How Credit Unions Can Deploy AI to Elevate Member Service and Reduce Costs

Introduction and Guest Background

Doug English: [00:00:00] Andrea Argueta, welcome to see you on the show. I’m delighted to have you join me today to help the credit union movement, understand and grasp and implement ai, uh, in credit union. So, uh, thank you for joining me. Tell me a little bit about, uh, you have a credit union background and that resonates powerfully, uh, with our listeners.
Uh, so tell, tell me, uh, about your credit union background and what you did and where you did it. Uh, and then how you came to do what you’re doing today and what that is.

Andrea Argueta: Of course. Well, first of all, thanks Doug for having us here. Um, I come from the movement. I was the COO of a credit union here in Washington, DC and led a digital transformation.
Um, think about all the projects that you could do at a credit union. We did them everything from core conversion, website redesign, rebranding, uh, digital banking improvements, launching a virtual [00:01:00] branch. We worked on those. Uh, our credit union was special in the sense that we, uh, were, uh, serving members of an international organization.
So we had members in 75 countries around the world. So digital was very important for us, and uh, I actually partnered with Glia, used Glia for a couple of years before I moved over to the Glia side. And what I do now is I advise credit union executives around launching digital customer service, leveraging ai, and just having a really strong unified interaction management strategy.

What Glia Does

Doug English: Hmm. So I’m unfamiliar with Glia. So tell, tell me and my listeners what does, what does G do

Andrea Argueta: of course. So. As we know in credit unions, there is lots to do and we don’t always have all the resources we would like to have. And um, I know right now that bar is only getting higher with all [00:02:00] the technology out there.
And so that’s where Glia comes in. Uh, glia eliminates the efficiency versus experience trade off by using AI to automate interactions, elevate agent performance, and transforming reporting and insights for leaders. So. One thing I always like to think about when we’re talking about glia is, yes, we come and solve these problems, but uh, when you partner with Glia, you want to imagine what you could do if you automate all the routine tasks and inquiries and really delegate those to ai and think about, you know, could you reinvest those savings?
Um, or agent hours or reallocate, you know, those agent hours. Um, or just right size, you know, the team, um, depending on, you know, what goals you, you have at your organization.

Glia’s Structure and Services

Doug English: So, so what, is Glia a standalone company? Is it a QSO [00:03:00] structure? What, what, what’s the organizational, uh, structure?

Andrea Argueta: Yeah, so, uh, glia actually, um, is a FinTech provider that has a QSO as well.
Um, our credit unions value us a lot, and we, we created that cusso for that reason. But, um, we really come in and provide, um, I would say, uh, first ai, uh, to all of the interactions. But we also provide, uh, digital customer service and um, uh, a voice solution.

Improving the Member Experience

Doug English: So the, the in, in the, it, it’s direct to member, uh, ai, which is really sensitive.
Right. Uh, you gotta get that right. And I know I was thinking about, uh, you know, your experience as a customer in banking and in and in medical and as like one of the things that is. Absolutely drives me crazy. Is ever entering data twice or entering data, being asked to enter data that you should already [00:04:00] have, not update, but it’s like you give me a blank slate and want me to reenter it.
Absolutely. Hate that. And it sort of displays incapacity and, uh, antiquated technology when I experience that. Uh, is that, is that part of, uh, of what you’re doing for credit unions and then can you gimme specific implementation examples like a. Some, you know, here’s, here’s a credit union that, you know, ’cause AI or it’s super new, we’re in 2025.
But you know, way long ago in 2024, some folks started doing something. So, uh, we’d love to hear about some implementation of specific tangible, uh, impact to credit unions and to the members.

Andrea Argueta: Yeah, so I would say that. We definitely improve that experience. We don’t want our members to be repeating themselves.
And so Ai, AI helps, you know, with that journey of providing quick answers to routine questions, but when they need to hand off to an [00:05:00] agent, we definitely provide that context so they don’t have to re-explain the whole conversation that they just had with a, with the ai, uh, with a virtual assistant. And so the agent can pick up right where they left off for a really positive experience.
And so we’ve seen a couple of organizations, for example, um, heritage Feral Credit Union, double Its Call Volume. Without increasing its contact center staff, which I know is super important at a credit union, how do we not continually, uh, increase headcount? I know that’s like the biggest line item that we have a big expense, and so that’s very important.
Um, and then, uh, right now they’re enhancing, uh, at least 90% of their member interactions. Um, similarly, service first, uh, federal Credit Union saved 69 hours per week with their AI powered phone assistant. And that automatically reduced wait times and abandonment rates, uh, which, you know, it doesn’t impact the experience.
If anything, it [00:06:00] enhances it because the way I like to think about it is when you’re using AI for those simple routine interactions, you’re basically freeing up your routine to really focus on those important conversations where it matters to have a human agent converse with you. So you’re making them, you know, that personalized service that credit unions are known for.
You’re making it more accessible to everyone.

Integration and Compatibility

Doug English: Yeah, it’s, it is super important to have that go quickly from if the AI can answer the question and answer it quickly and accurately, that’s fantastic. But often the only time I, I, I would imagine that, uh, um, some members would interact. Would be when it’s, when it’s weird, when it’s outside of their norm and it’s, it’s a little may maybe outside of, of the, uh, AI’s current ability to answer things.
So that handoff to, uh, the existing team and the call center is, is a really big deal. Uh, DD does. Is the platform agnostic as to to core or system, or [00:07:00] is it, uh, does it have certain requirements as far as, uh, a, any of the, uh, credit union software systems?

Andrea Argueta: So I would say that Glia, first of all, we have, uh, great partnerships with a lot of online banking providers, core providers.
So we’re, we’re purpose built. So we have these relationships already. So we have productized integrations with, you know, the most, uh, popular, um, providers out there and partners out there. So that’s one. Uh, but also just, I, I think we’ve been able to help a lot of organizations, uh. Regardless of their, of their, um, providers there, we were, um, really leading the way in those integrations.
And so I would say that, um, we do a really good job of, uh, discovering what your requirements are, what the technical requirements are, and providing a, a solution around that.

Implementation and AI Strategy

Doug English: So the implementation, um, you, you mentioned two credit unions and, and so they. Went [00:08:00] through the process of, uh, of determining that they wanted to implement ai, uh, and that was this, their, and it’s okay if you don’t know.
Was this their first implementation of AI and the implementation was in a customer service capacity? Is that right?

Andrea Argueta: So it was definitely in a customer service capacity. Um, I’m not familiar if they have other AI within the organization. And, and I bring that up because I know that right now a lot of, uh, credit unions are looking at what their AI strategy is going to be.

Doug English: There’s a lot of options. Yeah.

Andrea Argueta: Lots of options where they’re going to invest their time and money, but I do know that. I think something that they wanna be thinking of is the feasibility and the value that they’re gonna get, and there’s definitely high impact when you invest it in member experience because.
It really touches every single part of the member journey. And with purpose-built solutions, the feasibility is, is pretty high as [00:09:00] well. And so it’s a likely win and that’s the way they wanna be evaluating where they’re going to invest when it comes to ai. Right? There’s other solution where it takes a lot of, uh, work upfront and the value is low.
And so, you know, that’s where. It might be more of a risk to invest in those areas. And so as they’re you really thinking about this, especially now in strategic planning season. Mm-hmm. I think biggest they should be asking themselves when it comes to AI investment.

Automating the Member Journey

Doug English: It’s, uh, you know, on, on this podcast we’ve, we’ve talked a about all kinds of, uh, AI related subjects, but one of the ones that really stood out for me was a system that, uh, took all the policies and procedures for customer service, uh, and, uh, created a, uh, uh, a central source of truth, uh, and a single consistent answer for the internal team, which is.
Step two, right? Your tool is step one. Ideally it’s the [00:10:00] same data, uh, I assume, right? You, you guys ingest the policies and procedures from the internal tool, probably the same tool as the, uh, the human beings use you, uh, ingest that into your, uh, AI and then answer the questions, uh, that way is, is that correct?
And then that way the AI and the customer service representatives give the same answers.

Andrea Argueta: Yeah, so I think that’s a, a, a really good question I would say that you wanna be thinking of when it comes to ai, about the entire member journey, right? It’s, uh, the entire interaction. And what I mean by that is there are different parts that make up, um, a member interaction with your team.
And so when it comes to ai, you wanna make sure that either you’re automating or augmenting the entire interaction, and. What I mean by that is at the beginning, you know, you can have member facing AI to answer those simple [00:11:00] routine questions. Then you can have, uh, when you transfer it to an agent, you can have tools that help automate the agent work or augment the agent.
Agent work. Mm-hmm. And that could look like in different ways, right? You can automate the, the transcript that gets put together to transfer from the bot to the agent. You can provide live recommendations for the agent.

Manager and Executive Applications of AI

Doug English: Yeah. Well, I love that idea.

Andrea Argueta: Yeah. And, um, you can, you know, do a lot there. And then you can also think of the other stakeholders that we’re not, you know, thinking about when the interaction ends.
Um. Also, you know, you can automate the, the summary or, or the notes that you’re taking about the interaction using ai, but it doesn’t end there. Then it comes the manager work, right? The manager needs to do quality, um, uh, analysis on the, on the interaction. So QA on the interaction. And that is very manual.
I’ve talked to a lot of credit unions and with a lot of heavy work, [00:12:00] they might get to at least 1% of the interactions. And so when you think about that, you really don’t have a clear picture on what the performance truly is. And so using AI to automate QA and get not 1%, but a hundred percent of the interactions evaluated for.
You know, strategic things that you’re looking for that is game changer. And that frees up the manager’s time to be more strategic, and then it doesn’t end there. Now the, at the executive level, you’re looking at performance at a higher level. So how do we automate that? How do we augment that too? So I like to think that the entire interaction needs to be either automated or augmented using ai.
And it’s helpful when you have, you know. A partner that can consolidate all of that, right? That you’re not giving, you know, one AI tool, the agent piece, another AI tool, the member piece, and it doesn’t fit together, but it’s really, truly looking at it holistically.

Early Implementations and Results

Doug English: Yeah. The, the [00:13:00] implementations that you mentioned, how old are they?
When, when were they put in?

Andrea Argueta: Um, I would say, uh, last year.

Doug English: Yeah, that’s about as old as it gets in ai. Right. So 2024, they were put in and, and can you, can you state those, uh, stats again please? Yes. About those two credit unions?

Andrea Argueta: Yes. So, um, heritage Fertile Credit Union double its call volume without increasing its contact center staff.

Doug English: Um, but interesting, double the call volume. So the call volume like occurred because the members had more needs, uh, and, and that they were able to. To meet those needs with no additional staff. I wonder, like the first question I think is like, wow, why did that call volume increase so much? But that’s a whole separate issue, right?

Andrea Argueta: Well, I, I feel like we’re, we’re helping, uh, credit unions in different scenarios. One, there’s those that are growing quickly and they’re growing quickly, but it’s impacting their efficiency. [00:14:00] Right. So when they’re looking at their efficiency ratio, they’re, they’re seeing kind of those results of, of that growth.
Whether it’s, you know, it can be in all areas, share growth, loan growth, member growth, and we come in and support that to make sure that as they continue to grow, um, they, they won solve the current issue of efficiency, but they’re also future proofing that growth. Mm-hmm. And then we’re also seeing those that are experiencing, um, low growth.
And so we wanna make sure that we can help them. Become as efficient as possible so they have the bandwidth to invest in growth activities, deepening those relationships, you know, onboarding new members, um, offering the new either money market account or share certificate, right. Or the new mortgage, um, that we’re offering now.
And, and then we have those that are experiencing, I would say, um. Uh, both, right? That they have the greatest pain in that aspect. They are inefficient, they’re not growing, they’re stagnant. [00:15:00] And it’s, you know, again, how do we free up the time to really think through where are they going to, uh, reinvest for growth?
Like what they’re gonna prioritize for growth. And last but not least, we have those that are, you know, leading the way. They are growing, they’re efficient. Um. But as they continue to grow, they might start to see those growing pains, or they might just be really looking to continue just. To stay as a leader, right?
How do they keep that leader, uh, space there? Um, and I think with AI, they can definitely do that because they’re future proofing their growth, but they’re also, you know, adopting technology that right now I think it’s becoming table stakes. Everyone’s exposed to AI in all sorts of businesses, in all sorts of areas of life.
And so, you know. A credit union that adopts it sooner is going to be well ahead than than others. And what I like about Glia is that I, I think that Glia is really, um, just. [00:16:00] Providing credit unions a way to access this technology. So it’s bringing parody in terms of technology. Right. We, we don’t have, you know, for example, a Bank of America might have a whole team of AI within their organization.
Yeah. And we, we, we don’t always can afford credit unions

Doug English: need FinTech, right. We, we need FinTech to help, uh, keep this, uh, this industry.

Andrea Argueta: Yeah, so what I like is that Glee has really taken that into their hands and created a tool that they can, one easily just, you know, there’s a process to get it up and running, but they’re not really building it.
And that’s a big decision to make. When do you build and when do you buy. Mm-hmm. And I think for something like member experience and member service. It would take so much work, so many resources, a lot of new talent to hire, to be able to get something up and running quickly and up to use it. And that’s actually powerful.
You know, we’re not talking about a basic FAQ bot. We’re talking about virtual assistance [00:17:00] that can really do about, you know, 800 types of, of, um, uh. Question. They can answer about 800 questions in different ways and then know how to transition to a human when needed, what journey to take. And that takes a lot of work.
We’re talking about, you know, AI consultants, a data scientists. Um,

Adoption Trends and Implementation Speed

Doug English: how many credit unions have said yes so far?

Andrea Argueta: I don’t have the exact number, but I know that, um, it. It’s, you know, there’s Can’t be that big.

Doug English: Yeah.

Andrea Argueta: Credit union’s adopting this every week, like going live with their virtual assistants every single week.
And um, I’ve seen organizations that maybe last year were like, we’re not sure how members are gonna react now. Thinking about seriously. Okay. Now’s the time. Where do we start, right? Mm-hmm. Because you don’t necessarily have to start on the member side. You can also start on the backend. You can start automating your agent work your [00:18:00] manager work, right?
Negative work while you prepare for the, uh, member side, if that’s what you choose. But we also have a lot of credit union that are leading the way where they’ve gone all in. Having virtual assistants on the voice side, on the digital side, and also

Doug English: how long does it take? So you, you decide that you’re, you’re going to, uh, try to, um, use it in the call center fashion that you mentioned.
Excuse me. How long does it take from deciding that you’re gonna do this to uh, to it being live?

Andrea Argueta: So I think there’s lots of components to that because they might be, that they’re also rolling out, you know, the voice solution, right? They’re, they’re replacing their contact center solution, or they’re launching digital customer service for the first time.
But I would say that we’re talking about, uh, you know. Potentially two months, three months max, depending on the resources that you’ve had, that you have available. I’ve had a credit union that was doing this right before their core conversion and we were talking about weeks. So it really depends [00:19:00] on

Doug English: Before the core conversion.

Andrea Argueta: Yes, yes. ’cause they were really looking for, um.
A cost effective way to handle that peak right of, of calls that you get during a conversion. And so they decided to go through this route because it would be the, the best way to invest those funds in a long-term solution versus a solution for a couple of weeks. You know, when you, you, when you outsource to another third party contact a couple of weeks mm-hmm.
Basically just getting resources for those weeks and that money that’s spent, it was already spent, you’re not getting anything out of it. So I’ve seen a lot of organizations really think through this, and not just for core conversions, but generally like, you know, they have 30 part 33rd party contact centers.
Um. Providing solution to overflow after hours, and it’s very costly and it’s a lot of, it still comes back in tickets, right? At least 40% is what I hear from others and was my experience as well. [00:20:00] And you’re paying for that. And so what I see across the board is that you can significantly, significantly decrease the number of interactions that you’re paying for with a third party contact center, or we’ve had some customers that have gotten rid of that altogether.
Mm-hmm. And have replaced it with a solution that works 24 7. It’s, and is

Doug English: it lower cost? Can you save the credit

Andrea Argueta: union money? Oh, for sure. You, not only do you save money, but you have a, you know. A fixed, um, uh, expense going forward, you know what it’s going to cost. And I like to think that this virtual assistant really becomes part of the team, right?
And it’s, you know, it doesn’t, and it

Doug English: should become, uh, should be increasingly more accurate, more effective member of the team, right? Because it only gets better.

Andrea Argueta: Correct. It’s learning from all the interactions it can do transactions and also, you know, it doesn’t take breaks. It’s available 24 7. So now you can go to a 24 7 model without the additional expense of, you know, that headcount.
And you’re also getting [00:21:00] comprehensive reporting too.

Measuring Performance and Continuous Improvement

Doug English: Yeah. Yeah, yeah. And, and, and that, that’s gonna give you the feedback loop to make it better. Do you have any examples around that? Like actual, like. Here, and again, I know it’s super new, maybe you don’t yet, but I’d love to hear about like, uh, uh, you know, we, we implemented, uh, and, uh, the tool was producing this level of effectiveness as far as the, uh, I don’t, I don’t know what the metrics are for a contact center, but then we, we dialed it in a little bit more effectively and made some various modifications and it got even more effective.
Are there any, um, any instances like that, or is it too early?

Andrea Argueta: So, um, I can give you some stories that we have. Um, one organization that on average was saving about $41,000, uh, by using the, um, AI solution. Um, they were. Seeing fi almost 60% reduction of average [00:22:00] wait time.

Doug English: Now, is this versus outsourced? Is that the, is that what changed as they were outsourcing and they changed the outsourcing to, uh, ai?

Andrea Argueta: Yes. Yes. Um, and one of the things that I would say from this is,

Doug English: uh, can you tell us who that was?

Andrea Argueta: I don’t know

Doug English: if No, no, it’s all right. Okay. Go ahead.

Andrea Argueta: If I can. That’s a great question. Um, but I would say that that’s one, one example also that’s without considering, um, just thinking about the time to deploy, it was less than a hundred days to deploy something like mm-hmm.
So we’re talking about this, you know, this is, this is quick when you think about mm-hmm. In terms of implementation, it can be really quick. Um, one thing I want to highlight really is. The reason why you can deploy so quickly, it’s because, you know, we’ve, we’ve put in all this work into building all these, um, paths that the virtual assistant can take.[00:23:00]
When I bought, um, ai, I wanna say maybe like seven years ago, uh, we were getting a spreadsheet and it was. We had to come up with the questions and that took a lot of work and we’re banking professionals, right? We did. We’re not sitting there thinking about the 10,000 different ways one single question can be asked.
That’s what Glia has done, and making sure that the virtual assistants do a really good job of understanding the. The metrics. Um, sorry, question. So that means you

Doug English: had to see the system with a whole bunch of, uh, of knowledge from, from some source, right? Is that, is that something you can tell us about how that works?

Andrea Argueta: So, I. I don’t think I would go in

Doug English: that. It’s okay. All right. I, I ask questions. It’s okay to say no to them?

Andrea Argueta: I would say so. Um, we do a really, we’re really focused on credit [00:24:00] unions and banks, and so we do a really good, a, a really good job of understanding, you know, what are the needs of credit unions and banks.
And so we’ve made sure that we have, uh, built. For around 800 customer goals, knowing what are the main goals? Like we, we’ve taken a deep dive into understanding what are the goals of these credit unions and how can we automate. We’ve been very thoughtful about what needs to be automated and what should remain agent, um, first, right?
So for example, uh, I’m sure a member would not mind if. A virtual assistant providing them with an A, b, A routing number. Mm-hmm. And in fact, text it to them so they don’t have to take notes, but they will care if you want like a deep consultation around what’s your best share certificate or mortgage product.
And you’re trying to handle that with a bot, right? Mm-hmm. I like to think that Glia is freeing up the time for the team to focus on those conversations that matter. So that’s how it was really built. And we’re also thinking about how do we. Provide [00:25:00] responsible AI for all stakeholders. So it’s not just member facing.
It’s not just agent, but we’re also considering managers and and executives. So we’re really, really thinking about the entire interaction completely.

The Future of AI in Credit Unions

Doug English: When is AI going to be able to. To, uh, look at my activity in, in an account, uh, and, uh, and, and suggest, uh, things that are in your interest as far, you know, like, uh, uh, Amazon watches.
What you look at and keeps track and then suggests things for you. Uh, is, is how, how far away is that from, uh, um, a member experience?

Andrea Argueta: I’m sure that, at least for this part, I know that we, um, and I don’t know if we can exclude this question, but ’cause we don’t really look at them, we’re not like digging into the member’s data in the core.
Mm-hmm. We’re not that, but we are looking at the interaction itself.

Doug English: Just at that, [00:26:00] that moment’s interaction, not at like all the, the things they’d done in their checking or credit cards, right? Not, that’s a different thing.

Andrea Argueta: Not for now. Yeah. No, no, not. Okay.

Doug English: That’s fine. Yeah. I didn’t, I didn’t think it was a there yet, but I, I, I love the idea of, uh, of just getting more and more custom so that you can provide more custom member, uh.
Member experiences, anticipate questions before they come and, and, you know, answer the question today and the one that they’re likely gonna come up with. ’cause we look back in the logs and we see that every 15 days you call to check on so and so thing. So instead of waiting for your call, we’re gonna answer the question for you ask.
Right. That’s where we should be headed.

Andrea Argueta: Right. And you know, you bring up a great point. I know that we’re, we’re looking at what other. Results can we expect from ai? And I know that time, after time, I keep hearing from credit unions that there’s a lot of competition out there. So they’re thinking about how do they increase their membership?
And that’s really become like a longevity strategy, attracting [00:27:00] younger generations, attracting different, uh, communities and just expanding their reach. And I know that another way to be thinking about the solution is what we can do to just provide more accessibility to different communities. One example, one credit union was able to begin providing service in Spanish, and they saw a hundred, 428% lift in their Spanish child.

Expanding Access and Inclusivity

Doug English: Yeah, I assume it’s an immediate, like the AI doesn’t care, right? It just immediately can switch from one language to the next without any, uh, any, any skip at all. Right?

Andrea Argueta: Yes. And, and this virtual system was able to contain about 87% of the Spanish language, um, interactions. And I think that that just speaks volumes of, um, the, again, the mission of our, our, our credit unions, our people serving people, and just being able to expand service to all sorts of communities and, yeah.
Doug English: And 24 7 and maybe cost savings and more better and, and [00:28:00] increased member service. All those things, I think can, can potentially come together from, uh, AI implementation, which is why I’ve been doing a lot of podcasts on it. I think it’s a really, really critical thing that the credit union movement embrace a lean into, uh, take a little bit of risk with, but make it part of your strategic plan to become a leader in ai.
You, you absolutely must. I, in my opinion, you, you must, in order to stay relevant.

The Role of AI in Strengthening Human Connection

Andrea Argueta: I think today is not like an if or a nice to have. It’s a must. And we wanna think about ai, that it’s really not replacing human connection, amplify it, right? To make the member experience better. And I, you know, I think that’s how we should be thinking about it and just asking ourselves, where are we gonna have the biggest impact for our credit union?
Because I feel like a big question that we should be asking ourselves is if we get to automate. A lot of the interactions, we’re technically increasing the capacity of our team, and we [00:29:00] wanna be thinking about, okay, what do we wanna do with that? Right? We get lots of choices there. We get to think through, do we reinvest that in our back, in our members?
Do we provide more dividends? You know, do we offer a new share certificate or, or money market account, you know, for some time? Or, um, we can also just. Um, I think, think through. Um, do we reinvest that in?

Doug English: I bet that’s a while out though, right? Right now it’s an increased spend. The savings takes a while to show up.
I, I, I hope to heck, that’s the promise of ai, right? That we decrease, uh, overhead costs and, you know, obviously that might, that might decrease some staff. Uh, but, uh, increase.

Andrea Argueta: In that sense, Doug, like it is actually faster. Like if you deploy voice AI within the first month, you could potentially automate 40% of your volume.
So the question becomes what do you do with that team capacity? Right. Do you reinvest in the growth activities, deepening the relationship, just having [00:30:00] them focus more on that high value, uh mm-hmm. Member complex issue, or do you retrain them? Um, another question is, you know. Again, if you are using voice AI to, uh, reduce or eliminate the overflow in after hours, then you have a monthly, um, reduction of your spend.
And it’s significant. These contracts are, you know, they charge you per minute, anywhere between $1 to $2 per minute. So we are talking about significant savings and we’re not talking about six months down the line. Once you deploy or you start seeing that right away. And also, you know, just thinking about contact centers usually have a lot of turn.
Potentially choose not to backfill, right? Because the volume has, is now taken care of by that virtual assistant. Uh, but I think you can also then think about do I reallocate these resources in another area of the business for business development? Do I modernize my branches? Like there could be so many things that you could be doing.
What

Closing and Final Thoughts

Doug English: great questions you get to ask, [00:31:00] uh, when those problems present themselves. Yes, for sure. So, if our listeners wanna learn more about Glia, uh, where would they go to to find out more?

Andrea Argueta: Well, glia.com. There’s, uh, our website. We can quickly, um, answer questions from there and we can also, um, we’re on social media also.
If you have, um, your partner. With, uh, your online banking provider, um, we’re, we have partnerships with most of them, and so you can reach out that way as well, so there’s any way, get in contact with us.

Doug English: Very good. Andrea, thank you for helping the credit union movement Power ahead, uh, with the critical win, uh, in uh, AI implementation, it must be done.
The only question is when is your gonna credit credit union gonna do it? Where is your gonna credit union going to do it, and who are you going to do it with? Uh, with that, we will, uh, thank all of our listeners. Thank you, Andrea [00:32:00] and any final thoughts for our listeners today on the, uh, need to implement ai, uh, in credit unions? Just any, any final things you wanna close with?

Andrea Argueta: Yes. I would say that AI right now is the way to help our people. And the, what I mean by that is. If you implement ai, you’re going to be able to have the time to have those meaningful conversations to make a difference, and that means getting someone that first home or getting those savings for their kids’ college education, those conversations that matter, we get to have them because we’re automating those that aren’t needed to, to, to be had by with an agent.
So. Make sure that you’re reaching out to as much people as you can, increase your reach. You can do that with AI and continue helping the movement.

Doug English: Andrea from [00:33:00] Glia, thank you so much for joining me today.