Intro

Is AI underdelivering? Or are we asking the wrong questions? This episode breaks down what actually leads to business ROI with AI (and no, it’s not more automation).

Overview

What if AI isn’t the silver bullet—yet—but the bottleneck is human, not technical?

In this episode, Brian Milner chats with Evan Leybourn and Christopher Morales of the Business Agility Institute about their latest research on how organizations are really using AI, what’s working (and what’s wildly overhyped), and why your success might hinge more on your culture than your code.

References and resources mentioned in the show:

Evan Leybourn
Christopher Morales
Business Agility Institute
From Constraints to Capabilities Report
Delphi Method
#93: The Rise of Human Skills and Agile Acumen with Evan Leybourn
#82: The Intersection of AI and Agile with Emilia Breton
#117: How AI and Automation Are Redefining Success for Developers with Lance Dacy
AI Practice Prompts For Scrum Masters
Join the Agile Mentors Community
Subscribe to the Agile Mentors Podcast

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This episode’s presenters are:

Brian Milner is SVP of coaching and training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.

Evan Leybourn is the co-founder of the Business Agility Institute and author of Directing the Agile Organization and #noprojects; a culture of continuous value. Evan champions the advancement of agile, innovative, and dynamic companies poised to succeed in fluctuating markets through rigorous research and advocacy.

Christopher Morales is a seasoned digital strategist and agile leader with over 20 years of experience guiding organizations like ESPN, IBM, and the Business Agility Institute. As founder of Electrick Media, he helps U.S. and European businesses harness AI to make smarter, more sustainable decisions in a rapidly changing world.

Auto-generated Transcript:

Brian Milner (00:00)
Welcome in Agile Mentors. We are back for another episode of the Agile Mentors podcast. We've kind been a little bit off and on recently, but I'm back, I'm here, I'm ready to go, and we've got a really good episode for you today. I've got two, two guests with me. I know that's not a normal thing that we do here, but we got two guests. First, we have Mr. Evan Layborn with us, who's back. Welcome back, Evan.

Evan Leybourn (00:23)
Good morning from Melbourne, Australia.

Brian Milner (00:26)
And Christopher Morales is joining us for the first time. Christopher worked with Evan on a project and we're going to talk about that in just a second, but Christopher, welcome in.

Christopher Morales (00:35)
Yeah, good evening. Nice to be here. It's very late here in Germany. So this is an international attendance.

Brian Milner (00:42)
Yeah, we were talking about this just as we started. I think we have pretty much all times of day represented here on this call because we've got morning here from Evan. We've got late evening here for Christopher and I'm kind of late afternoon. So we're covered. All our bases are covered here. But we wanted to have these two on. They both work for a company called the Business Agility Institute. And if you have been with us for a while, you probably remember Evan's episode that we had on last year when we kind of talked about one of the studies that they had done. Well, they put out a new one that I kind of saw Evan posting about. And I thought, wow, that sounds really, really interesting. I really want to have them on to talk about this. It's called From Constraints to Capabilities, AI as a Force Multiplier. The great thing about the Business Agility Institute is they get into the data. They do the research, they put in the hard work, and it's not just speculation. It's not just, that's one guy's bloated opinion, and do they know what they're talking about or not? So that's what I really, really appreciate about the things that come out of the Business Agility Institute is they're factual, they're data-based. So that's what I wanna start with, I guess, is... What was the genesis of this? What did you guys, how did you land on this as a topic and how did you narrow it down to this as a topic? Where did this start?

Evan Leybourn (02:07)
Well, quite simply, it started from almost a hypothesis around so much of the conversation around AI. And let's face it, there is a lot of conversation around artificial intelligence and specifically generative, predictive and agentic AI. Focuses on the technology. And yet when we talk to organizations, a lot of them don't seem to be seeing a positive return on investment, a positive ROI. And we needed to understand why, why these benefits of like three times products or operational efficiency product throughput, three times value creation, Why weren't companies seeing this? That's really what we were trying to understand. Why?

Brian Milner (03:01)
Yeah, that's a great basis for this because I think you're right. There's sort of this, I would imagine there's lots of people out there who are kind of going through their business lives and hearing all these incredible claims that people are making in the media about how this is gonna replace everyone. And now it's, yeah, we can, I mean, you said 3X, I've heard like, 10 or anywhere from 10 to 100X, the capabilities of teams and that they can now do all these amazing things. And if I'm just going through my business career, I'm looking at that from the outside going, is this fact or is this fantasy? this just a bluster or is this really, really happening? So I really appreciate this as a topic. A little bit of insider baseball here for everybody. You guys talk about in this report that you use a specific method here, the Delphi method. for data geeks here, or if you're just kind of curious, would you mind describing a little bit about what that means?

Evan Leybourn (04:00)
Chris, do you want to take that one?

Christopher Morales (04:01)
Yeah, well, so the idea behind using the Delphi method was actually inspired by my sister. She had done a periodic review that utilized this method. And essentially what it is is we utilize rounds of inquiry with an expert panel to refine the research, the feedback that we're getting. And so we collected an initial set of data. reviewed that data, tried to analyze it to come up with a consensus, and then repositioned our findings back to the experts to find out where they stood based on what they gave us. And really trying to get all of the experts to come to an agreement in specific areas. In the areas that we found gray space, for instance, or let's say, data was spread out, right? Those were really the areas where we're really trying to force these experts to get off of the fence and really make an assessment. And it was proved extremely helpful, I think, in this research because what I find in the AI space is that there is plenty of gray. And we really wanted to get to some stronger degree of black and white. I'm not going to say these findings are black and white, but I will say that in order to guide people, you need to give them degrees of confidence. And I feel like that's what we wanted to do with this.

Brian Milner (05:31)
Well, that's the great thing about research though, Is it can give you information, but there's always the story. And it's really kind of finding that story that really is the crux of it. So we open this saying, fact or fiction. So just hit us up with a couple of the, maybe some of the surprising findings or some of the key things. For the people you talk to. Christopher Morales (05:38) Mm-hmm.

Brian Milner (05:53)
Were they seeing these amazing kind of, you know, 100 X of their capabilities or what was the reality of what people reported to you?

Evan Leybourn (06:01)
In a few cases, yes. Maybe not 100x, but 8x, 10x was definitely being shown. But the big aha, and I won't say it was a surprise, was really in a lot of organizations, the teams that were using AI were seeing

Brian Milner (06:03)
Okay.

Evan Leybourn (06:23)
absolutely massive improvements. People talk about going from months to minutes in terms of trying to create things. And so there's your 100X. But when we look at it at a business level and the business ROI, when we look at the idea to customer from concept to cash, when we look at the overall business flow, very few of those organizations saw those benefits escape from the little AI inner circle. And so that 10x or the 100x improvement fizzles into nothingness in some cases. negligible improvement in the whole organization. Some organizations absolutely saw those benefits throughout the entire system. And those were organizations who had created a flow, who created organizational systems that could work at the speed of AI, especially some of the younger AI native organizations, if you want to think of them that way. But no, most organizations those 10x, 100x kind of goals were unachievable for the business. And so when I was saying 3x, by the way, what we sort of tended to find is those organizations, mature organizations with mature AI programs and systems. we're generally seeing between a 1.2 to 1.4x improvement to about a 2.8 to about a 3.2x improvement. So that's like a 20 % to a 300 % improvement if you want to think of it this way.

Brian Milner (08:15)
Wow. Well, that's nothing to sneeze at. That's still really, really impressive.

Christopher Morales (08:19)
yeah, it'll make a significant difference. I think for me the interesting thing about the findings is that there's two areas that I think will pose a really interesting question for people who read the report, and that is this idea of being very intentional about identifying your goal, right? I don't know how many organizations are really meaningfully identifying what their expected outcome is. And I think the other thing, which we didn't really talk about much in the report, but I think plays a role in the conversation that's kind of bubbling to the surface here today, has to do with the human element inside of the organization. And while all of the organizations that we spoke to said that the human was a very important element and prioritized, There was a challenge in identifying specific initiatives that were being put in place to account for the disruption that the technology might have on the staff or the employees. And that wasn't surprising. That was kind of expected. But I think it's interesting that, you know, eight months after we released this report, I would argue that that's still the case.

Brian Milner (09:36)
Mm-hmm. Yeah. Yeah, that's fascinating because you're right. It's, it's, that's not the story you always hear, because you, you are hearing kind of more of taking the human out of the loop and making it more of just this straight automation kind of project. I want to ask really a question here though, Evan, said you made the distinction about it being more mature, groups, more mature organizations. I'm just curious, is that translate to, is there anything that translates there into the size of the organization as well? Did you find that more larger organizations had a different outcome than smaller, more nimble startup kind of organizations?

Evan Leybourn (10:14)
So age more than size. Younger organizations tended to be more, well, mean, they tended to be more agile. There's more business agility and through that greater benefits out of AI. These things are very tightly tied together. If you can't do...

Brian Milner (10:18)
Hmm, okay.

Evan Leybourn (10:38)
Agile or if you don't have agility as an organization, you're not going to do AI particularly well. And a piece of that goes to what you were just talking about in terms and you use the word automation, which is a beautiful, beautiful trigger word for me here because the reality is that the organizations that utilized AI, specifically generative or agentic AI, to automate their workforce rarely saw a high, like a strong return on investment. It basically comes down to generative predictive AI, generative and agentic AI tends not to be a good automation tool. It's non-deterministic. You pull a lever, you get one result. You pull the same lever tomorrow, you will get a different result. There are better tools for automation, cheaper tools for automation. And so we're not saying automation is bad. We're just saying that it's not the technology for it. The organizations that used it to augment their workforce were the ones that were seeing significant benefits. And now there are caveats and consequences to this because it does change the role of the human, the human in the loop, the human in the organization. But fundamentally, organizations that were automating or using AI for automation were applying an industrial era mindset and mentality to an information era opportunity. And they weren't seeing the benefits, not at a business level, not long term. And in some cases, did more harm than good.

Brian Milner (12:28)
That's really deep insight. That's really amazing to hear that. I'm interested as well. You found some places that were seeing bigger gains than others that were seeing bigger payoffs. Did you find patterns in what some of the hurdles were or some of the kind of obstacles that were preventing some of these that weren't seeing the payoffs from really taking full advantage of this technology?

Christopher Morales (12:52)
Yeah, absolutely. mean, we identified some significant constraints that, interestingly enough, when we talk about this, we obviously do workshops. So we were just at the XP conference doing a workshop. And when we talk about this, we identify the fact that our position is that the challenges to AI are a human problem, not a technology problem. And the findings reflect that because of the constraints that we found. only one of the major constraints was associated with technology and that was data primarily. The constraints that we identified had to do with normal operations within a business. So long budgeting cycles or the ability to make a decision at a fast rate of speed, for instance. These are all human centric challenges that independent of AI, If you're trying to run an efficient organization, you're trying to run an agile organization, right? Able to take advantage of opportunities. These are all things that are going to come into play. and, you know, as we like to say, like AI is only going to amplify that, right? So if AI can show you 20 more times, like the opportunities available to you is your organization going to be able to pivot? Do you have a funding model that can provide the necessary support for a given initiative? Or is the way things that run within the organization essentially giving you AI that provides you information that you can't move?

Brian Milner (14:31)
That's a great, yeah, yeah.

Evan Leybourn (14:31)
And think of it this way, if you're expecting AI to give you a three times improvement to product delivery, can your leaders make decisions three times faster? Can you get market feedback three times faster? And for most organizations, the answer is no.

Brian Milner (14:51)
Yeah. Yeah, that's a great phrase in there that Chris was talking about, like the AI will just amplify things. I think that's a great observation. And I think you're right. this is kind of, you know, there's been a thing I've talked about some recently in class. there's a... I'll give you my theory. You tell me if your data supports this theory or not. I'm just curious. You know, we've been teaching for a long time in Scrum classes that, you know, there's been studies, there's been research that shows that when you look at the totality of the features that are being completed in software development, there's really a large percentage of them that are rarely or never used, right? They're not finding favor with the audience. The audience is not using those capabilities. And so my theory, and this is what I want you guys, I'm curious what your thought is. If AI is amplifying the capability of development to produce faster, then my theory is that's going to only expand the number of things that we produce that aren't used because the focus has been sort of historically on that it's a It's a developer productivity issue that if we could just expand developer productivity, the business would be more successful when those other former studies are saying, wait a minute, that may not be it. We need to focus more on what customers really want. And if we knew what they really wanted, well, then, yeah, then productivity comes into play. But That's the human element again, right? We have to understand the customer. have to know. So I'm just curious again, maybe I'm out on a limb here or maybe that doesn't line up, how does that line up with what you found?

Evan Leybourn (16:41)
So the report's called From Constraints to Capabilities. And Chris, we spoke about the constraints. So maybe let's talk about the capabilities for a second. for the listeners who are unfamiliar with the Business Agility Institute, the model that we use for the majority of our research is the domains of business agility, which is a behavioral and capability

Brian Milner (16:45)
Ha ha. Yes.

Evan Leybourn (17:04)
Now, in that model, there are 84 behaviors that we model against organizations. But in this context, more importantly, were the 18 business capabilities. And so what we found was that the organizations that were actually seeing an improvement weren't the ones with the capabilities around throughput. So one of the capabilities deliver value sooner. That wasn't strongly tied. So the ability to deliver value sooner wasn't strongly tied to seeing a benefit from AI. But the ability to prioritize or prioritize, prioritize, prioritize, something so important we said it three times, was one of the most strongly needed capabilities. It correlates where organizations that were better at prioritization, at being able to decide which feature or area, what thing to do was the next most important thing. If you're got AI building seven or eight prototypes in the same time you used to be able to create one, great, you now have seven or eight options. Not that seven or eight are going to go to market. but you're going to decide, you've got more optionality. So it's not that you're be delivering more faster, though in some cases that is obviously the case, but you've got more to choose from so that if you make the right decision, you will see those business benefits. But the capability that had the strongest, absolute strongest relationship to seeing a benefit from artificial intelligence was the ability to cultivate a learning organization. That's not education, that's around learning, experimentation, trying things, testing things, being willing as an organization to say, well, that didn't work, let's try something else. And those learning organizations were the ones that were almost universally more successful at seeing a business benefit from their AI initiatives than anybody else. So yeah, just because you can develop features faster, it means nothing if it's not the right features that the customers want. And that comes from learning and prioritization and there are other capabilities unleashing. workflow creatively and funding work dynamically, for example, that came out strongly. But I just really wanted to highlight those two because that's the connection that you're looking for.

Christopher Morales (19:43)
Yeah. And if you think about your question ties directly into something that we heard at the conference we were just at, likening to technical debt. So we're actually starting to see the increase in technical debt because of the influence that AI and software development is having in the creation of code and so on and so forth. And so... I think that what you're saying is spot on in terms of your theory. And I think that this speaks to what I believe we should really kind of amplify, right? AI is going to amplify certain things that aren't positive. I think leadership, think businesses need to start amplifying a conversation around... Are we approaching this the right way? What are the ultimate outcomes that we may see? And can we take that on? So if our developers are increasing the amount of technical debt that we have because we've integrated AI or adopted AI, what are we doing about that? What is the new workflow? What does the human in the loop do on account of this new factor? that we need to take into place because ultimately things like that make their way to the bottom line. And we know that's what CEOs care about.

Brian Milner (21:02)
Yeah, wow, this is awesome. I just want to clarify with sort of the learning organization ability, just want to make sure I'm clear. What we're saying here is that it's organizations that already have that kind of cultural mindset, right? That the background of a learning organization that see a bigger gain from this, or are we saying that AI can makes the biggest influence of impacting how learning an organization is.

Evan Leybourn (21:34)
The first, ⁓ the arrow of causation is that learning organizations amplify or improve or are more likely to see a benefit from AI. It's not a bad, and I should say we're not looking at how effectively you can

Brian Milner (21:35)
Okay.

Evan Leybourn (21:57)
deploy an AI initiative. It's about a we looked at AI as a black box. Let's assume or as in the cut through the Delphi method, the companies that we were speaking to had been doing these for years. These were mature established organizations. And the so it wasn't looking at how effectively you could deploy AI. But rather You've got AI, it's integrated. Are you seeing a business benefit from it? And those organizations that were learning organizations were more likely to be seeing a benefit, much, much more likely to be seeing a benefit.

Brian Milner (22:40)
Yeah. There's one phrase that kind of jumped out at me that I thought maybe one or both of you could kind of address here a little bit. I love the phrase, kind of the metaphor that you used in there about shifting from a creator to composer. And I'm just wondering if you can kind of flesh that out a little bit for us. Help us understand what that looks like to move from a creator to composer.

Christopher Morales (23:01)
Yeah, I'll start, but I think Evan will touch on it as well, because I do think it's a fascinating position, is how I'll phrase that. So when we think about creator to composer, we're talking about a fundamental shift on how a human is utilized within an organization. So if we eliminate AI from the equation, The human, your employees are acting as creators at some level, at some degree. Okay, so I have a media background, so I'm doing a lot of marketing. And I think that this is appropriate to use as an analogy, because I think a lot of marketers are utilizing AI right now. So independent of AI, that marketer is required to take into consideration all of these different factors about the business, create copy, let's say. create a campaign, do all of this real like hands on thoughts and levels. Now you bring AI into the equation and there are certain elements of these tasks that are being supported, offloaded in some cases. I'm not gonna get into my opinions about what is right and what is wrong here, but what I will say is there is a change in that workflow. And so what is... fundamentally at play here is that that marketer is now working in conjunction with something else. And so it is critically important that that marketer develops the skills to compose with the AI in a sense of, now know how to direct, I know how to steer a conversation, steer a direction. in order to get to a meaningful and hopefully valuable output utilizing the assist of the AI. And Evan, I'll toss over to you because this is the area, just so you know, Brian, this area of the report is the one that this podcast could turn into an hour and a half long podcast.

Evan Leybourn (25:08)
So I'll try not to make it an hour and a half, but just to build on what Chris said.

Brian Milner (25:11)
Ha

Evan Leybourn (25:12)
So this created to compose a shift, it changes the role of the human in the loop. It changes the responsibilities. And there's a quote in the report, AI is an unlimited number of junior staff or junior developers if you're a technologist. And that comes with some deep nuance because we all know that junior staff there is a level of oversight and validation required. So if you're creating through your AI colleague, let's call them that, if you're collaborating with AI, the AI is creating, then every human shifts into that composer mode and moves up the value chain. So your junior most employees, right? start to take on what would be traditionally management responsibilities. Now, this isn't in the report, but this is sort what we found after, right? Was that there were three sort of skill areas that needed to be taught to individuals in order to be effective and successful with AI or to collaborate in an AI augmented workforce. The first one was product literacy. So the ability to define and communicate use cases and user stories, design thinking techniques and concepts, the ability to communicate what good looks like in a way that somebody else understands, this somebody else, of course, being the AI counterpart. And product literacy, again, your senior employees have that, but that's got to Everyone now needs that. The second is the skill of judgment or critical thinking. The ability to, for anyone here who has a background in lean, pulling the and on court. The ability to and the confidence to, which are two separate skills, actually say, no, what AI is doing here is wrong. We're going to do something different. I'm going to say something different. I'm going to suggest. I'm going to override AI. I'm going to pull the hand on cord and stop the production line, even though it's going to cost the organization money. But because if I don't, it's going to be much, much worse. And so that ability to use your judgment and the confidence to use judgment, because let's face it, AI can be very compelling in its sounds accurate. So you've to be able to go, hang on, there's something not right here, and use that judgment. And then the third is around feedback loops, or specifically quality control feedback. Because as a creator, the first round of feedback, the first round of quality control is implicit. It exists inside the heads and the hands of the creator. Like you're writing a document or creating a... a marketing campaign, you go, oh, I'm not happy with this, I'll change that, or maybe not that word. You're a software developer and say, oh, I don't like that line, that's not doing what I wanted, I'm gonna change it. So the first round of feedback, the first round of quality is implicit. But once you become a composer, the first round of feedback is explicit, right? Because you're taking what has already been produced. And so the, what we, What we found post report is that a lot of people do not have the skill or haven't, sorry, have not learnt the skill, how to do that first implicit round of feedback explicitly. And so it gets skipped. so AI outputs get passed through into... later stages of quality control and so forth. And obviously they fail more often. So it's a real issue. So it's those three skilled areas that we would say organizations fundamentally need to invest in, in order to enable their workforce to be augmented, to work with AI effectively. And the organizations that have those skills, the organization with who have individuals with those skills at all levels from the junior most employee are more successful. Now, I'm going to add one thing to this. I'm going to slightly go off topic because it is the one of the most common questions that we get when we teach this topic or we talk about it at conferences. And that is

Brian Milner (29:44)
Yeah Yeah, please do.

Evan Leybourn (29:56)
If AI replaces your junior employees and your junior employees go up a level, what's the pathway for the next generation to become the senior employee? And this is where I have to give you the bad news that no one has an answer for that yet. These very mature, very advanced organizations Right? Many of them were trying to figure it out. None of them had an answer. and that's the, and I'll be honest, I personally, and this is just Evan's opinion, believe that this will become or must be a society level problem, or solution to that problem. it will require businesses alongside governments, alongside, education institutions to make some fairly substantive shifts and I don't think anyone knows what they are today.

Christopher Morales (30:53)
Yeah, and I would only say to that, and again, there's so much I would love to inject here, but I will say that this is an opportunity, and I always stress that, because that is a little sobering when you think about that idea. But I really, really strongly encourage organizations that are evaluating this to, I understand the considerations about efficiency and bottom line benefit.

Brian Milner (30:53)
Yeah. You

Christopher Morales (31:20)
towards AI, and I appreciate that wholeheartedly. But I think this is a real opportunity for organizations to take a step back and really think about the growth path for the talent that you have in your organization. Because augmenting your workforce with AI, are studies, Harvard Business Review put out a study that indicated that an augmented employee was more productive and enhanced as if it had been working with a senior staff member and collaborated at a level that was equivalent to working within a team. So there are studies that show real benefit to the employee having an augmented relationship with AI. If an organization can take two steps back, think about that pattern, think about that elevation strategy for your talent. you're going to be doing so much more to keep yourself sustainable in what is arguably the most like, you know, I don't know, I don't even know the word I'm looking for. It's, the most chaotic time I can think of for businesses when it comes to technology adoption.

Brian Milner (32:23)
You Yeah, I agree. But there's also sort of, I don't know if you guys feel this way as well, but to me, there's sort of like this crackling kind of sense of excitement there as well, sort of like living on the frontier that like there's this unexplored country out here that we don't really know where all these things are going to shift out. But gosh, it's fun thinking that we get to be the ones who kind of do that experimentation and find out and see what's the next step in this evolution? What's the next growth? The patterns that we've used previously may not apply anymore or apply in the same way because so much of the foundation underneath that system has changed. So we got to experiment and find new things. I love the call there, the learning organization, that that being the primary thing that If we have that cultural value, then that's really gonna drive this because we can then say, hey, this isn't working anymore, let's try something else. And that's how we end up at a place where we have new practices and new workflows and things that will support this and augment it rather than hampering it being a constraint, like you said, yeah.

Christopher Morales (33:48)
Well said. Well said.

Brian Milner (33:50)
Awesome. Well, this is a fascinating discussion. I really could go on for the next couple of hours with you guys on this. is just my kind of hobby or interest area at the moment as well. So I really appreciate you guys doing the work on this and appreciate you sharing it with us and sharing some of the insights. Hey, and the listeners here, hey, they got a bonus from the report, right? You listed extra things that didn't quite make it in the report. Just make sure you understand that listeners, right? You got extra information here listening to us today. ⁓ So just any last words from you guys?

Christopher Morales (34:19)
Thank Yeah.

Evan Leybourn (34:24)
Just for the folk listening, treat AI not as a technical problem, but as a human and a business opportunity, requiring human and business level changes. Don't just focus on how good the technology is, because that's not where the constraints nor where the opportunities truly lie. I would also just like to call out that if anyone listening wants to learn more about any of these topics, the capabilities, the domains of business agility, visit the Business Agility Institute website, check out the domains, download the report. But we've also launched an education portfolio and we'll be running a different education course on each of the capabilities over the next, I think it's every two weeks almost until the end of the year. So please come and join us and let's go deep into these topics together.

Christopher Morales (35:21)
Yeah, and I would just say, Brian, to all the listeners out there, don't fall into what I think is a common fallacy, which is where we're going is predetermined. It's already set in stone. I think as Agilists, we know the power of flexibility, the ability to pivot, and the ability to utilize data and information to inform what our next move is going to be. And I think this is a classic case of you control the narrative. You control what AI looks like in your organization, in your team, in your workflow, and you have the ability to carve out how it impacts your world. And so I encourage people to look at it that way. Empower your humanity, empower your decision making. The AI is here, it's not going anywhere. So embrace it in the best way possible.

Brian Milner (36:22)
Yeah, it seems oddly ironic or maybe appropriate to quote from the Terminator movie here, but it sounds like what you're saying is no fate, but what you make.

Christopher Morales (36:32)
Prophetic, Brian, that's prophetic.

Evan Leybourn (36:37)
I love it.

Brian Milner (36:37)
Awesome. Well, thank you guys so much. I really appreciate you guys being on and obviously we're gonna have you back. you know, when you guys come out with new stuff like this, it's just amazing to dive deep into it. So thanks for making the time at all kinds of times of the day and coming on and sharing this with us.

Christopher Morales (36:55)
You're welcome.

Evan Leybourn (36:56)
Thank you.