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Starting and running a business or managing one isn’t for the faint of heart. You’re balancing internal and external forces in a continually changing landscape. You’re building strategies, and banking on the future – no matter what it holds. This is where Cadence Bank’s In Good Companies comes in. We share our wealth of knowledge, and insights from noted industry experts, to guide you through the forces shaping business today.
We’re back for Season 6, and this time, we’re setting our sights on the future of work. We’re asking the big questions, like:
What will your career look like in 2030? Or 2050, even?
How is ESG shaping the future of companies?
And how can we leverage AI to our advantage?
We bring together experts from across the board, from Silicon Valley to multinationals like EY, to help you stay on the cutting edge of business. And we get to know those who are building the future of our companies; because at Cadence Bank, we want to hear the human side of every success story.
Hosting our stellar range of guests this season is our new host, Ari Marin. He is a Cadence Bank Senior Vice President and family enterprise advisor, whose specialty is consulting with family-owned and small businesses. Ari’s idea of “good company” is being around creative, insightful people with unique and inspirational stories. For Season 6, he brings in his curiosity and ambition to In Good Companies, to lead discussions with our guests, and bring listeners across the U.S. all the information they need, in one place, in under 30 minutes.
Ready to launch into the future? Then join us!
In Good Companies - Season 6
Episode 5 - “Driving Performance: when AI takes the wheel, with Bob Trotter”
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[00:00:00] Bob Trotter: We are still at the beginning of a tremendous journey. AI has been around a little while but this is the type of technology that I think you are going to have to embrace. So I do encourage most organizations, at least to start the operations. But the way this thing is going to look five years from now, let alone ten years from now is going to be vastly different from what we're seeing today.
[00:00:00] Ari Marin: This is In Good Companies, from Cadence Bank. The podcast where we share the forces shaping business to navigate the opportunities ahead. We’re empowered to help! Through this podcast — or any of our more than 350 locations across the South and Texas. Because that’s what Cadence is all about: The expertise and flexibility to do business on your terms. I’m your host, Ari Marin.
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[00:00:00] Ari Marin VO: How are you integrating AI to your business?
I know, you’ve probably heard this question a hundred times. But it’s a big one and I wanna hear your takes. So do let us know in the comments. Are you using Chat GPT to draft marketing content? Or… Do you bring in AI to process your data differently?
Whoever you are – whatever the industry; you’ve got something to gain from Artificial Intelligence. A recent survey by McKinsey shows that AI could create millions of new jobs, and increase corporate productivity.
And if you’re anything like today’s guest – that prospect should get you fired up.
[00:00:00] Bob Trotter: Oh my gosh! There are so many things that are exciting about AI; it creates authorship of stuff that in the past would be much more difficult, whether it's marketing brochures or whether it's essentially taking technology and developing better technology out of it.
[00:00:00] Ari Marin VO: That’s Bob Trotter. he labels himself as “an Executive Partner and Fintech Evangelist”. But really… There’s a lot more to his professional journey.
Bob is someone who believes in the power of new tools to improve people’s lives. Over the last 25 years, he’s moved from business operations to IT leadership roles, in organizations like EY or ThyssenKrupp.
Today – as Partner for Gartner – he leverages his experience as Chief Information Officer to help other CIOs in their own development.
[00:00:04] Bob Trotter: I basically work in what we call our financial services vertical with CIOs and CTOs for banking and financial institutions, predominantly helping them with their strategy, what are they doing in terms of tech modernization, and of course, leveraging emerging technology like artificial intelligence.
[00:00:00] Ari Marin VO: Bob is the perfect guest to discuss the future of AI.
In this episode, we debunk the myth that AI is a threat to business. And we come up with strategies to use it for performance. From backend tasks, to product personalisation, we get to grips with what AI can do for us. And we find out how to use this technology, now!
But before we dive in – let’s address the question we’ve all had on our minds at one point or another. Why Artificial Intelligence? How did it become such a hot topic? And more importantly: why should we start paying attention?
[00:01:55] Bob Trotter: It's interesting, because we're in an interesting inflection point with regards to AI, and what I mean by that, there's always emerging technologies that will come in. There's a little bit of a hype, actually, Gartner talks about a hype cycle, which inflated expectations on what this thing is going to do and how it's going to disrupt, and then eventually, it levels off. I have been around a little while and I've seen some technology that has what I would call fundamentally changed some of the landscape, but in this particular case, I think AI is really groundbreaking. I think it's as large as the internet was in terms of changing the way people do business. And it's predominantly because even though AI itself has actually been around a little while, machine learning, AI models existed for some time, generative AI, when it came about, really made it available for anybody.
[00:02:48] Ari Marin VO: Just as a quick reminder here: Generative AI – it’s the AI most of us know. As a tool, it uses data to generate content like: text, pictures and much more. And its selling feature for the public is – it’s very accessible. So most of us know Chat GPT, for example.
And actually – when Chat GPT launched, that’s when everything changed.
[00:02:48] Bob Trotter: For the layman out there that suddenly they heard about this thing called ChatGPT, which basically went live from OpenAI, which was the main provider of it. It allowed for somebody just to play around and experiment. And what generative AI really is, it involves using these large language models which are prompt-based. It works very much like when you're doing a chat or a text, you're prompting it by you're putting some information into it and you're asking the question, and in a very simplistic terms, it's coming back with a response. And it provides what I would call a very humanistic type of experience.
You need to literally feel like you're talking to somebody because of the way it relates back. Prior to that point in time, AI was more difficult for people to access in the public sector because it was something that was done in IT offices or in large scientific labs, but now it became mainstream through generative AI. It made it available for the general population, and people started to understanding the power of this thing. What I don't think we understand is how it's going to really change over time. I don't think we fully understand what this thing is going to look like in three years, let alone, 10 years, but I do think it is a ground-shifting type of technology that people are all trying to learn how to use.
[00:03:41] Ari Marin: So AI is going to open a lot of work capabilities in the long term, but in your opinion, what is it really good at?
[00:04:18] Bob Trotter: In a simplest form, it's what I call authorship at scale. And what I mean by that is the most challenging thing for most organizations when they have a very large customer base, thousands of customers, certainly in the retail banking space, you're dealing with hundreds or thousands of customers. And what you're trying to do is you're trying to take the efficiency of some capability, and as best that you can, customize that offering to a lot of people. So what organizations tend to do, they segment customers into certain groupings, and they say for this grouping, this is the product offering or this is the service offering, or this is how we go to market. And that's extremely expensive to do. What AI does extremely well is it allows for customization and content at scale without having to segment into these large groups of customers. So it's what I call the authorship at scale, using, again, technology to do things that in the past required a tremendous amount of human intervention.
[00:05:18] Ari Marin VO: So yes - AI could add value to your company. You could open up your services to new customers, while keeping a personal touch that makes your product… special. Convincing, right?
Well, if you’re ready to start integrating AI… Here’s the next step. Start by asking the right questions.
[00:05:24] Bob Trotter: What exactly is your ambition? AI is everywhere, and a lot of people want to start dipping their toes into it or maybe afraid to be left behind if they're not getting into it. But I always start with understand the ambition of the organization. It could be as simple as, "Hey, we're not looking to change anything. We're not trying to create new business models. We're not trying to disrupt a whole bunch of things that are happening in the marketplace. We just want to be better at what we do. We want to enhance productivity." And so a lot of the things you're doing in the back office can be automated or augmented by using AI. But other people are more ambitious, and they're, let's just say, willing to take chances in saying, "Look it, I want to use AI to generate new products and new services, and actually create new offerings to the marketplace that in the past didn't exist."
And that's fine too. That's a totally different ambition. So normally what I'm coaching them is understand your own ambition as an organization, and then once you understand your ambition and let's say your risk tolerance and your appetite, say, "Yeah, I want to take the next step. Let me see how this might be valuable for my organization."
[00:06:34] Ari Marin VO: Once you know what you want to achieve; focus your attention on the data. What can you use it for? How will it improve your company? This is an important step, because not all data is equal!
[00:06:36] Bob Trotter: A lot of times artificial intelligence is heavily dependent upon good data. So many companies are using, if they're going to use data within their organization, it's falling under let's say the head of business intelligence or data analytics, and they'll say, "Look it, we've got our own platforms internally, but I want to be able to leverage these things that we don't have, like these large language models and these algorithms that exist. How do I do that?" Well, there are companies out there that now have these capabilities, so many times with the first thing that they do is they contact some of these organizations that come in. And it gets to another level, let me describe what that next level is.
The next level gets involved with, do you want this capability through artificial intelligence to just be restricted within your own domain? What I mean by that is I don't want any of this intelligence going out into the general marketplace. I want to basically use my own data, maybe bring in some outside data in the marketplace, create some capability, but then use it internally. If that's the case, that's going to be very different than using a ChatGPT, which is basically an open marketplace where you may not want to disclose some of your proprietary data. So the first steps that are involved is, again, what is your appetite? Where do you think the biggest benefit for the organization would be? And then who are some of the partner organizations that you could work with to basically start to model some of the data sets that you already own?
[00:08:03] Ari Marin: Can you share some examples, maybe some front office examples of companies that have integrated AI successfully?
[00:08:10] Bob Trotter: Yeah, there's so many different use cases that are pragmatic right now. I'd say one of the big areas that people are using it in is marketing. So again, we're saying, "Look it, I have thousands of customers, I want to be able to reach out to them all the time." I might have a lot of data on those customers, but it's very difficult for me and it's certainly not economical for me to have individuals trying to segment through all that data to figure out what type of content is relevant for this customer at this point in time. Let's say that recent college graduate who may be looking to buy their first car and they might be interested in a car loan, or that late stage of life, somebody that's nearing retirement that may be looking at getting into wealth management, and how do they manage their life savings?
Those are individuals, and they could be current customers, but to develop a targeted type of a marketing campaign around all these individuals is very difficult to do. So again, we tend to segment. This is where AI can be very, very powerful. It can customize that marketing on the front end. The other areas it can also do very, very well from the back office perspective, it takes a lot of content that's happening, like a call center, for example. So you get customers that call you all day long in your call center. People load tickets into the call center, whether they're simple, like, "I forgot my password to log in to get my bank account." Or, "I've got this particular issue and I can't particularly resolve it."
To be able to take all that massive amount of data to learn something about yourself, it seems like we have a lot of customers that are having this kind of difficulty or that type of difficulty. How do I change that into maybe a training session for them, or how do I take this information and actually generate some real-time type of exchange with them? Or links, if they're contacting me by chat and suddenly there's some information that I'm picking up through AI that says, "Hey, this information is available publicly, but they don't know how to get to it." All I’ve got to do is embed a link.
So contact centers. Call centers, contact centers are one of the best areas that people are starting to leverage AI to basically what I call very customize their type of interface with a customer directly.
[00:10:24] Ari Marin VO: Beyond customer-facing services, AI is useful on the operations side too. It’s a harsh truth but: the future of your company will depend on the viability of your tech.
[00:10:29] Bob Trotter: A lot of organizations have technology that's very old. It's written, let's say, going back to the 60s and 70s, and COBOL is not trained in college anymore, or even FORTRAN or BASIC. AI has the promise of doing code conversion, where I can take all this data technology with people that frankly I can't even hire out a college to maintain the code structures, and can I rewrite this code in something more modern, like Java or Python, so that I can actually work with current-day developers to actually support it?
[00:11:04] Ari Marin: There's certainly a lot of unease when it comes to AI with a lot of people, especially as it pertains to data security. And you spoke earlier about generative AI, the fear is it gives you a response that it's inaccurate, there's words that are associated with phantom data or phantom answers. So what's a good philosophy to practice navigating AI safely?
[00:11:31] Bob Trotter: So the word that we tend to use around these, let's just call them false narratives that can come out of AI, the word is hallucinization. I thought it was an interesting word, because when I think of a hallucinization it's totally made up. But what ends up happening is, again, we're still at an early stages certainly of using generative AI, we're still at the early stages. And it will get better over time, but it is not perfect. And as a result, when you're in AI, particularly when I'm talking about something in the public domain, you'll get responses back that are let's just say 85 to 90% accurate, and then other pieces of it that are wildly off.
So what I've seen organizations do is, again, you can use AI to do authorship, but it still requires editorship, and that's something that most organizations don't do, very honestly, unless you're a publisher by trade or you're involved with making content, you have people that author content and you have people that edit your content. Well, now for just any organization, you can leverage AI to gather information on a marketplace or gather information, let's say about a competitor, whatever the case may be, you're definitely going to need some editors reviewing the content that comes back before you determine what to do with it. So what I always suggest is that start thinking about who's reviewing the results of AI, again, the editor, before you start using it pragmatically in something you want to do within your business.
[00:12:57] Ari Marin: Another fear surrounding AI involves, I guess, replacement or employability. Maybe we can start to have that conversation.
[00:13:05] Bob Trotter: Well, it's very understandable. If you're used to providing a service, particularly something that is a high transaction-based service, and all of a sudden you see this tool out there that has a very human type of exchange, you start to get threatened about your job. Because part of your job is your knowledge. You have all of this knowledge, you've done this work for some period of time, you apply that knowledge to a transaction, and all of a sudden you see this tool out there that's able to aggregate knowledge, whether it be written, verbal, or in other forms, and respond to it and people start to get threatened.
What I think is going to happen in this, and again, we're still at the early days, I think that what's going to happen, AI is going to get embedded into a lot of the processes we have as an organization, and new jobs are going to be created out of it. And you're going to have new jobs, like prompt engineer. That's a word that typically we don't use in business. What is a prompt engineer? Think about that. A prompt engineer is somebody that is leveraging AI, they're prompting it, so they're sending something into it, they're asking it a question, and it's responding back. And it's summarizing things in a wonderful format. Somebody is still editing it, it might be that prompt engineer that's going to be editing and saying, "Hey, I can use this thing. I can cut it and paste it, and then push it somewhere else."
So I think what's going to happen is roles are going to evolve over time, and the most organizations that have really figured this out is they don't fear it to the point that they're saying, "Oh my God, this is going to displace a whole bunch of workers." They're embracing it, they're embracing it and saying, "Let me use this as additional tool. It can't replace everything that we're doing here, and we got all of this institutional knowledge that is embedded in the workforce. But what I want to do is use this new tool to be faster at something, to be better at something, to have a more holistic view of something that goes outside of maybe the individual's knowledge, but then to take that and customize it into something that could create value for the organization or our customers.”
[00:15:06] Ari Marin: So I'm a big Star Trek fan, and so I've always enjoyed, especially the ones in the 90s, but I've always thought of AI in the same way that they communicate with the computer. Where they're asking it questions and refine what it is they're trying to get to until the computer ultimately gives them the perfect response and the perfect solution to their problem, the prompts are the most important part of using AIs.
[00:15:28] Bob Trotter: It absolutely is, and like anything, it's going to get better over time. I really think if we look at our business models right now, we're not sure exactly how to embrace it. It was kind of funny, I remember going back, again, dating myself a little bit, to when the internet became mainstream. And organizations had, "Hey, we got to have a president of internet," do you remember that? Or vice president of internet service, those roles don't exist today. And that's why I hear companies say, "Oh, we got to have a vice president of artificial intelligence." I think what's going to happen is we use the internet today as just a mainstream traffic channel. It provides all capabilities, now we're able to move things up in digital formats. We can serve things up on our mobile phone just as easily as on our desktop, and a lot is just the mainstream use of the internet.
I think the same thing's going to happen here, is that what's really going to take place is that people are going to understand that this is a very powerful tool, but the humans still have to be involved in working with this particular tool. And it's going to allow for us to be better at what we particularly do, but we haven't even figured out how to organize ourself to leverage the tool. We're still going through that right now today.
[00:16:40] Ari Marin VO: What Bob is saying here… I think it does a lot to appease fears around AI.
Because it’s true – AI will change the way we work. It will bring in new jobs, and make some tasks… Less necessary. But change – it’s something we can adapt to. In fact, it’s even something we can enjoy!
So let’s be curious. Have a play. And find out how the tech can serve us. Trust Bob on this one: you can embrace AI, and stay safe with it.
[00:16:45] Bob Trotter: If you have a tremendous amount of IP in what you do, if your entire business is creating authorship, that's what Gartner does, we create research. So as a result, you're afraid, "Hey, wait a minute. If the stuff that we make a living on, which is research, goes into the public domain, then people aren't going to buy our research anymore."
Highly regulated industries, banking and financial services, much the same degree. You carry a lot of PI data, you carry a lot of customer data there, that obviously needs to protect it. So most of those organizations tend to be very cautious about it, and they have controls in place. And so what they do is they tend to think through a little bit, like for example, how available we're going to make this for our employees. And if we do make it available for our employees, how do we educate them on their proper use of it? A lot of people are updating their acceptable use standards, or how accessible is it when I go into the network to access it? So highly regulated industries that have PI data or certainly those that make a living on content creation, they're going to be a little bit more cautious, and rightfully so in terms of how they leverage AI.
[00:18:01] Ari Marin: Do you have any good tips to make the technology more accessible to everybody in the company?
[00:18:07] Bob Trotter: Well, I'm a believer that you can't hide from it. You can't just tell, "Hey, we're going to lock this sucker down and nobody's going to have access to it." Because not only are you missing on the opportunities for productivity, but I also think that inevitably it is going to be to the point where people are going to get access to it. If they can't get in through their network on their desktop, they're going to go in through their mobile phone, they're going to get access to it. So the tips that I usually talk about is have a discussion at the top of the organization that begins with what is our appetite, in other words, there's what I call proactive measures and then there's defensive measures. The proactive measures are what's our appetite and where do we think we can best use AI safely to enhance productivity or service our customers?
On the defensive side, where are we at risk? So if I look at our organization, where we have protected data, certainly customer data, or content, intellectual property, where are we at risk and what do we need to do to minimize that risk to our company and certainly to our stakeholders, like our employees and our customers? So I say, look at it from two fronts. Put together a small group of people that understand a little bit more about this and start to discuss what's your appetite and what's your risk tolerance, and then move into your use cases.
[00:19:24] Ari Marin: What about those people who just don't have the skills to maximize these opportunities with AI?
[00:19:32] Bob Trotter: I say that anybody should have the skill. If you can type, you can use AI. As a matter of fact, it doesn't even require that. Because AI can work off images, it can work off of voice fonts and things of that nature. I really think, to me, it's the same thing as simply saying, if people didn't have the skills to use the internet 30 years ago, I think everyone will be using AI in the future. Whatever timelines you've put in to developing the technology, add the exact same timeline to change management within your workforce. The change management is large, it's not as small. Because again, you have that fear factor, is this thing going to replace my job? I'm not sure exactly how to use this in my day-to-day operations.
You really have to invest an adequate amount of time to get people comfortable with using this technology, understand where there are concerns about risk associated with misuse of the technology, but then at the same point in time, get comfortable with using it in their day-to-day operations. And those are some of the things that most organizations are having to take into consideration, the change management is fairly large, and they need to understand that.
[00:20:34] Ari Marin: I still come across people who don't know how to type still, but yeah…
[00:20:38] Ari Marin VO: To each their own time, right?
But Bob - no doubt here, he is focused on the future. Which is why I want to ask him about AI regulation.
In the technology space, AI is making leaps but… The legal framework around it? It’s still a construction site. And in a few years things could look very different.
[00:20:53] Bob Trotter: There's no question. There's absolutely no question about it. The European Union has already got some tight regulations in terms of acceptable use of AI or how it can be used. The United States tends to be a little bit slower to start putting a heavy hand on policy. Part of that is because to be innovative, you have to be a little bit experimental on the edge. But the problem that I've seen out there is that in highly regulated industries, like financial services for that matter, if you're not sure what the guardrails are, you may not do anything at all. So it's the first time I've ever seen, and I think about true technology leaders like Elon Musk or Sam Altman who actually say things like, "Please regulate us, create the regulations." Rarely do you ever see business leaders asking to regulate it? A lot of people say, "Well, they're doing that because they're afraid of it. They're afraid of what it could become."
Yes, maybe, to a degree. I think they realize that if you're in a regulated industry and the regulations haven't been defined, you're almost afraid to invest a lot of money into it. If I'm going to invest two, five, $10 million in a technology that eventually someone says, "Hey, that's outside of the guidelines of what we're going to allow from a governmental standpoint," you're going to be a little bit leery. So right now, what I think is going to happen is the industry is asking for regulations to come in to set the parameters, and as those parameters start to be established, particularly in regulated industries like financial services, you're going to see just a dirge of new AI offerings that are starting to happen.
[00:22:27] Ari Marin VO: The conversation on regulation is far from over. We’re going to hear more and more about it in the next few years. And — you know — that’s a reminder too… That while AI has been around for more than a decade… And we’re gaining access to this technology… We’re just getting started.
[00:22:28] Bob Trotter: We are still at the beginning of a tremendous journey. AI has been around a little while, generative AI ground shifted things less than two years ago. But we are at such an early point in understanding what this thing is going to look like in the future, that I think for us to predict how to apply it and what the world is going to look like is very difficult from where we sit right now. When we get to a point when we're leveraging AI every single day in terms of the interactions that we're having, and we don't even think about it, we don't even call it AI just because it's part of our normal business operations, that's when we're leveraging it to the full potential.
But at this point in time, I don't even think we know the full potential of this opportunity. So if there's one thing I'd like to leave it at, this is the kind of technology that I think you are going to have to embrace. I think it's something that it's better to get involved with it even once you've identified your risk talents, so you can learn from the way you're experimenting around it. Sitting on the sidelines right now is probably not wise. So I do encourage most organizations, no matter what your risk tolerance is and no matter what investment level you have, it's probably better to get into the pool, at least to start the operations. But understand the way this thing's going to look five years from now, let alone 10 years from now, is going to be vastly different from what we're seeing today.
[00:23:43] Ari Marin VO: Ready or not, here comes AI. So whether you’re a CIO planning for the future, or an entrepreneur with a vision, consider Bob’s advice:
First things first – know your tech. AI tools can produce a large range of results. Take Generative AI: it’s very good at creating content and scaling authorship… That means it can take data and tailor it for specific needs. And that’s great to market your product to a new audience… But it might not be the right solution for… Your internal processes, for example. Different companies, different capabilities.
So before you dive in, ask yourself the right questions. Instead of “what should I do with AI?”; ask what can AI do for me?.
Define your goal. Figure out what data you want to collect; and where it’s going to go. If you partner up with an external organization to get started – what will you be sharing with them? And with the public? Cover all your bases; that will help you mitigate risks.
Having anxieties around AI is normal. For many of us, this is still foreign ground. But the more we get acquainted with this tech, the less of a threat it represents.
AI will be an integral part of our jobs… Our skills… And soon – you will use it without thinking. Remember when the GPS wasn’t your way to get around? Then, it became our day to day. AI is no different.
And one more thing: change takes time. So as a company, get together and discuss how you want to implement AI, little by little. Make sure no one is left behind… And keep an eye on what’s next. Because from regulations, to financial services and operations – AI is only getting started.
As Bob would say…
[00:23:43] Bob Trotter: At the end of the day, we will get this under control. The regulators will come in, they'll create the safety guardrails. But more importantly is that leveraging AI appropriately is going to make the human experience better. It's going to make us more productive as a workforce. It'll create new job opportunities. I actually think it'll be net neutral. The jobs that might be lost from it are going to be offset by the jobs that are going to be gained from it. And in the end, it's going to be so embedded in terms of what we're doing that we're not even going to call it artificial intelligence.
Ari Marin VO: I want to thank Bob Trotter for helping us hack the AI business mindset today. His trust and excitement for the future is something we’ll hold onto, whatever comes next. As for me… I’ll see you next week, for a new episode of In Good Companies. Speaking of which…
Ari Marin: Where do you listen to In Good Companies? Apple? Spotify? Well, now, you can check us out on YouTube.On the Cadence Bank Channel we’re posting full episodes and some exciting new bonus content. To find us: search Cadence Bank or click the link in our show notes.
Ari Marin: In Good Companies is a podcast from Cadence Bank, member FDIC, Equal Opportunity Lender. Our production team is Natalie Barron and Eydie Pengelly. Our executive producer is Danielle Kernell. This podcast is made in collaboration with the team at Lower Street. Writing and production from Lise Lovati. Sound design and mixing by Ben Crannell.