: But if you bring that paint into other verticals, like over into the place where we fire clay.

Speaker C: Mhm.

Speaker B: Um, it'll burn, it'll explode.

Speaker C: Right, right.

Speaker B: So that paint that does really well in one vertical is dangerous in another vertical. And that's the same way data is, you know, a dispute means something to the law department.

Speaker C: Right.

Speaker B: Different than it means to someone who's in uh, following reg E compliance.

Speaker C: Right.

Speaker B: Different from someone who's in a different part of the bank. And so terminology, data definitions, um, data mapping and then the ultimate data ownership, um, is, is so important. So, and you said it really well when you were talking. If, if it's garbage in, it's garbage out. And then you can add into your um, into your inventory that yes, AI can 10x your, your operations in, in efficiency.

Speaker C: Yeah.

Speaker B: But the mistakes move at 10x also.

Speaker C: They do, they do. And that's where I think the scary part of it is, is this what, this is so rapidly changing and can rapidly change a company and, and the impact is huge. Um, can I read this?

Speaker B: Easily fixable. Yeah, please do.

Speaker C: I grabbed uh, and I call lots of different leadership groups and so this came from. It's an anonymous of course, posting. Right. And so um, I'm curious to hear how everyone is handling AI. I work for a tech company where it is pushed down our throats daily and we are consistently told to be agile and adaptable and develop our own resources using the tools that have been provided. I understand it. I accept that AI is me, part of our future. But I am struggling on how to be adaptable and what that looks like for leaders. I will have to make cuts in headcount to fund AI and I have yet, haven't yet found a way that it truly makes my life easier and certainly do not see it reducing headcount at this point. Uh, what are you doing or building to truly make AI work for you, to help shape and secure uh, what you have in place and for your team's future. Literally just came across my feed right before I got on this call with you Jim. But that I think is what we're faced, uh, companies are going to start to be faced with. And this is, this is a leadership group. So these, these, it's a women's leadership group. So these women are putting out and it's anonymous. Right. So she's afraid. Put yourself out there to say I'm being told I have to use AI and I don't know how to use it, but I, I know I need to, I need to fund it. So that's another interesting thing as a leader and all. And in all these different companies, you've got to fund it, you got to figure out how to get it out of your budget and how to reduce headcount to actually fund it, but you're not really quite sure what to do with it. And so that's where it comes as super critical. At the C suite and at the boardroom, we want to use AI, how are we going to use it? What direction do we have to give our leaders in order to help them to actually make good, sound decisions on behalf of the company and their teams?

Speaker B: Absolutely. And I, I can't help but go back to that question and let's take people out of the. We can't in our lives, but let's take people out for a second. Yeah, but when you hear that question, that question screams M team job, company security. And so do the, do the CEOs understand what they are doing to their companies by trying to deploy AI in this method where they're saying we want everyone to use AI because one of you is going to get really smart and do something really good.

Speaker C: Yeah.

Speaker B: And pushing AI on them. Do they realize that the response of the, of the labor force is. They're threatened, they're confused.

Speaker C: Yeah.

Speaker B: Ah, they're, they're being asked to make efficiencies that are going to cut staff.

Speaker C: Mhm.

Speaker B: It's, it's, it's. Doesn't it seem like it's the wrong strategy?

Speaker C: It is the wrong strategy. And I think they do realize that because I do think, you know, to give, to give CEOs a little bit of, of a break. They're getting pressured. Right. They're getting pressured less. And how do you become more efficient? How do you operate at a much a, uh, much faster speed, higher speed with that's, you know, less expensive? Um, and it's the entire C Suite. Right, the entire C suite. Starting to own that. Those types of decisions. But also on the other hand, to your point, then how do we make sure we give our people what they need? And if we have to be transparent, we'd be transparent. Yeah. We might need a cut. You know, our goal is to cut 20% of our, of our labor force by using AI. But let's make really smart sound decisions and let's make sure we're doing it for good and the outcomes that we're trying to achieve with it as opposed to use it, use it, use it, figure it out type of thing. So I think maybe they're not hearing that side of it as to. Yes, but your people are terrified and they're not quite sure how to use it and what the end goal is that we're trying to accomplish. Just use it and figure it out. It's probably not good enough for, uh, these leaders that are downlines, uh, and they're looking for that direction and we need to provide that guidance. Um, and I don't know that the C Suite knows yet what to do with that either. Right. I just know we got to use it. We got to figure out what we got to do with it next too.

Speaker B: Yeah. And then I, you know, and I didn't mean to belittle in any way the environment that these CEOs are in. Um, they're new at this too. They don't get and understand that. But when I'm in rooms with them, my, my conversation is to get them to, to, um, to maybe not bite on the hysteria.

Speaker C: Right, right.

Speaker B: And, and to recognize that those that do, they appear to be winning the race at this point, but they're accelerating their demise as well if something goes wrong.

Speaker C: Yeah.

Speaker B: And I, I don't know of an organization that has properly set up its data ownership, its governance shop, um, yet. And so a CEO that focuses on that.

Speaker C: Mhm.

Speaker B: That focuses on governance of the data and spends this year focusing on data governance from, from a very strategic perspective and managing data from the C Suite from the executive office.

Speaker C: Right.

Speaker B: Um, with a Chief Data Officer. Um, then, then you will never have to guess where AI is going to be most efficient or least efficient. You just, you're, you're able to use it in a very safe and confined way.

Speaker C: Right.

Speaker B: I will tell you that. Um, you know, another thing, and I'm just babbling on, but one of the controls that the C Suite has when we run companies. Right. One of the controls we have is, are the employees. The employees are a, uh, control. And they're a control because we give policies that say how they can act and what they can do.

Speaker C: Right.

Speaker B: And so we kind of get used to that control operating almost to the point we forget that it's, it's there.

Speaker C: Yeah.

Speaker B: Now introduce AI, which represents hundreds of those employees all at one time and has no controls.

Speaker C: Right, exactly. And we give them a lot of leeway because we think we have the policies in place. Right. And then sometimes we realize we don't and so we got to put a new policy in place. But again, like you said, this is a new frontier. Like what policies and what are we putting in place? But it's funny, you triggered a thought because I think the chief. What, what was used to be the Chief Digital Officer is now needing to become the Chief Data Officer and it's not the same person. So.

Speaker B: But that, that CDO is,

Speaker C: uh, it used to be, now it's data. Yeah. We've really lost the game now. We need to get into the data space and make sure that we're being smart about the data before we, we make decisions.

Speaker B: Yeah, yeah, I hadn't. I guess I am not aware of boards with CD Chief Digital Officers on them. But the Chief Data Officer is, is that, that's. If I didn't make that clear in my conversation earlier, that's my, that's my mission is to get that out into the marketplace that rather than chase AI models today, get your data house in order, get a Chief Data officer over the top of your data and spend this year, or at least for six months of the year really pounding and focusing on data governance.

Speaker C: Yeah.

Speaker B: Um, and managing that. And that will go a long way in managing the risk.

Speaker C: Right.

Speaker B: And then you can deploy AI models in a much, much more risk adverse, uh, environment where you can make mistakes and you can learn from them. And it's not as costly or as expensive.

Speaker C: Yeah. I mean it's a, it's a critical role. And it's funny that the 8 billion fintech company that I work with, uh, they've got a whole massive program initiative to get the data. Right. To get it clean. Um, and so they've also got a whole AI program initiative. Massive, you know, so they're, they're trying to do both. It may be that we need to kind of bring them both together and make sure. Same page.

Speaker B: And Betsy, I want, I want to be real clear about one thing. Okay. AI does not need clean data. One of the reasons AI is as powerful as it is is it can operate with, with unclean data. That's. The cleaning is not an issue here. At issue is who owns it.

Speaker C: Who owns it.

Speaker B: What's the policy around using it.

Speaker C: Right.

Speaker B: And what is the common nomenclature and naming standards across all data?

Speaker C: Right.

Speaker B: That it, that it connects to. And so that Chief Data Officer is. They're, they're, they've been doing that their whole life hopefully.

Speaker C: Right.

Speaker B: And yeah. And can set that up. And, and once you have that set up, then you can start to work with AI and understand the risks that it's bringing to the C suite.

Speaker C: Exactly. Yes.

Speaker B: Because today, and you heard it here first, but you get a rogue employee or a rogue group of employees that really, truly understand AI.

Speaker C: Yeah.

Speaker B: And they're inside of one of our nation's largest banks.

Speaker C: Mhm.

Speaker B: They can turn that bank inside out in a big hurry.

Speaker C: Yes, they can.

Speaker B: Without proper data governance.

Speaker C: Might be happening as you and I are talking right now.

Speaker B: Right.

Speaker C: Probably. Probably is a huge risk and that impact is going to be felt. It'll be a huge one. And I think we're maybe, maybe waiting to see which one might. Because I didn't do what we're talking about here today. Right. So.

Speaker B: Right. But it's just at the beginning. So if we, if we talk about it, our clients will pick it up and they'll do it and, and we'll start to have these conversations. But that in the, in the dot com boom, everyone had to have a website today, yesterday. It's got to be done. Let's do it. No one understood what it was, why it was there, how we're going to transact business over it.

Speaker C: Yeah.

Speaker B: Right. And so, um, you know, this one will slow down a little bit.

Speaker C: It will give it a few years. We're still in this, we're still in the, you know, everybody's hurrying and investing and trying to figure it out and get it all right and put it in place and it'll eventually find this curve and come back down again. But, um, it's an exciting time. It's a scary time. Um, and you know, I'm, I'm kind of eager to watch, to watch it and to be a part of it and to engage with leaders that are really trying to grapple with what it, what we're trying to do here. Mhm.

Speaker B: What I would say to your friend or the person that made that, the, the person that posted the post is, is, I would say AI is your friend. You, you will learn that. But the best thing that you could do is interview each one of your employees and find out what it is they spend the most of their time on.

Speaker C: Right. Correct.

Speaker B: And once you have that all inventoried, you can begin to allow them to work more efficiently and faster doing the same things by deploying AI in those different areas. Whether it's letter writing, whether it's research, whether it's, you know, transactional, um, auditing, uh, you know, all those things. There's, there's a lot of ways AI can make help, make her job much more efficient. And it's just unfortunate that it's kind of being put to her the way that it is.

Speaker C: Right.

Speaker B: Um, that makes it, it is a

Speaker C: mindset shift to your point. To me, AI is an enabler. Right. So if we can leverage it to enable us to, to do some of the, the common tasks that are just so, you know, easy. Um, you know, I use it all the time, all day long, in lots of different ways and lots of different things. Not just to help proofread my emails, but, you know, to help me crunch data when I'm doing 360s and to, to help me have conversations and to say things. Things. Right. And to coach people. I mean, I use it all day long. And so I think if we, if you come from a mindset of, okay, how can we leverage this to enable us as a team and as a leader, you're going to be much more successful than if you're showcasing that fear and that negativity. You're going to have some problems. Um, and so you need to change your mindset. And we need to figure out, like, okay, what's the best way that I can use this? Maybe I can take some of my learnings and take them upward and outward to the C suite. Right. Here's how we can use AI as opposed to it feeling like it's being pushed on us. And I know that's a hard place to be, especially when you're kind of sitting in that, that middle, you know, that middle leadership role. Um, you've got peers you've got to recount. We're pushing on you. But again, be it. Be a steward of it and really try to enable it and be a champion for AI because it's not going.

Speaker B: That's great.

Speaker C: Awesome.

Speaker B: Betsy, um, Kaufman, fantastic.

Speaker C: Thanks, Jim.

Speaker B: Thank you so much.

Speaker C: Thank you so much.

Speaker B: Yeah. Thank you for being with us today. I really appreciate it.

Speaker C: Thanks.

Speaker A: That was Betsy Kaufman.

Speaker B: And if there's one takeaway from today's conversation, it's this.

Speaker A: Strong institutions are not built by avoiding regulation. They're built by understanding it, operationalizing around it, uh, and integrating strong strategy into it. Uh, compliance is not a cost center. It's a trust engine. As we move deeper into season four, we're going to continue unpacking the real mechanics of consumer finance. What regulators are watching, what institutions are missing, and where opportunity exists for those willing to think differently. If you found today's conversation valuable, share it with a colleague, forward it to your compliance team, and send it to someone in product or risk who needs to hear it. And remember, at financial forward. We are not here to count complaints. We are here to understand them.

Speaker B: I'm Jim McCarthy.

Speaker A: Thank you for listening. We'll see you next time.