Damon Gatison Podcast Transcript
Damon Gatison joins host Brian Thomas on The Digital Executive Podcast.
Brian Thomas: Welcome to Coruzant Technologies, Home of The Digital Executive podcast.
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Welcome to The Digital Executive. Today’s guest is Damon Gatison. Damon Gatison is a senior financial services executive with more than 25 years of experience spanning investment management, investment banking, broker-dealer operations, and enterprise transformation.
As senior vice president and head of North America at Synthesis, he serves as a partner leading the company’s North American expansion, collaborating with colleagues across the globe to deliver AI-driven solutions to industry clients. Through enterprise transformation, AI engineering, data and analytics, and digital platforms, Synthesis focuses on building systems and capabilities that organizations can sustain, not just solutions they consume
Well, good afternoon, Damon. Welcome to the show.
Damon Gatison: Thank you so much for having me. Looking forward to it.
Brian Thomas: Absolutely, my friend. I appreciate it. And making the time, jumping that time zone. You’re in New York, I’m in Kansas City, so I really appreciate that. And Damon, if you don’t mind, let’s jump into your first question here. You’ve built a 25-plus year career that spans investment management, investment banking, broker-dealer operations, and now leading enterprise transformation work at Synthesis.
Walk us through your journey. What were the key inflection points that took you from the financial market side of the world into the world of AI-driven transformation consulting?
Damon Gatison: You know, it’s interesting when you ask about key inflection points, ’cause I think you have multiple aha moments within your career where you’re realizing that you can do more or you could do better or you can do both.
And I think that happened to me multiple times. So starting out in consulting as an analyst and associate, I wanted to kind of just learn what consulting was all about, get my feet wet in many areas, learn different techniques, learn cer- certain skill sets. That really made me fungible, made me a utility.
I used to run track in high school and I was, uh, I used to run… I used to be a sprinter and a jumper, as well as part of certain teams, so I was able to be very fungible across, um, the track meets, and I wanted to make that into my type of career. When I started out in consulting, I said, “Hey, this is great.
I’m learning a lot of different skill sets in different industries that are adaptable to between each other.” And then I said, “Hey, I don’t want… I want to focus on something. I want to really kind of hone my skills in one area,” and went squarely into financial services, primarily in asset and wealth management, working for some large conglomerates, um, on Wall Street.
And in there, I was doing a lot of strategic finance, which got me a good glimpse of the entire organization or product strategy, investment strategy, and how it was run, how it made money, how it operated, and what really made sense. So, things became more complex. You have, you know, different investment strategies coming out now, these w- exotic investment strategies such as ETFs and various funds, and then you had all the hedge funds coming about.
And that was my timeframe. That was my era where I was able to learn a lot about the way investments work and how you could really kind of give back to the client and make sure that the client was receiving the best return for their money And through that, it made me think about, you know, maybe I should go into consulting, ’cause consulting was an opportunity for me to really kind of understand the industry, but also from being on the client side, understand how to work with the consultants, and from a consulting side, understand what the client was looking for.
So, you know, a lot of the technologies that were coming out at the time, the advisor workstations and the, the various, um, robo-advisories that were happening at the time really kind of helped me understand technology and how it worked with compliance, and then also made me start to think about data. And a lot of the data that happens right now is a lot of, a lot of these, uh, wealth managers and asset managers had these mounds and mounds of data from the client’s standpoint that they just weren’t using, they weren’t taking advantage of.
And so, we were building these large, scalable platforms that took clients’ data so that by the time the client walked into the door, the, uh, wealth advisor or portfolio manager knew everything about the client. As you start to get… As it starts to evolve and it starts to get, you know, bigger and broader, you had the large wealth transfer where a lot of your aging wealth advisors were kinda moving on.
You had younger wealth managers coming into place, and then you had this transfer of assets, the big wealth transfer of, like, 2018 to 2020, which is still kind of going on now, where you have people who are investing at a younger age, individuals who want more information. They wanna know more about their investments.
You’ve had the big spring up of ESG a few years ago, and all that really excited me. I started to learn more and more about it, and then I said, “Hey, you know, how can we bring all this together?” And what’s happening at Synthesis is a great thing where we’re looking at the entire client journey and client experience for wealth management, asset management, just to name a few, and how we can inflect that with artificial intelligence.
And what’s happening with artificial intelligence is it’s really taking these massive data structures, these large language models, and it’s helping us to develop these specific Workflows, these specific protocols that the wealth advisors and portfolio managers become much more smarter about their clients.
They’re able to offer various investment strategies and better processes for their clients to get them the information that they need to make the right decisions on their investments or their portfolio and book of business. So, with all of that happening, we’re seeing that the execution of these platforms is becoming paramount for the wealth advisors and portfolio managers to be competitive.
It’s helping them really understand some of these more exotic investment strategies better, as well as, as the processes of agentic AI and things that we’re doing there are helping our overall enterprise clients run a more sleeker, better operation, and also not necessarily, but also take out some of the human error, um, and build human efficiencies as well as teaching and training those in the field.
Brian Thomas: Awesome, thank you. I appreciate the backstory, and I liked how you started out, really, you wanted to do more. You wanted to do better for the world, for your clients, and you stepped in that role of consulting. And what was cool is you, there’s this parallel, your athleticism, the way I heard it, was you really contributed to your work ethic in that space.
There’s a parallel there. And initially you were working in the financial markets, learned a myriad of various types of investments, platforms, et cetera. Then your pivot to consulting I thought was interesting, and of course now leveraging AI and helping your clients really be more competitive and, and leverage some of that technology to stay ahead.
So, I appreciate that. And Damon, Synthesis emphasizes building systems and capabilities that organizations can sustain, not just solutions they consume. That’s a point of distinction in a market full of AI pilots and proofs of concept that never make it to production. What does sustainable AI capability actually look like inside a financial services client, and where do most firms get it wrong?
Damon Gatison: You know, I would, I would say a lot of the enterprise clients that we work with, um, need to stop ignoring some of the down factors of their processes and their protocols in their business. So, for example, you have a lot of people who are doing the same job for years and years and years, and they’re doing it the same way over and over again just because that’s the way it’s been done.
That doesn’t necessarily translate to that’s the best way and the most efficient way for that process or protocol to be enacted. So what’s happening with AI is everyone’s saying, “Hey, you know, let’s take some of these pilot programs and let’s give it a shot and see what happens.” But they haven’t really thought out the long-term effects of artificial intelligence on that individual and on that role.
For example, you may have someone who’s, who’s sitting there doing the work the same way they’ve been doing it for the past 20 years. AI comes along, they think that they’re gonna lose their job. They don’t necessarily wanna give the right information for the process.
Um, you have individuals who have been doing things on scratch paper and on the s- on the side, outside of the technology. So there’s these little nuances that they don’t necessarily translate well into the new workflow that’s being built around the AI tool, uh, for proper operation, and things just fail.
So, you have to have this certain governance in place, and that governance has to start from up top. It has to come up with a governance structure that says, “This is what makes sense. This is what we’re gonna use AI for, and this is what we’re not going to use AI for,” which is just as important. Um, and that helps build that operational sustainability so that everyone knows their play, everyone knows their place in, in, in, in the AI initiative and bringing it from just a pilot to an actual, uh, business operation.
One thing that we found is the data is not necessarily cleaned. The data is not necessarily efficient. There may be five different sources for the same revenue line item on a, on a, on a, on a document. How’s the document going to know when it’s, when it’s, when it’s automated, how’s the document gonna know which, which data source to go to?
Um, which one’s most updated? It could sometimes be a, a timing factor, uh, or other issues that they have. So those, so the data cleanup has to happen. So the data foundation, the data models have to be right. Um, Synthesis comes in, we help do that from the start. We help build a strategy around what’s the data, why the data, um, what’s the technology, where are the protocols coming from, and help them build that understanding foundation.
Once you get that understanding foundation, you can start with the business transformation initiative. What do you wanna change? How do you wanna change? And even more importantly, sometimes, how do you measure change, right? Um, because a lot of times with a lot of these op- with a lot of these operations, you know, AI comes in, it helps, it makes things more efficient, but are you really getting the bang for the buck that you expected?
Are you getting the reporting that you’ve been looking for? Um, are you really just, are you, is, and are you really just kind of getting that overall business added value that you thought you would have? Instead of just doing a pilot for three months and maybe making this something that’s more sustainable and long-term.
So with that governance in place, with that oversight in place, and then obviously a change management happens where individuals are truly trained up on the new processes, everyone understands what’s going on, it’s properly documented, and it’s something that’s very sustainable for the future, that’s where Synthesis kinda comes in, and that’s what we’re, we’re focusing on, from taking us to a pilot into reality
Brian Thomas: Thank you.
I appreciate that. And a lot of people right now, uh, are struggling. And, you know, just, just managing a project alone, if you’ve- if you’re wearing a hat in a role as an executive or leader, uh, that’s, that’s full time. I mean, you, we and I both know we’ve worked 50, 60 hours a week easily just in wearing one hat.
Yeah. But that’s where synthesis comes in. But I thought it was interesting just to highlight a couple things. People really are still doing the same process they’ve been doing for many decades, and, and, and now with AI, they’re thinking, “Well, gosh, we just grab AI, buy it off the shelf, and throw it in, it’s gonna work,” and, and that’s where you, you definitely, um, really made it clear that it’s more than that.
When you leverage these technologies in a, let’s say, a business transformation initiative, there needs to be a game plan, communication, building support for the pilot making sure that compliance and governance guardrails are in place, and that data foundation needs to be right from day one. So, I appreciate those insights.
And Damon, next question here, financial services is one of the most regulated environments to deploy AI in fraud detection, know your customer, credit risk, client life cycle management all carry real compliance weight. How are you helping clients navigate the tension between moving fast on AI and maintaining the governance, auditability, and model transparency regulators are increasingly demanding?
Damon Gatison: I would say that one thing that’s very important is, you know, your risk management has to be really properly in place to help identify, um, assess, and obviously mitigate those risks that are associated with those AI systems, right? That helps build a sense of trust or sense of comfortability between the masses.
One of the biggest things about AI is everyone… no one knows what’s fake and real anymore. Everyone’s trying to decipher between, you know, what’s the real image of the data, what are the real… what do the numbers really mean, um, and how can I trust this? So when you’re building out these algorithms and you’re building out these data models, you know, it has to be very transparent to the user group or to the operators what it really means and where are your vulnerabilities in security.
Um, I think one thing that we ha- that we’re very good on is helping to identify that governance model of who knows what and who is the kind of gatekeeper of knowledge and information, um, for certain areas of the program. You have a lot of frameworks out there like the NIST, um, I think it was a AI risk management framework, um, which is around how g- how organizations should manage AI risk.
Um, I think something like that is very helpful. Something like that is very poignant, um, as a foundation. It may not be, you know, one size fits all, but it’s something that’s very foundational and transparent and helps to build the collaboration, um, between the folks within risk management and operations, um, and compliance too.
You know, everyone’s interested in making sure that they are adhering to the proper rules that the regulatory bodies are inflicting or imposing, and also staying ahead of them, right? So, you wanna stay ahead of the regulatory bodies. Um, and obviously communication is key for that, um, from a risk management standpoint and being very proactive.
So kind of thinking ahead, uh, for the, you know, your robotic process automations, um, and just anything else around, you know, your machine learning model, your machine learning and large language models, I think are very important there as well. Synthesis helps look out. We have certain frameworks that we leverage that help us look out for certain key pitfalls, um, of an AI implementation and AI initiative that can help keep us out of those, keep us out of those dark spots and keep us, you know, moving towards that golden, that golden source and North Star, um, area of added value
Brian Thomas: Thank you.
Appreciate those insights. Uh, obviously risk management is key when implementing AI in these organizations. And of course, in regulatory environments, uh, following the NIST framework is always a good idea, and staying a step ahead of the regulatory, uh, bodies. Uh, you talked a little bit about that. It’s really, uh, uh, can be kind of a, a cat and mouse game, you know, if you’re not on top of, uh, the day-to-day and, and some of those regulations that you need to, uh, adhere to during these types of projects, especially in these industries.
So, I appreciate that. Mm-hmm. And Damon, last question of the day. Looking ahead maybe three to five years, the financial services landscape is going to be reshaped by agentic AI, autonomous workflows, and a fundamentally different relationship between humans and machines in the front, middle, and back office.
How is Synthesis positioning itself and its clients to be one of the firms that shapes that future rather than reacts to it? And what should industry leaders be doing right now to make sure they’re on the right side of that transformation?
Damon Gatison: Um, I think something that industry leaders should start doing right now is having the conversations.
Um, don’t be afraid of AI and what are the possibilities that it could, that could, that it could hold, and be exploratory. So we’re having conversations with a lot of clients right now about what their AI governance is, what their AI apprehensions are, um, and then what, uh, what’s the realism around AI.
Which, you know, we had one client, which was interesting. We talked for an hour and a half about all the things that they want to do with AI, but they have an AI governance council that just won’t approve anything for eight to 10 months. Eight to 10 months from now, AI is gonna look a lot different. So you’re gonna have, um, some of those who are early adopters, who are kinda going slow and steady wins the race.
You have others who are gonna be very, very well prepared and kind of hit, hit the AI boom at the right stride and start really kind of integrating it into their processes and into their, into their business models, um, which is also another way to approach it. But you just can’t stay still. Um, those who stay still, those who, who are, who are the laggards, um, who fall behind, they’re gonna find themselves constantly being behind the movers and the shakers of the industry and losing competitive ground.
Now, what I found is, I, what I think is very important is, you know, when you’re looking at the workforce transformation, AI is more of a collaborative tool, collaborative tool with humans. Um, it’s something that can help alleviate some of the human error. It can help make us more efficient if used right. I think, um, with the organizational Infrastructures that are in place, you’re gonna see that a lot of these companies are able to really build these strong data foundations, um, and be better prepared and understand their business better.
Um, and people are just gonna learn. They’re gonna learn more. Um, it’s gonna be a chance for them to kind of really kind of train their, their people on AI literacy and what they really should understand about the tools, about the processes, not just how to use it and what the technical adoption is. I think it’s very important, um, that the firms that win the most, um, they really start to integrate AI into their business, um, you know, in the areas that make the most sense.
So when you have some of these high compliance, highly regulated areas such as the investment strategies and things, you wanna make sure that you’re doing that right because you can’t just buy any platform off the street that’s going to give you the answers that you need to make the right investments decisions.
But in operations, in some processes such as accounts payable, um, you know, accounting and finance, those are some good ways to start first. Some of the processes within the business lines, those are some of the good ways to start first in operations, and that will start to get you a little further into the comfortable space of AI.
And then now, now AI’s actually started to run some of your business lines. It’s starting to support some of your business lines, and it’s starting to get you to that next step where you’re ready to take some of the more aggressive challenges that AI’s ready to handle, um, for your business success and for your business growth.
Brian Thomas: Thank you. Appreciate that. Um, let’s highlight some things again, Damon. As you said, you need to start, organizations need to start having these conversations now. Remove that anxiety around AI. You know, look at AI as a team member, uh, a platform, a technology that’ll help humans be more efficient in all their work at all levels.
And of course, you talked about AI governance. You know, AI governance councils, committees need to be more nimble with the speed of evolving AI and other technologies, uh, because it is moving really, really quickly. And of course, as you said, when implementing AI, start with the areas first that make the most sense, and I think that is some great advice.
So, I appreciate that. And Damon, it was such a pleasure having you on today, and I look forward to speaking with you real soon.
Damon Gatison: Absolutely, Brian. Thanks so very much. You have a wonderful day.
Brian Thomas: Bye for now.
Damon Gatison Podcast Transcript. Listen to the audio on the guest’s Podcast Page.
