Navigating AI and Agile Practices in 2025

Curious about the future of Agile in 2025? Join Brian and Lance Dacy as they dive into the rise of AI, hyper-personalization, and how teams can balance innovation with customer focus. Plus, discover actionable insights to navigate a rapidly evolving landscape—don’t miss this forward-looking discussion!

Overview

In this episode of the Agile Mentors Podcast, Brian and Lance set their sights on 2025, exploring how AI is transforming Agile practices and reshaping customer engagement.

They discuss the shift from output to outcome metrics, the expansion of Agile beyond IT, and the critical role of leadership agility. With practical takeaways on fostering continuous learning and delivering real value, this episode equips teams and leaders to stay ahead in a fast-changing world.

References and resources mentioned in the show:

Lance Dacy
Accurate Agile Planning
Subscribe to the Agile Mentors Podcast
Advanced Certified Scrum Product Owner®
Advanced Certified ScrumMaster®
Mountain Goat Software Certified Scrum and Agile Training Schedule
Join the Agile Mentors Community

Want to get involved?

This show is designed for you, and we’d love your input.

• Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.
• Got an Agile subject you’d like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com

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.

Lance Dacy is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®. Lance brings a great personality and servant's heart to his workshops. He loves seeing people walk away with tangible and practical things they can do with their teams straight away.

Auto-generated Transcript:

Brian (00:00)
Happy New Year's Agile Mentors. We are back and a very happy New Year's to everyone who's listening. Welcome back for another episode and another new year of the Agile Mentors podcast. I'm with you as always, Brian Milner, and we have our friend of the show for our annual kind of tradition now. We have Mr. Lance Dacey back with us. Welcome in, Lance.

Lance Dacy (00:23)
Thank you, Brian. Happy New Year to all of y'all. Happy to be setting this tradition. think it's two times now, so we'll just call it a tradition, but I love it. Thank you for having me.

Brian (00:32)
Very glad to have you here. The tradition we're referring to is that we like to take the first episode of the new year and just take a pause and kind of look ahead a little bit. What do we see coming up? What do we think this new year is going to be like? Obviously, it's a year of change. Here in the US, we'll have a new president that comes in. I'm not going to get into whether you like that or not, but it's new. It's going to be a change. There's going to be differences that take place. And I know there's a lot of differences and changes going on just in the way businesses operate and how things are run and lots of new technologies, lots of new trends. So we just thought we'd take a pause and kind of scan the horizon and maybe give you our take at least on what we're hearing and what we're seeing. And you can see if you agree with these or not. We'd love to hear from you in our discussion forum on the Agile Mentors Community afterwards if you have other thoughts or opinions on this. let's get into it. Let's start to talk about this. So Lance, I guess I'll start. I'll just turn it over to you and ask you that generalized question. Give me one point or one thing that you've been reading or seeing recently that you think is going to be a really important thing for us to kind of be prepared for or look out for here in 2025.

Lance Dacy (01:44)
Great question, Brian. There's so many things out there, and I thought we could start by looking back a little bit. if we're okay with that, just let's summarize, you what did we see happen in 2024? You mentioned, you know, 2025 is a year of change, absolutely, but 2024 was definitely a different kind of year as far as my experience is concerned and seeing a lot of industry trends that are just popping up out of nowhere. Now we are fans of agility, which means we embrace quick, efficient changes, but there's things going on in 2024 I never predicted

Brian (01:52)
Yeah, yeah.

Lance Dacy (02:19)
fast. And so I think we've got to reshape the way that we're thinking about these things. I think the topic of mind, one of the biggest shifts that I saw in 2024 that I think will continue in 2025 is AI. So that artificial intelligence is a big word that we keep lumping into a lot of things. And I just wanted to take a pause a little bit and say, I know everybody's got a little bit different experience about AI, but in particular, as it relates to product development and agile delivery, which is what this show is basically focused on, I thought we could look at some insights of what happened in 2024 with that. And so I think I call us babies at it right now. And I know that may be a bad term, but I have a lot of experience with AI and machine learning and things like that. But as far as the use of it, I feel like we're all a little bit more of babies on how to use it in the day-to-day work that we're trying to accomplish. And I think that comes with learning something. I embrace that. I don't mean that as a downplay, by the way, but that we're all babies. I'm just saying we're less mature about it. We're experimenting with a lot of things. And I don't think that some of the AI is all good. I I embrace it as a thing that's going to help us later on, but... I thought we could just share our experiences of how we've seen this thing manifest itself. I think tools like AI driven, I'm going to use the bad word JIRA, but in place of that, just use any product backlog management tool that you see. And I've seen a lot of organizations not just talk the game of, we use AI for our backlog management, but I'm talking about backlog prioritization, sprint planning capacity. And I believe what's happening is it frees teams up to do more of the... value driven work that we're going to see a lot more of in 2025. So what I mean by that is when we got automated testing and development, if you remember those days, it freed the developers up or the testers, should say, from doing less of the does this thing work to more of how does it feel using it as a human being, you know, automating that. So I've seen things like JIRA, with AI with JIRA and GitHub co-pilots, you know, reshaping the value creation in the teams and eliminating the need of having to do very low level tasks. So what is your thoughts on that and do you have any experiences of that as well?

Brian (04:36)
Yeah, for sure. There's a couple of things I've found that just kind of some stats I found from some different places. you know, listeners know I'm kind of like a data geek here. want to know where the data comes from and want to make sure it's a, yeah. Yeah. You want to make sure it's a solid source and it's not some questionable, you know, sketchy kind of, well, I asked 10 of my friends and here's the answer, you Right, right. Exactly.

Lance Dacy (04:48)
Good hand. I love that. or a FBI.

Brian (05:02)
But so there's a couple of things that came back. One was, I think Forrester is probably a pretty good source of information. They have some pretty good rigor to their process. And they have a thing that they put out every year. This one's just called the Developer Survey. And this is the one that they put out for 2024 that I'm quoting here. But a couple of stats from that that I found interesting. One was, 49 % of developers are expecting to use or are already using general AI assistance in their coding phase of software development, which, you know, maybe higher than most people might think. But it doesn't surprise me too much. I think that's probably kind of what I'm used to it. Understand saying, you know, an assistant co-pilot, that kind of thing. They're not saying 49 % have been replaced. They're saying 49 % are being assisted. by that and that seems about right. Maybe again, maybe a little higher than some might expect, but that seems like not too big of a shocker.

Lance Dacy (06:04)
Well, the animation too. So when you talk about assistance versus letting it run it, I saw a gentleman on LinkedIn, which is also a good. I wish we could interact more with our users on this call, because I'd love to hear their perspective. But I heard somebody say, let AI write my code. No, thank you. Code is like poetry. It has to be refined over time. It has humanistic qualities. And I was like, man, that's a really good point. But when I try to show my kids how to create a Ruby on Rails app to do an e-commerce site and I type it into chat GPT or whatever tool you use, I was amazed at how quickly it was able to put together. mean, you got to still know the file structures and things like that. But I don't know that developers are just going to say, I was going to write the whole thing. think they're, I think it's saving us keystrokes. I think we talked about that last time as well, but that's an interesting, interesting take.

Brian (06:50)
Yeah. Yeah. So I thought, I thought that was interesting. There was another, you know, I'm kind of, I'll move around between these two sources basically, but there's another source that I saw where there was a Harvard Business Review article. posted this on LinkedIn a while back, but it was a kind of the source of it was about a survey that they did to try to determine the impact on the job market. And one of the things they did was now their data was from July, 2021 to July, 2023. So this is a little bit older data, right? The survey was trying to say in analyzing the job postings on freelancer job sites specifically, and they tried to identify ones that might be affected by the advent of chat GPT, because that's the period where chat GPT really started to come onto the scene and started to become prevalent. And what they found was about a 21 % decrease in the weekly number of posts and what they call automation prone.

Lance Dacy (07:35)
Yeah.

Brian (07:47)
jobs compared to manually intensive jobs. They said riding jobs were affected the most 30.37 % decrease, followed up by software app and web development 20.62 % decrease and engineering 10.42 % decrease. But the interesting kind of thing is they found it kind of towards the end of that there was some increases and their kind of conclusion was that there was actually an increase in demand of the kinds of work that required human judgment and decision-making. And so that kind of ties back into what you were saying about let AI write my code whole, completely no, there's still a requirement for that human judgment and decision-making. I think this is why I'm not afraid of it, right? This is kind of, I don't want to make this an AI show, it's about the future in 2025, but when we had a...

Lance Dacy (08:17)
All right. Right.

Brian (08:40)
When we've had AI shows, that's one of the things I've said to the audience here is that I'm not so afraid of AI being sort of the doom and gloom of it's going to destroy profession or destroy. It's going to change it. But I don't think that's any different than any other. A great kind of analogy I make is when we started to have testing automation. It didn't do away with testers. This is just another tool that's going to be in our tool belt.

Lance Dacy (08:51)
Guy net.

Brian (09:05)
And I think our challenge is not to, you know, we're agilist, not to resist change, but to try to adapt, try to find ways that we can align and incorporate and get the most out of it. So, yeah.

Lance Dacy (09:17)
I think the most part of that though is, Brian, too, what most people fear. And I agree with you, we won't make it an AI show. just, we got a couple of points to make on this. But for the first time ever in human history, we now have something that might be more intelligent than us. And that is scary because there's some AI neural network engines that people can't explain how it's working anymore. They put it in place. And then it's like, we're not quite sure how it's doing all of this. And that's a scary thing, obviously, that can get out of control. We've never really had to face that. So we do have to be aware of that, but you know, let's go back and peel it back. Hey, we're, trying to plan a backlog with AI and we're trying to write a few Ruby on Rails code. I'm not letting it run my life yet. And one day it may already be doing that. I just don't even know it. I don't know. We won't get into that debate, but I think the thing is that we need to take pause of in the agile industry. is we embrace new technology as long as it's helping us deliver faster to our customers and save us time and efficiency. You know, I tell teams all the time, Agile is about delivering the highest business value items as early as possible with the least amount of cost friction, know, whatever word you want to use for that. Well, AI might help us do that, but I want to caution that. I think you and I were just talking about this. I wanted you to bring up that news story element that we were talking about. where people are just pushing content out there and kind of desensitizing us to is that important information or not? And I think AI needs to tag onto that. So I didn't know if you could share that real quick and then I want to share some metrics that I've seen some teams capture. There's a lot of teams now adopting these things called Dora metrics, which was created by a DevOps engineering group. And it's amazing to me now that we have real data to see, well, we have embraced AI.

Brian (10:45)
Sure.

Lance Dacy (10:59)
does do some things or not, I'd like to balance the good with the bad on that. But can you go over that new stuff that you were sharing with me?

Brian (11:05)
Yeah, no, it's just a conversation I've been having recently with people, they're friends of mine and kind of, you're probably feeling the same way about this in certain places, but the breaking news alerts that you get on your phone, you get those things all the time and I've had friends and I have discussions about maybe it's time to just turn them off. There's just so many breaking news alerts and that's kind of the issue, right? Is that there are so many that are now classified as

Lance Dacy (11:23)
Yeah.

Brian (11:31)
breaking news that you kind of look at that and say, this isn't really breaking news. You know, like if something really major happens, yeah, I want to know about that. I'd like to get an alert about something that's truly breaking news. the, you know, have major news sources, apps on my phone and get those breaking news alerts all the time. And some of them are just things that are minor, minor news that I would be much better served seeing in a summary and like a daily summary or even a weekly summary on some of the things. Right.

Lance Dacy (11:50)
Yeah. Or if at all, like you don't care about the sub undersecretary of Parks and Lighting in Minnetoca. You know, I don't know. It's just like, thank you for that information. But I totally agree that I feel like we're getting desensitized to a lot of these words, buzzwords, if you will. And we as humans are going to have to learn in this environment. And I'm trying to teach this with my kids as well, because they're the ones suffering the most from it.

Brian (12:04)
Right. Yeah.

Lance Dacy (12:22)
It's just inane information out there and you're filling your brains with the main things. So AI is great because it's allowing people to deliver more content, but is that content of substance or they just trying to market to you and get you, I forget the word you use for it, but, you know, keep you on a leash. Is that what you said? A small.

Brian (12:42)
Yeah, yeah. Yeah, that's, yeah, that's kind of what we were saying about this is that I think that the kind of conclusion that led me to is that I and I've seen this trend, I think in other areas as well, as I sort of feel like maybe with bigger companies, more than others in today's world, there seems to be a shift a little bit that, you know, for example, that that breaking news thing, it's not it's not something that benefits the customer, right? As the customer, I don't think there's a customer out there that says, I really love all these minor news stories appearing in my breaking newsfeed. But what it benefits is the company. It benefits the source because it keeps you engaged. It keeps you coming back and it keeps that ping to keep you engaged. And that's what they're trying to promote. That's good for the... Yeah, that's good for the company, but it's not good for the customer. I think that there may be, we may see some real kind of shifts I think happen in...

Lance Dacy (13:21)
Or me, it keeps me frustrated and I leave them.

Brian (13:34)
Some of those big companies maybe have moved too far in that way to favor their company's interest over the customer. And that leaves a door of opportunity, I think, for smaller companies to say, well, we're going to be all in on just what's best for the customer. And I think customers will appreciate that and will reward that because it's annoying otherwise.

Lance Dacy (13:54)
That's what I want to focus on because the last part of this AI conversation I want to have is I like a lot of what Gary Hamill, he's a management professor at a lot of different schools recently. He visits a lot of companies as well, but I really like the way he delivers his content and how he's more innovative and thought. I mean, I tell people all the time that management and leadership has not seen any innovation in 150 years. It's about time. that we start learning how to create cultures for human beings that are bringing gifts and talents every day to make things better for our customers. And Gary Hamill is a really good source if you're interested in those kinds of things. And so he emphasizes how AI has reshaped value creation by eliminating those low-level tasks that I think we all can embrace and are allowing agile teams to achieve unprecedented efficiency. Now... We are babies immature with this technology. So maybe these news organizations and the ones that we're going to kind of say, you're not doing a good job at it. It's not because they're bad. It's just we're learning how to use a new tool and hopefully customer feedback will change that. But I wanted to hit on these Dora metrics. Dora metrics are, I think they were created by DevOps research and assessment. That's what they kind of stand for. And there's four major categories. that Dora metrics measure as it relates to more of an engineering benchmark. Like how well are we, if you're an agile software development product company, Dora metrics are really good for you to look at. know, metrics can be misused, so be careful, but they're measuring outcomes. You know, what is our deployment frequency, which could be an output metric, because who knows if you're releasing the right things, but let's not get into that conversation. deployment frequency, lead time for changes, the change failure rate of your changes, and the meantime to recovery of those changes. I think those are really four good performance benchmarks. And they're starting to surface a lot in organizations that I work with. So you kind of use tools like Jellyfish or something to overlay over Jira. And all these tools are great, but these teams are using AI. And I found that we finally get some real data that says, how well is AI affecting those core metrics if you were measuring performance benchmarks of the software that you're delivering. And so this report that was created by the 2024 Accelerate State of DevOps report, they categorize organizations and performance clusters like elite, high, medium, and low. And based on their performance across these metrics that I just mentioned earlier, they're evaluating and guiding their software delivery practices. And so the impact of AI adoption was really cool to see on the DevOps Launchpad was a site that I saw this on, that the integration of AI into the development processes, as we were just talking about, has mixed effects on those door metrics. Can you believe that? So a 25 % increase in AI adoption correlated with a one and a half percent decrease in team throughput and a 72 % decrease in the stability of the product. Now these suggest that while AI, you know, offers productivity benefits maybe for the individuals or the teams, it has a, you know, it's introducing complexities that are affecting the software delivery performance. So I want our audience to pay attention to that.

Brian (16:59)
Wow. Wow.

Lance Dacy (17:21)
and start using some of these maybe to push back on managers and leaders that are just embracing this new tool and say, let's just push this on the teams. So that's the impact of AI adoption. And then if you look at platform engineering, so if you look at the implementation of an internal developer platforms, you know, that are helping developers deploy code faster, the adoption of AI led to an 8 % increase in individual productivity. and a 10 % increase at the team level. Now that's fantastic. But these gains were accompanied by an 8 % decrease in change throughput. So while the teams may be able to make changes, what I interpret that to mean is the customer is not seeing the changes. There's an 8 % decrease in the throughput all the way as a cycle time, if you will, all the way to the customer and a 14 % decrease in the stability of the product. So that indicates trade-offs. that we all need to be aware of that AI might be helping us performance wise, but it's not helping the customer a whole lot if we're destabilizing the platform. So I haven't dug into those metrics a lot, but I wanted to share that with the audience because if you do find yourself in a position where people are pushing this, you can try to go reference those and maybe give them some, I always call it pros and cons, right? There's no really right or wrong when you're an agile team trying to make a decision. You got to look at the pros and the cons and

Brian (18:23)
Yeah.

Lance Dacy (18:40)
We might accept a pro, multiple pros that come with some cons, but we all look at each other and say, that's the better decision for our customer. And we live with those cons, whatever they may be. So I wanted to talk about that because it centers on what you were just thinking with the news organization. just push, we got more productive at pushing content, but was it the right content or is it destabilizing what people are using? And you just have to be careful of that.

Brian (18:57)
Yeah. Yeah, no, I think those are excellent points. I think that's one of the things I see kind of for 2025 as well is that we're still so much in the empathy of how AI really plays into how a team operates and how development works that I don't think we can really say ultimately what's the right way or wrong way to do anything yet. I think it's good for teams to experiment. I don't think you should be afraid of experimenting and trying things. But it all comes back to the basic principle we say over and over as Agilist, inspect and adapt on it. Try something and identify what works about it and what doesn't work. And if that means that, we're using it too much and it's causing too much errors, we'll back off, find the right point, and move forward with that.

Lance Dacy (19:41)
Yeah. Or where companies are using it bad. Like I have a story that we won't get into here where a CEO or an executive of the company was mandating that they use AI to do something not so good for the customers. And you want to be able to push on that as well. So I'm sorry to interrupt you on that, but I was just like, man, that's something.

Brian (20:07)
Right. No.

Lance Dacy (20:11)
Sometimes, like we want to self-organize around the experimentation. We don't want it pushed in like management saying, need to use this because I want you more productive and managers be careful of doing that. Make sure you understand the pros and cons as much as you can before you dictate.

Brian (20:26)
Yeah. Something else you kind of said triggered something to me. I know the, I think that, well, not in a bad way, but it just, you know, the metrics I think that you mentioned were really good metrics. I liked the idea of kind of measuring, you know, things like, you know, the failure, the bug rate, you know, like how many defects and those kinds of things I think are good metrics. But they kind of,

Lance Dacy (20:31)
What? Okay.

Brian (20:49)
point out a certain difference that I think that's out there that I think the business community is wrestling with. And I hear these questions all the times in class, so I know it's prevalent out there. But we talk about building high performing teams. And just the difference between that word performing and productivity. There's sometimes I think confusion or false equivalency. between those two, that performance equals productivity. And I think a lot of the metrics sometimes we see that get measured or that we try to measure even, kind of expose that, as that's what's really the issue here, is that we're really trying to make that false equivalency between the two. It's not saying that performance has nothing to do with it, but

Lance Dacy (21:15)
Right.

Brian (21:32)
You know, this is the simplicity, the art of maximizing the amount of work not done is essential. You know, I'd rather have low productivity, but what we produce is high performing, is highly valuable, is something that matters, right? And I think that's kind of those kinds of statistics like you were bringing up, you know, what is our failure rate of things we put out there?

Lance Dacy (21:44)
Yeah.

Brian (21:54)
That is, I think, a performance metric to say, the old phrase, slow down to go faster. Right, right. Maybe the reason that our failure rate goes up and we're having problems with this is that we're trying to go too fast. And if we could back off, it ultimately makes you go faster if you have less bugs that you then have to go back and fix.

Lance Dacy (22:00)
Yeah, make hate, totally. Yeah.

Brian (22:19)
So it may be counterintuitive to certain organizations. Let's push them. Let's try to get everyone to go faster. But I think these new kind of metrics that you mentioned that we're trying to measure more and more, I think are starting to open people's eyes a little bit to the difference between those two words.

Lance Dacy (22:22)
I mean Well, in like the CrowdStrike situation, you know, that took down a lot of the airline systems, you know, I'm not saying they make, they didn't do a good job deploying and everything. All of us are victim of that kind of thing. But, know, to get us back on track a little bit, because you asked me the question, then I felt like I got us off on a tangent. know, 2024, obviously the rise of AI integration into

Brian (22:48)
Sure.

Lance Dacy (22:54)
the workflows that we experienced with Agile. And I just wanted to highlight, yeah, those are some great things, experiment with it. We're in our infancy. So there are a lot of things to discover that may not be so good. So start trying to put metrics in place. And I thought the Dora metrics, you know, as I've started discovering those, I'm a data guy and I'm like, yeah, as long as those are being tracked correctly, I think that's a good benchmark to kind of look at, hey, we're making a lot of changes in our software, but it's crashing the system. So change is good, crashing is bad. there's pros and cons, so we have to delegate that or figure that out. Now, the other one that you just mentioned, I thought I saw a great shift in 2024 from output related metrics to outcome oriented metrics. So the Scrum Alliance has a report, which we're all probably familiar with, especially you and I being certified Scrum trainers with, and we get a lot of data from them. But teams moved away from feature counts to measuring outcomes like

Brian (23:35)
Yeah. Yeah.

Lance Dacy (23:49)
customer satisfaction, user retention. You we teach this in our advanced certified Scrum Master workshops, the difference between output versus outcome metrics. And we've been doing that for five years. And I think it's really starting to take hold that management and leadership and maybe even teams are measuring the wrong thing. And I really saw the needle move in 2024 that people's eyes are opening that let's measure the outcomes of what we're doing. Sometimes that sacrifices individual productivity and performance for a greater outcome achieved at the organization or customer level. And we've been trying to articulate that for many years. And so I've seen a shift in that. And then also the rise of Agile beyond what I would generalize as IT. So Agile Alliance produced some information that I thought was interesting that Agile has expanded into health care or sectors like health care. education, human resources, HR, and those are typically what we would see the laggards, you know, back in the day, banking and healthcare and all those were the last people to adopt this progressive planning approach because of the way that they budget and finance and rightfully so. But those agile principles have been proven out far beyond software unpredictable type work and is going more into, you know, the different types of work environments and I think onto that is how it's getting involved more in leadership. So I don't know about you, but I've also seen people focusing more on building a culture of, I would like to call it leadership agility. So John Maxwell, you know, is a vocal person in the industry about leadership. And he underscored this idea that agile leadership. in driving transformation across non-technical domains. So not just a digital transformation, but non-technical domains is really taking hold in this idea of empowering cross-functional teams. You we've been saying this in technology for years, that the siloed development method is not good. Well, organizations are starting to see that not only in the tech sector, but why don't we put a marketing cross-functional team together with this other team? And that's what they talked about in 86. you know, in the new, new product development game. And I think I started to see the needle move a little bit more with leaders being more fascinated about leadership agility and driving culture change to meet the demands of cross-functional teams. And it could just be a by-product that technology has gotten easier to make these and focus on these things now, but psychological safety, know, sustainability and agile with, people having real goals and integrating.

Brian (25:59)
You

Lance Dacy (26:23)
What you see now is a lot of these eco-conscious practices coming in to product development, like the environmental, social, government's commitments as well, are making their way in there. So I want to just reflect on 2024. I don't know what you think. I'd love to interact with the audience too, but those are kind of the main things that I saw. And that will lead us into a good discussion of how we see that helping us in 2025. So what do you think about those?

Brian (26:49)
I One of the things I think that kind of stood out to me from what you talked about was the concept of how that plays in leadership. And I think you're absolutely right. think that is, I am hearing more of that in classes, people talking about that when they ask questions. You know, we've talked about for years that the fact that there can be sort of I don't know a better word to say but a glass ceiling sometimes in the organization for agile and how it spreads across and that leaders are often You know overlooked as far as getting trained in this kind of stuff and understanding it and I do see a rise in leaders trying to understand a little bit more about how can we You know incorporate this or even better, you know, how do we support? and nurture and foster this culture in our organization. So I think you're absolutely right. I think that is sort of a hidden or kind of a cheat code, if you will, for organizations to try to be more successful with the stuff we talk about is if you can have, it's not a top-down approach, but if you don't have the top on board, then they can really start to become a hindrance or a roadblock to the teams actually being successful with it. And so I agree. think that, you know, I'm hopeful that that shift is occurring. I'm seeing signs of that, you know, it's kind of always a little bit of a back and forth, you know, is it moving in that direction? Then I start to hear people say, no, we're having trouble. And the anecdotal little stories you hear makes you kind of not sure what the prevalence is, you know?

Lance Dacy (27:54)
Yeah Lose hope. You lose hope. I think, you know, the big takeaway for me for this as we talk about 2025 is it's going to be increasingly difficult and it has been increasingly difficult for any one individual company, product, service, whatever you want to call it, to differentiate yourself from other people. I've been telling my kids this forever.

Brian (28:18)
Right, right, exactly.

Lance Dacy (28:38)
that I feel I've seen a big shift from when I was back in early 90s, know, writing spreadsheets for people, they thought it was just unbelievable the work that I was doing because not everybody could do that. Well, everybody can do that now. So what I mean about differentiating yourself is, you know, AI is one of those things that you have to start prioritizing AI literacy because we've just talked about how immature we might be in some cases with this. But if we can ensure that our team members understand how to work effectively with those AI powered tools and letting AI be an active team participant, then I think we're going to start seeing even a greater problem with being able to differentiate yourself. So the main point I want to make for 2025 that I believe is going to be a real big focus is a is a hyper personalization of customer products. So there's a lot of companies out there that are really good. You just mentioned it with the news, right? Hey, I'm building your content, I'm keeping you engaged, but am I really serving you? Am I giving you your needs? And maybe it's okay if news organizations do that if you have a way to filter it and customize it. But really what I'm talking about is, and I'll go back to what Gary Hamill says about this. He says, the markets are crowded. And when you have the rise of AI and tools like Trello, Monday, and things like that, those are project management tools, right? Used to, you could be a better product company just if you would manage your work better. You know, you were using Scrum or Agile, you had an edge on everybody else. You could deploy faster and that was your secret sauce, right? But now that most people can do that now, what's your next up level in game? And he thinks it's going to be this hyper personalized customer solution and engagement.

Brian (30:06)
Right.

Lance Dacy (30:23)
where we need to invest in more customer discovery processes. You know how hard that is in teaching tech teams to do that? All we focus on is building the features, but how about we get better at customer discovery and really understand the tools that provide deep insights into their behavior so we can recognize that? know, several companies that I think are on the forefront of that, for those of you who are like, yeah, I'm concerned about that too. Where can we get better at that? I mean, go look at Amazon.

Brian (30:30)
Yeah.

Lance Dacy (30:51)
You know, Amazon uses highly sophisticated algorithms to analyze customer behavior, which enables them to produce product recommendations and help you buy things you didn't even know. You remember when we would teach like Kano analysis in a product owner class and they had six categories of features and one of those feature categories was an exciter or delighter feature. You know, the key to being a good differentiator is providing product and features that people didn't even know they needed. That's why customers are not always right, you know, on what they need. They're thinking about their reactive sense. And so how can we get better at predicting their behavior even more than they can and use AI and machine learning that allow for real-time adjustments? Because that used to take forever. You you think about Benjamin Graham's book on investing in the 1940s and 50s, trying to predict what the stock market is going to do is nearly impossible now. But can you imagine how he differentiated himself by doing all these algorithms by hand?

Brian (31:20)
Yeah.

Lance Dacy (31:48)
And so what I mean by that is we need to use AI and these tools to help do more predictive customer experiences. So Amazon does a good job. Netflix employs a lot of data analytics to help understand viewing habits. Starbucks does this. Spotify does it. So I really feel like in 2025, if you want something to focus on and you're a software product development company practicing agile, build literacy of AI tools with your team. Make sure we're using them the right way. Track the right. data, but more importantly, let's discover what our customers are doing and behaving and use the AI to help us decipher that information a lot easier so that we as humans can make a decision on where we spend the great scarce capacity of our teams building great products for them. And so there's a lot of things that go into that, but I feel like that's going to be the focus in 2025. That's what's going to separate the people that succeed even individually. How are you going to differentiate yourself from a market pool of people out there? You need to start learning how to use these tools and differentiate yourself. That's the for 2025.

Brian (32:52)
Yeah. No, that's a great point. I'll tag on and say that I know there's this, people probably have heard of this, there's a social media kind of trend of if you use chat GPT or something like that a lot to go to it and say, tell me some insights about myself that I may not know, just based on all my interactions with you. And that was a trend for a while for people to ask that and then. they were shocked in some of the things that would come out from chat GPT. Well, what I found in taking a couple of courses and things about AI is, it's really good at taking a large amount of data and then pulling out things that you may not be aware of. I think that's going to be something, the more data driven we are, obviously the better because we have facts behind it. And as you said, it has to be the right, we have to collect the right kind of data. you can take a big...

Lance Dacy (33:19)
Yep. Yes.

Brian (33:43)
source of data and feed it into an AI like ChatGPT and say, give me five hidden insights from this data. Yeah.

Lance Dacy (33:50)
Yeah, stuff you thought about, right? I think insights, that's the way to put it. And I used to have a saying being a data analytics guy for 20 years. Most people and organizations are data rich, but information poor. And I would like to change that word nowadays to insights poor because

Brian (34:09)
Yeah.

Lance Dacy (34:09)
We may have all the data and tracking data, there's no harm in that, know, storage is cheap these days. So go ahead and track it all. You can report on it infinite number of ways. And that's the secret sauce. And I think you just hit it on the head that, just go ahead and start tracking stuff. Let AI, you can't ever read that amount of data as a human being and decipher it. Let the machine do that. But then you can test it. You can say, do I really believe that or not? Because you have a humanistic experience that AI doesn't have. So we should embrace that.

Brian (34:40)
Yeah, I agree. Well, I mean, I hope people are hopeful. I'm hopeful. I know when I start a new year, I generally am hopeful because that's just the way I try to start new years. But I'm hopeful for some of these changes. think the tools that we have are just making things, some things that might have been more mundane, a little easier for us to do. And maybe that allows us to focus. Well, like the data I brought about at the very beginning, you the fact that there's a rise in, you know, postings and companies needing jobs that require human judgment and decision-making. I think that's where we're headed is, you know, that rise in human judgment and decision-making skill. And that's something that's at least at the moment, you know, our computers can't do for us. And it really does require, just like you talked about, understanding our customers. I can't put an AI out there to try to interview all my customers and get deep. Well, but not and get the kind of deep insights I want, right? Not to find out what the real problems are. It wouldn't know how to question it enough and dig deeper into different ways to truly figure those out. So it requires huge human judgment and decision-making. And I think that's where we...

Lance Dacy (35:35)
you could. Right.

Brian (35:51)
now bring the value is in that area.

Lance Dacy (35:53)
Well, and people hate change, right? So let's just end with this. know, most people, customers, you change things on the product. You put a new car design. We usually don't like it. So you want to hang in there and not get too distracted by noise with that. mean, remember when the first iPhone came out, you know, older generations like this is too complicated. I don't want to use it. And there is something to say for that. But eventually that's what we use and we learn how to adapt to it. So stay hyper competitive in 2025. Foster continuous learning for your team. So stay updated on industry trends. It'll lead time to experiment and invest in your team's learning. Prioritize collaboration and innovation. None of us are smarter than all of us together. Break down the silos. Encourage the cross-functional collaboration. And experimentation is going to be key. Leaders and managers in particular. must foster an environment where it's safe to not do so well. I tried something, it didn't work, and I'm sorry about that, but I learned from it and I'm going to try it this way next time. That's not a huge thing right now. We need to foster that. The last one, focus on delivering value. Keep the customer at the center of everything. Use metrics to measure your real world impact, not just the outputs. And I think that's how we can summarize everything that we talked about. Those are the three things if we had to take away. continuous learning, collaboration and innovation, and focus on delivering value. Good luck in 2025, right, Brian?

Brian (37:19)
Yeah, absolutely. Absolutely. That's awesome. Well, I hope this has been beneficial to folks. And Lance, I appreciate you keeping our tradition and helping us look forward into the new year. obviously, a very happy new year to you and your family. And thank you for coming back and joining us.

Lance Dacy (37:35)
Yeah, likewise to you, Brian. Glad to do it. Hope to see you all soon. Thank you all.