Mitigating Business Disruption with Digital.ai’s Wing To

This is Techstrong tv. Hi everyone. Welcome back here to Techstrong tv.

Our next guest on Techstrong TV is Mr. Wing Two. Wing is the general manager of Intelligent DevOps over a DI digital ai.

Hey Wing. Welcome. How are you?

I'm well, thanks Alan. Thanks for having us on the show. Oh, always a pleasure.

You know, we've been covering digital AI since I think the very first day it was announced, right? Digital AI of course, was a a bit of a roll up. Uh, was it TPG was the Yep.

Venture company behind it. Yeah. Yes.

A private equity company bringing us together from five different companies is a certainly an exciting start. Yes. Yeah, I re I remember it well.

I re I, you know, we had good relationships with I, I think probably two or three of the original five. But anyway. Wait, before we talk more about digital ai, let's hear a little bit about your journey.

Yeah, so I've been in, um, the business of building software products for about 20 years. And they've always been in this space of products that help people deliver software. So that's, um, in the IT service management, which is now all coupled with AI ops all the way to DevOps and all those, um, release processes.

So in, it's been with a mutual companies somewhere around the kind of mid-size startup to, uh, large companies like IBM. And about maybe three and a half years ago, I had this opportunity to join this company that was coming onto the market called Digital ai, which was really bringing together, as you said, of um, several companies. And what fascinated me, um, one about our industry as a whole is just how so many of the silos are being broken down.

But, um, digital AI was really looking at how do we solve problems from across the software delivery lifecycle, particularly for enterprises around planning, release, orchestration testing. But what was really fascinating, which seems very, um, very, uh, relevant now, is how can we take all the data that's across the software delivery lifecycle and help businesses optimize their delivery, um, by making sense and use of it, particularly with ai. Um, hence Heon, uh, the name Digital ai.

No, when they first launched as digital, do ai, this is before, you know, chat GT and everything burst on the seed. People were, you know, AI was kind of interchangeable with ML ops, right? It was, it was battered matching and so forth.

So, um, and I thought it was kind of forward looking of them in, in hindsight, you know, sitting here today. It was brilliant, right? Because they were probably just maybe two years before this whole thing just exploded.

Um, of course digital AI though, you know, there's a couple of different style. I, I don't wanna use the word silos for a DevOps company, but you know, there's a couple of different types of products, businesses actually. It's all one platform now, probably, but, you know, different pieces of it.

If you would just give us maybe a quick recap. Yeah, no thanks. Um, so when we really focus around the enterprise, um, and tackling enterprise, uh, type problems, especially with things like scale, so type of things that we are looking at is how do we plan agile at scale.

So we're looking at organizations that, um, may not have multiple hundreds of teams and that they're trying to coordinate and do agile across that type of team, um, that type of, uh, organization. So, um, planning then we have strong release orchestration capabilities, again for helping manage multiple teams as they're trying to release software all the way from when they have, um, from the build to how they get into deployment, into production. Um, so specialized technology around continuous tests, particularly for, um, the mobile space.

And then in our security space, we also have technology that focuses on how to protect applications once they've gone, um, out into production. Um, so particularly mobile apps, uh, desktop apps, um, a range of them. Um, and then on top of all of that, which is something that we see as a, a, a differentiation is collecting all the data that's just available from all these systems and tools, BDR or some, um, third party and bring them together to really get a sense of what's happening across that entire sub software lifecycle.

I mean, it, it's, it's kind of, you know, sitting where I am here at my capper seat. It, it's, it's fun to see the evolution of these things. So Wayne, you know, we've been kind of skirting the issue, but let, let's jump into it, right?

Gen AI has the potential certainly to change, well, to change everything, but as it relates to software delivery, probably the biggest change, I, I don't know since the, the, the commercial internet maybe. And the other thing about it is that the, the pace of of evolution of this is just staggering. You know, I mean, we've all gotten used to, you know, the crunch of internet time, but the hyper crunch of AI time is even faster.

Um, I'm sure you guys are seeing this. How are you preparing, how are you working with your enterprise customers, you know, to maximize the potential, minimize the risk. Now it, it's, it's really interesting went in time because as you say, there's, um, the technology has certainly just come on leaps and bounds and we're seeing, um, uh, tests that our, our customers are doing.

There's literature in the market and then also some work of course we're doing. 'cause the interesting thing about all of this spaces, we're also a software company. So we build software ourselves and experimenting.

We can really certainly see that there are, um, opportunities for, he output gains from developers. 'cause a lot of this is focused around how do we help developers, um, produce code faster, which should translate to both, uh, productivity and um, and innovation. So, um, the things we're seeing was we speak to, um, our, our customers is that they're really at that kind of, um, InBetween point that they're experimenting it with it, really want to adopt it.

And from the c the the exciting figures I and I had, um, um, some time with some CIOs and of course a number of them are getting pressure from the executives, from the rest of the executives saying, we're hearing about these great things, how can we tap into it? But then they've also got the compliance team saying, you've gotta be really careful 'cause we're also hearing about all these scaries things that are happening. So the type of things that we're seeing as we talk to the customers is that there's, there's, we're almost at that kind of tipping point of everyone wants to, how do they do it?

And then from that, we're seeing a few themes come out. One is, um, uh, clearly what can I do to really ensure that I minimize or manage the risk? And now the interesting thing around that is that the, because it's mostly AI assisted, that means developer working with, um, uh, working with some, um, uh, technology and so yeah.

Famous ones out there of course. Are things a copilot tab nine a code whisperer? No, no, no one to call anyone particular out.

But yeah, there's a range of them. Um, yeah, because they're using them in conjunction with developers, it's really hard to distinguish whether it's ai, dev, AI assisted or not. There's, there are no signatures or no ways of what, what telling certainly, um, at, with to the, um, current technology.

So then it raises important point of, well, is it any different from any other risk that an organization has if they were to just use, say a large number of, um, contractors or hired new set of, um, are junior above very wealth, um, resource developers. So what we're seeing is that along the organizations are really trying to manage this by saying, I need to make sure we use order checks, but we need to be very thorough about it. So we need to make sure that there is no code that released without having had, um, for example, order code reviewed, uh, making sure that it's run with tests, some level of test coverage and all the usual policies that people have within their, um, organization.

So the first thing is, um, how to manage risk by ensuring that we are very thorough and consistent with doing our checks. But then that leads on to the second thing of, well if I'm unable to ensure that I can actually release this into production, um, how can I make sure that I don't get, get bottlenecks on the next stage? 'cause only about 40% of time is spent writing code.

There's 60% of time actually sent, um, delivering and shipping code. So then this whole question of can we help accelerate the rest of the pipeline? 'cause it hasn't moved at the same cadence.

This whole idea of flow that things have to be able to flow consistently across, otherwise you just get bottlenecks and there's no innovation, there's no, um, output. So we're really looking at how do we also then help organizations automate as much of the rest of the process so that it moves at the same speed. Lastly, the last thing that's come up, which is really interesting and that's why slightly careful my wording earlier when we talked about the, the power of this because it's certainly increasing developer output, but some questions remain about is it increasing productivity?

Because some of the things that are coming out are, um, are we du producing more valuable quality code? We're just producing more code. So one, the things that we're seeing with a number of the customers we're working with are, can we help benchmark and understand the level of productivity of our organizations now and then measure and check as we apply it in the organization to see are we getting that level of productivity come out from the, um, from the the, um, from the actual, uh, output?

Or is it just that we're producing lots more code, but we're getting a lot of rework? There's a report out there showing that, um, you can get a high level of rework, um, that we get a lot of, um, there a lot of, uh, code generated, but we're not getting lot good reuse. So certainly customers are now saying to us, help us benchmark and also identify as we, um, use ai, where are the, where are the benefits?

Where are the bottlenecks? Because it doesn't come without cost. In fact, uh, the the worst scenario is you produce a lot of poor quality code, so you actually have more rework and it's cost you money because you have to then pay for these new tools.

Yeah. So it's, it's interesting because you, us usually you see organizations potentially jump on top of new trends about thinking ahead, but we're seeing people being a bit more kind of forward planning about it. You know, I've heard people say you could have it write code and, and a human edit fit or a human could write the code and the AI can edit it, but you don't want the AI to do both, Especially the same ai.

So Right. Especially the same ai. Yes, Because and that's the interesting thing 'cause you can use AI at all these different stages and I think that's gonna be the next generation.

There's so much focus on AI just at the developer stage, but who's writing the test? Who's creating the test, who's doing the code reviews? Who's doing the documentation?

But you need to make sure you're very careful about the AI mixture. 'cause otherwise it's gonna be this awful virtuous circle. Exactly like asking you To take your own code, You know, kinda breathing its own exhaust.

But Yes, I, I think the other thing to remember, and this is really for all the humans out here. So all you AI watching this can go off right now, but for all the humans out there, it's important that you learn to harness AI to make you a better, in this case, developer or tester or, or what have you. Not that AI's gonna take your job, but AI's gonna make you more valuable and enhance your value to your employer in the market, et cetera.

And I, I think, you know, if I, and and I talk to young people all the time coming outta school now, and I ask them point blank, how do you think AI is gonna affect your career path and what you're doing not this year but three years from now? And who can see beyond that? And you know, a lot of these kids would say, well I don't know how big it's gonna be.

Right? I don't know how big, well, it's gonna be big. There's no doubt it's gonna be big.

But I think people who get out in front learning how to leverage it and enhance their marketability are gonna be the winners. People who just kind of buried their head in the sand are going get run over here. I would completely agree.

I completely agree. I think this fear that it's gonna take away the jobs is, is is not really the point. I think this will create new opportunities or people don't understand how to use it.

Right. Serve them. Yes.

And I think, um, I think it really is just, um, another natural evolution with we, uh, I was chatting to a one of our developers because some of the code we do, um, um, is very low level. And of course people use to program being similar, but when people start to using high level languages or low code is always constantly changing. But what it should be doing, and a lot of our, you know, our tools around automation is really freeing up the drudgery of work so that people can spend their time being creative and really thinking about the value of the things they produce rather than the kind of mechanics of what they do.

They, they just need to understand the mechanics of it though. 'cause otherwise that's when people get, um, get, get uh, maybe a bit, um, uh, complacent and just assume AI will do it all. Certainly it might be possible in the future, but today it's a tool that needs guiding and I think the people don't know how best to guide it will be the, the really the winners of tomorrow.

Agreed. Agreed wing we're about out of time. Obviously the website here is digital ai.

Um, what's the best OnRamp other than, you know, okay, we go to the website. What's the best way to engage from there? Oh, I would say certainly, um, just uh, reach out, connect with us, would happily have a conversation.

We'd love to really discuss where people, um, the challenges they're having as they're thinking about adopting ai. Um, also even one of the things we're also seeing customers think about is how do I get ready for adopting ai? 'cause a lot of things we talk about in terms of, um, preparing the level of governance, heavy about the automation to reduce to they benefit organizations and they get ready for the use of ai.

Um, and we're always happy to hear about customer challenges. Um, it's a great way for us to learn, learn from our, learn from the market out there. Absolutely.

Thank you for coming up here, our tech drug tv. Say hello to our friends at Digital AI and we'll speak soon. Thanks Alan.

Thank you. Wing two, general manager, intelligent DevOps at digital AI here on Tech Trunk tv. We'll be back in a minute.