Why AI Literacy Beats AI Strategy: Digital Explorer’s Tony Moroney Reveals The Truth

[00:00:01] Tony Moroney: We’re moving into a world where it’s around intelligence orchestration. And some of those people will be people, some will be digital agents. And those digital agents will be doing their own reasoning and taking decisions and doing things. But as a leader, you’re going to have to orchestrate it all. And that challenges the very concept of leadership because I no longer have all the information. I’m no longer the most knowledgeable person at the top of the organization. I am actually dealing with people who are smarter and agents who are much smarter than me.

[00:00:28] Intro/Outro: Technology has transformed how we live, work, and connect. And behind that change are visionary people with bold ideas. Welcome to “The Bridgecast,” where host Scott Kinka explores hybrid work, AI, and our evolving future with tech’s brightest minds. Presented by Bridgepointe Technologies, the nation’s leading tech advisory firm. Experience a better way, the Bridgepointe way.

[00:00:58] Scott Kinka: With so many AI solutions on the market, how can organizations ensure they’re choosing the right technologies that deliver real business value? Today’s guest is at the forefront of helping enterprises navigate this AI-driven transformation. Tony Moroney is the principal at The Digital Explorer, where he advises global brands on digital disruption, innovation, and, importantly and pertinent to this conversation, AI strategies. He’s got over seventeen years of experience spanning industries like financial services, tech, and consulting. Tony has guided executive teams in harnessing AI and digital transformation to drive measurable business outcomes and create impactful customer experiences. Tony, welcome to “The Bridgecast.”

[00:01:36] Tony Moroney: Scott, thank you. Delighted to be here.

[00:01:37] Scott Kinka: How’d I do on the intro? Did we catch you from LinkedIn well enough?

[00:01:41] Tony Moroney: Oh, yeah. It sounded wonderful. You’re putting me on a pedestal now.

[00:01:44] Scott Kinka: Tony, tell us something beyond that, though, beyond the resume. Who’s Tony Moroney when he’s not shaping the future of enterprises for Fortune 500s?

[00:01:51] Tony Moroney: Well, as a Scotsman would say, I started my career in industry, worked in financial services, funny enough. About seventeen years ago, I moved into international management consulting. That had me working all over the world. Just before COVID, I came back to Ireland because I was traveling too much. And that brought me into academia as well as consulting and advisory, and from there into thought leadership. So I suppose where I operate is in the sphere of digital disruption, both from an advisory and an education perspective, and then I pull all of that together in terms of thought leadership. So everything I do is around digital disruption.

[00:02:23] Scott Kinka: And you, as you mentioned, you’re in Ireland today. We figured that out as we first got on the call. You’re in Dublin.

[00:02:28] Tony Moroney: I’m in Dublin, Ireland. Yes. So, the other side of the ocean.

[00:02:30] Scott Kinka: Born and raised? Yeah.

[00:02:32] Tony Moroney: Well, born in Kilkenny, about eighty miles from Dublin. But, yeah, I’ve lived in Dublin since, oh, I suppose, ’79. And, um, what do you do for fun? I love rugby. I love all sports, actually. I’m involved in martial arts. So I teach and I practice Japanese Jiu-Jitsu, usual social sort of stuff. So, yeah, plenty to keep me going.

[00:02:50] Scott Kinka: I love it. We’re going to jump right in, but I have to say this. I was in Cardiff. I used to have an office in Cardiff. I was in Cardiff the weekend of the Six Nations Championship a couple years ago between Ireland and Wales, where the championship, I don’t remember what year that was. It was probably, well, it was three or four years ago.

[00:03:07] Tony Moroney: Well, every second year, they rotate every second year.

[00:03:10] Scott Kinka: But Millennium Stadium is basically the modern-day Colosseum, and the place is just terrifying.

[00:03:14] Tony Moroney: Oh, it’s beautiful.

[00:03:16] Tony Moroney: And, you know, they can close the roof, and the Welsh love their singing. But the great thing about Cardiff is the stadium is right in the middle of the city. So you walk out, like, everything is beside you.

[00:03:27] Scott Kinka: It’s amazing. We’re having a pint at the Brains bar directly across the street, and you, you could literally throw an empty glass at the entrance of Millennium. It was amazing. Alright. Well, enough talking about rugby. Let’s jump into it. You know, AI, automation, the metaverse—I mean, things are going on at a faster pace than most IT leaders have the time to think about. In your recent articles, you pointed out a paradox. Right? When everything can be built quickly, what becomes valuable, I think, was sort of what you pulled out. Tell us a little bit more about that concept.

[00:03:56] Tony Moroney: If we think about it, on one level, people are arguing that AI, particularly generative AI, et cetera, is, you know, is a general-purpose technology. So if it’s a general-purpose technology, yeah, look, I believe it is. It’s going to be the same as electricity. It’s going to be the same as, you know, the Internet. But that means it’s table stakes and everyone else has it. So how are you going to differentiate? Because if we’re all using the same large language models and are all using the same embedded reasoning, well, we’re all going to end up with the same answers. So that will lead to a commoditization of many markets unless we think about, well, actually, what are the unique human factors that we’re going to bring to our business, and that is going to be used to drive how we use these models? But also, what is the ambition of our organization? And what are we actually trying to do for customers? Because the starting point has to be what are we trying to achieve with our customers, not what we’re trying to achieve with the technology. And my concern is that, look, there’s a lot of organizations blindly jumping into technology solutions because, on one level, you know, boards are putting executives under pressure. “Oh, you better be seen to do something with AI.”

[00:05:07] Tony Moroney: So AI washing is the new greenwashing. So they need to be able to say, “Oh, we’re doing something with AI.” But, ultimately, I think the real challenge is forget about the technology. Think about what is it you need to do for your business to be relevant and competitive in the future. And in that context, how would you get there? And in that context, what role does AI play in supporting that? And the problem that I see with organizations is, you know, they’re all grabbing large language models or generative AI or, you know, agentic AI. They all want agentic AI now. And in one of my articles, I posed this question, which was another paradox in terms of, “Well, whose organization is becoming more clever?” Is it the handful of companies that own the large language models, or is it the companies that are using the large language models? And that’s a really, you know, deep question that an organization should ask because it plays into the point you raised. Well, if it’s the former, so actually, the OpenAIs and these guys, they’re getting all the intelligence and becoming cleverer, and we’re all now utility users of their platforms. Where do we differentiate? Or how can we differentiate? Because we’re all going to get the same answers because we’re all using the same tools. And I suppose it’s an interesting question that I’m not sure many boards or many executive teams have stopped to ask themselves. And it’s probably because they have this fixation of being seen to do something as opposed to having a considered perspective in terms of why do we need AI in the first place. Because if you can’t answer that question, I would suggest, yeah, you’re doing some nice experiments, but that’s all.

[00:06:32] Scott Kinka: There’s a bunch of things that I want to pull out. I mean, one is the sort of AI for AI’s sake. Are we doomed to repeat cloud? Right? I think we, you know, part of this is in finding use cases. And we have—we’ve had quite a bit of conversation on this show, and I do, you know, in my day job, have a quite a bit of conversation around identifying use cases. But I want to unpack your thing about sort of the commonality of the answers coming out of large language models first. Is this where businesses just sort of need to focus their energy right now in terms of creating a little bit of AI muscle inside the business before they’re trying to solve problems? You know, number one at the end of the day. Like, is that a reasonable step in as opposed to trying to go do something really big? And then I think, partially, I think a corollary to that is the step to use case development around going a level beyond the LLMs and sort of just, you know, leveraging them for general intelligence while having specific intelligence around sort of RAG and agentic models that are specific to the business. But we’ll explore the second one. Let’s start with the first part of that, which was really around kind of almost businesses just focusing maybe on AI literacy first inside the business.

[00:07:38] Tony Moroney: Yeah. And, look, I would say, you know, most executives can spell AI, but it’s kind of stuck there. And the problem, Scott, is that if we think about, you know, the average age of your executive team or your board, they were born and were educated pre the Internet, pre the smartphone, invariably. And now we’re in a world where actually, you know, three years ago, we went talking about generative AI. Now everyone’s talking about it. We’re now talking about agentic AI. We’re talking about quantum. We’re talking about all these types of things. So at best, you know, the people leading our organizations are acquiring some knowledge of the different technologies that are coming at us. So the question really is, if we think about it as a technology, are we actually closing our mind down? Because most executives run scared when they think it’s a technology discussion. And for me, it’s not about the technology. The technology is a means to an end. It’s not an end in its own right. So if we think about the business and the business problems and the business challenges and the desire to be relevant and competitive in the future, that now puts any conversation around AI in context. So, yes, literacy is really important, but it’s not about teaching executives how to build algorithms. They don’t need to know how to build an algorithm. But they sure as hell need to understand, well, what does an algorithm do, and what type of outcomes will it drive in my business, and why have the safeguards in place to make sure that they are good outcomes? So it’s not about the technology because that’s just an enabler. It’s a tool. And I think this is the problem for organizations. You know, they’re being, you know, bombarded by tech companies, by, you know, consulting firms, whatever, and they’re coming at them, “Here’s a technology to solve all your problems.” Well, actually, as you alluded to, can we spend some time to actually talk about our problems in the first place? Because if we understand what we’re trying to do and we understand how we need to change the business to be able to achieve that, we now have a context for where AI fits. But we do need to invest in education because, you know, having a bland conversation around AI. Yeah. Well, for a lot of people, that’s not an engaging conversation unless we can actually bring it into, “Let’s talk about this in the context of our business.” What does it enable our business to do? How can it enable our business to achieve its aims? Let’s talk about it in that context. Let’s not talk about technology. Let’s not talk about large language models because, you know, most boards will be, you know, they’ll be going to sleep by the time you get to the end of that conversation. Bring it back to, let’s talk about the business, and let’s talk about what we’re trying to do here and how this could help us.

[00:10:06] Scott Kinka: Let me offer you an alternative. I’m curious to get your thoughts on this because we have this very same conversation with business leaders often. And very often, you’re like, “Well, what are you trying to solve with AI, or what are you trying to solve inside the business?” And the connection between the two things is very difficult to get, you know? Meanwhile, where, like, ChatGPT is the number one shadow IT app of all time, right, in terms of usage and the pace of adoption. Right? So it’s inside of these businesses now already. Setting aside all the security issues and privacy issues that unsanctioned use of AI inside of the business present. We’ll chat about that now, but instead, let’s set that aside for a moment. These end users are actually trying to use AI to do something inside the business. I mean, tell me about that as the source for a use case. Like, is that a place that you go with customers who are kind of like, “I need AI. I just don’t know what the hell I’m going to use it for”?

[00:10:56] Tony Moroney: Yeah. I’d always start with the question, “Well, why do you need AI?” Because I’m reminded of the old Westerns and, you know, the guys who come into town and you have the, you know, the, the black oil, and this solves every ailment. There’s a lot of that going on. So you really need to take it back to sort of say, “Look, if your problem is around customer experience, let’s unpack that. And where are we having challenges with customers?” If our problem is in terms of going to market, let’s unpack that and understand that. If our problem is around data, and look, let’s be honest, data is a huge problem because organizations, they’re not data-centric, and therefore, they’re going to struggle with AI. But let’s understand, actually, what our problem is, not apply one-size-fits-all solutions to every company. Because that’s not going to work, and it’s not going to help any organization get to where it needs to. It has to be about if the organization has clarity of its vision and its purpose, if it knows where it is today, and if it knows how it needs to change to achieve its ambition. Well, now we can have a meaningful conversation in terms of, “Well, what role AI could play within that?” And if that’s around creating a better customer experience, okay, well, how? What are the things we would need to do to create a better customer experience? Is it better insight? Is it, you know, real-time translation? Or, what is it that we want? And then we can see how AI can support the achievement to that. But just sort of saying, “Well, look, here’s one here. We can throw this into the pot,” because the issue you’re going to have is it’s going to be another technology overlay onto an organization, and your employees are going to be saying to themselves, “Is this management taking steps to basically rightsize the organization? And will I have a job?” And, you know, that never works out well in terms of technology implementations, as you well know, because people don’t embrace. They don’t adapt. They don’t use. How many organizations have you been in where, you know, there is loads of technology, but no one uses it? I was at one organization, and they said to me they were getting a particular CRM system. And I said, “Great. What’s that going to do for you?” And they said, “It’s going to give us loads of data.” And I said, “But you have loads of data today, and you don’t use any of it. So what’s going to be different?” They couldn’t answer the question, but someone had sold them a new CRM system.

[00:13:02] Scott Kinka: I mean, tech for tech’s sake. We go down these rabbit holes every couple of years. This isn’t sort of the new thing. Right? Now we’re, now we’re on AI. Help me out with this. You mentioned customer experience a minute ago, and, you know, we had quite a bit of chat about that in the open. It’s an area where we, you know, at Bridgepointe, spend quite a bit of time. And it also seems like the area where AI has been embedded, maybe without even calling it that to some extent, in the CX organizations of these businesses for some time. We’re often telling our customers, “Use what you got.” And they’re like, “What do you mean?” And I’m like, “Here’s all these things. If you have to go do some AI, there’s already embedded features in the platform that you purchased for CX or, you know, in your productivity suite. Like, let’s turn that on. Let’s figure out where we can baby step right now.” But do you have an opinion on why you feel like a lot of these use cases land in the customer experience category first?

[00:13:53] Tony Moroney: So I would argue, actually, a lot of them end up in the cost and compliance category first and foremost.

[00:13:57] Scott Kinka: Okay. Oh, well, tell me. Let’s, let’s explore that, but certainly address the customer experience-wise well.

[00:14:01] Tony Moroney: I think the issue there is that’s going to be table stakes. Everyone will do that. So being efficient and being compliant, well, you know, everyone is going to do that, and everyone will use AI. So that’s not going to differentiate your business. So unless you’re going to create new insights and value through AI, all you’re doing is matching your competitors. So I think CX is a really interesting one because, look, you, a brand is only as good as its experience. And if you can use AI to improve that experience, your customers are going to be more loyal to you because loyalty is very fluid these days, as you well know. So if we can create a really great customer experience, why wouldn’t your customers stay with you? And when I look at CX, you kind of say, “Yeah. Oh, look, you mentioned that there’s a lot of stuff in CX.” Yeah. There’s a lot of clunky stuff in CX. On one level, it’s a bit like, you remember, do you remember those scientific calculators we had at school where you used about five functions and you didn’t use any of the rest? So there’s a lot of that sort of things happening here as well. So they have systems that are capable of doing things, but they’re not using them. They don’t know how to use them. But equally, some of those systems are corralling them into paths that are not actually appropriate for what they really want to do. So I think part of the issue here, Scott, is to sort of say, “Well, okay. Let’s unpack CX and sort of say, what is it we need to be doing to excel and to really differentiate our experience vis-à-vis our competitors?” And how do we use AI? So that could be in a whole host of areas. Like, one of the most interesting ones, I think, is maybe if we went back a year or two years, you hired a new person into your call center. It might have taken six, nine months to train them. Well, now you might train them in a week because you got an AI agent in their ear prompting them real-time as to what they should do. And the AI agent may be translating a language that they don’t speak real-time for them as well. So this ability to really give a great service is up for grabs. And I think that starts to really change the dynamic in terms of customer experience. Because a lot of customer experience, as you know, is outsourced. But we’re not looking for labor arbitrage anymore. What we’re looking for is intelligent organization, and CX lends itself to that. And it’s a win for everyone because, you know, your customer’s going to be absolutely happy because you solved their problem. You gave them a great experience, or you helped them achieve whatever they want. And there was no sort of, you know, bouncing them around or saying, “Well, I don’t have that information,” or, “Actually, I need to transfer you to departments.” It’s there. It’s real-time, and it’s one and done. And the customer feels valued, that they feel you understand them, that you know them, that you can anticipate their needs, and that you can meet their needs. So CX is one of those areas, I think. If we—I think one of the most exciting things about AI is the ability for organizations to become truly customer-centric. Because a lot of organizations, they give lip service, “So we’re customer-centric.” And it’s in their annual accounts, you know, “We’re a, we’re a customer-centric organization.” Yeah. You’re not really because that’s not how you operate in practice. You don’t have the data to be able to do it, and you’re not organized or aligned around that. You have pockets of centricity, but you don’t operate as a customer-centric organization. Now, if we think about AI and we sort of say, “Well, if we want to be truly customer-centric, and we can sort out our infrastructure, so, you know, make sure we have all the data, the right data that’s interoperable, that we can, it’s available to everyone,” we can now start to move into a world where customer experience is real. It’s value-adding. It’s creating experiences that customers love. And that’s where I think the real prize is. So I would say, Scott, look, yeah. There’s things happening, but have organizations figured out that, actually, this could be the one where they can totally change their organization? No. I don’t think so. And I think, I think it is problematic in that, as with all disruptive technologies, the approach is, “Well, let’s add this in too.” And, you know, it’s not about adding in; it’s about rethinking your enterprise.

[00:17:52] Scott Kinka: So at a high level then, this is a matter of super-powering the humans in the customer experience operation rather than replacing them in a lot of ways. I mean, obviously, it depends on the business. And in some businesses, it may be a cost factor. But at the end of the day, what you’re saying is, from reading it back, you know, the opportunity to differentiate here is in doing more with the people that you have, getting them closer to the customer, not having them chase information, powering them with insights to be able to drive a much better engagement with the end client when they finally do get to a human, whatever point that is.

[00:18:25] Tony Moroney: And, look, Scott, you, if you, if you think about it as, you know, someone calls into the CX person, and they’re trying to navigate loads of screens, that’s not what we want them to do. We want them to be empathetic. We want them to be responsive. We want them to make the customer feel valued. So why not simplify all that and give them the information real-time so they can have the right conversation? So, so I think, though, the challenge will be, I think, particularly in the Western culture, this, you know, focus on quarterly results, the pressure point, “Well, well, can we just use this to take people out?” as opposed to, “No. Let’s use this to augment our people to your point so that our people are superstars.” And we don’t have them doing mundane work or, you know, chasing things they shouldn’t have to chase. But, actually, we have them in a situation where they have everything they need to give a great experience to customers because most employees want to serve their customers well. The problem isn’t their attitude. The problem is, you know, what supports them.

[00:19:24] Scott Kinka: Yeah. Completely. I mean, super interesting. Do you find—I mean, you work internationally. Most of the business, I mean, I have a history of working internationally, but, you know, in my current role, it’s largely domestic U.S. I find this so interesting around customer experience. Do you feel like consumer expectations are different culturally in Europe and kind of in the U.S.?

[00:19:46] Tony Moroney: Yeah. I, well, I think first thing is, look, what digital disruption has done is raised the bar in terms of expectations for everyone. But I think, yeah. Look. I mean, look. The Western culture is very individualistic. As you move more East, it’s more consensus, collective, whatnot. I think time horizons are also different. We tend to be very short-term and focused. Next quarter results, other parts of the world actually, no, they’re, they’re, you take the Middle East, it’s all 2030. What can we achieve in the next five, six years? You take other parts of Asia, “Well, what can we achieve in the next thirty years?” So we don’t operate on that basis. And part of what we’re now seeing with AI is that the large language models are becoming geographically relevant. So the Middle East is building large language models that are based on the Middle East. You know, China, the same; India, the same; Europe, certainly in France. They’re building large language models that are appropriate to their culture, their society, and to their value system. So I think that has yet to play out because we’re in early days of that. But, you know, for a whole host of reasons, including digital sovereignty, that is all on the cards today, and organizations all over the world are looking at this. And they’re having to, you know, you mentioned cloud. They’re having to be very strategic now in terms of, “Okay. Well, if we use the cloud, like, where is the cloud? What does that mean for our data?” If we’re going to use AI, “Whose platform are we using? Or whose large language model are we using? What does that mean for our data?” So this is actually in terms of our strategic decision-making; this is moving right up the board agenda. Whereas before, I think boards will be kind of, “Oh, yeah. Well, that’s just outsourcing, or it’s cloud, or whatever.” No, no, no, no. It’s actually this is actually at the heart of your organization now. And if you get it wrong, it actually has big consequences. So I think there’s a shift in terms of how organizations are starting to realize the game is different and that they need to have a considered position. And they also need to be thinking about this as a basis for competitive differentiation to the start of the conversation. There’s no point in us having the same models if we’re producing the same outputs because we might as well merge.

[00:21:52] Scott Kinka: It’s interesting. I’m going to bring those two things back together then because you talked earlier about, you mentioned kind of exactly that point around the commonality of the responses within an LLM, and we’re all depending on them. So how do you create differentiation leveraging them? And then, you know, just moments ago, you were talking about sort of LLMs sitting in regions of the world based around culture, based around the way that, you know, you would respond within those cultures, that kind of thing. So this is maybe more of just sort of a, I mean, certainly a hypothetical—but I’m asking you to kind of put your, your almost your ethical hat on here as we go. Does this provide us an opportunity to understand and overcome cultural differences globally, or do you think because the answers become sort of common to LLMs that will be almost more entrenched culturally in the regions that we’re in when every business is using the same LLMs and getting the same answers, and then you’re going to see the same thing in the Middle East, and you’ll see the same thing in Asia? What kind of risk does all of that present for us?

[00:22:51] Tony Moroney: Obviously, any market that you operate, you need to be culturally relevant in terms of anything you do. I think that we are probably, we probably are on the brink of big islands of AI, and they’re going to be geographic-specific. And if you’re not able to operate in that market on that basis, it’s going to become more challenging because, you know, a lot of this is down to, look. In an ideal world, the whole world would get on famously well, and we’d all share best resources, and we’d have great outcomes. And, like, that’s not the real world though. So the race going forward is, you know, AI is at the center of this race. And so AI, data centers, quantum, all these types of things. And the issue, so if you take Europe, for example, so Europe may be a little bit behind in terms of some of the innovation, but might be a bit ahead in terms of the ethics and whatnot. But Europe needs to address its own digital sovereignty. How can Europe actually, if it wants to protect its data and keep its data in Europe and make sure that everything that’s happening is relevant in a European context? Well, unless it’s very clear on how it needs to achieve that, it will be just like any other utility, and it’ll get whatever it’s given. So I think right around the world at a country regional level, these discussions are now happening in terms of, “Well, if we take it that the future is going to be different and it’s going to be an intelligent economy, what does that mean?” The economy and society is intelligent. “What does that mean in terms of what we need to have to safeguard, to build, and what value system are we applying to it?” Because, you know, value systems differ around the world. And I’ve done work in, you know, many parts of the world where the value system is very different. And their outlook, their temporal horizon, is very different. You know, it’s not three months. It could be several years. What they’re prepared to wait and get it right as opposed to, “Well, show me a fast result.” So I think all of these things are in play.

[00:24:46] Scott Kinka: I mean, it’s super, super interesting. And that’s a conversation we could stay on forever. Right? I mean, the whole kind of governance around AI, particularly in, like, I think, I find the whole EU experiment around this to be super interesting too. You know, when you’re like, “Well, I mean, AI in and of itself is also a means to develop very quick malware and leverage,” so, like, what happens when this depends on which side of the aisle you are? So I’ll just use general terms. The good guys decide, “We’re going to follow these standards,” but the bad guys don’t. And what are the good guys’ standards worth at the end of the day? And then it just becomes this; it’s almost the same kind of argument around the global economy and all of the other things that we have cultural differences on region to region. I don’t know that there was a question there, but I just find it so interesting to think about as we’re going through this.

[00:25:36] Tony Moroney: I think, Scott, you’re right. And, look, you know, the bad guys invest, if not as much, more, maybe, even. But this brings it back to from a governance perspective. If we don’t know all the answers because it’s still so fluid and it’s changing so rapidly, it needs a governance system that is principle-based and flexible at the same time. So the what are the underlying principles that we want to apply in terms of how we use technology in the business from a security perspective, from a ethical perspective, from a responsible use perspective? And those principles then need to be embedded into the organization. Now, I think there’s a growing awareness amongst boards and C-level executives that, yeah, actually, we need to get this right. But, like, I have a—there was a great paper the other day, and, basically, it was sort of saying, “Yeah, but the challenge now is going to be that these models are going to be so clever. They won’t actually tell you what their reasoning is. In fact, they might disguise it.” So you think that, “Okay, put that in a governance context. How are we going to manage that?” Because I don’t think I think one of the challenges—look, if we think of our organizations, they’re, they’re built on army-style command and control. But now we’re moving into a world where it’s around intelligence orchestration. And some of those, you know, people will be people, someone who will be agents, digital agents. And those digital agents will be, you know, doing their own reasoning and taking decisions and doing things. But as a leader, you’re going to have to orchestrate it all. And that challenges the very concept of leadership because I no longer have all the information. I’m no longer the most, you know, knowledgeable person at the top of the organization. I’m actually dealing with, you know, people who are smarter and agents who are much smarter than me. And my job is to make sure that the right level of intelligence is working together in a safe and responsible way that safeguards the organization so that we’re actually achieving great outcomes. Um, I’m not sure that organizations have worked this one through.

[00:27:26] Scott Kinka: No. No. Not at all. I mean, the other thing that occurs to me as we’re thinking about that, right, is the, you know, kind of the concept of, like, “History is in the hands of those who write it,” right, at the end of the day. So when we have autonomous agents working inside the business, and we’ve also got this idea that you’ve got kind of commonality of responses that are being generated in large language models over time that become the basis of our thinking, like, that’s way more the risk to me than the sci-fi version of the robots taking over the world, right, which is that, like, “History is written by OpenAI,” right? And they mean that, like, do you think about that risk as you’re consulting with businesses?

[00:28:03] Tony Moroney: Absolutely. I think organizations work on span of control. I think they need to work on span of intent. So what are we actually trying to achieve here? What is it we’re setting out to do? And then to start, understand through our humans and through our digital agents, or whatever you want to call them, are we actually operating to that, or are we doing something different? I can guarantee you, Scott, there are going to be court cases in the future where, you know, executives are going to be saying, “Well, I didn’t realize the technology did that.” And the issue is, look, the economists would say that humans are rational, and, you know, the psychologists would say we’re far from rational. In fact, we all do lots of things that are not in our best interest. Right? We drive past the gym on the way home and things like that. Right? But the behavioral economists would say, “Well, actually, we can predict just how irrational people are. We could take advantage of that,” and it has happened, you know, in the U.K. and financial services, et cetera. But if you now put that into the domain of AI, where AI actually can understand all these patterns and they can understand that, actually, customers are not doing what’s in their best interest and their parameters have been set to “Maximize profitability at all costs,” you can see what’s going to happen. And the issue is that by the time you find out, all of your customers have been impacted. It’s not like having a rogue employee who maybe you ought to pick up after a dozen cases or whatever, and they say, “Hang on. There’s something wrong here. Scott’s after being—what’s Scott doing? He’s outside the parameters here.” Well, actually, what were the parameters in the first place? Right? Because we don’t know because it’s, it’s opaque. And it’s not just one customer. It’s all our customers. So there’s a real challenge in this, as I see it.

[00:29:31] Scott Kinka: Wow. Tony, I have, like, fifteen more questions that I want to ask, but we have a social contract with our listeners that they can get this done while they’re on the treadmill. So maybe I’ll have you back, and we’ll figure that over time. So I’m going to rapid-fire some fun questions at the end. Alright. Here we go. Number one, what are you reading? Is there anything you’d like to recommend to our listeners?

[00:29:49] Tony Moroney: I’ve been doing a lot of reading around sense-making because I think it’s, I think it’s one of the issues that leaders need to actually get their arms around. I’m also reading the new book by Eric Schmidt that he wrote recently. It’s on AI and where it’s all going into the future. Fascinating book, and I’d recommend it to everyone. So, yeah. I mean, I, look, I, I read every day. I read eclectically, as you can imagine. And I have all sorts of reports, including reports from your own organization, but also reports, alerts, you name it. I, I’m, I’m an avid reader. So every day, I’m reading.

[00:30:20] Scott Kinka: I love it. Alright. Here we go. Next, I need a shameless prediction. This is loaded, obviously. It can be on anything, and it doesn’t have to be around AI or business. It could be around your rugby team. Doesn’t matter. But I want to look back eighteen months from now and see how right you were. So give me a shameless prediction for the next twelve to eighteen months.

[00:30:38] Tony Moroney: So, a half a dozen companies with one person in them that become unicorns.

[00:30:42] Scott Kinka: Okay. Interesting. Alright. I like that. That’s a quick one and a really quick answer. While we’re at it though, give me one thing about kind of AI that you believe very strongly that you think other people in the industry would push back on. Like, give me a controversial feeling that you have right now.

[00:30:57] Tony Moroney: I don’t think AI is about job displacement. I think it’s about augmentation. And I think, you know, if people don’t get their arms around that and start to rethink their enterprise and the role of people in it, it’s going to be a long, bumpy road and not necessarily ending up in a good place.

[00:31:12] Scott Kinka: Again, that’s a whole other episode. Right? You know what I mean? To talk about, but I think you’re right. Right now, I think the possibilities of AI bring out the worst in the “let’s get it in this quarter” part of Western economics. Totally agree there. Alright. Now, last fun one. Maybe it’s the robots taking over the world because we just mentioned it, but some dystopian future, depending on your favorite sci-fi, occurs. And, Tony, you’re alone, and you’ve got one app that still works on your phone. Only one. You get to choose which one it is. What’s your indispensable end-of-the-world app?

[00:31:46] Tony Moroney: Well, in the world we’re going to enter, there will be no apps. You will just talk to your AI, and anything you want, you can have.

[00:31:53] Scott Kinka: So that’s your answer. Right? Whichever your AI agent of choice is, that’s the app you’d like.

[00:31:58] Tony Moroney: It’ll do everything you want for you. Everything.

[00:32:01] Scott Kinka: That might be the most creative answer since “Flashlight,” as several partners have given us. Tony, I really appreciate the time. This was, um, super entertaining for IT leaders who were tuning in, aside from obviously connecting with Bridgepointe as we would hope they would do and asking “How do we get ahold of Tony?” How can they get ahold of you?

[00:32:18] Tony Moroney: Oh, I’m on LinkedIn. You find me on LinkedIn. Um, you know, I write prolifically on LinkedIn as well.

[00:32:23] Scott Kinka: Frankly, your writings were what got our editorial staff paying attention to you and then hooked this up. So, anybody, I highly recommend you check out Tony. He’s got great writing on the topic, and thrilled to have had you on the show. I hope we get an opportunity to do it again.

[00:32:36] Tony Moroney: Yeah. Perfect. Thank you, Scott. I really enjoyed it.

[00:32:39] Scott Kinka: Listen. If you made it this far, there must have been some good content, or you’re just a fan or a relative of mine. In either case, we appreciate you spending your most valuable asset with us, your time. We don’t take that lightly. We’d also like to say one more time that we’re appreciative of Bridgepointe Technologies and their belief and sponsorship of this show. We hope that if you or someone you know is thinking about your company’s digital transformation or simply the next IT project that you may not have the resources, budget, or time to get to, I’m sure that Bridgepointe, one of the country’s fastest-growing technology advisory and procurement firms, can help. Check out bridgepointetechnologies.com. Don’t forget the E on Bridgepointe, or simply reach out to me at skinka, s-k-i-n-k-a, @bpt3.net. Also, take a minute, please, if you would, to give us a five-star review on your favorite platform. It helps give us the visibility to reach other people like you. Thanks for listening to this episode of “The Bridgecast.” I’m Scott Kinka. And until next time, there’s a lot of noise out there in business and in life. Do what you can to be the signal.