Beyond Compliance: Building a Framework for Trustworthy AI

[00:00:00] Reggie Townsend: If GDP is some sort of measure—albeit adjacent—to describe that the people of the United States are doing well as a result of our economy doing well, then we will have missed the boat by a long shot in your organization. We’re dealing with a lot of that sort of thing at SAS as well, so this is just frustrating me.

When you’ve got senior-level folks who are able to produce a bunch—like, these are the highest token users, let’s say—they’re now able to produce at a level that suggests perhaps you don’t need as many junior folks to come in, right? What happens when those senior people leave? They go, but you’ve not built the pipeline necessary to sustain the organization. You’ve done a great job keeping the foundation model provider in business, and bless them—I’m all for their business. They’ll stay in business, but you won’t because you’ve hollowed out your employees.

[00:00:55] Scott Kinka: Technology is changing faster than ever, but the most important conversations aren’t about the tools. They’re about the ideas and the people shaping what comes next. On The BridgeCast, I sit down with the leaders driving change, from AI and cybersecurity to the infrastructure powering modern business. Together, we explore how technology moves business forward, what they’re seeing, what they’re building, and what it means for the rest of us.

I’m Scott Kinka, and this is The BridgeCast, presented by Bridgepointe Technologies.

Welcome back to The BridgeCast. Today, I’m joined by Reggie Townsend. He’s the Vice President of AI Ethics, Governance, and Social Impact at the data and AI company SAS. Reggie previously advised the US Department of Commerce through his seat on the National AI Advisory Committee. He also currently serves on the board of EqualAI, and we’re going to get into that today.

Over the course of his career, he’s led teams delivering data-driven outcomes across consulting, customer success, and cloud analytics, including work in health and life sciences where the stakes obviously can be especially high. Today, he sits at the center of a global conversation about how organizations can use AI to drive better business outcomes while importantly minimizing harm, protecting trust, and serving people more thoughtfully.

Reggie, welcome to The BridgeCast.

[00:02:23] Reggie Townsend: Thank you, Scott. I appreciate you having me here. Thank you.

[00:02:27] Scott Kinka: I always like to start with: How did we do on our AI-generated description of your career? How was it?

[00:02:34] Reggie Townsend: I think there is sufficient data out there now to have trained the models, so you did well. Thank you for that. I’m curious which model you may have used to pull that out.

[00:02:45] Scott Kinka: You know, I’d love to find out. Our editorial team does that, and they change them around, so I’ll follow back up with you on that. For those of our listeners who aren’t currently asking their model of choice about Reggie while listening to this episode—Reggie, would you just give us a background? Tell us about yourself. How did you get here? Where do you live? Where did you come from? Give us some backup.

[00:03:05] Reggie Townsend: Yeah. So, I was born and raised in Chicago, and I still live in the Chicagoland area. I’m all things Bulls, and all things that make Chicago great—the restaurants and all that good stuff. For those listeners who have not been to Chicago, come check us out—but in the summertime, because winter is what I call “character-building season.” We’ve just come out of character building, and now we’re coming into the fun time.

But seriously, I’ve been in the tech space for probably a little longer than I might care to admit at this point in my career. I’ve worked with some really small names you’ve probably never heard of, like Motorola, IBM, and Sun Microsystems, and I joined SAS about eleven years ago now. I was coming out of a consulting role and really saw an opportunity to get aligned with the leader of big data at the time. That was kind of the whole focus then. As a data and AI company, I’ve been much more focused on the data side of it historically, and here lately, I’ve been really embracing the AI side of it. I saw an opportunity, took it, and haven’t looked back since.

By the way, eleven years is as long as I’ve been with any company, so we have to be doing something good for me to stick around that long.

[00:04:27] Scott Kinka: Chicagoland your whole life?

[00:04:29] Reggie Townsend: Off and on. You know, family roots tend to bring you back, and so every time I go away, I end up coming back for some reason. I’m still here, and my wife and I entertain the idea of being somewhere else during every character-building season. We’ll see if that ever materializes.

[00:04:51] Scott Kinka: Reggie, I was in Chicago for a meeting and a training that I had to do with a supplier partner of ours who’s well known in the Chicagoland area. You can probably make the connections there in distribution. It was during the polar vortex when that happened a couple of years ago, and when I stepped outside, I had never felt anything like it in my life. So, I had my character built in about twenty-four hours there. I can’t imagine an entire winter, but I’ve been there in the summer as well, and I fully agree—it’s a beautiful place to visit. Love that.

Could you just give us two quick sentences on SAS for those who aren’t familiar with the company, just so they can orient your background?

[00:05:26] Reggie Townsend: Yeah. So like you previewed, we’re a data and AI company. We’ve been in business this year for fifty years, so we’ve been doing AI for longer than a lot of people have been alive—just to keep that in context. We grew up, of course, on the predictive analytics and machine learning side, and now, obviously, we’ve gotten into more of the generative side, and we can get into some of that if you like.

We serve a lot of organizations in the areas that really impact daily life, like financial services. If you’ve ever gotten a credit card alert saying that they don’t recognize you in Location A and think you should be in Location B, that could very well have been a consequence of some of the work that we do with major banking organizations. We do a lot of work with the public sector around the world, and a ton of work with healthcare and life sciences organizations. For example, all drugs that get cleared through the FDA here in the United States are a function of having to get validated using SAS. So, we’ve been around for a really long time, having a significant impact in areas that affect people’s lives. We’re a bit of a sleeping giant.

[00:06:40] Scott Kinka: I love that. Well, speaking of people impact, your title is a mouthful: Vice President of AI Ethics, Governance, and Social Impact. What does that mean, and why is it important?

[00:06:55] Reggie Townsend: You know, I kicked around that title for quite some time. I’ll tell you, Scott, I almost succumbed to the allure of abstracting it and just doing something kind of flowery. But when I thought about it, abstracting away the ethics, the governance, and the social impact would do a disservice to those distinct parts of the role. Each of those areas has a specific meaning.

I know you probably want to get into the heart of what ethics is and how that shows up in an AI context, but that is a distinct discipline, as is governance. When I talk about governance, I’m talking about scaling our judgment. How do we do that in ways that are meaningful in an AI context? When we think about social impact, we look at where AI is intersecting with society, and whether there are positive or negative stories at that point of intersection.

We’ve got leaders on each of those teams really focused on those specific areas. We throw in advisory work, all of our regulatory work, and accessibility work—I don’t want to forget about that. I have a team of leaders under me that focus on all of those critical areas, and I just thought it would be a disservice to their leadership to abstract away the important work they’re doing. That’s how I ended up with a mouthful.

[00:08:27] Scott Kinka: I love that. I think a lot of the conversation is moving there, and I’m glad that you answered it that way because the questions I have lined up are sort of around juxtaposing those things against each other. They’re not always perfectly aligned, which we’ll talk about in a moment.

What struck me when I did my homework and checked your LinkedIn is that when people see phrases like “ethics” in a title, they assume those individuals come from academia or policy. But you have a technical background. You’ve been a project manager and a product manager at the kinds of major tech companies you mentioned. Bridge that for me: What got you from practical, commercial uses to driving AI ethics from a practical lens rather than an academic one?

[00:09:19] Reggie Townsend: The origin of my participation in this space really sits with curiosity. Many years ago, I was trying to understand what this idea of artificial intelligence truly was. Out of that curiosity, additional questions were sparked. Fortunately, I’m surrounded by a bunch of really smart people here at SAS—a lot of PhDs in this space—and I just started asking questions.

Some folks turned me on to this idea of how AI is showing up in society, which was a burgeoning space at the time referred to as “responsible AI.” As I started to explore more, one of the things that quite honestly scared the hell out of me was realizing that if done wrong, this could turn out really poorly for a lot of folks. I didn’t like that prospect, so I decided that rather than sitting on the sidelines, I was going to jump in and see if there was anything I might be able to do.

Because of my commercial background, I think what’s unique about my perspective is that I’m able to come into this conversation with an understanding that you can do well and do good at the same time. They don’t have to be mutually exclusive. I accept that organizations have to make money, and at the same time, we should find ways to simultaneously help people. That orientation matters. If you focus purely on the commercial side, it takes you down a path where everything must be strictly efficient and every single dollar needs a high margin associated with it. Unintentionally, one might not keep their eye on the real purpose, which is that people matter. Profit matters because people matter.

My background focused on figuring out ways to earn a dollar, maintain trust, and ensure we’re continuing to live out the values we say are important. That approach struck a nerve with the folks who needed to listen at that given point in time.

[00:11:52] Scott Kinka: I love that answer. It’s funny, the next question I had sketched down here was actually just answered by you: How your commercial background allows you to see AI risk differently than a traditional ethicist, academic, or policymaker. It really comes down to that phrase: You can do well and do good at the same time. That’s a great dovetail to the next question. When people think about the broad-based categories you’re covering—all the elements of your title—they often equate ethics and governance with compliance and reputation management. They view it as a defensive function. Let me ask a different question: What are leaders missing if they are thinking about AI ethics purely as a defensive mechanism?

[00:12:45] Reggie Townsend: It complicates the definition. I’m a big etymology person, so words matter to me, and I like to dig into their derivations. When we talk about ethics here, Scott, we’re really talking about social consensus. I know there are all sorts of schools of thought around this, but to boil it down: What do we as a collective believe we owe to one another? What do we as a collective believe is good, bad, right, or wrong? That’s where you start to intersect with personal moralities.

At an institutional ethics level, we’re talking about the broader society and the relationships we have to one another. In that regard, I think the confusion in the conversation lies in thinking that ethics can be defensive. It can’t be offensive either, because it’s relational, contextual, and it shifts. The concept of systems came after the concept of ethics, but ethics respects this notion that, systemically, there are bonds and connections. If you pull one thread, it alters another part of what has been woven.

[00:14:10] Scott Kinka: Yeah.

[00:14:10] Reggie Townsend: And so, it just understands that. As a consequence, what is right today could very much be wrong tomorrow, so it inherently respects the notion of time. All of that is very important in the kind of work that we do because it defies the notions of rigidity and certainty. It necessarily puts you in a place of nuance, which I know is very difficult for us in 2026. It forces us to exercise judgment in ways that respect the current moment while preparing for when this time moves on. If we have enough humility, then we’ll move with the times, holding the view that all people are still connected. To add a bit of a personal editorial note: in doing so, we want to make sure that we do what is proper and best for as many people as we possibly can.

[00:15:24] Scott Kinka: I want to explore both the “do well” and “do good” categories. We’ve focused on doing good, but I want to pivot over to the “do well” portion because I think business leaders are tracking with this but wondering how to apply it. Let’s stay here categorically for a moment. When I engage in responsible AI conversations, you either have someone who gets it or someone who goes, “Well, it’s just technology. We don’t have ‘responsible word processing,’ so what are we talking about here?”

My answer very often is simple: At the end of the day, AI is informed by information delivered via the internet and the treatise of human thought, which is based on the people who wrote it—and maybe not based on real history. If someone says to you, “I don’t get it. Why responsible AI, and what’s the risk?” what is your answer?

[00:16:27] Reggie Townsend: At SAS, we talk in terms of trustworthy AI. Responsible AI—and I know I am absolutely in the semantic weeds right now for people—

[00:16:38] Scott Kinka: That’s good semantics. Go into it.

[00:16:40] Reggie Townsend: Because this notion of “responsible” doesn’t have a good counterfactual that people willingly opt into, we run into trouble. As an example, no one is going to say, “Oh, yeah, sign me up for irresponsible AI.” Right? That’s not a thing. This is also where we have to start talking about politics and the influence of political thinking. When I talk about politics, I’m talking about influence, not necessarily a Republican vs. Democrat sort of thing. The broader global politics around the term “responsible AI” is really steeped in the safety and “doomer” conversations. Who wants to be unsafe? But it gets rooted in a Luddite conversation, and it becomes problematic because people come to the table with predetermined stances based on those roots.

Coincidentally, when you look at a lot of the governmental conversations with respect to AI, a lot of it is framed around trustworthy AI. You can look at initiatives that came out of the first Trump administration, the Biden administration, the UN, the EU, or across Asia—it’s the same thing. They’re all talking about trustworthy AI because they recognize that this is fundamentally a conversation about whether people will invest their trust in AI systems.

Now, we can get into whether you actually trust the technology itself, but let me get to the point you were making about it just being technology. It’s completely agnostic; we don’t have responsible word processing, so why should we have responsible AI? Well, AI is not word processing. To shorthand it, AI is about automated decision-making. We’re talking about decisions happening by the dozens, hundreds, or thousands every minute on behalf of you and the rest of society.

If you understand that AI is a formulation of data, models, and decisions, then you have to ask: What’s the source of the data? Whose data is it? How was it derived? What’s actually in it? Is that data biased? The answer to that last one is always yes, but the question then becomes biased in what way? When we put that data into a model, is that model going to treat that data in such a way that it creates the expected outcomes? And oh, by the way, what are the expected outcomes, and what is the intended purpose? That is the complete lifecycle when we talk about AI.

So, why be responsible, trustworthy, or ethical with it? It’s because we are automating conversations and workflows every single day.

[00:20:14] Scott Kinka: We’re kind of doing cloud all over again, right? Leaders say, “Go get some AI,” and then it gets driven down to the IT department, which is trying to figure out use cases for the rest of the business that they might not fully understand. Then they meet with a consultant, and the consultant says, “Before we start turning knobs, we need to start with governance,” which is bound up in everything we’re talking about. Governance is the business’s decision on how to apply everything you just talked about practically. What does a good AI governance policy look like in a mid-market business that is just looking to improve processes or increase employee productivity? Practically, what’s happening there?

[00:21:00] Reggie Townsend: Practically, governance in an AI context is very much like governance in every other context where we put processes and policies into an organization—we’re just adding the flavor of the day.

We can talk about frameworks. We have a framework we refer to as a “quad” because it’s built on four pillars. It says we’re going to start with an understanding of the potential cultural implications of a given technology. A lot of folks underestimate the effect of normative behaviors in their organization. We rationalize it as, “This is the way we do it, it’s what we’ve always done, and it’s how we’re going to continue to do it.” Well, AI is a significant disruptor, and it necessarily requires us to rethink how we are organizing. If we are going to do certain kinds of work, what will the workflows look like going forward? And oh, by the way, what’s the psychological impact on the employees who are inheriting this new capability? There’s a whole distinct conversation to be had there.

That touches on the next pillar, which is operational dynamics—the literal effect on workflows. Another piece is the regulatory aspect: What is it that I have to do, versus what is it that I aspire to do from a regulatory perspective? Right now, that is a literal patchwork around the world.

The final pillar is oversight. Given all of these new dynamics in the organization, where does accountability sit? Where do monitoring and observation live? How are decisions going to get made at the frequency and pace required now, given the speed of technological change? And do we choose to accept that pace of change, or do we want to erect filters, borders, or gates that will govern the rate of adoption? There’s only so much change an organization’s nervous system can take.

That is the entire ecosystem of governance when we talk about AI. You can start with any of those pillars, but my medical analogy advice is always: Don’t try to cure cancer on day one. Let’s just find where the Band-Aids are needed and put them on. I always advise folks to think about it from those four pillars initially, and there’s a ton of work that we and others have done with similar frameworks.

[00:23:54] Scott Kinka: When you get outside of personal productivity and model use by employees—which is actually my next question—and you talk about codifying something into the business, I love what you just said. We’re always telling people to solve the most boring problem in the business first. I love your point about not trying to cure cancer immediately.

[00:24:14] Reggie Townsend: Yeah, I call them “pearls.” What are the things that are proven? We know we’ve done them because we’ve done them a ton of times. They’re repeatable, repetitive, and laborious—meaning, “Man, I don’t like to do this stuff, and nobody else likes to do it either.” Look for those pearls and go “AI-ify” that stuff first. Get some early wins and understand what the internal motions are inside your organization to solve those sorts of things. Then we can start to expand and scale. A lot of organizations get into trouble when they want to immediately stand up massive committees and make grand proclamations. It’s very, very difficult to be successful that way.

[00:25:02] Scott Kinka: This is The BridgeCast, proudly sponsored by Bridgepointe Technologies. At Bridgepointe, we deliver innovative technology solutions tailored to your business needs, whether it’s AI, network, cybersecurity, cloud, colocation, communications, customer experience, or managed IT. Partner with us to simplify your technology and drive your success. Visit bridgepointetechnologies.com to learn more.

Reggie, it’s interesting when we talk about productivity, the balance sheet, and how that moves forward. A lot of companies’ early focus on AI is just saying, “I’m going to make public models available to my employees to do things better, faster, cheaper, and be smarter.” I wonder about the way we measure productivity today, which is still, in my mind, a very post-industrial, post-WWII model of “how hard do you work and how many hours do you show up?” Is part of the governance and ethics conversation realizing that businesses genuinely have to rethink how they measure productivity?

I’ll give you an example of what I’m thinking. How do we measure the work product of someone from a younger generation coming in, writing the same number of lines of code in two hours, and going home, compared to someone from an older generation who needs a day and a half to generate those same lines of code but worked incredibly hard over the course of two twelve-hour days? Are we culturally prepared to address that potential concern?

[00:26:41] Reggie Townsend: The short answer is no, we’re not prepared yet. With your example, maybe we should even evaluate if “lines of code” is the right metric to begin with. This is the exact cultural matter I’m referencing.

A few years ago, when I was doing work with the White House, I brought up the idea that, on a macro level, our economy is evaluated for the most part by GDP, right?

[00:27:11] Scott Kinka: Right, mm-hmm.

[00:27:12] Reggie Townsend: And it’s supposed to be the definitive measure of success, well-being, and all things associated with economic health. Of course, we have other metrics like consumer sentiment, but GDP is the bedrock. The frailties of GDP as a accurate barometer for societal success are long-documented, so I won’t go into that. But imagine for a second that GDP is just a function of output. To your lines of code analogy, if we can output more, faster, with the use of AI agents, why wouldn’t we do it if GDP remains the primary target?

There are all sorts of tie-backs that the government has when evaluating companies in that regard, and we’ve built a significant industry around this concept. So you’re right—if we can just get more output as a result of this AI, what does that really mean to the people who used to produce it? If GDP is used to describe that the people of the United States are doing well as a result of our economy doing well, then we will have missed the boat by a long shot.

Bringing this back down to the organizations your listeners care about: inside your organization—and we’re dealing with this exact thing at SAS as well, which is why it frustrates me—when you’ve got senior-level folks who are able to produce massive amounts because they are the highest token users, let’s say, they are now able to produce at a level that suggests perhaps you don’t need as many junior folks coming in, right? What happens when those senior people eventually leave? They go, but you haven’t built the pipeline necessary to sustain the organization. You’ve done a great job keeping the foundation model provider in business—and bless them, I’m all for their business—but they’ll stay in business while you won’t, because you’ve hollowed out your own workforce.

Organizations are now having to wrestle with how to bring new people into the company at the proper pace. Crucially, since we no longer need them to just come in and bang out raw code, maybe the skill set we actually need from them is much more akin to management. These younger folks will be managing an array of agents that are producing the necessary output.

We still have to deal with the question of whether an organization can actually grow at a pace that demands the sheer volume of technical capabilities we can get out of these models. We still need to wrestle with the idea of workforce shrinkage within organizations and what kind of deleterious effect that might have on society more broadly. We can talk about new entrepreneurial opportunities, which are fantastic questions, but these are fundamentally matters of ethics and governance.

When people question why this is important, my response is that these are the exact strategic conversations that happen in this space. Unless you have a person or a team dedicated to these kinds of dialogues within the organization, you’re going to miss the boat strategically in the next five, seven, or ten years—if not sooner.

[00:31:18] Scott Kinka: I love that. I think it works in the other direction too. It was suggested on a previous episode with Elaine Varelas—we had a great chat—and she brought up a situation that was the converse of the one you just mentioned. She noted that a business might say, “I’m just going to hire the kids because they are already prepped on the tech.” But then the problem is they don’t even know what to ask the model because you’ve cleared out all the institutional knowledge in the business. It’s all completely wrapped up in that.

I have one more macro-level question before we jump into some rapid-fire ones. You mentioned the White House and spending some time there. I could easily spend an entire forty-minute conversation with you just around compliance, but let me ask this at a high level: AI regulation is increasingly fragmented across regions. You have the EU AI Act, emerging US frameworks, and then you have things happening in governments that we don’t trust. How should CIOs think about designing an AI governance model that is appropriate, but also durable across varying jurisdictions? Is this ever going to settle down, or are we just handcuffing ourselves while letting bad actors run rampant? How should a CIO approach this?

[00:32:33] Reggie Townsend: Yeah, I think it will settle. We have a little more transition in front of us, but I think it’ll stabilize. The way I advise senior-level leaders is to look at what is common across the different frameworks within the specific markets you are pursuing. If you are a global organization doing business in Europe and parts of Asia, a lot of the text associated with either their pending or active regulatory language is relatively consistent. One group might call it Gala apples and another might call it McIntosh apples, but they’re still apples, right?

What we’ve done with the quad framework I mentioned earlier is move to a layer of abstraction. When we talk about culture, operations, regulations, and oversight, allowing organizations to operate within that kind of construct puts them in the best possible position to adhere to changes as guidelines solidify. Obviously, you need to keep a close eye on specific legal definitions and localized mandates.

For those listening, just this week it looks like the EU is pushing out the high-risk expectations to 2027. That gives a little bit of a reprieve in that regard, but smart organizations would do well to continue building that internal muscle. Even if you don’t feel the immediate need to appeal to regulatory expectations, you can pull excellent, responsible innovation practices from these activities. We have certainly benefited from going through these exercises ourselves in terms of trying to think differently about certain processes, and it has actually generated some great product ideas. I would encourage people to take advantage of that.

I’ll also say to keep a close eye on the advancement of foundation models. We’ll start to see more applications built on top of them that you’ll have to monitor. From a foundation model perspective, we’re starting to see the US react with a different posture than we saw over the last couple of years. We may end up doing the right things for the wrong reasons, but we will likely end up with some global coalescing around regulatory expectations as a consequence of model development.

[00:35:27] Scott Kinka: Awesome. I could continue this conversation forever; I’m thoroughly enjoying it. We’re running up against our time, so I’m going to hit you with some rapid-fire questions, if that’s okay.

[00:35:36] Reggie Townsend: Okay, go for it.

[00:35:37] Scott Kinka: These are not traditionally rapid-fire style questions, so you’re going to have to figure out how to answer them quickly. What do most leaders completely misunderstand about responsible AI today?

[00:35:47] Reggie Townsend: They see it entirely as a conversation about bias and intentional harms, and I think that misses the boat in terms of the massive wave of unintentional consequences that could be mitigated if we just took a moment to look.

[00:36:09] Scott Kinka: Got it. And what is the biggest mistake—or perhaps the biggest missed goal—when businesses treat AI ethics or governance as a mere compliance exercise instead of a cultural or leadership issue? What are they leaving on the sidelines?

[00:36:28] Reggie Townsend: They’re missing the opportunity to accelerate and generate at a pace they’ve never been able to accomplish before because they are stuck remediating harms that could have easily been avoided.

[00:36:40] Scott Kinka: I love that. In ten years, when we look back at this moment, what will leaders regret not paying more attention to today?

[00:36:47] Reggie Townsend: That people are the ultimate reason that profits matter.

[00:36:50] Scott Kinka: Home run on that one. All right, let’s have a little bit of fun. Give me one bold prediction. It can be about AI governance, or it could be about a Chicago sports team. Just give me something we can look at over the next eighteen months and check your work.

[00:37:05] Reggie Townsend: I would love to say my Bulls will get an NBA championship, but that ain’t happening in eighteen months.

I do stand by my prediction that we’ll do the right things for the wrong reasons regarding regulatory structures. Ongoing model development is forcing that conversation, and enough governments want to get out in front of cybersecurity concerns primarily. That momentum will lead us down a path of having true global consistency around regulations.

[00:37:36] Scott Kinka: Got it. Give me a contrarian hot take industry-wide—something where people are going to say, “Reggie said what? I don’t agree with him.”

[00:37:43] Reggie Townsend: The so-called big tech leaders who get dragged quite a bit in the media? I think most of them generally care about human outcomes and have good intent. That will be my contrarian view. I believe they have good intentions, but good intentions weaken under pressure. When you’ve invested hundreds of billions of dollars, it creates immense pressure and a lot of perverse incentives. I don’t think they’re the villains they appear to be, but the structural incentives are simply outcompeting their personal values.

[00:38:21] Scott Kinka: What I love about that answer is that we spent this whole time talking about the defensibility of ethics and governance, and at the heart of it, Reggie Townsend believes that at the core, most people are good. I dig that.

[00:38:32] Reggie Townsend: Yeah, at the heart.

[00:38:33] Scott Kinka: Okay, what are you reading right now? Business, leadership, or it could be complete fiction—what’s on your nightstand?

[00:38:40] Reggie Townsend: Right now, I am making my way through Jonathan Haidt’s The Anxious Generation. I’ve got a kid who is right in that demographic and exhibiting a lot of those same conditions discussed in the book. I’m fascinated by the reality that he’s part of a much larger cohort than I ever realized.

[00:39:01] Scott Kinka: That’s amazing. It’s a heavy time right now; we’re contending with a lot technologically and emotionally, so that’s super important.

Now, one fun question before I let you go. It’s the end of the world—apply that in whatever way you want: zombie outbreak, apocalypse, post-nuclear, whatever. Either way, you’re in some dystopian future alone. You have your phone, and only one app still works. Which one is it?

[00:39:30] Reggie Townsend: I might be a prisoner of the moment because this is a relatively new app for me, but it’s called Fitbod. It’s an AI-generated personal training regimen. If it’s the end of the world, I might as well go out strong, right?

[00:39:46] Scott Kinka: Well, you know, sometimes people wonder what the end of the world will actually require, and in a zombie apocalypse, you’ve got to be able to run.

[00:39:54] Reggie Townsend: Yeah, I’m going to run fast and knock a couple of them over.

[00:40:00] Scott Kinka: A good friend of ours whom you may know from LinkedIn, Neal Ford, has been on the show a couple of times. He says the best thing to have in a zombie apocalypse is a slower companion.

[00:40:11] Reggie Townsend: There you go! Although they are a 360-degree bunch, right? They come from every direction.

[00:40:17] Scott Kinka: Well, Reggie, we’re going to end it there. For listeners who want to learn a little bit more about your work at SAS or connect with you, what’s the best way for them to make that happen?

[00:40:24] Reggie Townsend: Well, of course, find Reggie Townsend on LinkedIn. From a SAS standpoint, visit sas.com/responsibleinnovation.

[00:40:31] Scott Kinka: I love that. Thank you. This was a great episode. Looking back, I can tell you this has been one of the most stimulating conversations I’ve had, and I really appreciate your time.

[00:40:42] Reggie Townsend: Oh, shucks. Thank you, man. I’m happy to come back; we can double-click on a ton of these topics.

[00:40:48] Scott Kinka: We would certainly love to do that, and hopefully, we’ll catch you on a future episode.

For those of you who are listening, that is a wrap on this episode of The BridgeCast. If this conversation gave you something to think about, please hit subscribe and leave us a five-star review. We love all the kudos, and we promise we won’t let it go to our heads. Share it with your team. We’re super excited, and we’ll catch you soon on another episode of The BridgeCast.

Listen, if you made it this far, there must have been some good content, or you’re just a massive 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 how appreciative we are 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 a upcoming IT project that you may not have the internal resources, budget, or time to get to, you’ll reach out. 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@bpt3.net.

Also, please take a minute 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.