Rethinking Trust in the GenAI Era with Mahi Sethuraman

Transcript

[00:00:01] Mahi:  Good talent might fail at times, like you have pointed out, but, yeah, they need to learn from their mistakes, and they work very hard to, like, apply it and make sure that they get it right next time. I think what separates the good from the great for me is really the rate of learning and how leaders actually use that learning to then get many decisions right from the first because I think with time, even good talent will be able to deliver value, but good talent gets there fast, great talent gets there faster.

[00:00:35] Intro:  Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives share how they cut through the noise and act on what matters most. We go beyond the headlines to explore the pivotal decisions, opportunities, and inflection points that define their careers and shape the future of the companies they led. It’s time to cut through the noise and get to the signal.

[00:00:58] Kyle:  Welcome to Signal to Noise, Kyle Langworthy, partner and head of the AI practice at Rivera Partners. We are a global leader in executive search and the largest functionally focused search firm on the planet. We’re dedicated to the function of technology for organizations of any scale across all industries. Today, we are joined by Mahi Sathuraman, a seasoned and quite arguably battle-hardened technology executive with two decades of experience building financial technologies used by millions and millions of consumers. Today, Mahi is the vice president of consumer engineering at Credit Acceptance. Previously, she served as VP of engineering at Affirm, Ally before that, and just shy of almost fifteen years at American Express prior. Mahi, it is a pleasure to have you with us today.

[00:01:52] Mahi:  Thank you so much for the lovely introduction, Kyle. I am looking forward to it.

[00:01:56] Kyle:  So, as a way of starting and kind of diving in, there’s a question we like to start with everything that brings us back to the podcast name here. What is the biggest signal that you’re paying attention to right now? And just as importantly, what noise are you tuning out?

[00:02:12] Mahi:  That’s a fantastic question. As a new leader in the subprime auto financing space, I am focused on deciphering signals about consumer help in this segment, understanding the competitive landscape. I am also very interested in understanding how consumers are adopting to placing their trust in virtual AI agents and voice bots, given our focus is on repayments. And as a result, I am beating out noise that comes out almost daily or hourly about fundamental LLM model changes, the tool stack changes, which models are actually going out of commission soon. It is a very noisy cycle if you are paying attention to it on an hourly basis. So, that is something I am beating out, but I am looking for macro signals in how our consumers in production use cases actually trusting virtual AI agents, because we think there’s a lot of noise in there.

[00:03:16] Kyle:  Super interesting. Just to double click on that a bit, can you tell me more about some of those macro signals and what it is you get from ingesting and understanding them?

[00:03:28] Mahi:  For example, in this segment, in subprime space, there was a question on, do consumers do well in adopting digital technologies? Because primary repayment and collection strategies had been focused on the voice channel because that was existing before any of the digital channels came out. I find that consumers are able to adopt mobile channels, and those who adopt mobile apps early on are more profitable. And so, this gives us opportunities to experiment with new chat technologies, new push notifications to be able to reach them with much more cost effectiveness. It is important to not lose their trust. Uh, so definitely taking a very start-small-grow-big approach and really looking for insights on how some of these strategies will drive the bottom line.

[00:04:24] Kyle:  Incredibly interesting. And I’d actually like to start the first part of this with reflecting back on your career in financial services and fintech. It was dawning on me as I was looking back at some of the dates that you had joined American Express when Amazon launched AWS. And so cloud, mobile, digital payments, ecommerce. I mean, there’s been so much advancement in so many directions. Can you fill us in a little bit on the difference in role that technology played at American Express from when you joined to fifteen years later when you left? How did that role of technology change?

[00:05:07] Mahi:  I think you have definitely touched upon some of them. When I joined American Express, the iPhone hadn’t launched. We were just, the “ dot-com” era was just catching up in the enterprises. We were migrating from Java, JSP, portlets, ecosystems, sometimes even C++, into a whole new world of World Wide Web and the first few years at American Express was focused on adopting even early phases as early as 2008. I worked on personalization capabilities that personalized merchant and card offers for our consumers who are logging on to our newly launched front-end applications that were globalized for about 139 different cases and different languages. And that was a phenomenal thing because prior to that, we had to figure out how to deploy into different regions, making applications differently. So, the advent of even the updates within consumer experiences on the web and then transitioning to all those capabilities to be powered with data, consumer segmentation, like Bayesian models that I did, that provide click-to-conversion less was the next frontier. And then came cloud, right, as AWS picked up, Google Cloud picked up. The next set of years were focused on, “how are we going to scale our applications and data on the cloud?” And that was a fantastic ride. And I would say the next generation of that was how we evolved from going from traditional Hadoop-based data ecosystems into then with the advent of streaming in and I’m talking now from 2016, 18 onwards where the data ecosystem started to get very exciting with the ability to actually have ML platform ecosystems come in, and we could build capabilities that helped us move massive petabytes and terabytes of data in a matter of minutes and hours. And then scaling that onto the cloud as well so our ML workloads could start to scale and data and give us insights faster.

[00:07:23] Kyle:  I’ve wondered, I was wondering if you could shed some light for us through all of these different major pivotal shifts in technology. Did customer behavior and consumer behavior change in response to all the technical innovation? Were your organizations constantly playing catch up to consumer demands and what they wanted in terms of capabilities?

[00:07:46] Mahi:  I would say the consumer is always expecting more, always adopting new technologies. Just look at the consumer adoption of ChatGPT when it first launched. So, you always have to be anticipating and keeping up with the technology trends that consumers are adopting. The very earliest signals of this for me was how quickly consumers actually pivoted to mobile as a form factor and channel. And even though we were still predominantly web based in the early 2000s, the actual usage of the web applications on mobile devices as early as 2008, right, as soon as the first iPhones came out. So, that has been fantastic to watch and that’s why I am now focused on how consumers are trusting fully AI capabilities. I think because they are adopting, they will adopt if they can be assured that there is trust and there is, the companies are focused on their best interest in opening up these opportunities to consumers. And it’s been interesting to see the impact that machine learning has had in consumer experiences that has become so seamless to the consumer when they actually interact. And I think this was very evident in my tenure at Affirm. The product has been primarily adopted by Gen Z and millennial consumers, consumers across the credit spectrum. And they have personalized all of their consumer product experiences, including decisions on credit offers, underwriting decisions that are so seamlessly packaged in the consumer experience, including even on the merchant side in terms of the pricing and deals that we put out for different segments of merchants. So, I think that has been fantastic. I always want to say that consumers know to trust when he can be convinced that there is benefit for them in adopting.

[00:09:51] Kyle:  Do you see any difference in the kind of trust you’re gauging and winning from the early days when everyone’s grandmother said, “oh, never put your credit card on”, you know, on a website. Right? To all the different types of trust we’d had since then with mobile and all the complex platforms to now, it feels very Gen AI synthetic vs authentic. Like, has trust always been a similar issue, or are there any differences to the kind of trust problems we’re tackling today?

[00:10:23] Mahi:  I believe we are at the cusp of testing what trust means to the consumer yet another time. When internet security was still a big heavy factor, people didn’t feel comfortable putting bank details or credit card details. You had to work on identity fraud mechanisms, convince them that it was safe, give them fraud alerting when something multifactor authentication. We have come a long way since the advent of the World Wide Web and getting to a point where people want to do digital payments, right, and share-of-wallet increase for wallet transactions across both ecommerce and in-store. So, it has definitely been a work in progress to where we have actually now in a place where consumers trust us, where ecommerce and in store transactions. The reason why I say we are at an interesting juncture is because the typical forms of biometrics, the voice that we used to be able to rely on to say, okay. We have voice biometrics now or to authenticate who the consumer is on the voice channel, for instance, can now be easily spoofed, if you will, with the advent of generative AI. So you have to be extra careful in how we actually add security to some of these experiences that we wanna experiment with? I’m not saying we are already there. But we’re certainly thinking about what that introduces in terms of trust, what does it mean to secure our systems that are actually interacting with consumers in those ways? So, I think it’s going to evolve because we have to rethink what is trust in a generative AI world and the new age of AI, I think.

[00:12:12] Kyle:  I’d like to shift to some career transitions for you and kinda talk about yourself and your journey through your career. Can you tell us about a pivotal moment in your career or leadership journey? What changed and how you navigated it?

[00:12:28] Mahi:  For me, the pivotal moment that comes to mind is the first time I was promoted to vice president leading the finance data engineering organization at American Express. And the reason this story is important is because it depicts the courage it actually takes to pursue opportunities that align with your passion. Because prior to that, I had been leading global digital experiences for the two sides of the American Express network for consumers, merchants-led personalization capabilities for the consumers. But data had always been my passion and the advent we talked about, the advent of data streaming and how the data ecosystem was changing and the federation of machine learning that was then possible had actually piqued my interest. There was a role in the finance engineering organization that was looking to transform the strategic data assets of the company that were used for C-suite investment decision-making. The hiring manager at the time was looking for someone with expertise in managing the legacy systems that were already available in the enterprise, but also looking to transform without a lot of clarity around what that transformation should be. Because the role aligned with where I wanted to invest my time for the future, I’d actually set up expressed my candidacy for the role. The hiring manager at that time did tell me that he was looking externally to fill the role because all my experience at that point had been leading digital experiences and less on data. So, I knew it took some influencing, but over the course of subsequent conversations, I was successfully able to influence them to depict a vision for the future of how and what that transformation could look like and eventually managed to convince him to take a bet on me and hire me into the role. And that was a pivotal moment because in three to four years, we completely were able to stand up systems that were able to process all of American Express’s monthly AR and AP data in a matter of hours and built out a good team that could keep the road map going for many more years. Some of them are still there. So I think that was a pivotal moment because I did shift from being just purely focused on consumer digital experiences to then being in the thick of driving data analytics transformation for the company.

[00:15:02] Kyle:  How did that shift empower you for the roles you’ve had now across a bank, a credit card, purely technology forward lending platform? Because the role of data has increased immensely across all areas of the technology suite. But how did that shift in particular pave the path for the next few moves?

[00:15:25] Mahi:  I believe it has been eye-opening and very critical for me because it has moved me from a pure technology leader, very focused on making sure you have the best ML platforms and the best data platforms to actually translating data into insights and being a business leader to say, you can have data data everywhere, but if there’s not a, you know, drop to drink, then that’s a problem. And so I now fundamentally, even in my role today, look for my engineers to be called partners with our product analytic organization and build data fluency, understand KPIs, metrics, really look for, “why does this data matter and what data are we not seeing.” And I think that’s how it’s fundamentally shaped my view of how I lead teams as well. It’s always been part of me, but here at Credit Acceptance now, we wanna build more awareness within our industry on the broader opportunities here by building a relationship with the consumer.

[00:16:35] Kyle:  You touched on it a little bit there around hiring and what you look for in engineers. I was really curious if you could tell us a little about your organizational design philosophy as it relates to needing to have nimble teams that can move quickly, that can experiment, that can fail and make mistakes. And at the same time, we’re talking about financial services. We’re moving money, and mistakes have very real, often immediate financial consequences. How do you balance these two, what feels like conflicting or competing elements?

[00:17:11] Mahi:  I think that’s a good question. I think that’s, like, two questions integrated into one.

[00:17:17] Kyle:  Maybe three or four.

[00:17:18] Mahi:  Three or four even. My philosophy for any organizational design is to have a sense of shared purpose and clarity of vision in terms of what is this organization driving impact for the business and then influencing change and nurturing innovation. Because as we think about art designs, I am very focused on having the right author and collaboration infrastructure that also enables the organization of the organism and an entity to evolve with changes. That actually, so, a mentor once told me, it’s like a living organism – it has to function for what it actually eventually needs to do. And that purpose can sometimes, in the short term, change a little bit but if you are starting from a three to five year vision and that vision is clear and it’s aligned and bought in, then how we collaborate and nurture innovation and change, but give them a sense of ownership, then organizations can be learning more. They tend to understand why this experience is important to the company. Why does this data matter? What happened with what we launched? And do we understand where we need to get better to improve the experience? That’s largely how I think about it and leaders are there to demo roadblocks. Think about where, hopefully, some organizational dynamic collaboration and monopoly working and making some of that for them. I think that’s been helpful. And I think that it also actually speaks to both large enterprises like American Express, where it can take a lot more upfront legwork to build business capabilities for change, winning key allies or powerful stakeholders in your honor to understand and advocate for that change is time consuming and slows things down, that may not be the case with high growth startups like Affirm, but there is always a need for understanding why anything needs to change to begin with.

[00:19:30] Kyle:  Well, thank you for walking us through that framework. And having that as a backdrop, how do you think about identifying talent and your philosophy around hiring into this framework to enable this framework? And particularly for you, like, what separates good from the truly great folks when you’re looking to bring talent into your organizations?

[00:19:51] Mahi:  So one, I often ponder upon. For me, good talent are leaders who inspire even those around them to improve their performance and their caliber, who can demonstrate enough adaptability because they have to be able to articulate why what they do matters in the broad picture. Good talent might fail at times like you have pointed out, but, yeah, they need to learn from their mistakes. I think what separates the good from the great for me is really the rate of learning and how leaders actually use that learning to then get many decisions right from the first because I think with time, even good talent will be able to deliver value, but good talent gets there fast, great talent gets there faster. And they use their contractual learning effectively in building those raw functional relationships to make sure people buy in a lot quicker. And that is, I found, a very difficult skill to teach from an external party but if people have the imagination, the ability to understand how to connect the dots better with that learning, I think it worked really well for me.

[00:21:08] Kyle:  How have you found, has any of this been impacted? Everyone keeps saying that the only constant right now is that the speed of change is increasing in this world of AI, LLMs, Gen AI. Has that speed of change, because you’ve seen through cloud, through mobile, through ecommerce, digital payments, has that difference or increase in speed of change impacted how you hire it all, how you build, how you deploy? What impact at all has it had on your leadership?

[00:21:40] Mahi:  To me, this is why I landed on the rate of learning because change is constant. And as you have pointed out that in several decades of rare change has been happening slowly but surely and to stay relevant, especially as technology leaders, and make the right decision, you have to constantly be a beginner’s mind that have good curiosity around how does this technology have the ability to impact the industry that I’m working in. Like, I’m in the collections repayment space, which is why I think voice and bots and agentic flows means operational efficiencies. And I am looking for talent who are equally excited about the technology opportunities that come in, but who are good at connecting the dots. They are able to say this technology, its strengths, it’s knowing their weaknesses as well, applies to this problem that I am looking to solve for this business. And they are able to clearly show me how they have thought about that connection, that transformation, and be more pragmatic about and aware about any of the challenges that come with these technologies as well. And I’m looking for hiring people who can keep up with that learning because it is moving. It is even accelerating exponentially where I think everybody has said that we are in the potential growth decade of where things are gonna change so fast that even fast learners are gonna have to like, that’s why I think the daily news cycle is noise, but it is important in the short term.

[00:23:28] Kyle: So with all of that, I mean, what advice would you give to the up and coming generation of fintech leaders? What advice would you carry forward to aspiring fintech leaders who want to make an impact in this increasingly fast changing industry?

[00:23:44] Mahi:  I just had this conversation with a leader of mine not so long ago on the need to actually get hands-on with some of the latest technologies coming out. So, you get a good feel of calendars and the kind of trainees that you might have to face when you’re implementing or bringing this technology at scale within a large enterprise. I think most substitutes I equate, even though this is probably an analogy, but I equate software engineers to be similar to doctors. Doctors are constantly updating as journals come out, new research articles come out. AI is in a similar boat, software is in a similar boat. There is a need to keep up, keep your eyes wide open, but we focused on experimenting, getting hands-on with technology, yay, to earn the respect of the engineers that you’re looking to hire and attract so you can talk similar things or be sounding boards or be that word of caution when things are moving fast around you as people are innovating. And I think that’s the one advice I can say is get building and, like, get hands-on because no matter where you are in the organizational level, it is very hard to lead without some level of hands-on expertise in these new technologies.

[00:25:13] Kyle:  It’s probably one of the most common things we’re asked, particularly in the AI practice, is that combination of vision setting, communication, stakeholder management with technical expertise and competencies and keeping their thumb on the pulse of what’s happening. Because a lot of leaders right now feel relatively comfortable about their three or six month road map, but get pretty nervous when they think about the two year road map and the impacts of GenAI and AI-powered capabilities. There’s a lot of fuzziness there around, are we building the right platforms and infrastructure? Are we preparing ourselves to be well positioned?

[00:25:51] Mahi:  Absolutely.

[00:25:52] Kyle:  So I’d love to shift to this part because I think we’re coming up on time here to the rapid-fire section where we’ve got a number of questions I’ll,  kind of, fire through or rifle through, and we’ll just kinda go one after the other. Best piece of leadership advice you’ve ever received?

[00:26:08] Mahi:  By far, the best advice I’ve ever gotten is from a mentor of mine who said, Mahi, you should have absolute clarity of vision both for your business and for your personal self in terms of where you want to be, three to five years, and you must pursue it relentlessly without compromises. You can make adjustments as the environment shifts around you, but don’t let go of your dreams easily because if you don’t advocate for yourself, no one else will. And the way I actually internalized this is I talked about my vice president role. I had said no to two other vice president roles prior to that as well because it didn’t align with my passion. So, I think that advice helped me steer in the directions that were more aligned with my passions and were better decisions in the long run. So, that’s the best piece of advice I’ve ever gotten.

[00:27:04] Kyle:  One tool, book, or framework you think every leader should know?

[00:27:09] Mahi:  For me, because I drive so much transformation and change in most of my roles, I highly recommend the book, “Influence by Robert Cialdini.” I think it’s also great for anybody in sales or influence roles or influence without authority to be understanding the foundations of psychology that move us.

[00:27:31] Kyle:  You may have already touched in the first one, but personal leadership mantra.

[00:27:36] Mahi:  Stay hungry, stay foolish with Steve Jobs doesn’t work. I think taking a beginner’s mindset to be curious and always learning and having a good growth mindset and never to box yourself. I think that’s my leadership mantra.

[00:27:50] Kyle:  In one word, what does leadership mean to you?

[00:27:54] Mahi:  Courage. It takes courage to operate in this ambiguous world that we live in today.

[00:28:01] Kyle:  What one piece of unconventional advice would you give to the next generation of leaders?

[00:28:06] Mahi:  I would say maybe for leaders, I have heard a lot about a few individuals maybe driving outsized impact for the company, whether you take a 10x engineer or a hero culture, anything that you want to talk about. I have found, I think it is better to, in the long run, focus on driving collaboration, good teamwork, and unlocking every engineer, every team member’s potential. And so while it might be tempting to build teams or product suites around one or a few sets of individuals, real complex problems require collaboration. So. I would say even though the convention in technology might be 10x engineers, we go after them, they can drive outsized impacts, I think you want to eventually build cultures where there’s opportunity for both the high impact individual to drive as much value as a person who’s able to watch them, observe them, and grow at that same level. And that’s the type of leader I wanna be.

[00:29:13] Kyle:  And last one, if you could send a message to every executive in technology right now, what would it be?

[00:29:19] Mahi:  Resist slapping the AI label on every strategy that you’re looking to do. Truly understand if AI is a strategic enabler and really sift through, do you really need generative AI or regular machine learning can still drive impact? LLMs are great, but they’re still limited in the type of problems that they’re good at solving. So, trying to do a hammer and nail approach with Generative AI doesn’t work. So, I would say that’s the only advice I can give right now.

[00:29:52] Kyle:  Well, that wraps up today’s episode with Mahi Sathuraman. Mahi, thank you so much for shedding light on your journey in fintech and your leadership in scaling technology platforms and all of the future about where we’re going with financial technology and consumer-driven experiences. If you enjoyed this episode, please be sure to subscribe, share, and leave a review for more leadership stories and tech driven insights. Stay tuned for the next episode of Signal to Noise.

[00:30:19] Mahi:  Alright. Thank you very much for the opportunity to have this conversation today. I totally enjoyed it.

[00:30:26] Outro:  Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.