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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#843: Pega's Matt Healy on the hidden costs of outdated technology
00:25:57
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- [00:00]
A
What if the biggest obstacle to your AI strategy isn't the algorithm, but the 20 year old software your team is forced to use every day? Agility requires not just a willingness to adopt new technologies like AI, but also the courage to dismantle the legacy systems that hold your people and your processes captive. Today, we're going to talk about the hidden costs of outdated technology. While many leaders are focused on implementing the next generation of AI, new research suggests that the legacy systems still running in the background are not just inefficient, they're actively eroding employee morale, productivity, and could even be a major factor in employee turnover. Welcome to season eight of the Agile Brand Podcast. This season we're going all in on Expert Mode, MarTech AI and Customer Experience, talking with the people and platforms behind the brands you know and love. Again, I'm your host Greg Kilstrom and I help Fortune 1000 companies make sense of martech AI and marketing ops. Hit subscribe or follow to make sure you always get the latest episodes and leave us a rating so others can find us as well. PEGA provides the leading AI powered platform for enterprise transformation. The world's most influential organizations trust pega's technology to reimagine how work gets done by automating workflows, personalizing customer experiences and modernizing legacy systems. Since 1983, Pega's scalable flexible architecture has fueled continuous innovation, helping clients accelerate their path to the autonomous enterprise. Learn more@pega.com to help me discuss this topic, I'd like to welcome Matt Healy, Senior Director, Product Strategy and Marketing at pega. Matt, welcome to the show.
- [01:46]
B
Thanks Craig. Thanks for having me.
- [01:48]
C
Yeah, always good to see you. So yeah, looking forward to this topic here.
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A
Before we dive in though, why don't
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C
you give a little background on yourself and your role at pega.
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B
Yeah, sure. So as you mentioned, I help out with the strategy and the go to market for our PEGA platform. So that includes helping think about how we are enabling enterprises around building enterprise applications faster, using AI across the software development life cycle and then helping them automate their back office processes, their customer service workflows now increasingly through the use of agentic AI. And as we're going to dive in, a lot of what we're doing as well is helping enterprises really accelerate the transformation process through those sort of two lenses and thinking about getting off of legacy systems in the mix there.
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C
Yeah, yeah, and we're definitely going to dive into that quite a bit. And one of the things so Pega recently released some Research, and we'll talk about that a bit here. And the research highlights a pretty major disconnect that leaders are chasing AI innovation. Certainly we talk about a lot on the show. I think everyone talks about it many hours of many days while they're chasing this AI innovation. You know, employees are still struggling with frustrating legacy systems. Right. So how, you know, from a strategic perspective, how does this technology debt undermine that company's broader and strategic transformation goals?
- [03:22]
B
You guys talk about AI on this podcast?
- [03:24]
C
Yeah, Surprise, surprise.
- [03:27]
B
Okay. You're the one. Oh, yeah, absolutely. So if you zoom out and you think about the broader transformation goals and Legacy's impact. Right. This has been the case where Legacy has been an anchor at really dragging down transformation opportunities and the execution for decades, probably. Right? At least many, many years. And the conversation to this point has been around like, how can we accelerate to the cloud, how can we accelerate to microservices, how can we improve the customer experience and get to digital? Right. So the imperative around getting off of Legacy has always been there. I think that AI now increases some of the urgency where if your data is trapped in on prem data databases or it's trapped in proprietary data, data structures, whatever it may be, you're just unable to unlock, you know, the sort of fuel that you need to power AI driven transformation now. So there's all the same imperatives around getting off of legacy systems that have always been there. Customer experience, automation, you know, overall cost and maintenance and operations. And now this just adds more fuel to the fire. The good news is I think that today where we sit, AI is not just the urgency to get off of legacy systems, it's not increasing just that perspective of it, but it's also helping provide a solution or a path forward. So where yesterday or years ago, it would take you years to get off of a legacy system because you needed to understand what's in there, you needed to translate that into your future state goals. You needed to then write down your sort of prioritization about how you're going to buy piecemeal, pick out different parts of that legacy system, then you need to build the application, you need to test it, blah, blah, blah, blah, blah. And that's all manual back in the day. Today, AI can offload a lot of that work and that's really part of what we're focused on here, is accelerating the transformation journey by using AI to help you understand your legacy systems, reimagine them and then deploy them as new systems in the cloud.
- [05:41]
C
Yeah, well, and there's lots of reasons to do that, of course. I mean there's a lot of what I would call business goals and customer goals even around that. But there's also the research states over a third of employees would consider leaving their jobs due to poor technology. So there's an employee experience component here. And what I think we all know is unhappy employees lead to unhappy customers and it's that, that, that, that effect there. So you know, how should leaders reframe this from legacy being solely an IT issue to a critical business risk that impacts things like talent retention and even competitive advantage?
- [06:24]
B
Yeah, absolutely. I mean everyone talks about how the expectations of consumers have shifted over the years, right? Towards yeah, away from like passing on manual documentation and emails and whatever, towards digital self service, proactive, preemptive, simple, guided. That's what consumers expect. The funny thing that, you know, maybe comes up less is like those consumers also work somewhere. Yeah, totally. Like I, and like I work, I work at a company. I also engage with companies, you know, to get services done. Like I expect banking to be simple, I expect, you know, insurance to be simple, etc. Etc. So when I come into work and then I log into, I don't actually do this at pega, but like I log into a terminal based mainframe application, right. That's just completely disconnected from my life outside of work. And you know, having to make that shift and you know, to more manual platforms, to less guided platforms, less intuitive, less accessible, it's just jarring. So you know, as you mentioned, this is really disgruntling for an employee. And I think part of that is, you know, some of what we found is around 50% of employees said that they're like they're not their legacy but their, their tech stack that they're given. The platforms that they're given don't allow them to be their best, it doesn't allow them to do their best work, which I think just leaves a lot of people unfulfilled, frustrated, which then leads to retention. So you know, enterprises end up in this sort of churn state and then when they do bring someone new in, it takes a long time for them to get up to speed. How can you expect to bring in like a new college graduate into a place where they have to hop across 12 different systems and they're each a different technology type and some of them were written back in the 1980s and 1990s. It's just infeasible. So it's a, it's a sort of, it's just a snowball effect that they have Gone.
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C
Yeah, well, and I think a lot of organizations have tried to solve this as a training issue as well. So, you know, if they just knew how to use it better then, you know, they'd be happy and you know, all that stuff. But you know, the research certainly shows that it, you know, I'm sure that helps. You know, it helps to have mastery over what you're doing. But it's not just training, it's, it's a performance and it's a usability problem. Right. And so, you know, when an organization makes that decision to modernize, what, you know, what steps should they take to replace that? You know, what formerly was friction with efficiency. Especially when in, you know, in the enterprise there is no like rip and replace. There's everything's, everything's going to happen in phases, even if it's quick phases, you know, so how, you know, what are some practical steps that they should take knowing that it's a multi step phase? Let's just say, yeah, definitely.
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B
And this also, it kind of gets to where AI has changed the paradigm. So my answer a couple of years ago would have been way different. But today, you know, I think for step number one is understanding what you're doing today and what's in your existing platforms. So with AI, we can now, you know, take in all of the source code. We can take in the sort of user manuals that are provided to someone who's actually leveraging one of these systems. We can take in even videos of a user leveraging the system in their day to day and just get a sort of analysis of all right, what are the, what are the end to end processes in here? You know, how does it conform to best practices? Where are the opportunities to optimize? What's the impact of maybe poor experiences that are going on in these platforms and just give leaders, you know, sort of an understanding, an independent understanding of, you know, what's the experience, where do things fall apart and allow them to sort of think about next steps. And then, you know, from there AI can really help take a lot of those next steps and not just sort of rebuild what you have today into a newer technology which is, you know, some of the older approach was just, all right, I've understood my source code as it exists today, that might be in cobol, let me rebuild it in Java, but not change anything about how it works. What you can do now with AI is take the concepts, take the processes, take the regulations, take the business logic and actually reimagine everything that's being run through it to deploy an optimized process, an optimized experience that streamlines and automates a lot of what was previously done manually. So that's sort of how we approach it. But it definitely starts with that overall understanding, which you can do at a sort of portfolio level and then pick the sort of best places to move forward with that next approach.
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D
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- [12:32]
C
And of course it's while there's often ambitious goals in, you know, in the transformation, you know, a lot of the workers in your survey cited rather simple things like automation of repetitive tasks as a key need. So you know, while there's that, that longer term vision and, and things being being implemented, how can leaders strategically deploy AI to again not just achieve those, those far off things, but solve some really practical tools for, you know, reasons. We already discussed the morale, the, you know, all of that stuff.
- [13:09]
B
Yeah, definitely. This is again like, you know, maybe I'm too much of a pragmatist, but my downfall too pragmatic. But this is again where like I like to start with visibility, you know, leveraging mining tools, some like workforce intelligence type tooling to understand like what are people doing. I get like there are going to be the squeaky wheels who complain about certain parts of the process and it's like, yeah totally, that's good input but let me see what's going on on the ground. What are the clicks that they're making, what are the sort of like long poles in assignment processing that are actually happening just to have that understanding. And then, you know, I know AI is all the rage and agents are very capable, so it might end up being that like, AI is the solution to the problem. And I think increasingly so it will, but it's not the only solution to the problem. So it also is really important to sort of understand the problem at hand and really think through, like, what's the best tool for the job. And it might be some more deterministic approaches or maybe, maybe AI is the solution there. But, you know, you got to think through independently before you just get to, hey, we need to deploy AI here to deliver on our strategic imperatives or whatever it may be.
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D
Right, Right.
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C
Yeah. And so let's talk a little bit about how we measure then. So the report also just details feelings of, you know, frustration, exhaustion, demotivation, you know, things that are palpable when you're in the room with someone, but often hard to quantify. And yet, you know, we, we know they have a real business cost to do, to churn. And some of the things that you mentioned before. So, you know, beyond things like uptime and support tickets, what, what are metrics that leaders should be tracking to measure some of this, you know, impact of, of of modernization on the employee experience?
- [15:09]
B
Yeah, I think, like, one really good thing to look at that I know increasingly I've heard enterprise leaders looking into in the talks I've had is how specialized are their various teams. And that's a good indicator to complexity of their work and complexity of their systems. So, you know, take a look and find out, like, if a customer service issue comes in of a certain type of, based on its characteristics, could it go to 12 different teams because they're the only people who are sort of skilled up to handle it. And to know the various codes in the systems that they need to look for, the various lookups, that's an indicator that, hey, it's time to maybe simplify a little bit. It's time to rationalize. And I think that also unlocks some really tangible business benefits as you can think about employee fungibility and generalization and being able to, you know, make sure that you, you always have someone available to deliver on the work that you have, regardless of the type of work. So that's, that's a really good one. I think it's also, you know, some of the more apparent ones, like track how many applications does an employee use every single day, how many times are they switching across them? Obviously, there's productivity loss involved with that there's just training time that's involved with that. So just getting an understanding of what, what does an employee's day look like. And then you can sort of use that as grounding in terms of how can you simplify.
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C
Yeah, yeah. And then so from, from the CFO's perspective, let's say modernization projects, you know, sounds expensive, sounds like a, you know, a massive cost center. Right. So how do you build the business case? I mean, you know, we've talked about a lot of, a lot, a lot of proof points here, here. You know, how do you connect investments in things like better internal technology to tangible outcomes? Like some of the things you mentioned, you know, productivity, reduced errors, lower turnover, you know, how does that case get made?
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B
Yeah, I mean, I think AI has changed both the numerator and the denominator of the ROI calculation. So, you know, the case, the business case, has to be made in a holistic sense in that there's the apparent costs of your legacy system, there's the operations, the maintenance, the support, the things that show up as line items, and then there's the less apparent costs and really the opportunity cost in that if you got off of the system, what could you do in terms of driving new business, in terms of driving growth, in terms of driving personalization and automation? So you got to take all of that into account to really calculate, you know, the benefit. And then the cost is, you know, something that used to be, if you were looking at it like if you were to get off of a mainframe, and we had a great presentation, I'm gonna plug a little bit by a client at aws Re Invent Unum, an insurer. And we had one of the VPs of ITU that we've been working with talk about his journey around mainframe modernization. And he was like, hey, yeah, we're doing disability claim modernization, trying to get it off of the mainframe into the cloud. And we tried this about, you know, a couple years ago and we had SIS come in and give us quotes. And they came in and they were like, hey, this is going to cost you, you know, $25 million and the project is going to take seven years. And this was pre AI, and now with, you know, naturally our approach, but, you know, now with the AI driven approaches, he was able to get from COBOL to a working application in the cloud for their disability claims in 90 days.
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C
Oh, wow.
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B
So the denominator of these calculations has shifted dramatically. So it's really important to look at the latest and greatest approaches to Modernization, because I think a lot of these ROIs have been unlocked which will hopefully allow people to start to get off these legacy systems faster.
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C
Yeah, I mean that's not even like seven years to seven months.
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D
That's seven years to 90 days.
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C
Jeez. Yeah, that's, that's amazing. So, you know, as, as, as these transformations happen and they're gonna happen more and more, you know, as, as these technologies, as AI is, is adopted, what does the day to day experience of those employees that, you know, used to be on the mainframe, used to be using all, you know, know all those tools and the, the repetitive tasks, you know, what does it start to look like and you know, how does their role change when technology is more of a partner rather than an obstacle to getting things done?
- [19:58]
B
Yeah, I would also, I would be a fool if I made any like, strong predictions of where this is going. Right. Just given how fast the, the technology space is moving right now, I do kind of envision that everyone will have AI assistance. Work will be a lot more of a conversational manner, just engaging through some sort of agent fabric with a ton of AI under the hood which can actually execute on what you're trying to get done. So I think we will all sort of be naturally lifted out of platforms and systems and be moved towards guiding AI in doing a lot of work. So that's, that's where I see it going. I think, you know, there's obviously a long way to, towards that. That's going to be a little bit of a journey from where people are. I think the thing that we'll probably see, you know, on the way to that journey is just simplification and a little more fungibility, as I mentioned. So, you know, I was, I was working with a client who was getting off of the mainframe and they have people in their back office who have been working in the mainframe for 30 years and they know like all of the tips, all of the tricks, they know every single code they have to pass in and you know, I think we'll see a lot less of that as there's turnover in the industry and as people's expectations shift. So it'll start with simplification and then I think we'll see the emergence of AI sort of driven work and assistance over the next couple years.
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C
Yeah, yeah, I love it. Well, Matt, thanks so much for joining today. Got two last questions before we wrap up here. If we were having this interview one year from today, what is one thing that we would definitely be talking about?
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B
I hope that in a year. Right now the conversation is very much around how quickly can we adopt AI at scale, which is good. Everyone's trying to cut costs, everyone's trying to stay ahead and be the disruptor, not the disrupted. I get it. I think in a year the conversation will shift to how can we provide the human touch, how can we best utilize our people in the moments when our customers need it and be really intelligent about that so we don't appear, we don't show up like a robot wasteland to our customers. So that's the conversation I expect. That's kind of the conversation I hope to be having.
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C
Well, I have to have you back on to have that conversation then.
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B
Absolutely.
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C
And last question for you. What do you do to stay agile in your role and how do you find a way to do it consistently?
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B
I get hands on, I make sure I read about new capabilities, new advancements, and I make sure that I actually see if they work and make make the conclusion for myself again, I'd like
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A
to thank Matt Healy, Senior Director of Product Strategy and Marketing at pega, for joining the show.
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C
You can learn more about Matt and
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A
Pega by following the links in the show notes and make sure to check out Pega World 2026, June 7 through 9 in Las Vegas. Pega provides the leading AI powered platform for enterprise transformation. The world's most influential organizations trust pega's technology to reimagine how work gets done by automating workflows, personalizing customer experiences and modernizing legacy systems. Since 1983, Pega's scalable flexible architecture has fueled continuous innovation, helping clients accelerate their path to the autonomous enterprise. Learn more@pega.com and thanks again for listening to the the Agile Brand podcast. If you like the episode, hit subscribe and drop a rating so others can find the show too. And if you're interested in consulting, advisory work, or if you need a speaker for your next event, feel free to reach out. Just visit GregKilstrom.com that's G R E G K I H L S t r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
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B
The Agile Brand.