The Revenue Reality Check: Why Great Products Fail with Declan Sheehy

Speaker A: Welcome to the Tech People Podcast. My name is Ken Coyne. I'm your host and founder, as well as an ambassador for OPS talent. I believe at the heart of any success story are the people who made it happen. Diversity, creativity, and innovation, where nurturing people can lead to an unbeatable formula. I created this podcast to share the experiences of some truly inspirational leaders on a journey to success. Enjoy the show. Welcome back to Tech People. Today we are joined by a guest who bridges the gap between massive institutional scale and cutting edge of fintech disruption. Declan Sheehy has spent 25 years in the engine rooms of global finance, most notably helping scale operations from 200 million to over 31 billion, all while maintaining a spotless regulatory record. He's the author of the definitive new guide, Disruptive Innovation in Financial Services. CEOs and founders listening. Today's conversation is a reality check. Declan argues that innovation isn't a lab project or a cool app. It is a total business architecture. We're going to dive into why revenue is the ultimate market. Verdict on your innovation. How to treat compliance not as a handbrake, but as a commercial differentiator that builds institutional trust. And the Innovation Ambition Matrix, a framework for ensuring your product actually solves a problem or paying for it. If you want to move beyond the MVP and build a platform that can actually survive and thrive in a regulated world, this is the episode for you. Welcome back to the show, Declan.

Speaker B: Oh, thank you, Ken. Look forward to talking to you again.

Speaker A: No, pleasure to have you. And, um, looking forward to learning more about this book. But before there, before we get there, please tell us a bit more about your background.

Speaker B: Well, um, I've been in the financial service industry for over 26 years. 18 years of which was with HSBC under the asset management, um, area. I'm happy to say we grew a business from about 200 million to about 33, 34 billion globally. And that was a really fun journey. And then I jumped to the other side. As opposed to working with hsbc, I started working on the fintech side and the vendors that provide services to financial services. So at my core, I've always been in the financial industry and predominantly in the uk, but also across the US and Asia and beyond.

Speaker A: Okay, and listen, you've decided to write a book, Disruptive Innovation in Financial Services. Turn me why now? And, uh, who is this book targeted at?

Speaker B: Well, it does capture the 26 years of experience over the years, and I wanted to provide a toolkit. We're moving and we're changing so quickly. Within Our industry. So I want to help C suite executives, fintech entrepreneurs, innovation leaders to equip them with the tools to succeed in this digital transformation services. There's a big question, is it an art or is it a science? Is there is this creative art, artistic kind of idea but the idea by itself will never get implemented. So my book kind of blends the idea but also with the tools to help you succeed. Based on my history and I mean

Speaker A: I know based on what you just mentioned there that you mean the reason we have uh, guys like yourself and this, these type of books is that a lot of companies, a lot of people, they get, get it wrong, right? I mean they try to innovate. I mean in your experience, why is that?

Speaker B: Yeah, I guess it's the concept or the scope of innovation. A lot of people and even still to this day would look at as a discrete event or a technology project maybe. But innovation at its core has to be embedded systematically across leadership, culture, strategy, operations, financial discipline and the product. Not just one of them. You need innovative leaders, need a culture that strives it. And what I found from my experience, if all of these combinations uh, don't exist then that's where you're going to have an issue. If you take like Revolut for example, which is a very well known UK challenger bank, originally they really exceeded in the majority of all those sides between culture and product, et cetera. But they put some of the compliance elements in the, as a backstep. I didn't necessarily focus on that as much in the beginning and as a consequence it took them a lot longer to get regulated here. So again it's a really holistic approach. And that's my book tries to, tries to cover the book. It goes across seven parts. Now those seven parts we talk about leadership, culture, revenue, commercial strategy and the customer experience and trust which is so important nowadays. You cannot be innovative without uh, having the trust of your customers.

Speaker A: Yeah, well I completely agree on that point. I mean uh, it is all about customer experience. Right. And the apps. And I meant to say it before, big congratulations on the book. Really great, great, really enjoyed it. I know your background has been a lot in terms of reg tech. Right. And what I was really interested, you mentioned a lot of different points of, you know, what you need to build resident, you know, technology and innovation there. You mentioned culture, people, customer experience which uh, what was interesting I found in the book is the whole point on regulation and I've come across being like I was a financial systems consultant myself and regulation has Always been I suppose a uh, pain or you know, on the back of many companies trying to add new technologies, build new technologies event they feel it's an in hindrance. But you argue the opposite in your book, you know, which is interesting. Uh, so tell me, how does that work in practice and how can companies change that mindset, moving away from regulation as the enemy of innovation, but and doing the opposite to that?

Speaker B: Ah, it's a very good point and very good question I guess historically and specifically in the financial services because it's a very heavily regulated environment and it should be because you're mean, you're in trust of other people's money from old people's pensions to school. So every day it's a very important kind of um, aspect. So but traditionally people are looking for a phrase called alpha, give me technology, give me innovative solutions so I can make more money. But just in parallel to this is also regulation making sure that it is a regulated solution and it should be within the architecture. And I give maybe my HSBC story again as an example of that, that we scale it from 200 million. We were over 31.6 billion AUM, but with zero regulatory breaches. And this wasn't a compliance achievement, this was like an architecture from day one. We wanted to be safe, to build trust. And we asked ourselves what controls do we need to bring into our whole architecture for providing this service to our clients. And I'm happy over those 18 years as we expanded from region to region and then asset class to asset class, we kept that regulatory kind of backbone. And if I look at the outside world, this firms like a blackrock Aladdin, like um, they started off as a risk engine and now they're just dominate most asset managers, banks, insurers will have blackrock Aladdin. So they used innovation and they've used regulation as a selling pitch. And um, there's Archax who actually Graham kindly um, provided a forward for my book. They're the first FCA regulated digital securities exchange and they built a compliance first infrastructure from day one. Which means as we move more and more towards digital assets, these infrastructure would give clients trust. And then we think about maybe this is like in the past, but if you look at stuff like say cybersecurity, it's not a feature that you add. You should embed cybersecurity straight away within your architecture. So I guess it's the message here, but it shouldn't be a friction, it should accept that it's a part of your architecture to succeed in financial services.

Speaker A: Okay. But do you still see it? I uh, mean there's a balancing act there between getting the customer experience right versus you know, addressing like the regulation.

Speaker B: Well, correct, that's, I think AI is a good example right now. So right now with AI you can create really fast solutions, you can create maybe research, you can create suggestions. What should I invest in? All these sorts of things but in a secure regulated way. It needs to be explained. And then there's a trade off between this because if you're just training a model but then uh, the answers come back, it can be kind of quick to bring to product, it can give quick to give an answer. But to be able to explain this, how did these answers come in? Basically it takes a longer time to deliver this to market and also it involves more people and ah, kind of more questions. Obviously if you work with a partner, for example like a data science as a service, what have you and you have a partner that can help you with this. Explainable AI is quite key but if you don't then that's where. And eventually there's that friction between maybe the use of AI by making it explainable because the more you try to explain it, the more you're going to have to try to build and develop it unless you get some smart partners to help you.

Speaker A: That's a great point. And uh, it's interesting you say that because I've been reading more and more about you know, LLMs and having full auditability, full traceability and uh, a lot of it's questioning and going back and questioning, you know, do we go back to traditional AI machine learning and quality models versus at the latest and greatest in the LLMs? Again it's about getting that balance right.

Speaker B: Yeah, it comes a lot down too as well. It's like trust. Would you trust an LLM right now to manage your pension? And if you have a query on it, it can't give you an answer or uh, would you trust it for a medical advice? You God forbid something is not going too well with you on a health perspective and an AI model gives you an answer, no, you want it to be explained. And I think that's quite biological and I think that's how from an AI point or a regulator. Well we got the AI act within Europe. Uh, it's going to go more and more this way and you can say well I'm not going to do these pieces. Well you're going to find pretty quick these models that you're using within your industry where you're generating revenues from you're going to have to re engineer them. You're going to spend a lot of time redeveloping all these things. Well, if you bring that in at the beginning, like training, retraining, explainable, you're building a product originally that's going to provide a lot of value and service to your customers and you want to have to redesign it to be regulated.

Speaker A: Yeah. Okay. And what I liked about your book is that you know, you focus on revenue, pricing even, you know, and it's important even if you're your customers internal, there still has to be a cost to it. Right. And it still needs to value. But there's loads of amazing great products out there. But a lot of them, you know, they don't seem to make uh, a progress, they don't make money, they fail. And your experience, you know, why is that?

Speaker B: Well, if I put it to one sentence, solving a problem or solving a pain quite too often you can have uh, maybe a nice product idea. And a product idea by itself, if it's not tested to the market, it is still just an idea. It's revenue is not a consequence innovation, it's a test of it really. So you have an innovative idea and you're going to get a financial reward if you're solving somebody's problem. If you're solving a pain, the market will basically be a verdict of whether you solve the problem worth paying for or not. Right through the years I've met so many people who have a good idea and I'll say that's a nice idea. And then they ask well would you pay for it? And that's a completely different question. And most time it's no and, but this where innovation is kind of really exciting because I mentioned about it's not just the product, it's like the people and the strategy, but it's also can be the pricing. I think I'm seeing more and more models where they have these kind of pricing models which, where you could have premium or you could have where upfront or pay as you go. If I look at some of the successes, this would be like Robinhood. The Robinhood provided I guess a trading platform not unlike any other trading platform really on an innovative perspective. But what they did provide was a commission free premium breakdown where as you start using them more, you start paying more. And from that innovative idea they started generating more, more revenues. Or you could say Stripe for example. Another example, they developed a very friendly AIs so other firms could integrate as a part of an ecosystem into Stripe for transactions so that is like how you're using innovation, linking to revenues.

Speaker A: Okay. And you know, innovation, it's great but I find it's so difficult to keep up with it now. And you know, even looking forward for the future, it's like even if you look at the AI and the model is changing on a weekly basis. But in your book you might, you know, you talk about a 10 year outlook which I mean, goodness, it's very, very hard to cover. I mean we're talking about, you mentioned, I know you mentioned AI, uh, tokenization, the whole crypto thing, quantum computing, which is probably coming for the future. Web3. I know there was a lot of talk in the last couple of years and I haven't heard much recently. I'm guessing that's still very active. But if you were going to sit down with a CTO or a CPO in the regtech space, what would you tell them to focus on first?

Speaker B: I think technologies I tell them to focus on actually that they're all connected. Similar as I was saying, innovation goes across people and strategy and product finance. I say, I think if you look at one by itself, you're going to be in trouble. AI is going to be embedded by tokenization. It's going to be embedded by Quantum computing, by Web3. They're all, in some ways it looks like they're all independent sources but they're all intertwining. But I think the more you have how different technologies get on board and how they um, like they got a perceived value and then they drop isn't a value for society and then they increase and they become kind of normal. But I think that the one out of all of them would have to be AI since those like ChatGPT and all these models came in. Ll models like a couple of years ago, we start using it for, I guess for research and insights like these autonomous decision making agents. They're here and there's going to be more of them. Embedded intelligence making decisions, virtual advisors, advisors. Well beyond these kind of um, robo advisors that you see that's not even 10 years. I think this is going to be even within two to three years. And I'm seeing this like the workforce. I actually just wrote uh, another article about how the workforce is going to shape. Everybody talks about how AI reduces jobs or may reduce jobs, but we'll still need people. Those remaining will have different skill sets. How you build a team now is so even right now. It's so different than how you built a team five years ago. There's so many tools to, to help you.

Speaker A: And then.

Speaker B: So that's where I think AI is going to be a massive shift, not just for your product, but also how you deliver that product internally. The skills will have completely changed what when you're hiring. So that's something that's going to be really important to consider. And then, um, the other piece, which is, it's catching up now. And I remember the time where blockchain had more of a momentum than AI and our blockchain is coming to a point, the digitization of assets are. And, um, even put it more simpler, the digitization of anything that's of value. So be a property, be it even my beautiful book. And anything that's like, uh, anything that is of value that you want to exchange, you want to transfer and have a very clear contract towards it. That is here. All the big banks are investing a lot of money. There's a lot of money market signs example that are all tokenized right now. That's not going away and that's going to get bigger and that's going to have a significant change across the financial services industry. Um, but for tokenization to exist, it needs to have strong AI for smart contracts and kind of logic. And then on top of that, you've got quantum computing. We spoke about cybersecurity and etc. That crypto, again, it's a bit further down the line, but it is coming. Once we have quantum computing, this is going to significantly change how we deliver products, how we. I even like the thought process, the amount of processing that we can do so quickly. But. And then Web3 is kind of similar, but they're all a unified kind of connection. So I think there's a lot of exciting things coming over the next ten years. And, uh, we talk about the technology, but I think how it's going to affect the workforce. We talked a lot about how it's going to affect the customer, but I also think we need to consider how it's going to affect the workforce.

Speaker A: Okay, I'm not going to ask you for your opinion on something else. Look at it from a different direction, right? Because I see a lot of companies, okay, it's all AI, AI. We must invest in AI. We have to get AI going and they start looking at AI. But should they not, like, look first, what are the problems we're having, guys, and what's the best solution? Rather than saying, okay, AI, uh, has to be a problem. I feel that we're going to, we're just looking at new technologies and we're trying to find solutions for the problems. We have these new technologies and um, we necessarily might not be the solution. You understand what I'm saying?

Speaker B: Yeah, I totally agree. And I guess it ties into, I guess why is it focusing about like creating product that's not generating revenues. It's the same internally that like innovation isn't just phrase AI and some technology. It's how this technology is going to solve a problem of a pain. And I guess maybe the trend of this conversation where we've been talking about is that like being a fintech or being a service provider of these things, but it's also the clients, it's also the bank, et cetera, they have to think what pains do we currently have, huh? And I guess there's different levels and implementation of AI. Uh, I always like this phrase of having high value people no longer having to do low value tasks. That, that's great, you know, saying like email collection or writing notes and et cetera. So I think that there's a shift there where, where AI, uh, could potentially help these things. But are they going to necessarily increase your revenues? It'll give your team maybe a bit more free time to carry out activities. But the real pieces, you're talking about the pains, like how can we get more market share, how can we create more effective product? How do we know the products that we are creating is more effective? And first is through the analysis and then it's using the AI to solve those solutions. But then it's not just AI, it's like, how do you do it? Like a financial firm may not necessarily be an AI specialist and why should they be that? Their focus is on investment, so is finding the right partners to help them with that journey.

Speaker A: Okay, Declan, well listen, great insights, really enjoyed the conversation. Thank you for your time today. If people want to learn a bit more about you or want to buy a book, please feel free to share.

Speaker B: Oh yeah, well my, my website is one blackwater.com that's in the word one. You can find me on LinkedIn under Declan Sheehy. And my email is Declanone blackwater.com and I'd love to answer any questions and uh, make, get more connections.

Speaker A: Great. Declan, thank you for your time today.