Transformation is the Goal. Agile was the Starting Point

Discover how the transformational process that leads to business agility can also enable things like AI readiness, cloud optimization, and security remediation.

Video Transcript

Mike Cottmeyer

Why is a company known for Agile transformation, talking about ai, talking about private equity, talking about all these different things?

And I don’t pay a ton of attention to our marketplace, what we might consider like the agile community or the agile thought leadership community. But it seems like the people who I hear talking about ai, because everybody wants to put AI on top of whatever it is they’re doing, they’re talking about one of two things. How do I do AI for code generation? How do I become a more effective, incremental and iterative developer slash software craftsman, what have you? And then the other side that I think is interesting is how do I leverage ai?

I’ve seen things like story writing or process stuff. I’ve also seen it in this space of iterating outcomes with ai, which I think it’s fascinating to me. I think it kind of misses the point when we think about something like Agile and ai, what we’re fundamentally asking ourselves is how does Agile enable ai?

But I think to even answer that question, you have to define agile as LeadingAgile defines agile, which has been contrarian in market for a long time. We started forever ago with this idea teams backlogs, working tests, software right? Over time, that evolved into really fundamental principles like encapsulation versus orchestration with the, and then that kind of evolved into the product extraction metaphor, everything getting wrapped in tests, having data owned by the team underneath the application.

So, it’s interesting to me that when we take a LeadingAgile’s point of view on what does it mean to really do agile well, right? We want to extract technology components, wrap teams around them, align them to the business, get them into a state where they can deliver incrementally and iteratively where we can get fast feedback and then we enable it with agile practices and we measure the right things and we change the culture over time.

I think when you take a more expanded and integrated view of agile and agile transformation, the way that we’ve thought about it and our change management methodologies around getting there, I think the insight is that when you take that expanded view of agile and agile transformation, what we’re fundamentally doing is that process is getting ready for ai. That process is enabling private equity mergers and acquisitions or business capability focused models. It’s enabling us to effectively scale the cloud and strategically take costs out of it. It’s effectively getting the organization to be able to deliver better, faster products that are more applicable to market.

One thing that we’re starting to talk about that I think’s really, really fascinating is around security is when you think about security differently, right? More application security. How do we ensure that the application is going to be secure and maintain IT security? When you take those facades we’re talking about and you marry them with our expansive view of agile and transformation, it kind of starts to make sense how this thing we’ve been doing around enterprise transformation is an enabler of all these other things. To your point you made a little bit ago, I think what’s confusing is when you start to think about how do I use Scrum to do AI better, or how do I use copilot to become a better agile developer? I think that’s too small.

Now I’m bridging it back the other direction. I’m just talking myself into this. So it’s like when you look at Agile as a fundamentally different operating model that requires transformation, what you’re fundamentally doing is you are enabling these things that we’ve been talking about. So what leading Agile’s I think doing is recognizing to some degree that the word agile has been largely commodified. The practice of agile has been largely commodified. It’s been largely reduced to the implementation of practices or culture or values. And I think it’s necessary for us to credibly speak about this, that we have to say no, it’s not just agile that enables ai. It’s the transformative process that allows you to build products better, faster, more efficiently, get them to market in time, are the same fundamental things that are going to allow us to solve ultimately any problem in a technology enabled business.

The ones that we’ve chosen to focus on in the short run, the experiments that we’ve tried to pull and go to market with in addition to enterprise transformation are private equity, mergers and acquisition, strategic cloud optimization, security and AI readiness. I think that’s a really fascinating hook, but it requires this reinventing agile story that Tim and team helped us put together a few years back. When we really think about reinventing agile, we think about what does it mean to do it at scale? We think about alignment between technology and the business. We get all the benefits of agility and those benefits of agility enable these other things we’re talking about. I think that’s the core that we’re trying to hunt out and the story we’re trying to tell.

Does that make sense?

Philippe Bonneton

Yeah, yeah, yeah. And it’s interesting because I didn’t grow up with LeadingAgile, right? I’ve enjoyed LeadingAgile about a year and a half ago now, but what resonates with me a way I like to explain the companies that do it right, what do they have? What does good look like? I like to think about it as the three steps, and those are the physics. You often talk about the physics of how things work, which I think can apply to many things we discuss from security to private equity, asset modernization, know what to work on starts there, right?

You could talk about it as the investment mix, as the how much investment do we put towards maintaining what needs to stay the same? How much investment do we put towards building what needs to be created and where we need to innovate. So know what to work on, understand where the value is across your organization, across your operation, and figure out how to prioritize the work that’s going to drive the most value. That’s step one.

Step two, know how to do the work efficiently, whether it’s product delivery, whether it’s securing your applications, whatever it is, how do you drive the maximum value with the limited dollars you have to do it? So deliver product, deliver security, deliver your next AI implementation, finish your cloud migration in the most efficient way possible.

And then the third step of this, and I think it’s critical, and something that really speaks to LeadingAgile’s DNA, is how do you measure that you are doing it right? How do you measure that? What you thought had the most value, the most potential value to be worked on where you prioritize your investment and then applying your efficient working methods? How do you measure that it’s actually happening? And then once you start to measure whether you are driving the outcome you are looking to drive, you’re securing your applications the way you need them to be secured for the business, not only can you say, can you validate that you have something that’s working here? You have a thread that’s working, but you can also quickly pivot. You can quickly adjust.

So, this is a loop, right? It’s know what to work on, work on it, well measure it and be able to pivot or adjust very quickly. I think. So without getting into the agile dogma, it’s more like that’s how I imagine. That’s how I tell the story of leading agile.

Mike Cottmeyer

What you just walked through was ineffectively the team’s backlogs, working tested software story or working tested product story. And then what we’ve historically said is that scale teams backlogs working tested, software structure governance and metrics. So if you get the application architecture and the teaming design, right, you get it fed the organization’s highest value priorities. You give it the ability to deliver incrementally and iteratively and get feedback from market and ultimately figure out how to measure progress against what we’re doing, then we win.

And so, the client wins. And so yeah, it’s really fascinating. That’s right. The underlying DNA becomes the DNA of everything that we do. It just depends upon what problems we want to solve. Our CTO, James Hester was the one who coined the term facade. And I think it’s an interesting thing. You’ve heard us drop it over the course of the last hour of talking to each other.

The reason why we chose the word facade is because the core of what we do, the core of organizational change and technology alignment and modernization, and that creates the conditions where we can do the incremental and iterative delivery, align that to the priorities and measure the heck out of it. That’s the underlying DNA. And then the facade language comes in and says, okay, when we do that, we enable ai. When we do that, we can extract these value props for a private equity firm and facilitate mergers and acquisitions. When we do that, we can secure the application architecture. When we do that, we get AI ready. That’s what I think the interesting hook is, right?

Philippe Bonneton

And to your point, it’s both. You can apply this principles to how to do ai, right? But then what’s going to be very interesting for us is to figure out how to embed AI in those steps, working on the right things, prioritizing what’s going to drive value and understanding your investment mix. How do you bring your data? How do you make your data work towards that goal towards figuring that out, working more effectively, delivering products more efficiently? Same thing. How does AI help you get there? And then measuring and recommending the pivot that you need to make or the learn, harnessing the learnings from you having prioritized something in your backlog, having quickly turned it into a real life product or application. And then having the ability to feedback early on maybe faster that you could do it humanly, I guess with ai, are we going in the right direction?

How can AI help answer that for you? And so what I like about this, the physics of this is that if I look at the handful of client scenarios I have that are active right now that I have in mind that follow, I mentioned the example of the AI lead with 30 pilots. What do I work on first? How do I work on my pilots to implement them in an efficient way? How do I measure, how do I know I’m on the right track with them and how do I pivot? It’s true for so many things. It’s true for a private equity firm that comes to us and says, I need to do a tech turnaround of this company I just bought. Okay, well, there’s a million ways you can go about rationalizing the tech, the IT stack of a company you just acquired as a PE firm. What is the 10% of the work that’s going to matter the most when it comes to your exit criteria or to your exit valuation?

Is the team you acquired, the product team or the engineering team, the legacy product team or legacy engineering team of the company you acquired as a private equity firm, are they equipped? Are they designed, do they know how to work with that data? Do they know how to work with those methodologies? They were not built to do that. They were built to drive the company all the way to where it got when you acquired it, they might not be built for this next wave of valuation multiples that you’re trying to inject into this asset you just purchased. So again, across so many different scenarios, I can see how this plays out. And I think to tie it back, this is why an agile company with those principles and those physics at play is able to talk to clients about all these problems that seem different at the facade level, but ultimately always reveal the same sorts of operational organizational technology challenges underneath.