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A conversation with Taran Lent, Chief Technology Officer at Illumia

“Great companies, you’re never deciding between a great idea and a bad idea. You’re looking at 20 great ideas that all have merit and you can do three.” – Taran Lent

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Taran Lent, Chief Technology Officer at Illumia, about how his organization moved from individual AI experimentation to enterprise-wide capability. Lent describes building an “enablement task force” that deliberately avoided becoming a governing bottleneck, instead creating conditions for employees to play, learn, and share what worked, before this year’s push to elevate personal AI habits into shared team and company-wide skills, standards, and vetted tools. He talks about using AI as a contrarian thought partner and even having it grade his own interviewing and meeting behavior, while cautioning that transcripts alone miss tone and body language. The conversation also covers the governance side of scaling AI, including a stakeholder review process for new tools and skills, the four-to-six-week approval timeline for new vendors, and guardrails to prevent incidents like an unauthenticated internal dashboard. Lent connects this to Illumia’s recent merger of Transact and CBORD, crediting a shared “humble, hungry, smart” culture for making the integration smoother, and closes by arguing that judgment, creativity, and human discernment remain the differentiators as AI adoption accelerates.

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This episode is part of the Facilitation Lab Podcast. See all episodes

Show Highlights

[00:00:00] Introducing Taran Lent And New Friction
[00:02:30] AI Loses Its Social Stigma
[00:06:00] Using AI As A Contrarian Thought Partner
[00:09:30] Having AI Grade His Own Interviews
[00:14:00] Building An AI Enablement Task Force
[00:20:30] Zach Kass On AI Working Invisibly
[00:23:30] Traffic, Tragedy Of The Commons, And Self-Driving Cars
[00:29:00] Vetting And Governing New AI Tools
[00:34:30] AI-Assisted Code Reaches 90 Percent

Taran Lent — LinkedIn
Illumia — LinkedIn
Voltage Control

About the Guest

Taran Lent is Chief Technology Officer at Illumia, the company formed from the 2024 merger of Transact and CBORD. He describes himself as an engineer by training who has spent his career building and scaling technology companies, and at Illumia he leads engineering along with security and compliance responsibilities. Lent talks about designing tailored interviews and using AI as a feedback partner on his own leadership behavior, and about steering his organization’s shift from individual AI use to shared, enterprise-wide AI skills and tooling. He frames the recent merger as a test of culture, emphasizing that Illumia looks for people who are “humble, hungry, and smart.”

Transcript

Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align and how to move forward when the path isn’t clear. That friction, the human side of change is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Taran Lent, Chief Technology Officer at Illumia. Taran is an accomplished entrepreneurial leader with a proven track record in building and scaling technology companies throughout his career. Welcome to the show, Taran.

Taran Lent: Hey, it’s great to see you, Douglas. Thanks so much for the opportunity. Looking forward to the conversation and what we might learn from each other today.

Douglas Ferguson: Agreed. I always enjoy our chats. Excited about diving into this one. So I guess to start off, we’ve spoken about this topic quite a bit. You’ve attended one of our executive dinners and have attended the mastermind that we do monthly as well. And I’m curious, since you know the topic well, what news emerging for you since we last spoke? I know that you’re experimenting with a lot and trying new things. What have you encountered most recently in this world of AI and working alongside AI and the frictions that come along with it?

Taran Lent: It’s a great question. Well, it’s obviously a rapidly evolving space. The visual I have in my head is a snowball rolling down a hill and with each turn, it’s picking up more speed and more surface area and it’s getting bigger and it’s becoming kind of a juggernaut. But I just think it’s such an exciting time to be involved in technology. Anytime you have generational emerging technologies that kind of change paradigms the way you think, it’s just fun to have that in your career. Before I get into the details of it, I think it’s worth noting that humans have a terrible track record historically anytime new technologies come along predicting what it means and how it’s all going to play out. I don’t think with electricity, with computers, with the internet, if you kind of go back, a lot of people were wrong about how things were going to evolve and turn out. And I think you have people that underestimate emerging technologies. I give people that overestimate it. And I think it’s okay by the way. It’s okay not to know the answers, but what we’re trying to do and what I’m trying to do personally is just stay on the edge of the learning curve, play and experiment a lot, learn from other people, share stories. And if you do that with technology, whether it’s AI or something else, your chances of ending up in a good place are better than not is my opinion of that. To me, I think the biggest thing, the biggest thing I’ve noticed is maybe just social. There’s no stigma of any kind when people use AI. I just noticed that when people use AI, it’s perfectly okay to say, “I’m using AI.” I think there was a period where people would use AI and present it as non-AI work. And now people are using AI out in the open and the public and in a very genuous way. It’s like, “Hey, let’s bring AI into this and help it brainstorm, help us do critical thinking, help us automate.” And so I think everybody that I’m interacting with, you included, it’s becoming more of an AI first. “Hey, let’s make sure we’re leveraging AI to help us do whatever we’re doing at a higher level.” Right.

Douglas Ferguson: Yeah. I was just at an educational conference recently and one of the tables stood up as part of the debrief and said, “We don’t care if it’s AI generated, we care if it’s true.” I though that was so profound in its simplicity because it’s like, let’s not put a stigma on the AI itself, but let’s hold ourselves accountable to what matters.

Taran Lent: One of the things that I’ve had an experience with, I would say early in my career, my superpower was my ability to write and structure information or be persuasive or make a pitch. And when AI first came out, I felt like my superpower had been neutralized because now I was really good at writing a five or 10-pager or a strategic document, and then suddenly AI, everybody can now generate a lot of content. And then I was reflecting on how I use AI to do my own writing. I’m not sure it’s actually any faster at this point, but I think it’s better ’cause I’m still providing a lot of content and a lot of context. I don’t worry so much about structuring and organizing it, but I’m using AI as a contrarian partner. “Hey, what are all the things I’m not thinking about? Rate this on a scale of one to 10. How could I make this a 10? What could I be wrong about? Is this concise enough?” And by the time I provide all my context and I do all my think about it from other angles and I iterate on it and then I review it and then make sure it’s in my own voice and my own style, it might be about the same amount of time I was doing before, but the quality of when I put it out there I would argue is better. I spend more time not on the creation of the content, but on making sure the quality of the message and the information’s really good. And I think the same is true for my developers, the people that are coding. So I think there’s an analogy between writing human prose and then writing code. What AI I think helps you do a lot is really iterate faster and have more cycles so that the end result in theory … And look, there’s obviously lots of cases where it does absolutely accelerate, but the only outcome is not speed. There are other outcomes in terms of quality, completeness. I think to your point, being right and being correct or truthful are important outcomes too.

Douglas Ferguson: Yeah, I can resonate with that higher quality. And it’s interesting too, it’s like, what you’re describing is maybe even runs deeper than quality because I would argue that the stuff you were creating before was probably high quality, but you might’ve overlooked something or you didn’t have time to uncover one little thing that might bite you down the road or you share it and someone asks a question about, “Hey, what about this corner case?” And you’re like, “Oh, I should have caught that.” And I’m finding I am able to include more things like that. I’m able to iterate more and maybe it’s more expansive and more thoughtful and inclusive of other ideas and perspectives.

Taran Lent: I think the inclusivity is really good. I mean, overwhelmingly for me, I love using AI as a critical though partner and as a devil’s advocate. And I’ll give you a couple of examples in my own situation and others, but there’s oftentimes friction between just since we run a product and technology company between product management and engineering, right. Because product management’s trying to say, “This is what we want to build and this is a saving,” and then providing inputs into engineering. And oftentimes I hear things like, “Well, you’re not operating the right level of detail,” or, “This story’s not clear,” or … And I’m like, “What a gift it is to a product manager if you’re providing an input to engineering before you actually share it to say, “Here are the personas of the different people on my other side.” Review what I’m about to provide from their perspective and say, “Hey, is this complete? Is there anything that would make it better? Is there something more I could provide? Is there additional research that I should do to make this great?” And so it’s such a safe place to critique your own thinking and your own work. And like you said, the inclusivity to think about … I absolutely do that with my stuff. I have a boss, that’s the CEO. I have a board of directors. I have leaders in other departments. And by the way, and I have different relationships with these people. Some are really harmonious and peer-to-peer. Some might have more dynamics to them. And so I love that, screen my communications through that and say, “Hey, thinking about these personas and thinking about the nature of our relationships, is this message going to be successful? Is it going to be effective? And what could I do to make it better?” But that’s just scratching the surface of what AI can do, but it’s an interesting launching point of we all work with it in different ways and I’m definitely not a person that takes the first output and said, “Hey, let’s take that to the bank.” To me, it’s all about iteration, A/B testing, alternative thinking. And so I think that’s been very valuable for me. And when I talk to other people about how I use it, I kind of encourage them, “Hey, are you …” I’ll give you one more example, Doug, before you … When I do interviews, I do a lot of interviewing. A big part of my job is recruiting talent to the company. I find AI is a great tool to help me design a really tailored interview for that person so I’ve already taken into account the resume, the job description. And so I use it to help design really tailored interviews. But what I find when I am done, I don’t have it grade just the candidate, I have a grade me. “Hey, how did I do as an interviewer? Did I conduct a good interview? Did I give them lots of space to answer the questions without prompting? Did I delve two and three levels deeper? Did I hit on all the questions that were important?” And I’ll be honest, the first few times it was some tough love. It’s like, “Hey, you’re not doing very good interviews.” I was like, “Okay, well, I need to …” And that was great. I love that I got that feedback and it’s kind of a feedback loop that’s making me think about, “Okay, what do I need to do different?” Right.

Douglas Ferguson: Yeah, I was going to ask about that actually. I love that you brought a story about reflection and feedback because when you were talking about the screening of messaging for peers and other individuals inside the organization that you had to collaborate with and work alongside, and as you said, some are more harmonious than others. So I was curious, have you used it at all for reflection or after the fact feedback maybe from email exchanges or meeting transcripts, like, how the meeting transpired, how you could have shown up differently?

Taran Lent: 100%. I have a bunch of skills and prompts that basically, for any meeting where I think I didn’t do a great job or I felt like maybe the dynamics weren’t right, I’ll go back and say, “Hey, evaluate.” Especially in my role, one of the things I worry about is I, because I have a high level role and the power of the post, I really work hard to make sure I speak last and give lots of space for people to do it. And so I kind of do it as a way of accountability, “Hey, did I …” I don’t want to be known as a person that necessarily dominated the meeting or the conversation or interjected my position too early because I could create bias for how other people think or what they say. So I really like that idea of everybody can … And it’s not necessarily meetings that I’m in. Sometimes there are meetings I hear about that I wasn’t at where there’s some conflict or some issue or some decision that people have different takes on what happened. And it’s a really fast, efficient way for me to get a sense of, “Hey, how did that meeting go? And was there anything from a cultural perspective that was not great that I need to know about and address and coach?” Right.

Douglas Ferguson: Yeah, much more efficient than trying to sit everyone down individually and try to figure out what was going on if you can go back to the source.

Taran Lent: You do need to be careful though, because that’s transcripts and body language still matters, tone still matters. There’s lots of other things that play into that. You have to be careful not to take just texts on a page and a transcript and an AI. As I get further and further, the question people are asking is what does this all mean for humans and what do humans bring to the table that’s special? And I still come back to judgment, creativity, resourcefulness. Those are always going to be valuable I think in our society and in business. And I think you have to remember, AI is probabilistic. It’s trained on what’s already happened and what’s already known. And so there’s still a lot of room for humans to use their creativity and the power of our minds to forge things that have never happened before. And AI can help us do that, but I think humans are still just innately so good at that part of creativity and invention.

Douglas Ferguson: Yeah. I’m curious, how have you been encouraging your organization to lean in to the human parts and to embrace AI and use it in ways that are going to benefit the business?

Taran Lent: So first of all, I’ll provide some context. If I wound the clock back, I’ll say two years, two and a half years ago, I would say that we were not where we needed to be. I would say we were kind of behind the curve, and for reasons that were good. As you know, we were going through a transaction and selling the business and Roper acquired a company and then they merged us with another company. So it consumed a lot of capacity just to work through those mechanics. And I think as a side effect of that, we weren’t leaning into AI as much. We woke up one day and said, “Okay, hey, we’re not where we want to be.” So I looked at last year. Last year was the year where we really enabled. So the first thing we did is we created what we call the enablement task force and the naming of that was intentional. We didn’t want it to be a governing board. We didn’t want it to be a central bottleneck. We said, “Hey, look, we want to create greenhouse conditions where people can start to play, learn, experiment, apply, and then share with each other what’s working and what’s not.” And so the task force was pretty big. We’re a thousand person company. The task force was 40, 50 people. And we started really the whole focus of the conversation is, “What can we do to grease the skids to make it easier for people to get started on their personal journey?” And that included things like creating, you know, getting an updated AI policy, making it very clear, “Hey, here are all the tools that are already approved that you can use.” We looked at our process for how we review new software requests and we could get new tools approved more quickly while still keeping true our security and compliance obligations. We started creating resources and opportunities for people to share. So one of the things we did is at our company all hands, we always showcase at least one or two AI showcases that we think that’d be interesting to the whole company. But I think that word enablement was really the key piece. We said, “Hey, let’s help people get started.” There’s money and licenses for people to have one or more AI tools. So we did enterprise deals with Anthropic and with ChatGPT. We’re a Microsoft shop, so we enabled the Copilot M365 for employees, which is amazing ’cause that has access to the Microsoft Graph, OneNote and Teams and Outlook and so forth. So last year I would say was the year of, “Hey, get everybody in the game.” And by the way, I lead engineering at the company, so the developers are a whole nother story. We forget that developers created this technology. They’ve been playing with it for many years. And so the developers are in a different place in this maturity curve ’cause they’ve been … Developers don’t like to do work that they find tedious. And so they were extraordinarily resourceful at automating things that we don’t want to do. And I’ll come back to that point in a second. But this year is more about, “Okay, hey, it’s not good enough to be using AI at a personal level anymore. We need to really elevate it to team and enterprise. Like, how do we create skills that can be shared across the company? How do we make sure people have access to prompts and standards and defaults that really represent our company and our principles, our values, our thinking, our branding?” So this year the emphasis is really on elevating it beyond team. And so it’s no longer okay to have something that you just do yourself. And there’s an expectation is if it’s really providing that value, how do you bottle that up and make it available to the whole team? I want to come back to the point that I made earlier is, and this is my advice to anybody doing this, success is contagious and success is kind of compounding. And the number one best way, and this would apply for any role, any function, any department is, and I use this quote all the time, “Just look for high toil, low joy work, work that can be automated or where AI can provide assistance or leverage.” And anytime you free up capacity so people can work on other things or more strategic work or more fulfilling work, those use cases just build momentum 100%. So we still are … And we’re on the hunt for that internally in our org. So we call it reducing the drag. Anywhere where we have friction, toil, slowness, we now surface that and say, “Okay, let’s just go back and reimagine how can we leverage all these technologies we have to make that better?” And then in almost every case, we can find a better way to do it. And we’re trying to do the same thing in our products for our clients. All of our clients are being asked to do more with less and tighter budgets. And the only way you can do that is leveraging technology to help you have leverage and to force multiply people. And AI of course is a great technology for that. And so we’re just looking for opportunities where we can help people do different work, more strategic work, or free up capacity. Even if it’s just five to 10 hours a week across an organization per person, that’s huge.

Douglas Ferguson: Yeah. It’s also making me think this idea of reducing the drag, reducing toil can actually move folks into a place where the work becomes more enjoyable because they’re removing the things that they like doing the least. And so that has an opportunity to maybe improve morale.

Taran Lent: One of the most inspirational things I’ve heard in the last year, we had Zach Kass, one of the founders of OpenAI, just an incredible thought leader. And he’s thinking about this technology on a humanity level. And he was our keynote speaker at our annual client conference this year. And he was talking about what a problem it is when children in particular are spending all this time on their devices and their screens. And there’s lots of science and research around that. But he said one of his hopes for AI for humanity is, “That technology has the potential to work silently invisibly behind the scenes to make our lives better so we spend less time on screens. ‘Cause AI can be assisting us in doing work and doing things so that we can spend more time with in-person interactions versus having to work through [inaudible 00:21:05]. And so maybe there’s a future where technology’s just kind of persistent behind the scenes, making our lives easier, better, and it’s freeing us up to do more of the stuff that’s really human, right.” So I really encourage people to check out his writing and his vision of that. But if that were true, that would be a pretty amazing outcome of the technology for us as a civilization.

Douglas Ferguson: Yeah. And a great example is driving a car. Our cars are becoming more and more intelligent. The artificial intelligence that’s baked into a car is very invisible to us, but we enjoy the benefits when it’s able to adjust the lanes or alert us that we might be falling asleep or all of these things. We’re not sitting there laboring over or thinking about it, but it’s right there when we need it and it can save the day quite often.

Taran Lent: 100%. It’s a great analogy. Adaptive cruise control to me in Houston traffic is like, I love it.

Douglas Ferguson: Yeah.

Taran Lent: I was an engineering major in college and I actually got to work on a traffic study project at one point. And what a lot of people don’t know is our highway systems in most cases could support 10 times more traffic if people just drove sensibly.

Douglas Ferguson: Yeah.

Taran Lent: You maintain spacing, we’re in the correct lane for what’s your next move is, collaborated with each other. It’s incredible. There’s a phenomenon called tragedy of the commons. And when you have an individual or many individuals acting in your own self-interest, you unwittingly degrade the system to everybody’s despair. And the other thing’s interesting about traffic, by the way, this is a fascinating thing, but in a traffic jam, it takes only one car, one driver to unblock a traffic jam. Not if there’s an accident, but just if one car just backs up and provides a lot of space around it and that will unblock a traffic jam in most cases in 10 minutes. So think about if you had cars that were maintaining spacing, allowing other cars to do what they need to do. There’s one, I think we would find that traffic would become less of a problem. And then the safety aspects of that, I’m sure you would have fewer. There’s just no way that’s not our future. There’s just no way that that’s not going to be a part of our future, right.

Douglas Ferguson: Yeah, absolutely. And also now we’re getting into self-driving car land, but I’ve long been dreaming of a world where, yes, the self-driving cars are more respectful and traffic becomes less of an issue, less traffic accidents. And we don’t have to fill our urban areas with parking garages ’cause the car can just go home or go grocery shopping or whatever.

Taran Lent: Yeah. 100%. One thing that’s kind of a corollary to that is I think human tolerance for bad software is going to be a thing. We are not going to tolerate bad software, bad design, bad workflow, because it’s going to get increasingly easier if an experience is not good or not efficient or not at the standard. AI is very good at analyzing that and comparing it to all the other code repos out there and best practices. And I just think there’s going to be very low tolerance for software that’s not well-designed, that’s not well-crafted and works well. And if you’re honest with yourself and you kind of step back, there’s a lot of bad software in the world. And you look at the app store and there’s no reason to have an app of any scale that’s below three stars, but there’s many of those. And I think expectations and the standard is going to get higher and higher and higher and it’s going to be easier to meet that standard. And I think that’s going to be great for almost every realm of life where we have technology and software. The expectation’s going to be very high. And if you just think about, look at my kids, my kids, we have this instant gratification society, but their expectation when they order something, whether it’s DoorDash, Uber or Amazon, when they order it and when they’re going to get it and the level of convenience that comes with that, it’s actually relatively new in the last few years and it certainly didn’t exist when we were growing up. And that just is an example of people are just going to have different expectations over time with how this technology works.

Douglas Ferguson: I wanted to come back to your point around this year being about the move beyond the individual and thinking about these kind of team and organizational use cases. And I’m curious from your perspective, what’s the pathway that you’re taking to get there and any early wins or what are you noticing?

Taran Lent: Almost everything that we do, you do have to think about it from a guardrail perspective and compliance. We sell enterprise software. Our customers depend on us for mission-critical solutions and in some cases their data. And so everything we’re doing has to meet that bar. And so as an example, that means … I’ll take skills as an example, right. People were individually building skills that were very useful for them or maybe they’re a really localized team, but there are absolutely skills that are valuable that could be shared across the whole enterprise. And so just as a simple one, we’re rebranding to Illumia and we have the Illumia branding skill that knows our design language, knows our colors, our fonts, our imagery, everything. And so we built a skill where any document you might be working on, a presentation, a document, a letter, you can have the skill, check it for brand compliance and put that in there. And so that’s obviously a use case that was a no-brainer for us. But even skills have risk. And so we had to put in place the foundation of, hey, if somebody creates a skill, whether it’s internal or external, and it’s going to be shared across the enterprise, what policies and process need to be in place so that we can review those, make sure that they’re okay and approve them and get them published and do that in a way that’s safe. And so that’s an example. And I get requests almost every day for some new something that somebody wants to try out. It could be a plugin, it could be this or that. And honestly, our old way of reviewing requests for these things wasn’t fast enough and it wasn’t modern enough to deal with this new world. And so that’s an example where to get to the next level, we had to say, “Okay, how do we think about this and how can we support this at scale? And how can we anticipate the volume of requests we’re going to see? And how can we get these things decided or approved? Or at least if it’s not approved, a decision with, hey, we can’t do this right now for these reasons.” And so that’s an example of for our company, we had to think about those things so that we could have an operating environment where these things can happen and employees can build these things and share them with each other, right.

Douglas Ferguson: Yeah. So I’m curious, and I would imagine a lot of listeners would be curious ’cause folks are either actively designing similar processes or re-imagining existing ones. Who’s responsible for that review process? Is it a group, an individual? And is it the same process for external tools as it is for an internal skill? How does that work and even get communicated out that something’s approved?

Taran Lent: Right. We have a stakeholder group because everybody adds some value. There’s architects involved, there’s security analysts involved, there’s IT people involved because there’s a lot of things that we want everybody to evaluate from their perspective. But at the end of the day, you have to answer questions like, “Who’s the company that created this? Is it a credible, legitimate company? Are they doing business with other Fortune 500 companies? Do they have a security trust center? Do they have any compliance or assurances? Do they have an AI position? Are they clear about whether you can turn this on or off? Do they use your data to train their models?” And so what’s good is there’s good frameworks for this, but you want to know who created the tool, how they’re supporting it, what the service levels are behind it. Have they given thought to what the potential of abuses of the tool or software could be? And then with AI, you have to think about, “Hey, what happens if it does go wrong? What if it does give you the wrong answer and gives you bad advice or hallucinates? How do you confirm whether it did or didn’t? What telemetry do you have in place to track over time and then how would you retrain it to get it back on track?” And so those are the types of issues you need to think about. One of the things that I think is quite interesting is ’cause Claude’s obviously, and Anthropic are moving really, really fast and they introduced, to people who don’t know about it, a bunch of plugins that I think are amazing. They have this operations plugin and it’s got all sorts of things that are related to operations of a company and one of it’s kind of risk analysis. So they have their own risk analysis skill. And what’s interesting is that when you use the risk analysis skill on some of the new features Claude’s coming out with, it’s quite honest. It’s like, this probably isn’t really ready for primetime yet for a company at our scale. Companies are all different. We’re a public company at scale and so there’s a different standard that we operate to, which might be different than a startup. But that’s even an example of, like, even you could even leverage AI to help you with your risk analysis and to think through, “Okay, what are all the vectors here that could be exploited or abused or cause unintended consequence?” And just so you can have your eyes open when you’re making decisions. And by the way, the goal is never for risk to be zero, just so … As the CTO and I also am responsible for security and compliance, the goal is never for risk to be zero, it’s just to be informed and have your eyes open and the benefits have to outweigh their risks. And you got to say, “Hey, I think this is worth it.” Right.

Douglas Ferguson: Yeah.

Taran Lent: Or, “Hey, mitigation’s in place,” or you can put other measures in place to make it manageable.

Douglas Ferguson: And what’s your typical turn time on approvals now that things are moving so fast?

Taran Lent: So what I would say is if it’s something new, if it’s a company or vendor we’ve never ever worked with before, and the party we’re working with internally is well-informed on what the process is, it’s usually four to six weeks ’cause there’s contracts, there’s usually redlining and there’s some …

Douglas Ferguson: Okay. Yeah, yeah.

Taran Lent: And now if it’s already an existing vendor and something we’ve been doing business with and they’re introducing some new AI capability to plug in or tool, we can kind of fast track that. But one of the things we’re trying to do too is how can you do some provisional approvals? How do you have different tiers based on risk? But what I tell people is yes, and by the way, people complain about the four to six weeks and I understand that. But what’s great is somebody’s got to make the argument and kind of push it through. But after that four to six weeks, then it’s approved and we can use it for the next decade. And so in relative timescale. And look, at the end of the day, our customers depend on us to be thoughtful and to be smart and to be safe. And for me, that’s part of the value proposition you get from our company is that you can trust us with your mission-critical processes and you can trust us with your innovation and we’re going to help you. And by the way, our customers are hospitals and healthcare and universities. They tend to be relatively conservative about these things. They’re absolutely trying to protect. There’s HIPAA and FERPA and all these things. But that’s an opportunity for us to be a thought leader and to help them create frameworks and structure for how you can safely adopt these technologies and apply them to your use cases.

Douglas Ferguson: What’s your approach to sharing skills with the broader organization? The reason I’m asking, I’m really curious because I see a lot of folks checking these things into Git, but then that requires anyone who wants access to kills to also have access to Git. So then there’s challenges there.

Taran Lent: Yeah. Well look, and the other thing too is people need to understand that the real value of the skills too is they’re going to evolve over time. So the first version that you put in is probably not the best and it’s not the last. And so the question is more, I think open source software is a great model to look to ’cause … So for example, we’re really into the Silicon Valley Product Group, product operating model at our company. And we had somebody create the SVPG product operating model skill so you can have it evaluate things. Well, that was just one person. We’ve got a lot of people that are well-trained on this and have points of view. And so the question is how do you create a space where people can review the skill, can add to it, you can add it, collaborate it, evolve it. Ultimately you do need version control. And so Git is a good way to do it. Not everybody has that skillset. So that’s where that board comes into play is, “Hey, get us your feedback on the skill however you want.” And then so long as we have people who help you can get it published and manage version control, there’s a way to do that. But look, I think you do need version control. You do need to have reviews to make sure, “Hey, is the quality there? Is there anything there that may not be in alignment with what we’re trying to get done?” And by the way, kind of a related thing to that, one of the things that’s happened in our company that’s unique is for 20 years, all the developers were in my department and we established process and standards, things like, “Here’s your annual secure coding, here’s how our pipelines work. These are the security and vulnerability scans that are going to get run on code when you check it in. When you download third-party software, it has to come from a repo that we’ve verified it’s coming from a legitimate source.” There’s all these controls around these things. And so my teams are expert at working this way. Suddenly now other departments are hiring developers or builders or creators, right, and they’re creating things and then wanting to publish them and share them, but it’s outside of my org, so they’re not necessarily subject to … And so the question is how do you enable that? ‘Cause I certainly don’t want to stand in the way of that. That’s just where we’re going. But how do you create sandboxes and process where people who are not engineering the products that we deliver to our clients, these are a lot of times our internal productivity tools. How do we put them in a position where they can build things and then release them, but we’re confident it’s … So just as an example of, I think you said you wanted me to talk about some failures too.

Douglas Ferguson: Yeah.

Taran Lent: I’ve seen people build solutions that had access to confidential information that we wouldn’t want our competitors to have, and they published a dashboard or whatever that had no authentication. It was open to the public. You could take the link and put it in incognito and I’m like, “Well, that’s an example of something you absolutely can’t do.” So then the question is how do I create it so that it’s really easy for someone that may not have that skillset to publish their app and have it behind their SSO authentication, and they don’t have to reinvent that wheel, but we can create a place where they can publish that, that you only can get to it if you’re an authenticated person that should have access to it? Point though there is, like, everybody’s going to start building. We’re going to see an explosion of builders and creators. And if you can anticipate that, how do you make sure you set up an environment where you can teach them what they need to know and you can make it easy for them to do what they’re trying to do leveraging these new capabilities?

Douglas Ferguson: And what sorts of guardrails and sandboxes might we create so that-

Taran Lent: 100%. Right.

Douglas Ferguson: … they can play and not worry about creating harm? So there’s a couple of things I wanted to hit on before we run out of time. One is I’m curious what sorts of shifts and impacts you’ve seen on your product development life cycle. Have there been things that you’ve just straight up removed or completely changed or things that you’ve tweaked to support these new ways of working and bringing AI into these moments?

Taran Lent: So I’ll start with talking about engineering and how it’s working there. But if we went back two years ago and you looked at it, probably less than 20% of our code was being AI assisted. If you look today, it’s more like 90 to 95% of the code that we’re putting out is AI assisted. That could be AI generated, it could be AI reviewed, it could be agentically-created code, but that just shows you the natural adoption curve within the engineers. We’re actually seeing, if we actually look today, it’s different based on the tech stack you’re working on, but if it’s a modern tech stack, we’re absolutely seeing 20, 25, 30% productivity gains on throughput. On legacy code, that may not be as standardized and AI models aren’t as trained on, we’re not seeing that kind of gain. And then there’s a few areas where we’ve seen orders of magnitude productivity. But where I’m actually most excited about is not on the engineering productivity. I think on the upfront product discovery, validation, research, prototyping with clients, I think that’s going to be where we see this extraordinary leverage and gains. And you mentioned it earlier, but product managers used to do surveys and advisory boards and all these different ways to get feedback. Now you can just go talk to clients either in person or over the phone, you can observe them while you can capture these transcripts and you run those transcripts through a bunch of AI, you can get insights about your products, problems they have, feedback for services, support. And to me, I think just the tools available to product managers to research, to uncover insights, to prototype, to get early feedback to fail and learn, it almost makes you want to go back into a product management career. I just think it’s going to be so fun for people. And I do think there’s going to be a convergence of, ’cause historically there’s a designer and a product manager and a tech lead. And I think there’s a category person that I think is going to rule the world. The technical person that has design sensibility and good business product acumen, I think in some cases I can converge all the way down to one person using AI in a really resourceful way or maybe two people doing that. But I think, ’cause if you can get validated work that’s really clear and then you provide that to an engineering team, they’re going to go wicked fast, much faster than they’ve gone historically.

Douglas Ferguson: Yeah. And I think the amount of information you can process, not only interviews that I’ve conducted or my team’s conducted, but also what about all the customer service calls and all the sales calls? And there’s so much that could go into learning and extracting insights.

Taran Lent: It’s the best form of feedback. It’s the best form of feedback. There’s no doubt about it. And innovation a lot of time is about seeing patterns that other people don’t see or seeing insights, you know, and people talk about looking around the corner. AI can absolutely help you look around the corner if you have enough data with enough signals and enough patterns, right.

Douglas Ferguson: Yeah. And then there comes the new friction, which is the discernment on which pattern matters and where we’re going to invest our dollars.

Taran Lent: Well, that’s never easy. And I tell people all the time, “Great companies, you’re never deciding between a great idea and a bad idea. You’re looking at 20 great ideas that all have merit and you can do three.” And that’s where we get back to the humanity of it. Like, yes, there’s data and yes, there’s science, but there’s art to everything and human judgment, human discretion, human intuition still has a role to play in this. And we all know them. There’s people that we work with that just are right a lot. They’ve got really, whatever it is, their life experience and everything they’ve read and how their brain works and how they connect dots. And that’s always going to be valuable in this world. And so people that operate that way, AI is only going to make them more impactful, more effective. That’s my view.

Douglas Ferguson: Absolutely. My last question, I know y’all just went through a merger and so you’re in an environment where you’ve got two different cultures, which sometimes can be vastly different and sometimes can be very similar, but rarely are identical. And we’re in this moment where people are asked to show up and work different. So there’s this transition in our ways of working and how we’re using these tools that are frankly evolving daily. And then you’ve also got two different cultures that are coming together. So to me, when I think about that, there’s some extra layers of complexity. I’m curious what you’ve noticed and has that been a smooth ride or is it kind of figure it out as you go? What can you share about that?

Taran Lent: It’s a great question. The culture to me is the X factor. Most companies have intelligent people, but it’s the culture that I think wins the day. I think we’re relatively fortunate the cultures of the two companies we’re putting together. So Transact and CBORD are coming together at Illumia. Because both companies were very purpose and mission-driven … At the end, look, what we do, we help colleges use technology to operate more efficiently and elevate their end user experience for students, parents, faculty, staff. The shorter way to say that is we use technology to make college even cooler than it already is, right. And then in healthcare, we provide solutions around helping hospitals and healthcare environments provide world-class food services. And we think about it when you’re in a patient in a hospital healing from something, food is medicine and food is care. And it might be the one bright spot in the day. And so our technology helps make sure patients get the right food based on their doctor’s orders and also that nurses, doctors, family are well-nourished when they’re in a stressful setting. And because we’re mission-driven like that, I think our cultures were more similar than they weren’t and we were actually quite eager to learn from each other like, “Hey, how are you doing this? How are you doing that?” But what we look for, I mean, we look for people that are humble, hungry and smart. So humble means you care about the team more than yourself and you put team first. Hungry means you’re competitive and you want to win for your clients, you want to beat the competition. And smart means you’re not only intellectually smart, but you’re EQ smart in terms of the culture and the dynamics. But those things matter. And so I think those people that kind of fit that criteria are people that tend to be more adaptable and willing to be self-learners. And look, what AI demands of all of us is that you lean in and you try, you experiment, you fail, you learn. And if you do that, you’re going to be fine. I tell people all the time, “Look, if you show up and you put the team first, you work hard, you’re learning, you’re experimenting, you’re keeping your skills sharp, you should not worry about the future for yourself or your career. If you’re resisting or you’re not willing to learn, you’re not willing to change, that might be a tough road for you, so …” But people have a choice. And for me, this is the most fun I’ve had my whole career just ’cause it’s just so exciting. How lucky are we that this is happening during our careers and that we can … And just put yourself in my shoes. If I can drive productivity in my teams, if I can accomplish the same thing with smaller teams, that means I can just self-fund more ideas and more innovation. It just means the dollars go further and I don’t need to go ask for money or resources. I can free up capacity and go be in control of my own destiny. So as a tech leader, that’s amazing.

Douglas Ferguson: Yes, totally agree. And as we come to a wrap here, I want to give you an opportunity to leave our listeners with a final thought.

Taran Lent: I’ll go back to what I said at the beginning. None of us really know where this is all going and that’s okay. So don’t pretend. I think there’s a whole bunch of people that are overestimating what this means for us. There’s a lot of people underestimating and I just would encourage people to just experiment, to play, to have conversations like this one, to learn, to share. There’s so much information out there, but I think we need thought leaders that are going to use this technology … All technologies can be used for good or for bad, so if you’re in this industry, be a force for good. Help steer this in the right direction. Be engaged and be active. And I think if we have enough people do that, we’re going to see a really amazing future that we’re all proud to be part of.

Douglas Ferguson: I agree. It’s been great chatting with you, Taran. I’m looking forward to catching you again soon.

Taran Lent: Yeah. Thanks again for the opportunity and you take care. We’ll be in touch.

Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.