Inside Story: Ally & Microsoft Groundbreaking AI Collaboration

Jim Marous (00:11):

Hello and welcome to Banking Transformed, the top podcast in retail banking. I'm your host, Jim Marous, owner and CEO of The Digital Banking Report and co-publisher of The Financial Brand.

(00:21)

The launch of Ally's self-built AI platform, Ally.ai, in collaboration with Microsoft, represents a measured yet trailblazing foray into next-generation technologies for banking.

(00:35)

Rather than reacting to hype, Ally is taking a principled approach to focus on productivity, security, scalability, and an improved customer experience.

(00:49)

Ally's emphasis on co-piloting change also signals the value of strategic partnerships between banks and tech players in steering AI's responsible adoption, providing a blueprint for financial institutions eyeing productive AI integration.

(01:08)

Joining me to discuss this exciting collaboration is Priya Gore, general manager of data and AI for U.S. Financial Services at Microsoft, and Sathish Muthukrishnan, chief information data and digital officer at Ally.

(01:24)

New cloud-based generative AI models for financial services much bring next- generation capabilities while emphasizing security, governance and business innovation. Sathish and Priya will share insider perspectives on Ally's deliberative approach to explore AI, the business impact seen so far, and why financial services need a tailored framework to harness AI's responsibility.

(01:54)

So, Sathish, let's start with you. Can you give a little background as to how you got to Ally and your journey to where you are today?

Sathish Muthukrishnan (02:04):

Jim, thank you for having me. Looking forward to the discussion with Priya. Excited for it.

(02:10)

I grew up in India and started my first job with Singapore Airlines in Singapore, and got to a point where I got offered a job to move to U.S. with United Airlines. I got here right before 9/11, worked for United for a little bit of time, going through those turbulent times, but learning a lot, and then got the opportunity to move to American Express in Phoenix, Arizona.

(02:36)

Little did I know, I was joining Amex right before 2008, the worst financial time for our country. Again, a great opportunity to learn, but leverage on the learnings from the calamity with 9/11 at United. I worked at American Express, had the opportunity to lead digital transformation, helping move Amex from a financial services company to a fintech company.

(03:02)

Then had the opportunity to move into a completely different vertical industrial, working for Honeywell as their first chief digital officer. There was local to Phoenix, but was reporting to the CEO and being part of problem definition was very exciting.

(03:20)

While I was with Honeywell, came the opportunity to explore Ally, a fully digital native organization, the largest direct-to-consumer digital bank in the U.S. and also the largest loan originator for autos here in the U.S. Took that opportunity to be part of a digital native organization and have been very happy for the last four years.

Jim Marous (03:48):

It's interesting, Sathish, as you describe your journey, in each situation you've come onto the scene of your organization at a time when you could rely on your learning from the past but couldn't rest on them because things were changing so fast during 9/11 with an airline and American Express during a major financial crisis.

(04:10)

So, as you got to Ally, obviously there's a lot of things going on at once, certainly in the digital space, but what motivated Ally to build an in-house AI platform and what capabilities does Ally.ai enable and what came about in the decision to partner with Microsoft?

Sathish Muthukrishnan (04:30):

Yeah, it's a great question. Like you said, I couldn't rest on my learnings. I joined Ally and was a bit bored in the first month, so it decided to introduce COVID, just joking there.

Jim Marous (04:47):

You're a precursor to bad times, I'm just saying.

Sathish Muthukrishnan (04:50):

I am not moving. I'm done.

(04:53)

So, Jim, the answer to your question lies in that situation. Many companies like Ally discovered what you can and what your real potential is during COVID. Everybody was facing this massive uncertainty. We were the first bank to introduce rewriting or deferring the loans 100% digitally. Other institutions were trying to do the right things, but if you have to do it end-to-end, you may rewrite a loan or a contract, but you had to call to a call center to have it finalized. And we all know what happened during the call centers during COVID. We were able to quickly build that software and do it end-to-end and showed us the potential, but more importantly, showed us what customer appreciated of us. And we always go by do it right, and to a fault. We want to do what customers want first, before we make sure that it also resonates with our internal customers.

(06:03)

And that muscle was exercised when last November OpenAI through ChatGPT showed up, and we figured this was a technology that's going to be here to stay, but also will allow us the opportunity to explore new areas. And that thinking is what led us to creating what you rightly termed as the Ally.ai platform, as the technology was evolving fast and we were all learning we needed to have a safer platform that we could completely control in leveraging this new technology. And that's how Ally.ai was born.

Jim Marous (06:42):

So, Sathish, sticking with you for a little bit, what's interesting about generative AI is it's a solution sometimes looking for a problem. And so, not only do finance institutions have to look and say, "Do we want to get involved with generative AI?" But we got to figure out where do we want this to be applied. If I'm not mistaken, Ally settled on the initial focus on improving internal processes such as employee productivity and even marketing. How did you come to where you wanted to apply the generative AI Ally.ai model at your company?

Sathish Muthukrishnan (07:19):

What a great question, Jim. You do not often have technology that can have an equal impact across businesses, across industries, really across the entire human race. I feel like generative AI can have that level of impact. If you think about the last 40 or 50 years, the people that were uber successful were the knowledge workers. And for knowledge workers to be successful, you had to specialize in software development, you had to specialize in building a business and launching it and making it successful.

(08:07)

What generative AI has the potential to do is break that down. English is something that everybody understands, and you could use plain English to leverage this technology to do what a knowledge worker did over the last 50 years, and have the same level or even a bigger impact. And you asked us about our partnership with Microsoft. We understood and wanted to take advantage of this technology, and had a working session with Microsoft in their campus in Seattle earlier this year, beginning of this year. We were pleased with the lean in that we got from Microsoft and the excitement and enthusiasm in co-innovating. And we found that as we were learning this technology, we have this hyperscaler who is ready to invest and take the journey along with us. And that's where the relationship started. And we were pleased we started there because of the results that we are already seeing.

Jim Marous (09:19):

So playing off that answer, Ally, as you mentioned, is a completely digital organization. Microsoft obviously is a technology forward organization. When you're comparing yourself to other finance institutions, what's maybe singular or maybe two advantages do you have as Ally being digital first when you're looking at partnering with organizations that are tech first? Because I think a lot of financial institutions get stuck on, "Okay, how do we hire the knowledge people? How do we build our team to be able to use the tool that generative AI has?" Do you think there's an advantage to Ally being the partner with Microsoft from the perspective of you both are coming from a starting point of tech first companies?

Sathish Muthukrishnan (10:08):

My answer might surprise you a little bit. It starts with the culture that we have developed within Ally, and it rests on a very simple phrase, "Do it right." And you do it right by your customers, you do it right by your employees, you do it right by your stakeholders and our partners. And what that does is allows me and my team to have a very strong and open relationship with all the stakeholders and the executive committee. We are collectively able to say, "Yes, this is a little scary to take risk and use generative AI this early on in its cycle, but it's going to create a big impact. Let's take this risk together and let's explore what we can achieve." That inherently, that culture that is built in, I cannot put a value on it, but allows us to go do these experiments and allows us to do what is right for our customers. So, that's a big advantage.

(11:13)

The second thing, obviously, you stated is a digital native company. And we all understand the importance of investing and persisting that investment in tech, which historically has allowed us to invest in our data ecosystem, modernizing our network and infrastructure, which are building blocks, and allows us to use this technology much easier than any other company.

(11:39)

And I'm so thankful for the executive committee, for the board that believes that you may not get the return on investment today, but you have to continue investing and persist that investment over a period of time, which now we are seeing the benefits and reaping the benefits from.

Jim Marous (11:57):

What a great answer. In my podcasts and webinars and writings I do, I often lean back to say, "At the end of the day, leadership's going to define whether or not it's going to be a success. Investing in technology without leadership, understanding the application of it, and embracing it, it just won't work." But you hit something that I don't hit off enough, which is really the difference between avoiding risk, which traditional financial institutions have had in their history, and managing risk, which is really a different view because it says, "We understand that everything's not going to be perfect, but we're going to embrace that and work from there."

(12:34)

So, Priya, you're at Microsoft. You haven't been there a very long time in the scope of things. Can you talk a little bit about your journey and what brought you to Microsoft and what has happened since you've joined?

Priya Gore (12:46):

Thank you so much also for having me. I would love to share.

(12:50)

So, I joined Microsoft about six years ago. And prior to that, I was working... Actually, I grew up alongside Microsoft, to be honest, in their partner community, working at a few different entities. Most recently, prior to joining, with a consulting firm that was specialized in building cloud-native applications with our customers in the Azure platform. So, we had a lot of work we were doing when the Azure IoT service came to life in Azure. As the Azure AI services became available, when we launched those, that was when I was prompted, if you will, to come join the company.

(13:27)

And so, my first job here at Microsoft was part of our Global Black Belt organization as the Azure AI leader in the America's time zone. And really, my job was simply to get out there and talk to customers about Microsoft's point of view on artificial intelligence. That was six years ago.

(13:46)

Six years ago, seven years ago when we launched our machine learning cognitive API services, we also launched an AI framework for responsible AI, and that was another thing that I was helping bring forward. If you imagine, most people didn't know Microsoft as an AI company. They thought of Microsoft for...

Priya Gore (14:00):

Most people didn't know Microsoft as an AI company. They thought of Microsoft for the great hero products that we've had, like Windows and Microsoft Office 365 and all these great solutions that we have. So fast forward to today and where we are now, it's just amazing and unbelievable. And the culture, to Sathish's point too, that we foster here at Microsoft is real. We live and breathe our mission every day to empower every person and every organization on the planet to achieve more. And certainly we're able to accelerate that mission with this innovation that we've seen in the generative AI space and being able to not only infuse that technology in our own solutions, but bring the building blocks to our customers in Azure so they can build their own Copilots and innovate very holistically and very prescriptively for their own customers, which we're really excited about.

Jim Marous (14:56):

So Priya, Microsoft works with a lot of different organizations across the marketplace, and it goes without saying that financial services is unique in many ways. In many ways they have to work, but in many ways that you have to partner with a financial institution. Where do you see the most opportunity within the banking sector when it comes to generative AI, and using the crystal ball a little bit, what do you see as being the upside based on where we are today to where we could be in a very short timeframe in the banking world with those firms that are digitally mature and willing to embrace generative AI?

Priya Gore (15:36):

Yeah, I'll start with this, just as a refresher, generative AI is really great at four things. So generative AI won't be the answer to all things. And we are seeing a resurgence in classic and traditional AI as well as part of this wave and era of AI. The four things that generative AI are the best for are content generation, knowledge summarization, code generation, and semantic search. So when you translate those patterns, if you will, to for example, the banking industry, I would say some of the things we're seeing, most low hanging fruit for banks to think about first, and I know Ally was in this boat as well with what they were doing, is thinking about client engagement, thinking about market research and content generation, and how can we actually leverage these capabilities using natural language, prompted with natural language. Frankly, any language, whether it's English or any language, to be able to reason over structured and unstructured data like never before has been incredible.

(16:40)

Going down the line we're also seeing opportunities for banks with presentations, pitch book generation, quote generation, things like that, being able to leverage this technology for that. Market surveillance is another use case. Document analysis using natural language, of course, that's very, very popular and that frankly is cross-industry, but very popular also in the banking world. And then being able to really engage other models. So I mentioned our pre-trained cognitive service models. These are speech, language translation solutions, document reasoning solutions, semantic search services. These are all things that when you bring those services into an architecture with the generative AI APIs and build these intelligent applications at scale, the power of what you're bringing forward and the, I'll say, amplifying human ingenuity just is on fire right now in a great way. And of course, we want to make sure that we're also helping our clients leverage all of this great capability with a sound, reasonable, responsible AI framework. And that's another way that we're helping banks as well.

Jim Marous (17:50):

Yeah, I love everything you said, that there are a lot of good nuggets here. And I'm thinking not every financial institution is the same. Obviously Ally has a lot of advantages in that they're really very digitally mature. They embrace what AI can do. They did that before generative AI came about. But when you look at how Microsoft Azure enables Ally to scale AI, how does it do it securely while managing risk? Because if there's one thing that can be said very clearly within the financial services industry is the hesitation is always going to be around what if something goes wrong? So when you're working with all financial institutions and everybody's throwing spitballs to the wall and saying what's going to stick, what are some cautions or watchouts that your team has seen when you look at AI, especially in banking?

Priya Gore (18:44):

Yeah, I would say this, certainly for Microsoft, regardless of the Azure AI platform or our platform in totality end to end is security is the number one most important thing. And that security posture for our clients. That's the business that we're in. We're in the business of providing secure cloud ecosystems for our clients to innovate. So that's at the forefront. The interesting thing with something like generative AI, and I'll use the Azure OpenAI service as an example. The Azure OpenAI service is basically a secure service leveraging all of the great capability that OpenAI has developed and brought forward to the market, but bringing those exact same models into the Azure ecosystem so that our enterprise customers, all of our customers can leverage that capability in their own secure tenant, in their own ecosystem of data repository, their ability to reason over structured and unstructured data sets, leveraging these great capabilities and these models, but doing so safely and securely.

(19:46)

So for example, a lot of times we get questions like, "Hey, when we leverage your service, are you going to be using our data to retrain the model?" The answer is no, because when you use the service in something like Azure, you're able to reason over your own data in a private instance. You're able to think of it as a one-way street. So we're bringing you the capability of the model, but we're not pushing back any training of that model back out into the wild. So that's a simple example of how we're bringing very mature, secure infrastructure, if you will, with Azure forward in giving our clients the ability to leverage this great technology in a safe way.

Sathish Muthukrishnan (20:30):

I could add to that, Jim, very specifically what Priya stated. We have our own private instance of OpenAI on Azure, which itself is terrific because we know that nobody else has access to that instance. And as she also stated, while we use the technology, the data that has been used to trigger the technology is forgotten after the session. So it's not learning from Ally data, it is not retaining the memory of the interaction. And that's why Ally AI is so critical. So what we lose in training the model, we are able to use the data in refining our input and triggering the model and also argumenting the output from the model. And Ally AI has become so powerful and critical in how we use generative AI for our initial set of use cases.

Jim Marous (21:32):

[inaudible 00:21:32], you actually took my next question away from me, so that was pretty good because I was going to ask you about when you're looking at intimate consumer data, how do you balance the capabilities with the security? Because sometimes we look at this and say, "Well, there's a lot of things we can do," and then it gets down to, "Should we," and then it gets back to, "Can we still deploy something?" So we had an interview with a gentleman named Brian Romley on around four months ago, and he talked about a potential future where we could build these individual generative AI models for each individual, where we can actually have generative AI learn on the go and build conversations and engagement with a consumer based on their data, their conversations, their transactions, all these things. Do you see a time when we really get that intimate with the ability to deploy an AI model to understand a consumer on an individual basis where this almost becomes your personal financial consultant?

Sathish Muthukrishnan (22:37):

Jim, this is my opinion, and I'll tell you where the consumer behavior is going. We went from a very generalized experience provided to consumers to a more personalized one. My prediction is it'll go to a personal experience from a personalized experience. That's one. The second thing that you want to think about is customer data, and how customers guarded their data and what they thought was personal has changed over the last few years. Your personal phone number or email or your home address was very sacred. You are probably not sharing it with anybody else other than your friends even 10 years ago. But look at it now. Everybody has your phone number, it's easy to find if they don't have it, or your home address or where you live. And you're willingly giving it to somebody that you've never met through Uber Eats or DoorDash. So how customers think about their data has changed.

(23:38)

However, we need to protect the sacred data that allows us to interact from a business standpoint with our customers. And I go back to Ally AI. Ally AI allows us to strip out anything personally identifiable to our consumers or attributable to our consumers before we even send that request outside the Ally firewalls. And when the output comes in, we're able to regenerate it. And apologize for getting slightly technical here. People that are using generative AI models are using a technique called RAG, and what RAG allows you to do is maintain context between the conversation and allows you to build up and connect the dots with the conversation. Generative AI is not able to do that effectively.

(24:34)

And the largest open source that allows you to do it is called LangChain, which almost all companies use it. Not only do we take our security and safety of our customer data safely, we have built a module that'll strip out the PII and rehydrate the PII using LangChain that my team has contributed to the source code of LangChain for the entire industry to use. So not only are we moving fast, not only are we innovating, we're doing it safely and securely for Ally, but also being good citizens by contributing the code to LangChain for the rest of the industry to use.

Priya Gore (25:18):

No, this is an example of what we love in Ally as a partner and a customer. I mean, they're clearly, as Sathish stated, not just innovating and developing for their own use and for their own IP development, but then taking those learnings and those best practices and those very important code bases and democratizing it back out into the market and contributing that to the open source community so that others can benefit from that. And I think that that's a trend that we need more developers and product builders that are using this new technology to foster. I think that that's how we all become better. Even some of the things that we're doing out of Microsoft research, we do the same thing. We release things very early out to the market. We want people to use it safely, securely, with the right parameters around it, but to help us innovate and help us make it better. So I love that you're doing that, and that's a great example of the partnership that we seek to have with all of our clients that are innovating with this technology at scale.

Jim Marous (26:28):

It's interesting, both Priya and Sathish, but Sathish, in your perspective, you said the whole concept of saying... The concept of what's private has changed from the consumer's perspective. I mean, I go back to a day in financial services when we were required... I'm sorry, we were not required, but we were trying to get consumers' birthdates in the banking file. And not only did consumers not want to give it, but the employees did not want to ask for it, until the government required that as part of the whole PII and security issues they were looking at. But it's interesting because it really gets down to, from my perspective, value transfer. If you're going to ask for information or use information, you better be able to provide value and I'll be okay with it then.

(27:20)

I mean, I use as the example Amazon, my example is Amazon where we pay 130, $135 a year for Amazon to give us digital products that may be even at a higher price, but do it quickly. And we're okay with that because they've managed our data really well and given us value back. And when I ask a whole room of people, how many of you have Amazon Prime? Everybody raised their hand. I ask how many people have considered getting out of the whole Amazon Prime model? There's a few, and I ask, how many actually did it? There's none. And that's because we believe that there's a value transfer. And from Ally's perspective, I know from a personal perspective in being an Ally customer on the loan side-

Jim Marous (28:00):

... perspective in being an ally customer on the loan side. You use what you need to use to provide opportunities to me that are along my life scale. And in doing so, I'm okay with you using that. And that is so important because those organizations that actually let the consumer know how they're using data, but even more importantly, what benefits the consumer is getting from that, are going to be far ahead of the game because consumers are not going to think they're just asking for data or using data and putting it into the side drawer, to use an old analogy. So Sathish, when you look at your early results, and it is certainly early in the game and it is changing every day, what results have stood out from an early customer care perspective and also within the Ally organization as far as how you've deployed generative AI? What are some success stories?

Sathish Muthukrishnan (28:56):

Jim, first of all, thank you for being our auto loan customer. I cannot wait for you to become our banking customer and blow your mind with the experiences that we are going to provide you.

Jim Marous (29:07):

Always love it when we sell on the podcast. I like that. That's good.

Sathish Muthukrishnan (29:13):

From a critical view, I would love for you to use our products and tell us where we could even be even better.

Jim Marous (29:17):

Yeah, you're probably going to get that out of this conversation. So from your perspective, what are some success stories you've had from generative AI? Early success stories?

Sathish Muthukrishnan (29:26):

Yeah, I will tell you, not because I'm on the podcast with Priya from Microsoft, there is a mindset shift in using generative AI. In the initial days, and I believe we were one of the first, if not the first corporate customer, to use the private instance of Microsoft OpenAI. We're both corporations. Our legal teams were going back and forth with the contract on how and when we'll use it, how secure it is, et cetera. It was completely unclear. We were working through the processes. I can tell you the lawyers were turning around the papers on a daily basis. That's unheard of. This is not a knock on lawyers. By nature, they have to be very detailed and it'll take a few days for them to give us an answer. But between corporations, it was happening on a daily basis. That is the first success of generative AI. But I'll bring it back to-

Jim Marous (30:27):

On that, Sathish, what's interesting, that was being done at a time when it was like mercury on linoleum from the standpoint of what generative AI was. You talked about early in 2023, and it only got introduced in November of 2022. So the reality was lawyers are going back and forth with paperwork while what they're trying to do overall was changing in real time. So it's not like they even knew what the landing point... In fact, we still don't know what the landing point's going to be, but to have that iterative nature of that is really unique in that nothing was going to settle. Both organizations understood that change was going to happen, it was going to happen really quickly, and it hasn't stopped.

Sathish Muthukrishnan (31:14):

You nailed it. You nailed it. Let me add from our first use case, and I feel like this is the biggest success story for us and a big impact, is we started to believe that the ultimate leverage for generative AI will come from every employee at Ally, not just technology employees. So when our customer care associates started experiencing the automated summary of the call they just had with the customers, they came out and said, "Wow, it's capturing things that I never would've captured before. It's more than 85% accurate. It's saving me precious minutes or seconds, and I am allowed to singularly focus on customers. That's making my impact on the customer much more." So those are the early successes, but let me tell you what has happened since.

(32:12)

We have over 205 use cases, and this is from this morning, hot off the press, use cases that my team is working on across different businesses and functional areas in the company. Majority of the use cases are coming from our customer care associates. They now understand the technology, they understand what it can do, and are proactively influencing and forcing technology to deliver on it. We are thrilled. This is just the early days, but what we can do for Ally and our own employees initially is very, very promising.

Jim Marous (32:50):

That's interesting. It's another one of those differences that you probably don't see because you're not at a legacy financial institution. But when all employees are pulling in the same direction towards a common goal and not being afraid of change, working for Ally, and working for Microsoft for that matter, if you're afraid of change, you're in the wrong business because it's always changed within the organization. Legacy finance institutions are usually saddled with legacy leadership that honestly has really on the surface never done anything wrong. They continue to make profits, they continue to do well, but that also becomes their hindrance because we as human beings most don't like change. Now, we may embrace it at times and like it sometimes when it works in our favor, but because you're both working for organizations that is part of the definition of the organization, it makes it so... Sathish, you brought up such a great example where employees are getting together and saying, "Let's find other ways we can in some ways displace ourselves for the better good," without being fearful of losing their job. It simply moves them forward.

(34:04)

I think either you or Priya said earlier, the humanization of the digital transformation, where early applications of AI had to do with risk models and fraud and things like that. That was a very clear indication that we were going to be assisted by AI, not displaced. But some models right now, some of the things that could happen-

Sathish Muthukrishnan (34:25):

Yeah, that's very true, Jim.

Jim Marous (34:27):

Yeah, so some of the ways it's being used right now actually could displace jobs, but doesn't have to displace humans if they continually evolve with the marketplace. So Priya, I think one of the things we see when we interview organizations is that we at some point will clam up and not want to use generative AI at all. Or in other cases, we may think generative AI can be the savior of everything. The answer is obviously somewhere in between. But when you're looking at any type of organization, not just a financial institution, where have you seen instances where organizations have had missteps with generative AI? Either in the definition, deployment, or actually in the application of it? And just as importantly, where have you seen maybe cases where there's been overly enthusiastic view of what it can do, and then the disappointment sometimes because-

Priya Gore (35:25):

It starts with... Sathish mentioned this earlier. Really, I think the culture of the organization is everything, because if you can safely and I'll call it securely, but then really just humanly describe what the technology is, take the time to educate people about what it can do and what it can't do, what it's really good at and what it should never be used for, then you're setting your employees and you're setting your teams up for success. And you're setting them up to be part of the solution and part of the equation versus feeling, " Wow, leadership is coming in and bringing this AI technology that's likely going to take my job away in three years." No. There's two schools of thought. Those companies that are behind closed doors thinking about those things versus those like Ally that are thinking about how do we actually find ways to bring more value to our employees and bring more value to our end customers with innovation and leveraging this technology in a safe and secure way, and educating people why we're doing it, what does it bring you for that incremental value in exchange for using it?

(36:35)

And finally, I would say the example with client care at Ally and those teams now bringing forward their own ideas, I would say not to displace themselves and be not threatened because they're excited and they see, as mentioned in the example, their quality of care for their end customer went up, because they were able to spend more human time engaging with their client versus punching notes in a system because the system was capturing and recapping and summarizing the call for them. We also see examples, again, in the customer care realm where clients, they're looking for the bank or the insurance company or whatever it is, they want that agent to be probing about them and what's going on in their life. What's happening right now for them and what do we need to do to help look out for them? That's what people want from their financial institutions, their insurance agents and things like that. Generative AI is bringing that human ability to focus on people, people focusing on people, forward. I think those are great examples.

(37:47)

One last thing I'll say. The other gotcha that we've seen that we want our clients to really think about is you need to also think about your data strategy. Because let's face it, artificial intelligence, the intelligence of AI, is everything to do with the data. And so if you don't have a sound strategy for your data, and remember that this is a new wave of AI with generative, where now we can reason over unstructured data. So documents, emails, images, you name it. Any format, we can now reason over that information. And so it's very important to think about your overall holistic data strategy and really thinking about that, so that you can really harness the power and the value of this generative AI technology and get the most out of it with this amazing and very valuable data that financial institutions have at their disposal.

Jim Marous (38:42):

So sticking with you, Priya, you work with a number of financial institutions right now, and it's funny because we're talking about this as if all this has existed for eight years and it has existed for one. But from the standpoint of what you're hearing in the marketplace, when you're visiting financial institutions and you're trying to show organizations what have been some of the really cool use cases, what are you seeing beyond Ally as far as some of the use cases that have been really low-hanging fruit?

Priya Gore (39:12):

Yeah, I'll give you another one. We talk about generative AI being great for co-generation. One of the hero, what we call Copilot solutions within Microsoft, that is out in the wild. The first Copilot solution that we launched a little over a year ago was the GitHub Copilot. And this is an example of a solution that we took to market where we can help developers actually have high-value coding experiences at scale. We're seeing a tremendous value there for our customers, not just in being able to be more efficient in building applications and checking in code and things like this as developers, but also what we're hearing from our clients and the findings and using these technologies is, "Hey, employees are actually expecting this technology to be leveraged. Developers are in high demand and customers are looking to employ and retain top talent in the space. And if you're not using the latest and greatest technology and tools for them to do their job and do it well, they're going to look for another place to go."

(40:20)

So it becomes an advantage, right when you start leveraging this technology. If you think about generative AI and the era that we're in with this technology, as you mentioned, it's still very young. If you think back, so it took 16 years for the mobile phone to reach 100 million users. Seven years for the internet. Facebook, 4.5 years. ChatGPT, three months. So it's an incredible moment in time, an inflection point, I think, societally. And because this is natural language prompted, and Sathish mentioned this earlier and I'm sure he has comments on this, it's something that is capable of becoming democratized. So I expect generative AI technology to be infused into every application experience that a company brings to market for consumers.

(41:13)

In consumer world, certainly we see it already with even just the applications we use in our personal lives. And then certainly on the business side and in financial services, the opportunity is endless. Think about every application experience that you bring forward for your clients or that you use internally for employee enablement is going to likely have generative AI capability as part of it. And the question you'll have to ask yourself is, do I build it myself or do I buy it? And that'll be a question that we'll constantly think about together, and the Microsoft team is geared to help our customers wade through that decision and help guide you along the way, because it is new and we're learning as we go, and it's an exciting time, but it's pervasive and it has the opportunity to be completely democratized across the human race, which I think is exciting, but also a very serious matter that we need to make sure-

Priya Gore (42:00):

... but also a very serious matter that we need to make sure we're taking responsibility for together as technologists.

Jim Marous (42:09):

So one more question for you, Priya, is that we're seeing a lot of organizations, small organizations, doing just amazing things in the marketplace. But to do so, they have to find partners that can deploy solutions at speed and scale.

(42:27)

I think reaching out directly ... And I may be wrong, but I think if a small financial institution wants to reach out to Microsoft directly, that's a little bit overbearing from the standpoint of I'm over asking what I can actually use. But I would imagine there's a secondary market of solution providers out there that are partnering with Microsoft on the financial institution's behalf where smaller financial institutions can make partnerships with those. Does that secondary market, for lack of a better term, exist today?

Priya Gore (42:58):

It does. We have amazing partners in the space of data and AI. We have amazing partners engaged with Microsoft along every part of our platform to help our customers embrace that technology safely and securely and give you that extra set of hands, if you will, an extension of your own team, to bring that expertise forward so that you can go faster and engage with technology more quickly in your business.

(43:22)

So we do have a mature partner ecosystem, and I would say that we have some newer partners that have been really, really game-changing in the generative AI space that we're also working with. So I would say too, like any company out there that's looking to leverage this technology, it's okay to reach out to Microsoft [inaudible 00:43:42] with a person within Microsoft that can then share with you this list of great partners that are capable of helping you scale the capability that you are building in-house to infuse this technology in what you do.

(43:56)

That's not new for us. We're very proud of the partner ecosystem that we fostered over the many years we've been doing this, but certainly in the generative AI and the data and AI space, it's something we look at regularly and we're constantly vetting our great partners to make sure they're ready to go, so that we can make those recommendations and get those connections made for our clients.

Jim Marous (44:16):

It's interesting to both of you that every day I wake up, it's been happening quite a bit now. In my mind, they're going to come up with another idea on how generative AI tools can help me. Trying sometimes to find which tools, which platform is going to help me the best, I end up testing.

(44:36)

So I think on my computer now, I think I have four tabs that have some type of AI tool that I test to say who's going to be the best at moving my thoughts forward? When you consider that that's happened on the consumer level ... Priya, you mentioned this, that the acceptance of generative AI has just been so astounding that you can't get to a cocktail party, we can't get to a podcast or a webinar without it being part of it.

(45:05)

So, Sathish, part of it also is looking at those leaders and saying where are they looking? What are they doing next? So if I'm going to get some specifics from you around what is on your to-do list in 2024 with regard to deployment of generative AI, what would I see?

Sathish Muthukrishnan (45:27):

There's numerous use cases that I'm particularly excited about. But I would like to talk about something that is different, and you mentioned this. AI, generative AI specifically, may replace jobs but will not replace humans. But we have to help in that journey. This is an assistive and a technology that augments anything anybody does on a daily basis, but not everyone has the ability or the opportunity to learn what generative AI can do for me. People may not even understand what a tab is, Jim. You have four tabs of generative AI helping you out.

(46:14)

We call this AI equity. How can we stick to our mantra, which is do it right? How can we do it right for our customers and communities in helping them move along in this journey? I don't have a specific answer, but I will tell you, we are spending our brain cells, we're spending time, we are collectively thinking about how to make an impact. Maybe we will call on Microsoft to help partner with us in educating the larger society and helping them understand how generative AI can play a role in uplifting their wellbeing.

Priya Gore (46:56):

Yeah, I just would add a quick comment on that.

Sathish Muthukrishnan (46:58):

Yeah.

Priya Gore (46:59):

I agree with you completely. I mean we have a wealth of content and self-paced learning material out there in our AI Business School curriculum. We're refreshing that right now and next quarter, that will be readily available for anyone to leverage.

(47:16)

But I would say beyond that, we also owe it to ourselves to share best practices of things we're seeing. I was with a customer a few days ago last week and they were talking about how as they were engaging generative AI into some of their solutions they've taken to market, they've actually launched a couple products, much like ...

(47:35)

Same pattern that what Ally has done, and they made sure that they ... They actually told me that they produced TikTok-like videos of what is generative AI, and then certainly building on those modules of ... Building out material that talk about, okay, what is the solution? How should it be used? Because it is, it's going to be a new skill I think also for people to engage these generative AI-infused Copilot solutions. To Sathish's point, these are designed to be assistant in nature, not to ... We named our stuff Copilot, not autopilot for a reason, right?

Jim Marous (48:09):

Yeah.

Priya Gore (48:10):

That's the intent, is to augment and to bring value to work alongside you, next to you at your desk, not do it for you and replace you. The human in the loop is going to be very, very important.

(48:22)

The expertise and financial services, great example. The expertise of the banking professional will absolutely need to be at the center of everything that we do client-facing to have credibility.

(48:35)

And so, the work we do will change and how we go about getting work done will change with these assistants and these copilots. But I do think that the ingenuity of the human and the expertise will still need to be harnessed in the value you'll bring. You'll be able to up-level the work you do every day if the adoption cycle and the learning cycle of how to use these tools is cared for and thought about by enterprises as they bring this technology in. I think that will be ... It's really what we need for, frankly, the human race in order for us all to really get our heads around what's happening here and do so safely and responsibly.

Jim Marous (49:10):

That's interesting because I think we forget where we each started in this journey. When people initially would say, "I think generative AI is a bunch of crock. It's not good. This is ... " I finally ... You very quickly get to the point, going rather than damning the technology, maybe you have to look in the mirror and say, "Are you asking it the right questions?"

(49:33)

We all realize as we've gone forward that in the structure of questions, it provides you just amazing answers. When you get that aha moment that says, "Oh, I asked this a little differently and I got massively what I wanted," you realize you have to think a little differently for talking to technology.

Priya Gore (49:52):

Yes.

Jim Marous (49:54):

So in our last question, I'm going to give you each something different. Sathish, from your perspective, what really excites you about what could happen next year in financial services and in Ally with generative AI?

Sathish Muthukrishnan (50:09):

Jim, we recently launched what we call One Ally, where we're bringing all of the experiences across all of the products into a single digital experience. That connected to what I stated earlier about customers' expectations of moving from more personalized experience to a more personal experience. GenAI can help power that.

(50:37)

So we treat every customer of Ally as if they are our only customer. The power of segment of one can be realized through generative AI. Collectively as an organization, how we can differentiate ourselves in serving our customers is exciting me tremendously for 2024.

Jim Marous (50:59):

Great. Priya, from your perspective, if somebody's just looking to get started, where do they start?

Priya Gore (51:07):

Yeah. I mean, listen, you think big and you start small and give us a call. No, I'm kidding. That was just [inaudible 00:51:14]. No, but, seriously, I think we are in a time of what we call the era of AI. I think that, as you're just getting started, I think you really do want to brainstorm these use cases, think about ... Nothing's off the table, but then I would say start with something, like crawl, then walk, then run. So start with something low risk that you can do to get familiar with the technology and get familiar with how it works and how you infuse it into, again, application experiences.

(51:46)

Remember, these generative AI models are APIs. So you bring them into an architecture of an application along with other services, and that's how they come to life. And so, learning a bit more about that, and we can help you with that, to get familiar with how they work and what they're good for and what they're not great for is the first step.

(52:05)

Then I think also looking at some of the existing, for example, Copilot solutions, whether it's something custom that Ally has brought to market and you're a banking customer of theirs and you're leveraging their tools. Great way to learn more about how this stuff works by having a personal experience with a technology and an application like that.

(52:24)

Then certainly like our Copilot, our Microsoft 365 Copilot, solution, which touches many, many knowledge workers, we're starting to roll that out with our clients that are in the early access program and getting a lot of great feedback on how that's really improving and empowering people at the knowledge worker level. Again, that's a great way to get started.

(52:44)

So I would say think big, start small, do your research, and really do call us and we'll engage partners that are really able to bring you along and help you create centers of excellence and governance teams that can help make sure that you're also thinking about this from an ethical and responsible AI perspective. We've got resources there to help with that as well, which obviously is very, very important in the regulated world of financial services.

Jim Marous (53:15):

Sathish, Priya, I cannot thank you enough. This has probably been my favorite ... I've said this before, but things happen. My favorite podcast I've done, not just because of the knowledge you shared but the leadership that you each individually have shown and your organizations continue to show.

(53:32)

You reinforced everything that we keep on trying to bring forward in this podcast, which is it all starts with leadership. Really, you need to have the people to support that and make it so that it's democratized what we're doing, and that it can be deployed against any size organization today.

(53:55)

Again, thank you so much. I'm going to have Leah contact you very quickly because we're going to set up another call a year from now and see where everybody is, because I know it's going to be very different than it was today. Thank you so much for being on the show.

Priya Gore (54:08):

Looking forward to it. Thank you for having us.

Jim Marous (54:10):

Thank you very much. Thanks for listening to Banking Transformed, the winner of three international awards for podcast excellence. We really appreciate the support we've received to make this endeavor a success. If you enjoy what we're doing, please take some time to show some love in the form of a positive review.

(54:29)

Finally, be sure to catch my articles that I'm doing for The Financial Brand and check out the research we're doing at the Digital Banking Report. This has been a production of Evergreen Podcasts. A special thank you to our senior producer Leah Haslage, audio engineer Chris Fafalios, and video producer Will Pritts.

(54:48)

I'm your host, Jim Marous. Remember, the future of AI-driven banking will often involve the collaboration between banking and tech leaders.

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