How Employee AI Supercharges Employee Service, Speeds Up Onboarding & Reduces Turnover

Hello. Thanks for joining. So in this webinar, we are invited quite a few of our customers. So special thanks to our customers for joining.

Lot of our customers are using our voice AI and chat AI to automate the incoming calls and chats.

Obviously, there’s a very high ROI project. Today, we’re gonna focus on the employee So as voice AI and chat AI automates the simple incoming request for for on the voice and the digital channel, the voice and chat which are actually getting to your employees are fairly complex.

So employee AI is kind of designed to help them answer those questions or supercharge the employee service.

Obviously, the turnover is a big issue in our industry. So how do you speed up onboarding with the employee AI? And, ultimately, with, kind of automating the simple call, making the job more interesting, employee AI can help reduce the turnover. So that’s kind of what we’re going to focus on in today’s webinar.

So let’s get started.

So today, I have Shri, founder and the CEO joining us. He will walk us through Interface dot ai and actually show us a demo of the employee I had John joining us as well. He’s the VP of customer success, and he will talk about two case studies, one with FEDN and another with, EFCU. These two customers are using the employee AI to achieve the goals we just kind of talked about. And my name is Jack Chawla. I’m VP of marketing, and I’ll be the host of this webinar.

So before we get started, let’s just kinda look at the agenda and take a quick quiz. So we’ll start with Interface dot ai.

We’ll introduce you to what is happening in the AI world and and kind of talk about our overall solution. Then we’ll talk about the business case for employee AI. It’s kind of very obvious, but we’ll spend some time showing the hard start data on how the employee AI is helping with the productivity and so on. Then we’ll show you the deep dive demo of the employee AI, and then John will walk you through the case study, and then we’ll open it up for q and a.

Okay. So real quick, let’s take a quiz. So this is a kind of a high level quiz to get a sense of, where you are in your AI journey, how how you deploy the employee AI, voice AI, and so on. So if you don’t mind, take a quick minute and answer the questions in the quiz. So the questions are, have you currently deployed employee AI? If not, when do you plan to deploy? Same question for the voice AI and the similar question for the chat AI.

Okay. Let’s look at the results real quick. So looks like twenty three person have deployed the employee AI, and the people who are deployed the employee AI do plan to deploy it very soon. That’s great.

On the voice, fifty fifty. Fifty percent of the deployed, that’s great. That’s probably the reflection of our customer base who’s using our AI. And then on the chat side, again, the percentage who has deployed is thirty percent, and quite a few people haven’t deployed yet, and they’re looking to deploy.

So we’ll show you kind of our overall solution, and then you’ll get a good sense of what is feasible with all this solution.

Okay. So with that, let me turn it over to Sri, and he will walk you through interface dot ai.

Thank you for everyone joining today. Really exciting agenda.

So we’ll get started with a quick introduction to interface AI.

You know, there’s a number of our customers here. Thanks to all of them for all the support and for years and and pretty much we’ve come a long way. We are the longest standing AI provider in the industry. Ten years celebrations we we were carrying for we’re planning to do this year.

It’s been ten years, and we’re all based in California as a as a headquarters of two hundred employees. As a company, we have featured and and gotten a lot of accolades and, you know, awards. Recently, featured as our our Interface AI as one of the best AI out there for banks and credit unions. And we are endorsed by CUNA Allied Solutions, and we won numerous awards at Finowate.

And most recently, we won the best AI award at American Banker. We now power over a hundred financial institutions. You know, this is another milestone to celebrate.

And we continue to be staying focused on financial institutions. We have no plans to venture into any other industry. We feel there’s so much to do here. We continue continue to be committed.

And as you all know, some of you are customers joining. You know, I come from a Caribbean family and and and as well, in the AI research background, bringing the best of the both the worlds and the my lifelong experience to continue to build solutions, here. So, you know, pretty much we have four different, suites of solutions at Interface AI. Right?

So, you know, Agentic Voice, which is our most popular solution, and and agentic chat, agentic employee AI, and a new suite of agentic solutions that we are planning to launch very soon.

We now, you know, have over one point, five million interactions every single day across all of our customers and continues to grow pretty rapidly.

So we we have talked we talked a lot about how technology of AI is evolving, you know, from conversational AI in the past with very complicated way to set up AI.

And, you know, pretty much there was some, you know, impact to member experience or a customer experience when we set that up. To, you know, where we are, generative AI, you know, is kind of makes things very straightforward. And then kind of where we’re going is agent AI.

In terms of technology, the evolution has been pretty rapid. And, you know, every day AI is evolving, you know, in a pretty rapid way. But how this technology evolution is gonna change our industry, right, both for the member and employee, and then we go deeper on the employee, today for the rest of the call.

You know, the the the way we see the world based on how these technologies are changing, is kind of you know, we we kind of started the journey, to be the one AI brain, for credit unions and community banks serving, your members and employees through AI. You see that blue brick, at the middle? You know, typically, you know, our, technology stack within a credit union has three layers. Typically, two layers. You know, system engagement, which is our online mobile banking systems, our loan application systems, our our contact center system, kind of system engagement, and your system of record at the bottom, which is your credit card processing system, core banking system, and things like that. Those those are gray bricks, which are often our technology stack.

But we started the journey to introduce this one AI brain. There’s a blue brick, which says system of intelligence that connects with your online mobile banking system, core banking systems, and both system engagement system record to create the system of intelligence. Right?

Well, kind of acting as one AI brain.

You know, this is this is where we are in our journey. You know, a lot of, our customers, as you are all here, this is kind of how you use, you know, our our technologies today and things like that. Right? So but where it’s going with the agentic AI is, something far more powerful.

Right? So, you know, we, you know, which is what we call system of interactive intelligence, which is your system of engagement and system of intelligence is gonna merge to become a system of interactive intelligence, which is you no longer would need, the systems your, members use, anymore. It will be more like a chat GPT, like AI assistant on voice or digital to get lot of things done, know, beyond, you know, what your online mobile banking or online application systems can do. These, like, bank GPT like systems could do a lot more beyond that.

Right? So that’s kind of our new vision, what we call interactive intelligence for banking, ensuring that there’s a chat GPT like or or what we call bank GPT kind of assistant for your employees and members, and we plan to deep dive today on the employee side.

So just to give you an idea why these two systems are so interconnected and what value they bring for you to have this, you know, one bank GPD for your employees and your members. Right? So it’s a significant what’s your what’s your what’s your cycle and benefit that offers. So this this kind of diagram kind of gives you this picture kind of gives you kind of a good idea about that.

Right? So lot of our, systems that member interact with like, let’s say we’re bringing a bank GPD for, you know, members. It’s it’s a similar systems that you would have to integrate with for even for employee interaction. Right?

So, you know and and pretty much, the idea here is, you know, you have a bank GPD kind of system that, you know, pretty much, you know, your members use, and that integrates with all of the back end systems. And this bank GPT kind of system helps provide, make these interactions not just automate task, but enhance those interactions to drive, new revenue through product recommendation or provide financial insights to drive financial well-being or personalize this interaction from the past interactions that a member have had or make sure those interactions are secure. Right?

But on the same, on this on the same lens, know, for employee two, you know, either you’re a CXO or a call center manager or an, an or an analyst, imagine having a bank GPT, kind of an assistant that you can go, you know, and and tap into all of these integrations and do quick research and get gather good insights or take actions quickly.

You know, there is so much possibility having this one AI brain across the board. Let’s say, for example, our BankGB for employees actually, you know, now can read and see your notifications on changing in compliance. Right? And imagine if the the BankGB for employees side being able to read those notifications and, make sense of it.

And your compliance manager could go in and say, hey. I received this NCO notification. You know, how does this impact my business? What all changes have to plan out for?

Right? And then the AI is able to tell them, here are the changes you could make. But at the same time, if the member is asking a question to the on the other side of the bank GPT for the members, imagine there’s a real time compliance. Right?

So because this one AI brain already knows it, it would prop it it has ability to probably even apply the compliance, to your member interactions on the day notification came without you changing anything. So that’s the power, of having one AI brain possible. There’s so many possibilities. Like, you know, even an example, let’s say, you know, you have a target as a credit union to hit, you know, you know, a a specific goal for your auto loans or your credit cards or checking accounts.

You know, your CFO or your as as a CEO, you could jump into this bank GPT, ask a question. Hey. You know what?

You know, give me a path to achieving a goal of increasing I mean, getting a thousand auto loans by end of the quarter. Right? And it can actually imagine it could go into your core system, analyze, you know, or any back end system, not just core, analyze all of your members who may not have auto loan, but also continue to filter further who may have the potential to buy an auto loan based on their age or based on their recent transactions.

It can predict how many of your members could even have an option to like, you who may potentially get an auto loan in next one quarter. And then it comes back and tells you, hey. You know what? You know, you you have a target to hit thousand, but here is about, like, you know, five hundred you can get.

But guess what? Like, if you can reduce your, net interest margin by, like, ten basis points, I did research in the in in all of your neighboring credit unions, you will be able to drive thousand auto loans. Do you want to do that? With a click, you know, and here is the financial impact for the CFO.

And if if they if the click of a button, they say yes, automatically in real time, when members are asking questions about auto loan, it starts promoting, knows that you have a target to hit then starts promoting this new discounted interest rate. So that’s kind of the possibility of one AI brain. Right? So you start your CFO or your CEO could start with the research, get the data, make a new product, and then, you know, has ability to seamlessly have that new pro you know, members start discarding those new products and things like that.

So there’s just tremendous amount of possibility here to put entire retail banking on an autopilot. Right? If cars can drive themselves, why not banking? Right?

So it’s the time to do that. Right? So and this is kind of our full stack with with that kind of vision. What is our tech technology stack look like.

Right? So we have a number of products. Today, some of you probably already use a few of them. And then we have this fraud, solutions and this agentic banking platform and banking integrations and whatnot.

Right? So real quick, I would like to take a a few minutes to briefly introduce our products and then kind of go into employee AI. The Wise AI, which is kind of a member facing AI we offer, is the most popular. It is pretty advanced with, you know, with the agentic capabilities that we’re we’re getting our few customers live.

The agent think it’s kinda mind blowing in terms of its ability to natural voice, custom voices, is beyond better better than human understanding and handling complex interactions.

You know, has ability to, being able to do upsell cross sell, provide powerful analytics, and text responses and all all of that. That’s our voice AI for your members. We also have chat AI for your members too, which has similar capabilities. Gone are the days you’re, you know, training manually, you know, what are the intents or workflows.

You know, you you literally point our AI to your knowledge base and, you know, it learns everything in a couple of, minutes and straight to answer, And it keeps up with that knowledge base as that gets edited on on the chat AI. And it even can complete transactions and help with spend analysis or savings analysis and things like that too.

And then comes coming to our employee AI, and we’re gonna spend a lot of time talking about this today.

You know, pretty much our employee AI, you know, is is like, again, like a bank GPD for your employees.

It has ability to instantly get access to policy and procedures. Gone are the days. Your employees have to hop between systems to find, you know, a a, you know, a policy or procedure while a member is waiting on the call or at the branch. Right? So those days are gone. There’s, like, one AI assistant they could go to to tap into that knowledge base. Doesn’t matter where the knowledge is.

Right? And then, pretty much as it evolves further, it will be able to do upsell cross sell. These are all different modules. You know, you can just take AI that can help with policy and procedure, or you can upgrade the AI to do do the work on behalf of you.

Right? Like, which is let’s say, if if your employee wants to block a card if on behalf of a member, there is two two ways that could happen. Right? One, AI could tell you, here is the policy and procedure you you need to follow.

That’s just that’s a kind of a basic model. But as you upgrade, there are capability where, you know, it will actually go get it done for you, calling the API RPA system. Right? So we’re entering a new world where your employees don’t need to know how anymore, right, which is kind of a significant part of the training we spend.

Right? So across the enterprise industry, there’s, like, about two hundred and fifty billion dollar goes in just employee training. Right? So just to teach them how to do some things.

Right? Like, that world is changing. Right? So employees need to know what, not how. So remember, every time if there is a there’s there’s an employee asking how to do something, it reminds you that that they’re not you don’t have an AI system that’s effective enough.

Right? So and then this subsequent capabilities of this employee AI is not just getting things done on behalf of them, calling APIs, but it’s also, have ability to coach them, in real time to do an upsell cross sell and things like that.

But that’s just the frontline capability. But as we spoke earlier, it extends beyond that to help, let’s say, your CXO or a or a compliance manager or a call center manager to be able to do lot of different kinds of questions, including, hey. Fix this email for me. Right?

So or, you know, find all the facts from my core and add this to my email and send it out, the research I did on for my CEO. Right? Like, you know, that’s kind of where we’re going with employee AI. So there’s a lot of industry, study about, the possibilities and kind of the benefit of such a employee AI.

I’ll let Jack chime in here. Jack?

Sure. So employee AI, obviously, can be used by various different population in your company. One of the important group of the people is contact center agents.

There are studies coming out showing the productivity increase across different use cases. So this particular case study which we are highlighting here is actually coming from quarterly general economics. It was done in the q one of this year.

So these researchers basically did a study with about five thousand, customer support agents in the fourteen five hundred SaaS firms, and they did a kind of a control group, which didn’t get the AI. And the and certain group which actually got the AI, and then they kinda figured out what is the impact on various different aspect, like the, productivity, for learning customer and agent experience for the agents who use AI and compare it with their impact before and after using AI to the control group, which never got the AI. So this is kind of a scientifically designed study to figure out what is the impact.

And they use a tool, g p d three. In this case, they’re just using a kind of a standard tool without any automated action. So let’s see if you can go to the next slide, Sri.

Absolutely.

And this is the kind of the executive summary. So you can it had an impact on the issues resolved. So fifteen percent more issues resolved by the agents who are using AI. So this is a huge uplift in the resolution of the issues. Here, they are they did it with chat, but it can apply to any channel. Then the average handle time went down by eight point five percent, and then, the agents were able to handle multiple, chats at the higher percentage as well.

And as you would expect, the gain for the novice agents, the, the new agents was much more than a highly skilled, because highly skilled obviously already know how to answer most of the questions.

And, there was a thirty percent uplift for the lower skilled agent. So especially in the contact center world where there’s a huge amount of turnover, you always are dealing with the agents which need to be kind of, helped when they’re joining. So as she mentioned, huge amount of training cost going on. With AI, you can potentially change your strategy on how you kind of, onboard the employees.

And the interesting thing is all the productivity increased without any impact on the customer. The The customer sentiment actually went up, and the escalation to the managers, went down. Right? So I’m pretty sure everybody has this experience.

You call the contact center. You don’t like the answer. You either hang up and call again and get a different agent to get the right answer, or you say, let me talk to your manager. So with AI, you can consistently answer all those questions, and that reduces the manager escalation.

So you have set first call resolution and everything kind of goes up. Next slide, please, Sri.

I just wanna add, Jack.

I think you are spot on. Like, the for call, this will be substantially more. Right? Like, for example, that fifteen percent issues resolved per hour, thirty cents, you know, chat per hour, that that could be pretty huge, for voice in a big way.

Right? So it could be several dollars of impact in in the bottom line and things like that. And there’s also accuracy, you know, accuracy of responding, especially if you also have a high network individual kind of in your in your in your customer base, you know, providing that high touch service you often do, but that accuracy becomes even more important. Right?

Like, we have seen a lot of financial institution with high net worth individual start their AI journey with employee AI first, and then eventually go to the member AI, all of that. So there is some something to gain for every kind of financial institution.

Right.

Is the next slide, please?

Yeah. So digging a little deeper into productivity and efficiency details.

So immediate lift, like, as soon as you kind of deploy AI, the the lift is immediate. Right? And if you were to kind of take a traditional training approach, it takes a while to get people trained. And as she mentioned, with AI, you don’t need to know the answer.

You just need to figure out the right question to ask. And luckily, in the context in the world, the right question to ask is what the customer is asking. So it becomes very, very fast ramp up with using AI rather than the traditional training to ramp up the audience to the, to the agents. And, resolution rate goes up.

There’s no quality trade off.

And, again, AI is helpful on moderately uncommon issues. That’s common sense. But the point to make here is with automation, on the member service with the voice AI and chat AI, the questions which will come to the to the to the agents will be uncommon, will be complex.

And AI is actually helping. So it’s kind of a synergy there. With automation, you kind of automate the simple, and then the complex, augment, with the AI for the agents.

Sri, anything to add here?

No. I I mean, it makes sense. You know, to lot of this is kind of, you know, common sense kind of, approach to the benefit of AI. Right?

So, you know, removing the how part is the most important. Right? So the and if if if training goes down and the response time goes high, the accuracy improves, and our cost customers and members are happy. Right?

So Yeah. I had an experience even in American Express. I I shared that with you, Jack. Right?

Like, you know, I was traveling, trying to, you know, withdraw cash from ATM using my American Express. The agent didn’t even know how to do that. Miss misled me a couple of times. This is a challenge in every size of the financial institution.

Right? Regardless of how premium they project themselves and tell you, hey. We have this wide glow local American support and call center agents are here. And guess what?

You know, they even had challenges to answer basic questions.

So yeah.

In in the defense of agents, this job is very complex. I used to work in a contact center one time in early in my career. It’s probably very, very difficult. So having AI augmentation is a lifesaver for the agents themselves.

Yeah. Please.

Okay. So this is kind of showing you the the again, kinda we talked about novice agent gaining immediately. It kind of matches the six month veterans with the AI. So you kind of are gaining five, six months immediately by giving them the AI.

And the low skill the the agents which are low skill, generally, noise, their thirty six percent increase in productivity, and the style matches the high skill. Right? So you, in a way, are uplifting your new agents immediately to match the high skill agents.

And it’s also durable learning. So in the study, they actually turned off AI after giving the AI to the agents, and they saw that the agent retained their knowledge. And they were able to answer the question much faster even if the AI is turned off. Right? So this is kind of a very powerful, scientifically designed study showing you the impact of AI on the context of the agent. And there are other studies which are, studying different populations, and we’ll keep sharing those in our blogs and so on as we kind of go along.

With that, let me turn it over to Shri.

You know, we’re kind of going to take you through a a, like, a a little bit of a sneak peek into our employee AI, everything possible.

You know, our vision for employee AI as we talk, you know, we wanted it to be a bank GPT, one stop shop, one place you could go regardless of the role you’re playing in the credit union community bank and get access to any and every information that is that is access that is meant for you, right, with the proper access control. And then, you know, that that information or insight that you could gather not just from documents, not just from, you know, your SharePoint website or or or your shared drive, or policy repository, but it could be coming from, acquiring your core, core system or LO system.

You know? You know, how how what is my, you know, turnaround time on my auto loans? Right? So it should be able to go figure out that in LO system, how many applications came, and and and, you know, when did we approve or disapprove, like, long is the whole process?

Right? Like, you know, literally, it it would have access to every single knowledge, every single data within the financial institution, and can use all of that to answer and give insights that will help you make the best decisions in in in driving the business forward. Right? And so that’s kind of the vision here.

You know, kind of just take you through a day in the life with employee AI. Like, let’s say you’re CXO.

They wanna look at, you know, what is the ROI of our current credit card promotion trending versus last quarter. Right? So, they should be able to get answers to that kind of question or give me today’s net interest margin and flag any variances against the budget plan. Right? Or summarize member satisfaction score across all channels year to date. Right?

You know, or list the top three cost drivers that more operating, expense ratio this month. Right? So imagine being able to get instant answer to these things. You know, it it would make a huge difference and, you know, for us to kinda make right decisions.

Our managers asking, you know, how many mortgage applications are pending review? What’s the current average processing time?

You know, show the ten most frequent customer complaints log log past past week or generate step by step checklist for onboarding a new third party vendor.

Compare workforce headcount plans with actual, for q two and highlights gaps. Right?

Or an analyst asking, run a higher, you know, run a risk weighted asset analysis on commercial loan book, or fetch fire delinquency trend. Right? Explain the drivers of last, Friday’s p and l swing. What scenarios are stress testing model push for tier one capital below twelve percent?

Right? So, practically, all of these kind of questions could be answered, from not telling them how to go do it, but giving them the data from all these integrations and systems. Or a call center manager asking, what was yesterday’s average handling time? How much did AI guidance reduce it?

Right? Or a call center agent asking, how do I lock a debit card? Right? Or provide, provide the interest rate table, you know, or or branch staff asking, walk me through issuing or replacing a debit card or someone trying to, know, withdraw a lost cash withdrawal.

Like, how what steps I should follow. Right?

This is this is kind of the positive of a bank GPT for your employees. Right? So, you know, instant access to knowledge and insights, from every single system you have. Right? So, you know, what we’re gonna do is to show you a quick demo, you know, and how some of these each of the different personas and, like, kind of different kinds of employees in the creating could be using this.

Know, let’s I’ll invite my colleague, Laura, here to help me with the demo.

Great. So now we’ll start off talking about the first kind of employee, which is kinda most most needed.

This assistance is most needed for them, which is I empathize with Jack, he said.

Call center staff has the most hardest job. They’re put on the spot sixty times a day. Every single caller is gonna ask them a question. They gotta be prepared for that.

So let us show you how their experience is gonna be. Let’s get into the get into the view. Let’s click on that. So yeah.

So this is kind of the one stop shop view they’re gonna have.

You know, they kind of adapted itself to bring more information specific to what would be relevant to the call center agent apart from just answering the question. Right? So you see on the right on the extreme right, you have a panel which says authentication status. It shows you, if the caller is authenticated, already before the call handed off to an agent, you know, how successful which credentials they’ve already passed and whatnot.

On the middle section, it shows the conversation history because this is a call center agent using it. Right? So it shows, hey, what has happened so far with their conversation with the AI on the member side before the AI handed off to an agent. And then on the left side, see AI already pulled up a policy and procedure, showing you, you know, how to respond to that.

And then as you scroll down, there is a blue button. You click on it, view document. It automatically navigates even though you have thousands of documents. It automatically navigates and, highlights the right part of the document regardless of number of page it has and shows you, hey.

Here is where I got this information from. So you now you have a more detailed steps apart from the summary AI provided to kind of, you know, ensure the diligent execution of a policy and procedure is done. Right?

You know, in in pretty much, you could be asking and let’s say, member on the call could ask another question here to lock the card, for example, or or, you know, or password reset. Let’s go try that.

You know, member, you know, the agent determines you wanna do a password reset. So the AI now searches in the context of the same document, goes, pulls a, specific part of the document that has specific reset password, you know, policy policy there.

Right? So, it kind of resets itself even though it’s the same document, auto scrolls back to page number one and shows them how to reset online banking password. For for for, you know, for the audience here who are here who can probably going through any sort of, technique technical migration, let’s say, changing your credit card processing system, online banking system, or core system, or a lower system, this would be a game changer. Right?

Like, you know, in terms of being able to help your employees get up to speed on the new user manual, which could be, like, hundred page. That’ll be a lot of training and and whatnot. Especially during this migration, it also increases your call volume and put a puts additional stress on your, staff or you end up getting an outsourced contact center and whatnot. Having a an AI that can just read through a thousand page user manual and tell you, exactly what they need to do for the member query coming in may goes a long way, especially transitioning to a new system.

Right?

So, hopefully, that kind of gives you an idea.

And, you know, it this is not just about as I said, it’s not about just, responding to policy and procedures. It goes more deeper than that. If a member on the call ask again, we’re still talking about your call center agent. Member on the call ask, they wanna lock a card. Your agent could type it in and say, lock the card. Here’s a member number.

And, you know, push a button. The AI comes back and says, hey. You know what? You don’t have to go to your credit card processing system to navigate and do all all these things.

You know, I can take care of take care of, take care of for you. Right? So, it asks you to confirm. Right?

So in if you click yes, you’ll connect to that system through plug ins and complete that locking account. Right? Just the kind of the next step. Right?

Which is, instead of AI telling you how to do, AI will get it done for you. Right? So, that’s pretty significant. Like, you know, it saves significant amount of time.

You know, some of the studies Jack showed you is where, you know, saving of, you know, a minute or half a minute is all related to AI giving them policy and procedures to you know, guiding them what to do. But if AI can do the job on behalf of the agent, you know, by integrating the systems and take actions, that benefits can really double up. Right? So, you know, here, your agents don’t need to have three screens, ten different applications open, navigate between all of them based on member request.

Those days are gone. Right? They would come to one place and just which is kind of bang GPT and kind of type in a query, and, AI takes care of completing those actions.

And and, you know, it could go even further. Like, for example, if a member on the call asking, they wanna apply for a credit card. Right? So, now the the AI gets into a coach coaching mode.

Right? Say, hey. You know what? I’m gonna help you, get this, credit card as, you know, you know, get this customer excited about credit card.

Go ahead and ask them what kind of features they’re looking at. Right? So, and and the agent is typing in what member is saying, and and then it oh, it seems like member need a cashback credit card and kind of shows you, a a a a card here. So it shows you the card and, like, you know, and and and pretty much all the benefits in the card at the bottom.

So they have all the details to, kind of continue to get the member excited.

And, it’ll ask, hey. Tell me if member is excite satisfied, and and you go ahead and say yes there.

And it starts a online, know, an applications right away, integrate into, let’s say, your LOA system. So your agent could literally take the entire application, talk to the member right here on behalf of the member. Right? So we we’ve condensed that process for the sake of the demo, keep it short. But we’re gonna show you how just a few questions here. They could type it in and fill up the application on behalf of the member.

And, you know, it comes back, shows you a summary. You, again, check with the member on the call or the customer on the call, and you confirm. And, you know, you’re pretty much done. You don’t have to go learn your own application system. And by the way, as it completes it, it shows you an upsell opportunity saying, hey, you know what?

There is a you know, people who often apply for such a credit card. They may be interested in money market account. Do you wanna talk to them about the same?

So that’s kind of coaching mode of the AI. But this is kind of one one kind of employee who probably most need AI. Right? And and and then we’ll now switch to showing you how across the organization you could use this similar kind of AI. Right? So and we’re gonna start with, like, a, like, a branch employee real quick here. So let’s say, kind of a member walking in to a branch, their house is a lot large cash withdrawal.

They can quickly ask the same.

The AI can help the agent how to process that request.

You know, again, it gives you a summary, what you need to do, what are your limb limitations, what’s the process you gotta follow. And then you click on the view document. Again, brings up the document. This is your branch employee this time, like, you know, other you know, and we keep showing you other kind of employees how they can use. Right? So let’s try a a manager.

So we’re gonna ask a question.

Show the ten most frequent customer complaints logged this past week. Right? So and it didn’t it’s it comes back, shows you, hey. Here is all the details and top question top complaints. I’ve created a document for you, you know, and you can click on view document, and it kind of shows you, you know, the top, top complaints, from let’s say, you have some sort of survey going on. You fed the AI with all the data, and it it is able to process that and kinda show you the top complaints and things like that. So let’s say your CXO wants to ask a question.

You know, how is the ROI of a current credit card promotion trending versus last quarter? Right?

You know, it can kinda do that analysis and generate a report for you, that you can also get access to see. And, of course, like, you know, these continue to get more prettier as more capability get kind of kicks in. It it shows you a response there. Hey.

Here is the you know, kind of the the answer to the question, like, how promotion is trending. Right? So and then, you know, let’s kind of try one more probably like a a a call center manager, what kind of questions they could ask. Right?

So let’s say, what was yesterday’s average handle time? How much did AI guidance reduce it? Right?

So, you know, here, it comes back, shows you, hey. Yesterday average handling time was, you know, so many seconds. It is as you reduced by thirty seven seconds. Right?

So that’s kind of, you know hopefully, you get an idea, like, how, employees across the financial institution. We have customers already use this, and John is gonna talk more about it. You know, pretty much, you know, you have all of this, employees use it for IT related questions or HR related questions and whatnot too. So probably that’s a perfect segue, for John, for you to kind of, share how some of our customers are already using this.

Yeah. So while John is sharing his desktop, I wanna remind everybody that you can ask questions anytime. There’s a q and a area at the bottom. Start typing your questions. And once John is done, we will answer those questions.

Great. Thank you so much, Jack and Tree, for the intro. As Jack mentioned, John Terencelli here, vice president and head of customer success, at Interface. My favorite part of my job is speaking with customers about our products and how they’re getting value from them.

And that’s why I’m really excited to go through a little bit about what our customers are saying about this product that Sri, just shared with you. The information and the customer feedback that we’re sharing is also available on our website. We have transcripts from these from these more in-depth webinars, which occurred over the last couple of months. But instead of just hearing it from us, we wanted to give you a little bit of a preview of what our customers directly are saying about these solutions.

The first customer that we’ll talk to really briefly here is Afidian Credit Union, which was formerly known as, Rocky Mountain Law Enforcement Credit Union, and expanded rapidly in twenty twenty two, by relaunching its brand, expanding its membership, and really reaching an all time high in its business success. It historically had a small call center, only four only four members. And so to support this expansion, to support this growth, Afidian, turned to Interface and worked with us to build out a solution that would allow them to leverage this AI, in support of of their employees to better serve their customers.

So one of the key questions or points that we posed to Christine Wiley, the CEO of Afidian, when we spoke with her a couple of months ago was essentially, you know, thinking about your growth and your trajectory, what are the problems you’re trying to solve?

And from their perspective, there were there were four key issues that they were hoping to resolve with this solution. You know, essentially, first, solving for fragmentation of information. As as discussed earlier in the presentation, resources are scattered across multiple systems, across multiple experts, across multiple platforms. And having all that information available in one place would significantly streamline. The the second piece is really tied to that, which is, a lack of a signal source of truth. In many cases, employees didn’t have all the information or, in some cases, didn’t have the right information, which made it difficult to give a hundred percent accurate information to to members.

Third, really, this reliance on individual and institutional knowledge. It’s great that we have that we have employees who have been with us for many years who are able to answer all of our questions. But in many cases, this does create a bottleneck, and it creates a difficulty of getting information. How do we solve for that? And finally, how do we essentially speed up the process for getting all the information needed to support our teams?

And in terms of what is the impact and then what’s being seen on the ground here, we’ve seen some really exciting results, from Afidian.

And these are, you know, some direct quotes here. We didn’t, you know, create these, but the key one, that was mentioned is, you know, there’s universal praise. In direct quotation, everybody loves it. One of the things that we’re seeing with our customers who are using the solution is a a general and universal belief that this is supporting and helping their ability to better manage member needs in a fast, streamlined, and expedient way.

Staff are using this every day and are able to provide much more convenient service and consistent service and getting faster answers to decrease average time spent on the phone and ensure that costs are low and member satisfaction is high. In terms of some of the best practices that Afidian has been using to to implement the solution, you know, there’s really a few key ones, and you’ll see this as a consistency among many of the the the customers we’ve spoken with in our webinars. First, key to have organizational buy in. Ensure people understand what the value is and why you’re doing this.

And, you know, in tandem with this, making sure that the right team is there and ready to support, making sure that there are dedicated internal projects leads and people who are able to speed your transition to a truly AI powered workforce.

Third, you know, preparing the content properly. You know, I know we’ve seen some questions in the chat here about what exactly is incorporated and how does that work. You know, working very closely with our team to make sure that information is reviewed and organized in the right way that can really speed up this process and improve efficiency, and finally, executing really that strong implementation to comprehensive training and active promotion across the CU.

The next case study we wanted to bring up here is EFCU, which is the second largest credit union in Greater Baton Rouge area and the third largest in Louisiana. It has a membership of over sixty eight thousand and over one point two billion dollars in assets. And because of this significant scale, and continued growth and and, increasing in membership, EFCU needed a solution that was going to support its large staff, across its branches and its call centers.

So we spoke with Tyler Brooks, who is the VP of projects and innovation at EFCU a couple of months ago and asked him some of these questions as well. You know, what are the problems that they were trying to solve when they decided to go forward with Interface’s frontline assistant.

And very similar to what we’re seeing from Afidian, it’s a lot of the same things. You know, there is essentially a historical reliance on individual point people, the phone a friend mentality, a tie in to needing to sort through so many different sources of information to be able to get that correct answer and do it with high quality.

So as we think about similarly, what were the impact on the metrics that you know, the EFCU had seen and what is the adoption, overall, these results have been really positive. It’s worth noting that, you know, this is a a relatively recent launch, but it’s being used hundreds of times a week by the call center, freeing up a significant amount of time, for all of their agents.

And as you see here, yeah, as we mentioned, you know, six hundred and sixty five requests last month from the frontline team for sort for research and information through frontline assistant, which is significantly improving the speed to be able to support members and the quality with which they receive those answers.

So finally, what are the best practices that they see?

And similar to, similar to what we were seeing from Afidian, you know, in terms of how best to be successful, really a focus on sponsorship, management, and support across the CU as you’re rolling this out, making sure that there’s a dedicated team, that there’s a clear understanding of why, and a communication of the benefits. That it doesn’t just benefit you by helping you able to answer questions, more quickly, but it also allows you to better service your members with more consistent, speedy, and helpful answers. And as Sri showed in the demo, it’s also providing the ability to upsell and cross sell to additional services, the ability to help, you know, transform what we’re seeing not from just a a call center solution, but truly into a revenue revenue generating engine for the CU. So really excited to see those results and happy to kinda turn it back over to Jack, to answer some questions that have been coming up.

Thank you, John. Thank you, Shree. Okay. So this is the favorite part of, my favorite part of the webinar, q and a, and, we’ll start answering the questions.

On the right, you see this QR code. Feel free to scan the QR code if you would like us to reach out to you to set up a demo, either for employee AI or any of our other solutions. So with that, let’s get the q and a started. So the first question is from Kimberly.

The question is regarding the content, the documents, the information which, the employee AI needs. Does it need to be uploaded into, interface dot AI system? Or we can kind of, link to the wherever the information is sitting. So, Sri, if you don’t mind answering this question.

Yeah. Absolutely. So, you know, thank you for the question, Kimberly. You know, pretty much, the the AI, what we saw is ability to, give instant access information, take actions, on on the systems to complete a task, or do research, or get an agent coached, do an upsell cross sell or for a CXO to do a research or for a manager to get access to, you know, a lot of insights. Our system has a little tap into this knowledge, and sis, through any APIs and RPAs. Right?

And and that could be your core banking, a lowest bank’s credit card processing system. If if your knowledge is residing in a a SharePoint or a a shared drive, we can tap into those two.

Or you could just use our own system stand alone and load all of those, policy and procedure inside as well. Right?

You know, it’s flexible that way. There’s an it’s sitting externally or you wanna use local storage within the product, you can do that.

Often, kind of the other, players in the industry are, the the the employee are very focused on finding, pausing procedure. Doesn’t go beyond that, to take actions or coach an agent or help do your CX or do a research or or your kind of be beneficial for all other employees beyond just frontline staff. Right? So that’s kind of the uniqueness here that it it is useful across the board for all employees and and as well as, you know, it goes deeper in kind of integrations and all of that to find the data sources beyond just policy and procedure. Yeah.

Okay. Maybe I can ask a related question to this, Shri. So this information and then you have different population of people using it, executives, contact center manager, branch.

How do we kinda make sure that the information is kinda kept separate for different user groups?

Yeah. So we have a very strong SSO integrations. Right? Like, you know, you know, depending on how it is set up on your end, sometimes it could be straightforward or complicated. But, basically, this SSO systems can basically kind of you know, let’s say you use Microsoft Active Directory, like, you already have set up different kind of access control within your system. The AI can kind of inherit similar access control with the simple integration.

But if you don’t have that if you don’t have that, you can kinda manually then manage those access of different dataset to to your different group of people and users. Right? So you can either do manually or integrate with SSO to have a seamless integration.

Okay. Yeah. Okay. So the other question is about accessing this system from like, people use Microsoft Team and they want the information within Microsoft Team.

Some people will have contact center Yeah.

Kind of a system.

How do you kinda access this information in different So, you know, the this AI, you know, of course, is is kind of channel less in a way.

Right? So wherever you want this AI wherever you wanna tap the knowledge and insights and take actions of this AI could do, you can practically do that from anywhere. Right? Like, it could be your teams and and any other channel or a call.

Right? So all of those are possible. Right? So with the right system on right right system and licensing on your side, you know, the the the those are possible integrations to make it easy to make it accessible anywhere.

But when you do go out of the AI, sometimes there’s limitation to how the information gets rendered or what it can do, what it cannot do. Right? Like, given you know, the idea of the employee AI is we’re building a AI companion for work for every single employee within a credit union. It is gonna be your AI companion.

And that is gonna be big enough.

That is gonna be, like, its own stand alone tool is the right way to use it, either to craft an email or, like, you know, do a research and whatnot. But if if that is needed to be exposed through Teams or something, it’s definitely possible.

Okay.

Okay. So quite a few questions around comparing and contrasting other popular tools for employee AI. Obviously, Microsoft Copilot, ChatGPT. So can you kind of position how we are different, how this solution is kind of more aligned to this audience?

Yeah. Yeah. Really good question.

I think, like, you know, what we sell is solutions, not platforms. We sell solutions to problems for a for a mean, we’ve always focused on financial institutions who don’t want to set up an IT team to build take these platforms and build a lot of things around it to just be make it usable. Right? It’s very challenging.

And for a really large financial institution, probably thirty, billion dollar plus, it kind of makes sense. You could have a IT staff to kind of, you know, software development team just focused on just, you know, ingesting data and doing all these integrations and things like that. For many, that’s not an option. Right?

Like, which is having their own software development team to do all of this. Right? So what, what we mean by we don’t sell a platform, we sell a solution. The problem is, you know, we bring this technology.

First of all, significant r and d made to make sure this technology doesn’t hallucinate, doesn’t make up answers when it doesn’t know. You know, we are in a regulated industry. We can’t afford to have a hallucinated responses, and we are basing our decisions based on that.

There is a huge compliance risk. So we have done significant r and d to bring you a platform that doesn’t have the hallucination risk and is comp compatible for a regulated industry like ourselves.

And then beyond that, we all we have we bring you all these integrations out of the box and data fine tuning and make sure all of this is accessible and and you you’re able to kind of do the research using all data sources quickly and and and whatnot. But then connecting back to that one AI brain. Right? So not only you do research, you take actions to say, you know what?

You know, going back to my example, you know, a CFO doing research on how do we achieve the thousand auto loan target for the quarter, doing research on is there a way we can change the interest rate a little bit without impacting our bottom line, and then clicking a button to create that offer that now your member facing AI starts promoting. Right? So that just one AI brain for for your member’s employee is is something also kind of something beyond what, you know, these platforms from big companies can do. They’re not designed and and for for our use cases, and and it requires a lot of IT staff to set up things like that.

So, yeah, that’s that’s what the kind of how we differentiate Jack there, from reducing hallucination to zero to creating one AI brain and with the all the integration data to take actions and and, yeah, promote them to members too.

Yeah. Very similar to the vertical approach we are taking to the automation.

Exact yes.

Okay. So the next question is, does your frontline assistant currently integrate with Pfizer DNA core to perform transaction action if need be?

Yes.

I mean, at DNACore, there’s a couple of data centers. I can go with one one step deeper. Like, is it Johns Creek or Cherry Hill data center? Right? So we already have kind of VPN tunnels to them too.

So that that kind of make makes it easier. Right? So so, yeah, we do have integrations.

Okay. So here’s a interesting question.

Yeah.

An example referenced comparison of other FI rates in the local market to help determine the optimal low rate Yeah.

For auto and promotion. So where does this external data come from?

Yeah. So, you know, this, the bank GPT we’re gonna set up for you is going to tap into the general Internet to to do that research for you. Right? As as as giving you example, the CFO doing the research kind of saying, hey. How do I achieve this goal and, you know, what is the competitive offer I can make?

It would go do research from all of your potential competition in that region, tapping into Internet and and and things like that.

Okay. Yeah. Okay. Again, thank you, Shree. Thank you, John, and thank you for all for attending. Thank you all the customer who joined.

Please do reach out to us and let us know if you would like to do a demo.

Really appreciate your time. We’ll see you in the next webinar. Thank you so much.

Thank you.

Thank you.