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1% Better Fast 15 Podcast – Digital Engineering Quick Links

Learn more about Human Centered AI
Check out the Digital Engineering Healthcheck
Read Brian’s blog, Designing for Humans – The Future of Digital Engineering in an AI World
Connect with Brian Zielinski on LinkedIn
Connect with Craig Thielen on LinkedIn

Key Takeaways

• Digital Engineering is holistic: It’s not just software engineering; it includes quality, delivery pipelines, automation, and user experience
• Human-Centered AI accelerates, not replaces: AI should empower humans to deliver faster and smarter while keeping creativity at the core
• Intelligent Automation is evolving fast: Agentic AI and automation across CI/CD and security pipelines are essential for efficiency
• Speed of value delivery is critical: 80% of CTOs say faster delivery drives competitive advantage, and customers notice
• Start with priorities and quick wins: Assess maturity, identify your biggest pain point, and take practical steps toward improvement

1% Better Fast 15 Podcast – Digital Engineering Transcript

Craig Thielen (00:07)
Hello, I’m Craig Thielen and this is the 1% Better Podcast Fast 15. Today I’ve got Brian Zielinksi with me and we are gonna dive into Digital Engineering in 15 minutes or less. So Brian, you ready?

Brian Zielinski (00:21)
I am absolutely ready. Thanks for having me.

Craig:
Let’s do it. Well, let’s first start with who’s Brian Zielinski?

Brian:
Oh, I am a technology veteran of 25 plus years. My career started in Software Engineering and that’s definitely a passion that I still have. I still like to be hands on and maintain those skills. But throughout my career, I worked my way up to be a Software Architect and then a leader. And eventually I was VP of Technology of HR tech company and so I can involve facets that really digital solutions touch a lot more than just from a developer perspective. And I think it’s given me a unique view that I can hopefully bring to clients to help them produce better and faster.

Craig Thielen (01:07)
Super. Well, why don’t we start with terminology. So we started by saying Digital Engineering You mentioned Software Engineering So what’s the difference?

Brian Zielinski (01:16)
Yeah, Software Engineering is only one aspect of Digital Engineering. Really, the way that at Trissential that we see Digital Engineering is kind of this holistic platform of capabilities, all of which are essential for delivering transformative digital solutions. If you want to produce something that has value that will last, you can’t shortcut any piece. You can have great Software Engineers, but if you don’t have continuous quality, they’re not gonna deliver great products into production on a regular basis. so, and ultimately, if you don’t have a great delivery pipeline, doesn’t matter how fast everything else is before that, you’re gonna struggle to get releases out. So really it’s a concept that we see a number of different aspects playing together that produce ⁓ really a whole that’s greater than even the sum of those parts.

Craig Thielen (02:09)
Well, as they say, Brian, a picture says a thousand words. So this might be one of those cases where if we see a visual, maybe it’ll help understand what you’re talking about here. So are OK if I show our Digital Engineering picture here? Perfect. So why don’t you take us through this? Because I think this framework, so to speak, is, and we call it the Modern Approach to Digital Engineering, it really kind of puts all those things on a map and then glues them together. So maybe walk us through this.

Brian Zielinski (02:42)
Yeah, I think one thing I’ll point out is this isn’t a cycle that’s ordered in some particular way. It really truly is continuous self-reinforcing system where everything matters. This is not the SDLC… This is really just a view that we have of Digital Engineering as some of these capabilities that allow you to produce value much faster, much more efficiently, and always, always, ⁓ it’s going to be anchored in what we call Human Centered AI. So this is AI that puts humans first. It’s accelerating what we’re doing. It’s not replacing us. It’s making us more efficient, more productive, more capable. And ultimately, has to be underpinned. And the foundation of it all is lean, agile, continuous improvement. we work a lot of clients who have pretty good tech, know, they’ve got great Software Engineers, but they’re struggling with process. And so it’s another critical element to Digital Engineering. It’s not just engineering in the strictest sense of software, but ultimately engineering in producing value based on the strong fundamental principles of architecture.

Craig Thielen (04:00)
So what some of the things that jump out to me, Brian, on this is, you mentioned Software Engineering, user experience. I think most people understand what that is. If, if they’re not in the weeds of designing, developing software, there’s certainly, we’re all users of apps and whatnot… quality engineering, DevSecOps, cloud. These are things that have been around for a while. I’m going to get to the automation and Human Centered AI in a second, just as far as those components, what do you see as where organizations, again, these are not novel or new sort of constructs here, but where are you seeing that they’re not really being deployed or they’re not being integrated or they’re not being, they’re not as mature as they should be.

Brian Zielinski (04:51)
Yeah, well, think it’s really coming in, what is the focus of the organization? I’ve been with a lot of different companies myself in my career and worked with lot of clients since coming to Trissential and some organizations have focused a lot on getting great Software Engineers, but haven’t really thought about quality. It’s an afterthought for them. They think that if they have the best engineers, they’ll be able to produce high quality code without having to bring in things like test automation, which are essential for catching regressions and all the kinds of things that the engineers do. Having been one myself, I know I make mistakes. I need that safety net to make sure that whatever I’m doing is reliable. So I see very often it’s a lack of this holistic viewpoint of Digital Engineering and thinking any one area can be sacrificed so that you can invest more in another. And I think overall, that’s just, that’s a process that’s going to lead to it failing somewhere. And we really, really see, I think in terms of the big, one of the big challenges today is user experience. It’s changing so rapidly with AI, people are using natural language interfaces. So that’s a field that is rapidly moving and changing that needs to be rethought and really needs a fresh, again, approach that takes into account all aspects of this solution design philosophy.

Craig Thielen (06:20)
Yeah, I mean, you mentioned the user experience. That’s been a popular topic the last decade or so in the world of apps. And we all have such great user experience through the many millions of apps that are out there. But it’s changed dramatically in the world of AI now that we have a whole new way to interact with computing via this human language.

Large language models very different user experience, so I got to imagine there’s a lot of change there, which leads me to I want to get into that kind of the new kids on the block here… Intelligent Automation and Human Centered AI… of course you mentioned test automation, but that’s just one small component of an overall automated toolchain and then Human Centered AI… so maybe just talk about those emergent components to this that weren’t on this perhaps five or ten years ago.

Brian Zielinski (07:17)
No, I think for Intelligent Automation think that’s really being transformed very rapidly right now with agentic AI. And I think we’re starting to see that pay off, but I think we’re only just beginning to. And we’re really going to see a massively changed world in how we’re able to use AI to accomplish automation without bringing in what I would call kind of low code, no code engineers.

Thankfully, we’re not hard coding systems anymore and we’re using a lot of great tools ⁓ that have that low code, no code. But we don’t even need to do that. Ultimately, as Agentic really matures and becomes a reliable platform, there’s time for that to happen, but it is still paying off right now. And it’s something we see as really where Intelligent Automation is going. But there are a lot of ways to achieve that. Like you said, there’s things within the entire delivery pipeline in terms of automating CI/CD and automating security throughout your pipeline. So Intelligent Automation is pretty much a blanket term, but we see that as essential. If you’re not using that, you’re not getting the most out of your teams. And that really fits in well, I think with this concept of Human Centered AI, which is in a lot of ways sort of like this evolution of Human Centered design. It’s really saying humans should always be first.

Humans are the ones with a creative power that even the best large language models, today anyways, can’t compete with. And so we want people to use their trade of energy, use their experience, use the knowledge that they have, but accelerate the application of that into systems. So it’s really all about finding the tools that do things like extract user stories from business requirements. So very often, we’ve seen one of the challenges a company might have or a company culture might have is moving to agile, right? You have their waterfall, they’re used to it, they’ve been doing things that way for decades, and they want to move to agile. And one of the key focuses in an agile environment is being able to put things into a user story, you know, this sort of actor-goal-reason concept. And it’s hard for people to do it first. Well, what we’re finding is AI allows people to make that transition much easier. It’s helping transform the documentation that they have. Maybe it’s old spec sheets or it’s a user manual to create the context that allows them to interact with that AI and produce very high quality user stories. So it’s accelerating the maturity level for an agile process in ways that we’ve never had before. And that’s really just one way. It’s obviously, if you look at some of the elements on this visual, Software Engineering is being accelerated rapidly. Software engineers who are not using AI tooling are really doing a disservice to themselves and to those they work for. And this brings up, I think, one of the interesting trends that I’m seeing, and even in some of the clients I work with today is this last mile problem of AI that is really about people just not trusting it, not feeling like they need it maybe or that they can rely on it. And that’s especially strange but surprisingly common with Software Engineers I think there’s this reluctance to trust code that’s been generated even though it’s been proven to accelerate delivery times by at least 30%, if not more.

And so I think that’s something that I think we’re working on with all of our clients is, what are the tools that you can take advantage of at every step of the software or system development lifecycle that use AI to make humans produce value faster than they could before, but still, of course, be at the center and be in control.

Craig Thielen (11:20)
I think that picture really does a great job of making it simple and saying what are the main actors, the main components that we need to be good at. And if you look at it closely, there’s not a single one of those that you say, no, we don’t really have to worry about that. If you don’t embrace it and get on sort of an improvement journey with each one of those things, you’re gonna be deficient in a pretty serious way.

Either you won’t be able to deliver as fast, as high quality, you won’t be able to do it as cost efficiently, et cetera, et cetera. I think it’s a nice diagram, I guess, but let’s take a step back from that. What other trends and challenges, I mean, you’re working with different clients, different size organizations, different size teams, different maturity levels, different technologies. What are you seeing out there for trends and challenges and opportunities around that whole spectrum?

Brian Zielinski (12:14)
Yeah, I think the two biggest trends or what I kind of call it the Digital Leaders Dilemma is now more than ever speed of delivery matters and speed of value. think we have a stat that’s something like 80% of CTOs say that the ability to deliver value faster is essential for competitive advantage. And something like almost 70% of customers will switch platforms if they see a platform that’s getting updates faster when they sort of sense that there’s more investment happening there as they see more value being delivered even if it’s small and incremental. So this ability to rapidly iterate, produce value, and continuously deliver is accelerating and that’s posing its own challenges but it’s ultimately met with this ⁓ mountain of complexity that digital leaders are dealing with.

If it’s tech debt, I mean, the last 20 years of technology have seen, you know, changes of frameworks. It used to be every 10 years, you could rely on a platform to change. Now it’s every two to three years, an entire front end framework goes obsolete. So we’re seeing mountains and mountains of tech debt from systems that aren’t even that old. But then we’re also seeing the need to integrate into so many different platforms. And biggest challenge there, is very often that was done with what I would call unintentional architecture. someone didn’t go in and think, how can we build this with a platform engineering mindset to ensure that we can play in this ecosystem that has all these various dependencies? And they even thought they were doing microservices when really they were just creating overly complex monoliths. It’s been a kind of perfect storm of need to move faster and harder than ever to move faster. But of course, without a doubt, AI is changing that. AI is continuing to push the need to deliver faster. It’s creating even higher expectations of consumers and business leaders that value will be produced faster, that change will happen faster. But it is also that tool that if you don’t take advantage of, you’re going to struggle to be able to meet that case.

And we see it as really a way to revolutionize things like TechDev. In the past, it was really challenging to move from, say, one development platform to another. You wanted to move from Java to .NET. You didn’t really do that. It was crazy. But now, it’s quite a bit different. We have the tools and capabilities to make that a lot easier.

Craig Thielen (14:49)
Yeah. Yeah.

Yeah, it seems like there’s a such a dichotomy with tech debt simply put is if you had to describe it in a single term, it would be unintentional architecture. Almost all companies have an architecture that they would never set out to go and build. It just doesn’t make sense. There’s redundant technologies, applications, usage. There’s out of date software. There’s stuff that’s not even compliant. There’s all sorts of redundancy and and there’s lot of reasons for that. There’s acquisitions and there’s different business leaders have different ideas and different technology leaders and there’s a burn and a churn and a lot of organizations are complex. So that’s the dichotomy is we have an increasing complexity internally and externally with compliance and competition and all this and then you have the need for speed and so you don’t have time to go fix the tech debt.

So with that, that’s kind of the world we live in. What would you give anyone who’s at some point in the journey, what are some good ways to get started on a journey to unwire that and get on a path so you can change and go faster, but yet you still have some cleanup work to do.

Brian Zielinski (16:08)
Yeah, I think number one is assess your priorities. Maybe it’s you just want to move faster. Maybe you need to operate more efficiently. Maybe you just need to be more reliable and you have quality challenges. think find out what is the single principle. They all matter, but which is the single principle that you need to anchor to right now to show value to your leaders and to accelerate the building of value for your organization. Once you’ve established that, I think it’s really, it’s starting to build the process of understanding how do you have the right tooling and processes in place. So understanding maturity level is really key. That’s why we’ve come up with this concept that we call the Digital Engineering Healthcheck – really lightweight, very quick assessment and recommendation. It can be done in less than two weeks that we, based on what we see in the industry and all the clients that we’ve talked to and all the large assessments that we’ve done, we can now rapidly do a maturity assessment that provides a heatmap and recommendations for quick wins. I think this is what from some of my recent clients that they’re seeing most value in is, yeah, we’ve had assessments and we’ve had roadmaps that talk about what we’re gonna do for the next year, but very few focus on what do I do right now to start producing more value. That’s more of the agile mindset, right? It’s really the way in which…

Craig Thielen (17:33)
Something that’s practical. Something that’s practical It’s practical, they can implement now and they can build upon that they don’t have to reinvent. Makes sense. Well, great. Well, hey, thanks, Brian. That went by fast. Appreciate it. Thanks for being on 1% Better.

Brian Zielinski (17:39)
Absolutely. Absolutely. Thanks for having me, Craig.

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