Name: INDUSTRY DEEP DIVE: HOW FINANCE COOs ARE LEADING THROUGH TECHNOLOGY’S RISKS & RETURNS
Uploaded: 2025-06-10
Duration: 18 min 29 s

Financial institutions are grappling with unique challenges in the age of AI, including keeping up with head-spinning innovation at legacy organizations designed to manage risk. How do these leading COOs in finance lean into long-term stability while staying agile in the face of change? You’ll hear about how these global institutions are using ground-breaking tech to modernize the customer experience, detect fraud, conduct cutting edge research, streamline processes and more. 

INDUSTRY DEEP DIVE: HOW FINANCE COOs ARE LEADING THROUGH TECHNOLOGY’S RISKS & RETURNS

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Well, OK, as Diane said, AI has the potential to be transformative to the finance to the finance function. So I want to start by asking both of you. You, you, you're both in big financial institutions. Where does your institution, Jack and we'll start with you. Where are you on your AI? Path Thank you. So let me start by just providing a brief background. RBC Royal Bank of Canada, uh, is one of the largest global financial institutions. More than 100,000 employees as part of our AI journey, happy to say that we started our MLAI journey almost a decade ago and where we are just recently earlier this year, we shared in our investor day and made a very bold, ambitious goal that by the end of 2027. We will generate $700 million to a $1 billion of enterprise value through AI-based initiatives. And the reason we were able to make such Uh, bold back is based on how we have, uh, responsibly invested in technology over the period of last decade starting from Whether data analytics, API, private cloud, public cloud journey, uh, machine learning, AI, and now what we all are really hyped about generative AI. Bye. Yeah, thank you, Rashmi. Same thing. Yeah, so you know, TIA is primarily a retirement company. We oversee about $1.4 trillion in assets. I'll walk you a little bit through our AI journey. We are a very mission oriented company. So it was important for us to ensure that when we innovate, we innovate in a responsible manner. So one of the first things we actually did was establish a responsible AI policy which tells our employees the dos and don'ts of AI essentially. We also then created a center of excellence for AI. We have 3 major business lines. We have our retirement business, Novien, our asset management arm and wealth management, of which I'm a part. Each of these businesses identifies the use cases based on the, you know, their work process and what they need, and they work with the center where we govern those use cases. We train employees, we monitor the usage of AI, we deck the right capacity to it. So really setting up, I would say first and foremost the controls, the governance was important. The second thing we wanted to do was obviously create uh the ability to build AI solutions. So we set up our own in-house proprietary AI platform called TIAA Gate. Um, it's in-house, but I will tell you our strategy in this space is to be hybrid, to be hybrid hybrid, yeah, because you know, the best solutions are not going to always be in-house. So the approach we're taking is to say. We'll leverage third party solutions with providers we already work with, for example, you know, we may work with Salesforce and use one of their agents, and then where the solutions are unique, for example, our lifetime income products are pretty unique in the industry. So for those solutions, an AI option is best done on our own proprietary platform. So really our strategy is going to be hybrid, um, and I would characterize the evolution of AI is. We set the tools. We've got the governance, we've got the controls. We've started the training, upskilling. We've experimented with use cases. I think the next phase is really beginning to do the use cases at far more scale. Yeah, yeah, hybrid for you also at RBC. Yeah, it's very, very much aligned and I think as I, as I was saying, right, as part of the last 10 years of journey. All the lessons we learned from the previous technology that came and we all were talking in various conferences, right? We learned a lot. We harvested all those lessons learned, whether it's related to data privacy, the security aspect of it, making sure we understand the ethical nature of uh using the technology, being responsible about it. So similar to what Rashmi is saying, right, we, we applied that as part of our cloud journey because in order for us to leverage public cloud, we had to go through the same rigor on top with respect to data privacy and security. We use this foundation to build a similar level of governance for our AI, and now we are taking that for JNAI, right? And as part of that AI JN AI journey, we also have, uh, responsible AI principles that we have established and it's publicly available. So if anyone wants to check it out, you can, you can access it. It's a publicly available information for RBC and as part of it, we have also invested and created Borealis AI Institute. Which is basically, uh, employing 100+ PhDs, world's best, I would say AI scientists, and 800+ machine learning AI developers and data engineers. Uh, and, and the reason for that is that has helped us. Understand not just the business applications and the power of AI, but also understand how we can Leverage all the investment we have made over the period of the last many years using technology because at the end of the day with the recall of AI GAI or agent T AI, it has to tap into your ecosystems of the data which is your primary asset that you are hosting uh related to your customers across the line of business. Um, and then what we have done is we have created a very, uh, detailed development and governance framework. To allow various teams in a large RBC organization to understand, learn, design, develop, and test AI and AI model in a very responsible manner, and there is a very clear guidance and governance when they can make a decision how and when to take this to the production, so there is a very well laid out structure. Got it. So let's get very specific this time. What are the current use cases that you are working on now? Yeah, you know, I would categorize the use cases we typically see in wealth management in three different patterns is how I describe them. Uh, content summarization, content generation, and large scale data analysis for the purpose of insights. There's also core generation, by the way, but that's far more, I think, in the tech space. So these are the patterns we see. The impact of a lot of these use cases is AI as a co-pilot and AI not as an autopilot necessarily for our employees and advisors. So I'll give you an example of a use case. We are working on a meeting preparation. A use case for our financial advisor. So when an adviser is meeting with their client, they often have to spend time preparing for that meeting, right? What were the last notes from the last interaction? Did the client call between the last meeting and this meeting, and did they have a service issue? Are they on track with their financial plan? Are their life events likely to happen? This takes time, so we are actually. Looking at a solution where we can use AI to create a summary for the adviser that saves them time and then frankly go the next step and say give them recommendations on the next best action to have a conversation with the client on and by the way, Jeff, we think there is tremendous value in this because there are studies that have said financial advisors are spending 40% to 50% of their time on non-client facing activities. And close to 15-20% of that on administrative tasks. So the opportunity is clearly there to get more productivity. Yeah, and a follow up there, what you're describing would be equally valuable to many other financial companies. Are you creating your own? Are you using some that at least have elements that are being used elsewhere? We are definitely using creating some new ones and then leveraging others that we see can be applied enterprise-wide. So an example of another use case that's very common in the investment spaces you want to be able to summarize the investment researcher portfolio manager. uses to make investment decisions. By the way, our asset management firm Nve has to do the same thing, as do our wealth management investment advisors. We don't need two separate use cases, and that's where that center comes into play so that we can govern and leverage core capabilities. Yes, use cases now at RBC. Yeah, so. The way we look at AI and AI is two ways, I would say. One, how we can provide, uh, services, products and services we are offering to our. Our clients and the experience we are giving to our employees, how we can do this thing better. And secondly products and services or the experience that we wanted to offer to our clients and employees that were not possible that something now AI and GNAI is making it possible now. So these are the two different buckets. So when we talk about use cases for the period of the last 57 years, we have, uh, Made AI machine learning AI based products and services available to our clients across different line of business. Uh, if I talk about capital markets, we have Eden, uh, that's a brand name, uh, that is a MLAI based model that helps with equity trading, and it allows our client to have a better efficiency and Uh, quality of their trades. Now we are taking to the next level and it's all helping us with the research and insights and as, as Rami mentioned, right, enabling our frontline, uh, team basically to focus more on engaging with clients and having a quality, uh, conversation versus doing a lot of backend research work by themselves. If I talk about from a retail perspective, uh, through our RBC mobile app, uh, we have made available know me insight. Uh, what it does is basically it looks at all your financial cash flow transactions and allows at an individual level to avoid you to go below a certain balance and it automatically if you if you're given permission to move money from savings to checking because you have set up autopay. And it knows that there is a bill coming and it's roughly about this amount. So we don't want our clients to go through insufficient fees type situations. So we are leveraging technology to make our clients' life easier and same way to small market customers, they are able to look at their cash flow and understand and predict and help them properly run their business and and focus on their business rather than worrying about the, the financial cash flow aspect of it. If you, if you go into the broader enterprise side, same way there are a lot of use cases, but the foundational is. What we are most proud of is this BorealS AI and and 800 to 900 top of the best of the best world's talent have created a proprietary large transaction model, we call it atom. It is Running across 1 billion events that we generate every day, and it was trained using 65 billion client transactions. So we are, we are sitting on this intelligence that is allowing us to unleash the power to help our employees better serve our clients and our clients receive the product and service. That they were hoping but we were not able to provide so far the quality and experience that they were hoping for. Yeah, super exciting. There's a human element in this as in everything, it does seem to me that whenever there is something new like this, especially profoundly new, uh, and I think especially so in the finance function, there will be resistance to change, right? It's inevitable. How do you respond? So you know, I would say. Given the nature of the use cases we're seeing AI as a co-pilot, it, it really means humans who use AI will replace humans who don't. And so getting the human element right and getting them ready to use AI, I think is to your point, the biggest focus area now, and I think you need 3 things for that. You clearly need to give them access to tools. You need to give them the skill set. Right. Um, and then you need to, frankly, in our experience involve them in the creation of the solution. So co-creation is a big lever. um, so to give our folks access in March, we took our proprietary AI platform and created an associate version of it where people can now and we're rolling out access throughout this year so our employees can go in and use AI for their day to day tasks. Whether it's editing documents, writing emails, creating meeting notes from a Zoom session, you know, whatever it is, um, but it gets them comfortable from an upskilling perspective, we have focused a lot on, it's an interesting concept. We've established guilds. There are about 9 different guilds. These are informal networks in the company where employees come together, learn, and experiment. We've got over 5000 employees already members of the guilds. And then I will tell you co-creation we had, you know, we have estate planning attorneys who create, uh, give estate planning council to our clients. They spend a lot of time going through the estate planning documents and they're testing two different AI solutions, giving us a lot of feedback, and their involvement in it has been, I think, instrumental in driving adoption, you know, for both of you. There is also a leadership element here. I mean, you are at the heads of large organizations within your organizations. What's the leadership lessons you've learned so far in this very uh, profound change, so. All the, I would say leadership lessons that we all have aggregated over the period of many years, 1020 years when new technology came, I would say all that applies one or the other way, right, because I, I think your question is specific to AI AI, so I'm going to switch and say, assuming that we are not chasing technology for the sake of technology, we are applying business plans, we are being responsible about it. We are thoughtful about it. We are thinking about hybrid and not just going one way and then learning later, right, that this was the wrong path. So all those given what AI and GAI, if I, if I make it specific leadership lessons, is one is around. being in a highly regulated industry as a global FI. How do you, how do you create an ecosystem? Where the individual end user, the client, customers. is also trying to figure it out what does AI AI means for them. So we have partnered with a company called Kohre. Which is basically, uh, helping us create uh a financial services GNAI framework platform to allow, uh, creating a platform where our end user and client can develop their product and applications using GNAI. Why? Because we want to make sure that there is an independent party, uh, who we are partnering with who is protecting the security and privacy aspect of the data. We are sharing the relevant information, uh, and the, and the learning that we have. And the second part is as part of the broader journey. First, you have to win hearts and minds of your employee when you are going through this change. But same thing applies to your client, same thing applies to your community as well. So we have established respect AI. As a portal, as a framework where we are openly sharing tutorials, framework, knowledge article, that is also something available in public. So if anyone wants to to check it out, to help yourself, help your company, do that. And the reason is this is where we are. We cannot just take this new technology and say. Smart enough, we understand, we figured it out. Let's just use it. We need to think about the overall ecosystem. Great, Rami. So I would, I would echo, don't do tech for the sake of tech. I would say focus on the foundational. I've called it fix the leak. Don't just patch the drywall, do that. The other thing I do want to say is the skills of the future are going to be different. There's going to be a premium on certain skill sets, so speed or structure. Insight over title, judgment over certainty and frankly clean slate thinking or continuous improvement. So we as leaders need to ensure we are evaluating for these skills, hiring for them, performance managing for them. That's where the future is. Uh, that's a great note on which to end. And so I want to thank Jakin and Rajmi so much, really good. Thank you. There you go.