How AI Is Reshaping the Way Enterprises Build Software - With Tim Sears of HTEC
The AI in Business Podcast · 5 Jun 2026
Source: https://podcast.emerj.com/how-ai-is-reshaping-the-way-enterprises-build-software-with-tim-sears-of-htec
Transcript: https://podcasts.opennash.com/shows/ai-in-business/how-ai-is-reshaping-the-way-enterprises-build-software-with-tim-sears-of-htec-22d9a2/ (via OpenNash Podcasts, machine-generated)

Welcome everyone to the Emerj AI in Business podcast. Today's guest is Tim Sears, Chief AI Officer at HTEC. HTEC is a global engineering firm focused on AI centric software and hardware development, working across financial services, med tech, automotive, telecom, and enterprise software from more than twenty engineering centers. In this conversation, Tim examines how AI is reshaping the software development life cycle and why the most important shift has nothing to do with individual productivity and everything to do with how teams work together.

He argues that software development is turning out to be the killer app for AI, and that understanding exactly why that is true is the clearest lens available for how AI will eventually transform every other business function. Today's episode is sponsored by HTEC. AI is moving fast, new tools, new research, new use cases every week. Emerj synthesizes what matters most so senior leaders and practitioners can stay ahead without getting buried. Join the more than 85,000 subscribers and get the most useful AI business insights delivered to your inbox.

Visit Emerj dot com slash ad one. That's E M E R J dot com slash a d one. Now, the conversation with Tim Sears. Tim, it's great to have you on the Emerj AI in Business podcast today.

Hi, Yolandi, thanks for having me.

I I'm excited to to pick your brain today. Uh, I wanna get into the real stuff real quickly. You have described AI deployment problems in the past as more of a people problem than a tech problem. I like that. Uh, I wanna I wanna dive into that first. We it's it's quite a striking place to start, especially when our topic is software development life cycles today. From where you sit today, what has actually changed about how enterprises build and ship software, and where are most of them still running the same playbook they've been running for the last five years?

Oh, pretty big question. Uh, I mean, I would say a lot is changing. We talk about change, you know, old versus new. Well, it's pretty clear that the tools, you know, AI in in various forms, various modes, offer tremendous potential for productivity gains. We haven't figured everything out about how to put them to work in the best possible way. It's pretty clear that what looked like best practices twenty years ago are being outmoded, but we don't have, you know, uh, we don't have a gold standard of how things should be done today.

That's that's in the process of being developed. In olden days, you know, say three years ago, we had, you know, requirements gathering, which is a pretty lengthy process and it had to be done super carefully so that your precious software engineers could focus on doing exactly the right thing, iterating through things like Scrum and, you know, Agile. And then there's a whole variety of deployment and hardening tasks around deployment and everything around that whole process is being affected.

Today, we see a wide range of comfort and skill in terms of using AI tools and fitting in new ways of working, and it's having a tremendous impact on individuals. And I think the next phase is really about figuring out ways for teams to be highly effective using AI. Software development, business in general, is a team sport. AI is helping certain players do performance enhancing things, but we need to the teamwork aspect of that is really going to be.

AI needs to become a catalyst for teamwork in the future. We're really in the process. The whole industry is in the process of figuring that out right now. So, in a couple years, I would expect the landscape to be quite a bit different. We could be maybe a lot more prescriptive.

I know that you've spoken publicly about that gap between AI experimentation and and production deployment. Does that gap show up differently in engineering teams than it does in other parts of the business?

Well, I think looking at engineering teams is really going to give us all the evidence about how to figure out how to use AI more broadly. Maybe this will sound like a little bit of a departure, but I think it's it's quite relevant. Software development itself is kind of turning out to be the killer app for AI. And all of the hurdles and benefits of that actually are are illuminating. It's very instructive to understand exactly why that's happening.

First thing you need to know, and everybody kind of does, is that if you've sat down with a chatbot, for example, and typed something in, you know, you get this pretty interesting, it's pretty good, but is it really what you need? Is it really correct? So, the output of LLMs, rightly, is not totally trusted. Obviously, it's getting better, much better and fast, but it's not completely trusted. And so, but if if you're asking AI to output code, you have a way to remedy that.

You have compilers and you have you have tests and you have, you know, you can see how it works in deployment, whether it's in test or production, et cetera. So you have ways of validating the output of LLMs with less human judgment, which translates into lower cost. And that's why I think we're seeing software development get, you know, eaten up or really transformed, if you wanna use a nicer way to phrase it, by AI. And I think that this will extend it to softer areas of business as we go forward, as we figure out how to do.

What's the equivalent of validating the output of LLMs and baking that into your production process? We could get into that a little further if you like.

Absolutely. So, if if we wanna get into that further, where where would we start?

Well, the first thing I think we need to do is watch how it will be successful in software, like the use of AI, that is. And I think that when we see the shift from AI being a an enhancer of individual performance, you know, we can see that in our own lives where if you just if you're not a programmer, maybe you're using AI to help your writing ability. Okay, so everybody's getting better, more productive, you know, the average is moving up.

But, you know, how are we using AI as a catalyst for teamwork? You're not really. Are we sharing chats? Are we sharing knowledge bases and things like that? And so I think as we see the software engineering world figure out how to really maximize the use of AI, that's going to have broad implications for business. And so the the thing to watch for is when AI becomes a catalyst for teamwork. And in software, that means that non-technical people will be able to put their fingerprints on a project.

Teams will be iterating super fast. It won't be sprints so much as continuous iteration. Whenever something interesting happens and the AI is able to make an improvement. So that means sprints, things like that, if you know that word, are are gonna fade into essentially continuous iteration. Okay, and so until that time, productivity gains will be uneven. It'll be the people that are really optimistic or excited about AI are gonna use it, and they're gonna learn how to use it.

Maybe they get some benefits, maybe they waste a lot of tokens, but there'll be a lot more use there. But when the when we figure out ways for teams to share, you know, an AI in service of a common goal, then we're gonna see dramatic productivity gains. We're really on the verge of that. The industry's figuring that out right now. So, when productivity gains are tied to teams, we're gonna see big benefits. Until then, tied to individuals, we see some, but progress will be very uneven.

So, I think that's the that's what I see in software. We can actually extend that to other areas of business for different business functions, which will look more and more like a computer program over time.

I I'm guessing that there's also a lot of as assumptions that go with this. Uh, what is the assumption that most engineering leaders are holding right now that, you know, that if in two years' time they look back, they'll say, Oh, that was obviously wrong. Is there any anything that you can pinpoint at this site?

Yeah, I think there's some trends in place that we should extrapolate and we can extrapol- they're not extrapolating means we're at a certain point and we're going to get to another point and we can have some confidence, some high confidence in some cases, where we're going. So, the number one thing is that the quality of the foundation models has improved a lot. If you look at say, three years ago, they were certainly intriguing and there was a lot of maybe I wouldn't call it maybe hype, but also hope that, uh, things would improve and they did improve dramatically.

And at that time, some people it was okay to still be skeptical, maybe even a Luddite, maybe even, you know, anti or resisting. And I think in the software engineering world, that's changed dramatically. Folks that were digging in their heels or skeptical or whatever you want to call it, that's fading rapidly. And so you don't see that as much, but you still have. We still have a wide range of attitudes and and capabilities among teams about how to work together using AI to help them accelerate as a team.

And so I think that we're going to see, as we get more confidence in using the tools, we'll see kind of a, you know, a reemergence of a consensus about how to use things, and that that will happen in the in the context of two things. There may be some bottlenecks in terms of availability. We see that right now, actually. But I expect the longer term is we're going to see a lot of improvements in inference speed and inference costs, more importantly.

So that's that's one background, and then the models will continue to improve. And so those trends, I think, are firmly in place and we can expect, you know, even more interesting things happening powered by AI going forward.

There's a lot to to dive into there, actually. I I think it's worth noting for our audience that Tim has quite an intense and impressive background, education, and experience around all things AI. Which means that when you talk about what changes when AI is properly embedded in a software development process, you're not just describing a a technical integration. You're describing a different way of working. And I think that's why you love using the word team so much.

It's a it's a different way of working that has to hold up in a business that is under pressure. So, you're the right person to ask this, actually. What actually changes in how teams are structured, how they operate day-to-day, and what they're able to produce when AI is built into the SDLC rather than just layered on top of it?

Okay, wonderful question. So, I think one thing that I think a lot of business leaders are excited about is this idea that we can re-engineer business processes to be powered by AI. It looks like a big opportunity for cost savings, efficiency, and also q- quality of the experience in that business process, whatever it might be. That could be paperwork at an insurance company or it could be call center, anything you can imagine where data is flowing through a company and people are interacting with it is going to improve.

We obviously software powers a lot of that and will power more of that. And but, you know, we don't have a D, you know, and there's still many, many processes where we don't really understand them in exactly the same terms as say, something like a web app. Essentially, all of the knowledge about how to build web apps and how to deploy web apps and how to measure the impact in the marketplace, you know, it's getting outside of engineering, it's getting into the business metrics.

That culture is going to have to spread a lot more widely inside all nooks and crannies of the business. And so that product management expertise is not commonly found inside a call center. You might have a manager of a call center who's managing people and training them and troubleshooting and helping them in all the traditional ways, which are great. But if we want to save some costs and maybe take two thirds of the costs out of a call center, now it's a hybrid between humans and a bunch of computer programs at the end of the day, a bunch of computer programs.

So we're going to have to learn how to deploy and build and monitor and improve those systems. More or less like a, you know, like a system, like a web app, if you want to make a a a good analogy. And so I think that raises a lot of change management issues, and it stokes fear, of course, uh, and and so forth. So these are going to take time. And yet the people building the AI tools aren't the experts in figuring that out.

You know, the people that are going to have to figure that out are actually probably your listeners who are running a business. And so we can't expect AI just to be a magic bullet. We have to demystify it as much as possible so that it can become a tool for people to reengineer their business.

Uh, what I'm wondering about is how size will matter in this, um, if if we're starting this process, is there a team size or composition that AI integrated SDL C works best with or works against?

Probably you'll get a wide range of answers. So on that, I think smaller's generally better. I mean, if you have a small group of people, you know, adding more people increases the communication challenges. And maybe there's an AI fix for that, but in general, we like smaller sizes. So I don't I don't think size is the issue there. In fact, I think the leverage provided by AI is that, you know, small teams can do what big teams used to do.

So, a tendency would be to say smaller, but I don't want to be overly prescriptive on something like that.

Yeah, makes sense, but but starting where you can manage it more and where you can be involved more makes sense, and then obviously growing into into bigger teams. That also makes me wonder if if we're having the whole team involved and everyone, it's just it's a wider idea and a wider project. What does developer focus mean in an AI enabled team? What do developers stop doing and what do they spend more time on?

Yeah, that's a great one. I think that the whole process of building systems will change. You know, the requirements gather- let's start with requirements gathering. You know, that could be a lengthy process. You know, we're we're now telling our clients, you know, let's make that a rapid progress. In fact, you know, our client partners, effectively our salespeople, could be recording calls and turning those things, those conversations into requirements. They're certainly values that we want the system to have.

And so that can s that can start right on the initial sales call. I mean, it's kind of amazing. I've actually seen that happen. So, and then, of course, you know, you've got to specify the system, and I think it's more about architecting and specifying and being clear about that. It's less about, you know, typing the code and putting the semicolons in in the right place and all that stuff that is t was time consuming is, you know, and is challenging, requires skill.

That skill is more readily available. So, the developer job is is le- is more elevated in the sense of making sure the architecture's right, making sure that security matters are being dealt with early, being able to make the prototype, thinking about how we're going to get this prototype into production quicker. So, there's a whole bunch of things that are essentially all the grunt work is taken out, you know. So, all that stuff that looks a little bit boring is a ripe target for AI.

That means the work of doing this is much more elevated, much more consultative, much more interactive with our clients.

And that's great news because I think at the moment, at the speed that everything is moving on, if if if it takes you the same amount of time it used to and one hundred days of developing and testing, your program is already outdated. Your agent is already on retirement stage, almost, uh uh, um, current in the current environment. So, I'm wondering, I I've heard you talk about automated validation as a meaningful shift before. What does that change about who is responsible for quality?

Does it reduce the need for human judgment or just redirect it?

I think, well, maybe I'm a Luddite, but I don't think that we wanna get humans out of the loop on everything. I think that, you know, if somebody sends me a document written by AI, I'd like them to own it and not tell me that ChatGPT just wrote it for them. That just makes more work for me. So, we need people need to continue to own things. And so, really, these tools need to be efficiency tools for things that ultimately humans are responsible for.

So, you know, what exactly do you need before you're able to render judgment on something? Is, uh, you know, that's still going to be an issue. Just the way that you have frame it and the tools that you use to get to the answer are going to change,

And it it basically, um, I like the idea of telling everyone that, listen, we're we're appointing an executive assistant for each employee. How lucky are you? You now have an executive assistant. You're still the manager. You're still in charge of this assistant. And if this assistant messes up, it's it's your res- it's your baby. So that same idea is kind of replicated in what you just said. We're reaching the closing part of our conversation and we need to leave our audience with something that they they need to think of.

And for a senior leader who is responsible for engineering or product delivery and really wants to move on this, not just support it in principle, because I think we have a lot of that. We have a lot of support, but not a lot of movement. What needs to change about how they think before anything else can change?

yeah, I think it's an interesting thing. We see a lot of innovation in the sense that the tools are available and we see bottom-up engineers adopting, not everybody, but a lot of engineers, and it's leading to change flowing up through the organization. At the same time, we see essentially owners of companies like private equity firms, for example, mandating more AI in their portfolio companies. And, you know, these guys are not, uh, tech heads really, but they see opportunity and, uh, they're trying to force the issue.

So, there's change from top coming from top down as well. And so this all has to get resolved, you know, I think one thing that, and and you hear lots of hype and so forth in the marketplace, you know, you hear things like SaaS is dead and so forth. I think business models will change, the way we work will change, but enterprise software is changing, but it's not going away. One of the things that your audience might like to think about is that we're going to be using enterprise software in one form or another.

And I think that folks ought to realize that for the past, you know, twenty, thirty years, we've been benefiting from these great platforms, but in many ways, we've had to mold our, uh, ways of working to existing platforms. I don't want to name any names, some of them are our clients. So, but, you know, we've have sometimes you have a business process, you adapt it to the product that you bought, and you can't really buy that build that product, but, you know, or you it's a buy versus build decision if you really want it customized to your needs.

I think in this new world, couple things are changing. It's a really a build versus build. You're going to have an opportunity to build it this way or build it that way with h tech hx help or not with hx help. You know, there's an awful lot of opportunities. They all amount to building. So we're all going to be more engineer like in some ways. And I think that the other thing to think about is because the cost of software development is dropping in some ways that we sh- perfection will be expected.

So it's somewhat reasonable to expect perfection. Now, exactly how to get there, we're still figuring that out. But in a few years, perfection in enterprise software is going to be average. So it's a really interesting time to be working on that space.

I agree. And and it feels like what's what's interesting just keeps on becoming more interesting, especially if we just look at everything that has changed and developed and keeps on developing in the last year. So, um, anything that you want to leave our audience with today, something that they should be thinking about, something that they should take to the board meeting on Monday.

Well, I think that the companies that figure this out, that really look within and understand their own business operations in a new way, as we've described, will find themselves with a strategic advantage and would like to help them if that's possible.

Great. I think my key takeaway is here is one that software development is no longer just for the engineers. It is it's very much a team sport at this moment, and everyone gets to be involved because of AI. We're not fully leaving it to to the AI. Uh, we're just appointing ourselves executive assistants, becoming super humans with super capabilities and abilities because of AI. And I love the message that you're sending. It is positive and it is very practical to think about.

Tim, thank you so much for your time today. It's been lovely speaking to you.

thank you for having me.

Wrapping up today's episode, I think there are three key takeaways from our conversation with Tim. First, software development is the clearest model available for how AI will transform every other part of the business. The reason is specific. When AI generates code, you have compilers, tests, and deployment environments to validate the output with less human judgment and lower cost. Second, the individual productivity gains from AI are real but uneven, and they are not the main event. The dramatic value will arrive when AI becomes a catalyst for teamwork rather than an enhancer of individual performance.

And finally, the build versus buy decision that has defined enterprise software purchasing for thirty years is being replaced by a build versus bold reality. As the cost of software development drops and AI makes customization more accessible, the expectation of perfection in enterprise software will become standard rather than exceptional. AI is moving fast, new tools, new research, new use cases every week. Emerj synthesizes what matters most so senior leaders and practitioners can stay ahead without getting buried.

Join the more than 85,000 subscribers and get the most useful AI business insights delivered to your inbox. Visit Emerj dot com slash ad one. That's E M E R J dot com slash a d one. On behalf of the team at Emerj, we'll see you on the next episode.