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Scrum Master Toolbox Podcast: Agile storytelling from the trenches
How Agile Teams Can Start Adopting AI in Software Development | Wasim Osman
00:19:36
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- [00:04]
A
Hey there, agile adventurer, just a quick question.
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B
What if, for the price of a
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A
fancy coffee or half a pizza, you could unlock over 700 hours of the best agile content on the planet? That's audio, video, E courses, books, presentations, all that you can think of. But you can also join live calls with world class practitioners and hang out in a flame warfree and AI slop clean slack with the sharpest minds in the game. Oh, and yes, you get direct access to me, Vasko, your Scrum Master Toolbox podcast. No, this is not a drill. It's the Scrum Master Toolbox membership. And it's your unfair advantage in the agile world. So if you want to know more, go check out scrummastertoolbox.org membership. That's scrummastertoolbox.org Membership. And check out all the goodies we have for you. Do it now. But if you're not doing it now, let's listen to the podcast.
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B
Hello everybody. Welcome to our Wednesday where we talk about the biggest challenge and we show, of course, the example of a coaching conversation yesterday with Wasim. And by the way, Wasim. Hey, welcome back. Hey.
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C
Hey, good to be back.
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B
Yesterday with Wasim, we were talking about a book called Difficult Conversations. And a coaching conversation sometimes can be difficult, not because the people are difficult, but because the topic, the problem, the area we're investigating can be difficult because the goal is to turn both our attentions into what's going on, explore it and come up with experiments, because it is through action that we get to influence what happens around us. So Wasim, what is the challenge or problem that you want us to go in and investigate today?
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C
Absolutely. So I think the biggest challenge right now is kind of is a common one, I think across the industry, that is AI development, like AI emergence and what impact it has on development as a whole.
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B
Absolutely. Well, let's dive into that. Let's start with understanding the topic from your perspective. So in your context, what is happening regarding the adoption of AI in the context of software development?
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C
I think there's a big division between developers that are open minded into adopting AI driven development versus traditional development. Writing code by hand, although it has its merits, but the speed at which you can develop software using AI is astronomical in comparison. Even if AI hallucinates, even then, and even if, even if it generates a lot of garbage code, even then you will be able to deliver something order of magnitude faster than you would do if you wrote something by hand.
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B
And one thing to bring into the context is, I mean, let's be Honest. The writing of code with AI is never going to be as bad as it is today. It's only going to get better.
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C
Absolutely, absolutely.
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B
Tell me more about that. Because I mean that resistance, if you will, skepticism perhaps or, or outright rejection in some cases is something that I am familiar with. I work with clients all over the world and as you could expect in many of those clients, there's an AI adoption phase going on where engineers, and this means not just engineers, but also product managers and testers and managers in general are being asked to adopt AI native ways of working, whatever that may mean in that context. So tell me more about this distinction. Like what is happening? What is the division? Is it like most people are pro, some are against. Or is it most are against, some are pro? What's the context?
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C
I think because I also talk to software engineers from other companies, I have ex colleagues that I'm very connected to, I see that there is a general sentiment that can be about AI driven development that is divided into two parts. One is, that is rapidly trying to adopt it and the others are really not downright rejecting it, but they're still taking their time. And I think that could be very detrimental if they take too much time.
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B
Okay, tell me more about that. How do you see the dynamic evolving?
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C
So the ones that are rejecting the idea of like outright accepting it, they are feeling like it's not there yet and they feel like it's going to get mature and that's when we're going to dive into it. But the way that everything is getting developed, how rapidly they are being developed, like the AI models are being developed and like you just mentioned, the quality is only going to get better. The early adopters are going to a place where the late adopters will not be able to. So I think that's very important to understand for any engineering team.
- [05:14]
B
And when you, when you work with the people around you and the teams that are, you know, in some cases being asked to adopt AI in how they work, in other cases being encouraged or invited, depending how you want to call it. But, but what are the kind of, let's call it this, the standard behaviors that you see? Like what are the key behaviors? Because I mean rejection or skepticism, that's a very high level thing, right? Like what does it look like in practice when you're talking to people?
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C
So one is that when I'm talking to someone, they have a lot of questions. So the first question is, okay, I want to develop software. So when I'm actually writing code by hand, there is A specific process. Right. So I would be expecting the product person and the designer to come together, basically give us every sort of answer, every sort of questions that I have before I will dive in into development. So once I have everything kind of ready, then I start development. And when I'm doing development, there are some certain processes I follow. So with AI, what do I do? Would I just ask the AI to do everything? A one liner and then hope for the best or that's one. The other is because not everybody has like a lot of even leadership for those particular teams do not have experience with AI, they're not also guiding them.
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B
Like answer the questions, right?
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C
Yeah, yeah.
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B
And do you see people that are actually like proactively experimenting, like trying different things out and learning and sharing that with their colleagues?
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C
Yeah, I think so. Say for example, if in a software company there are like two, three engineering teams, unless a specific team or a few individuals from a team are assigned to do the research and then come back to everyone and then tell everyone, okay, so this is what we've found. Maybe this is some a way that we can adopt or transform our engineering processes into using AI. That could be one way of doing it. There could be other ways where the head of engineering or person who's like kind of in a leading engineering position to kind of go through that. And because that person has the most context, the most best bird's eye view of how software is being developed right now. So when that person goes over into the concepts that are being developed by companies like aws, like Amazon, basically coming up with that, the AI DLC platforms and some other companies are also championing that. So not necessarily the process that they're coming up with is going to 100% fit your case, but if your head of engineering or elite engineer goes through those processes that they're developing right now and then kind of like have like a AI DLC process laid out for you, then that could be a good place for the engineers to start without any friction. Otherwise there's a lot of thoughts that are not put into words or made any decisions on how software development should take shape with AI.
- [08:19]
B
One of the things that I've seen some of the clients that I work with do is kind of doing a little bit of research, putting a package together and kind of doing a training. Right. This is not the right way to do it, but this is how we're going to start, right? Kind of this because I mean, let's be honest, we're all discovering this together. The whole industry is clueless at the moment. And that's where we should be because it's too early in the cycle. We're all learning this, right? So we should be trying it out and we should be publishing, you know, blogging or podcasting about it. Like, like right here we're doing. So how have you guys, in the, in your context, how have you guys approached this getting started part? Kind of the creating the first sparks for the change?
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C
So we, I think like from sometime around, like in the middle of last year is when we started observing that AI is bec or at least showing the promise of becoming a crucial part of software development. That's when the company that I work for right now, so Orchestra SES the head of engineering over there. So he dived into. Understanding what is out there in terms of the software development processes being influenced by AI. So it's not only just into like how one company is doing it or defining it, but it's like a holistic research. And then he basically put the research for, documented all of that and made that available to all the engineering teams. We went over the entire process that he found out and then there were a lot of sessions afterwards, any feedback sessions, any questions that we had. And then there were more research that came in. A few of our engineers also dived in, into, into the research and then we were able to come up with a process that worked for us. So it's like a version of what we researched and, but catered more towards how we are doing something.
- [10:32]
B
And how did you get the team started? And then let's assume for now that we're only talking about the people who are not rejecting because for those we might need to have even a whole nother episode about, but for those that are at least not completely rejecting the idea, like, how did you get started? How did you socialize the idea? How did you get the teams to commit to doing something different? Even if it's an experiment, how did you get that started?
- [10:56]
C
So I think like the conversation started pretty early when we started to talk about, I think sometime in last year that even, even if jokingly that, you know, in a few years probably we're going to be rendered completely useless because, you know, whatever that we're doing, AI is going to get better and do much faster than we do. So we either need to probably make friends with AI or maybe get rejected by it. So even if this was like a casual funny conversation, but we were all aware of like how fast things are moving.
- [11:29]
B
High stakes, basically.
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C
Yes, yes. And I think I Bet you got
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B
a lot of scared faces when you had that conversation.
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C
I will not say no, but I think we took it very positively because I think we discussed about these quite in detail. And those conversations really helped us kind of accept the idea of like, okay, so if this is going to be a tool, we need to learn how to use this tool.
- [12:00]
B
Yeah, absolutely. So conversation, sharing ideas, exploring things together helps because people become aware and they start talking before there's any decisions. And I think that's a key part here. But what's the plan? Like, how are you pushing this forward now? Like, now that the conversation has happened? Technology is evolving. I mean, the models are so much better now than they were even at the beginning of this year. Like, it's not even funny how fast this thing evolves. How are you moving this forward now in that organization?
- [12:32]
C
So if you go through the few, like AI dlc, like the research and documentation that a few of the companies that are coming up with and championing that, you will see, see that there's a lot of, like the entire process is basically defining the problem statement and the AI having assessed that, and then asking you questions to define the product more and more as you keep moving forward. So it's a lot of questioning and answering with the AI with that framework on top of it. So I think like in the first iteration, it's just my opinion is that maybe it's asking a lot of questions that are not necessarily even relevant at some point, but you have to understand that the more information you feed off to the AI, the more context heavy it gets in terms of how your software is supposed to be. So maybe it's not going to immediately reflect on the part of the software they're developing right now, but it's going to help you in the future. So right now it's very like we have a lot of discussion about how to answer those questions about, like, how do we build this particular feature and then what other features does this feature affect? And they're like underlying questions that when you present these questions to AI, after getting like the entire context of how your current software works, it will ask you questions that you will never come up with. Right. So that's how it's making us kind of being as comprehensive as we can.
- [14:11]
B
So kind of treating it as a partner for now and then figuring out how to do things in practice in
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C
the future, that is a big aspect of it, but at the same time it's also helping develop software right now.
- [14:24]
B
Okay, so what are maybe the key lessons learned so far, what are the key lessons learned from the teams that have actually adopted it, that haven't just played it, they actually have tried to use it productively for the work that they're doing?
- [14:42]
C
I think the biggest lesson is that it's a completely different way of developing software than we used to before. So maybe it's a little bittersweet thinking that I have skills in actual writing code, but probably it's useless moving forward. But at the same time, I never reasoned with, like, say, for example, if I'm an engineer, I would think that I never reasoned with the software that is being built. It's like my own software has a voice now. So that never happened before. Right. So the software is telling me, okay, so you're going to build me like this? Like, do this or that? Or have you considered this? Like, oh, wow, I've never done that, actually.
- [15:32]
B
That's a great point. Right. Like, when you say your software has a voice, I mean, that means. And hopefully that's what you meant. At least that's what I understood. But that means that we are explicitly working with the software through the AI process. Right. So the way the software development process changes, at least in that case, is that it's no longer a single person in isolation figuring out what code needs to get written, but it's actually a person and a machine, in this case an AI or LLM, that are exploring the options of solutions so what might be written, and then figuring out through that exploration what actually would be a good idea to write. And if I add my own color to that, I think the important part of that process is that the exploration of the solution space is what we could never do before except when we were pair programming. Right. Like with pair programming, we would be exploring the solution space together with another partner that is a human. But now we're pair programming with AI.
- [16:42]
C
Yeah. And also, more times than not, AI is coming up with questions that probably we cannot come up with.
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B
And in fact, we can even ask that question, right? Like, we can say, hey, what are my blind spots? What have I not considered yet? And deliberately explore that solution space with AI. That's a great and wonderful world out there. It's great to be alive at this moment where we're rediscovering how to develop software. It was a pleasure. Thank you for sharing that story with us.
- [17:15]
C
Absolutely.
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A
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