#104 AI + Low-Code powered Development w/ Prasanth Reddy (Wavemaker)

Hello and welcome back to another episode of the Process and Automation Podcast with the Automation Guys. Yeah, here at the Process and Automation Podcast, Arno and myself are covering all things low-code, no-code, workflow, Copilot, AI. Yes, pretty much all the things around process and automation. And yeah, and of course today won't be different as we have another great interview episode planned for you. You know by now Arno and myself are very passionate about all the things process, AI, and automation. And yeah, we like to have a chat about these topics with others who share the same passion as we do. So without further ado, it is with great pleasure to welcome our next guest. He's passionate about creating impact by building software products and solutions. He works at Wavemaker and is Senior Director of Product Management. Welcome, Prashant Reddy. Welcome to the podcast.

Thank you. Thank you guys. Thank you, Sasha. Thank you, Arno. Very good to be here and talk to you guys this evening.

Right. Awesome. Yeah. Thanks, Prashant. So we are very excited to hear your insights today and learn more about Wavemaker. So to kick it off, can you please share with our listeners and with ourselves your background, what you do at Wavemaker, and how it all started?

Sure. Yeah, yeah, I can, I can do that. So I, right now my role at Wavemaker is to drive AI strategy and implementation at Wavemaker. So just before I took on this role with wavemaker.ai, I was, I was in a, I was directing the product management for Wavemaker, the low-code product. So this is something that I've started doing in just a few months, last few months. So really excited about my new role, which is to do all things to do with AI. But my journey at Wavemaker started 5 years ago, and I've been directing the product, the development of the product, and implementing, choosing what we implement, and working with engineering teams to get that done. So that's my role.

Perfect. Yeah, I think this podcast is called the Process and Automation Podcast, but Arno and I, we actually talked about this recently because of the AI sort of topic, we have to probably change it to the AI and Automation Podcast or something similar. So we have to still come to a final conclusion. Otherwise, like the AI and Process and Automation and Workflow. I'm just joking, but I definitely have to include that AI in there. So yeah, between Arno and myself and over the last 20 years, We have worked with numerous vendors to support companies with all things process and automation, of course. So where do you see the competitive edge for Wavemaker in the market?

Yeah, that's a good question. So let me talk a little bit about Wave, you know, the Wavemaker product and how the people behind and how we came to develop this. So a lot of, you know, we didn't start from automation and process, business process automation, that workflow industry. So we didn't come to this, you know, develop this product from that beginnings. So our beginnings were actually in enterprise software, Java application servers. So the core team that is behind Wavemaker actually developed the enterprise software, Java software, and we also developed a studio that lets people develop apps and then deploy them onto the app server. So this was, this was how we began. And then when the cloud era began, right? So we started opening out and thinking broadly about just enabling web application development and mobile application development. So we, so we, we focused in on custom software development enterprise. -grade software that is getting developed. And we saw that a lot of companies and teams, they want to develop web applications or mobile applications, but they don't have the right skill set. And then we focused in on trying to address that need. So that's where Wavemaker came from. And I would say the way the product— we position our product and how we differentiate from other people, other product in this space is that we offer— WaveMaker generates open source based code, right? So Angular for web, React Native for mobile, and the product itself is built to be extensible. So we let our customers build their own abstractions and then bring them into WaveMaker so they can extend the product. And it's very flexible. In that they can also write code. It's not very prescriptive that you just have to follow the low-code approach. Where needed, they can write their code and bring it in. And so it's a lot of— there's a lot of flexibility built into the product. So that's, you know, those are the 3 things that position our product in a space that lets it be used by a lot of— in fact, most of our customers are software vendors themselves. So we use Wavemaker to build an application that is customizable to their customers. So they use it. So that's where we are differentiating.

Okay. So thanks for that. And that sounds very interesting. I think that the market is fairly busy. There's a lot of people that brings out tools. There's a lot of tools. On the market as well in this space, especially sort of app creation, app generation. So it's, it's interesting to hear your story. And I think we always talk about use cases on the podcast because it's easier for our listeners to, to really try and understand exactly what the applications of the software. So granted, you've explained that you can generate sort of code using your platform for It sounds like, you know, in any type of application. What are the typical use cases you see out there that is impactful that has been created using Wavemaker software? Something that stands out, it's a very kind of special use case and it sort of could be anything that sort of caught your eye, not necessarily yourself, but perhaps one of the vendors that uses your product.

Yeah, that's a great question, Arno. So we can talk about, use cases that we saw a lot of product market fit. So one of the things that we realized is that a lot of ISVs, when they create their web product or a mobile product and then they take it to their customers, their customers want, want the product to be customized, right? They want to change the way it looks. They want to disable some features. They want to have like a pluggability with what they want, what they don't want. How it looks. So all of these, there is a suite of products, whether it's in banking, telecom, insurance, they have a core product and then they take it to market. But each customer that they take it to, they go through this long journey of customizing it, right? So that's the WaveMaker as a low-code product and the way it's built because of extensibility that is built into the tool. This is one of the use cases that we really found success with solving, solving this use case where we are enabling these ISVs take their product to their customers and then customize it very quickly without taking a lot of time so they can, they can start taking, taking their solution live very quickly and then start earning revenue on it. So that's, that's something that a particular use case that, that we really see a lot. and we've been able to successfully solve that and provide a pla— you know, kind of a product approach to building a core product and then customizing.

That's awesome. Yeah. Thank you. Yeah. So with all the, with the, with all the hype out there on, on all the different topics you mentioned AI earlier, so that's, that's one of the big topics you as well pushing at Wavemaker, but of course there are these other terms like intelligent automation, hyperautomation, still, still very, so obviously present there. So, but looking. With AI obviously now sort of in your mind, looking a bit into the future, where do you see things developing for, for, for companies in these areas? And obviously, yeah, very specifically, where does Wavemaker fit in going forward?

Yeah, so with, with generative AI, right? So that's, that's, you know, changed the way software is getting created and low-code products are in the area of enhancing the productivity of the developer. That's the core problem that a lot of these, a lot of products including WaveMaker are trying to address. But then with generative AI, you know, the software productivity or individual productivity is, you know, you can see that you can generate code, you can write things that are, you know, very quickly So these things are coming on as a capability. So we see that most of the— we have to embrace these technologies and provide solutions to our customers. But my thinking is that when we look at product development, a web app or a mobile app, apart from the individual developer productivity, there's also a lot of productivity loss that happens when there is transition between the teams. So when there is, when there's a, you know, you always have the design team that is creating the designs and then, and, you know, giving them to the engineering team. Then there's a lot of iterations that go to get the fit and finish right. And then you spend a lot of time in meetings trying to prioritize what to fix, what not to fix. So there's a lot of, lot of productivity loss that happens between the teams. And I think, I think what I am— what we are trying to do with Vamedha.ai is to address these productivity losses that happen when there is information that is exchanged between the teams and try to— we are trying to digitize that process in a way that, you know, it can be done easily and without spending a lot of, a lot of back and forth iterations. That's That's one of the places we are using GenAI. And yeah, so we're looking at all of the workflow that happens from the software development point of view and trying to see what kind of situations, what kind of places where GenAI can come and make an impact. And we saw with Copilot, individual developer productivity does get enhanced. But really, we are looking at more of a collaboration and team-wide productivity losses that are sometimes greater than the individual productivity losses. Yeah.

Yeah. And just obviously there are a lot of innovative technologies out there. I mean, GenAI is sort of just the latest addition. People are starting to use it. I think a lot of people are starting to understand its capabilities, what it's used for, its limitations. Of course, there are limitations as well to, to to GenAI. We see these first-hand every day. But just on AI in general, and, you know, and looking at that through a sort of digital transformation lens, what would your advice be for companies out there or our listeners if they want to get involved with automation projects using AI? Where's the best way to get started? I mean, what's your take on that? From a wavemaker perspective?

Yeah, so the digital transformation is a huge topic. But my take on that is that you be very, very specific to your customer journeys and stay true to making those simpler and not be held back by the way you do things. Currently, the current architectures that you have, the way things are implemented. Don't be held back by what those are. And then truly try to simplify your user journey and your end, you know, whoever your customer is, those journeys. And that will drive maximum benefit for any of the transformation initiatives. And especially in this world, I mean, we are, I think, entering into era age where experience or user experience will play a big role in simplifying that and challenging what earlier you might have thought, okay, this is not easy to build a UI around. So you have to revisit those with an AI lens as well. So staying true to your end customer and not being held back by whatever is the status quo. Within the company and architectures that you have. That would be, that would be my advice.

Yeah, I think it can sometimes be a bit of a daunting task for people to actually understand exactly where AI fits into the wider automation space. You know, the term digital transformation, Sasha mentioned hyperautomation. Of course, there's a lot of progressive technology that we talk about in this podcast. It's not just AI. We also talk about RPA, talk about traditional low-code workflow development. You know, integration, you know, we talk about analytics, data mining, process mining, task mining. So I think that if, from what I can understand, is that it's key to understand user journeys and key to understand what those user journeys should look like in terms of being sort of the best they could be and perhaps choose your technology stack based on that rather than a, forcing AI onto your roadmap because it's topical and it's something cool. So yeah, interesting take. I don't know if there's something you want to add to that, Sachin, from our side.

Yeah, of course, it's super important to not just be driven by the hype around AI, but that was very similar to all the other topics we had, isn't it, in the past. So it has to fit your use case. It has to fit your business. That's indeed, yeah, it's what you say, isn't it, Prashant? And yeah, I can only repeat that again. It's super important to not just think AI is sort of the magic super weapon. It can be, but it's not by default. So yeah, but it's good to see that that obviously made easy for developers to just pick up. So otherwise it will be just another thing they have to worry about, but doing it in a low-code way. With Copilot, GenAI, and that kind of stuff. It makes it really, really nice for developers. Just, just plug it in and see what it does really without thinking about the intricacies or the details about it. Yeah.

And apart from what our customers, I think everyone has an expectation now when they're using low-code products or any products that they expect it to be easy to use because Of course, you're expecting a low-code product to have GenAI-based capabilities to get you learn it faster. Like you said, Copilot, all these paradigms that we are seeing in the developer landscape, people are expecting to find these things within the low-code product. So we are actually looking at our own product experience and seeing where we can use GenAI to make it easier to learn, easier to create outcomes faster. So yeah, so it's, it's both inwards and outward as well. Yeah. Cool.

Yeah.

Very good. Yeah. Prashant, we have a tradition here on the podcast and our listeners, of course, like to know our interview guests, like to know you a bit more, more than just sort of the technical bits and the company bits. So that's why we would like to ask you a few quick questions. Would that be okay for you?

Yeah, yeah, let's go for it.

Yes.

Yeah.

All right, let's do it. So the first one we always have is who's your idol and why?

Who is my idol? Okay. All right. All right. So I'm going to choose someone. I play tennis, so I'm going to go with Roger Federer. Why? It's just, you know, the way he plays the game and makes you feel that, well, it's just, it's very magical when you see him execute some of the shots.

So that's, that's why.

Yeah, that's true. Even in his old age, isn't it? Or at the end of his career, it's just—

yeah, well, I think, yeah, I do love his tennis as well. He's a, he's a very good player to watch. That Rafa Nadal and Federer games is, you know, Up there with the favorites.

So yeah, unfortunately we can't see him at Wimbledon anymore. So I, yeah, maybe he comes back out of retirement at some point. Who knows?

You can still catch him on YouTube, you know, when you look at all the highlights.

Yeah, of course. Yeah. Okay.

So I could want to be you. Imagine you can get all the contents, knowledge of a book instantly. Which book?

Oh, okay. I would say any of these AI books, because there's a lot of, you know, a lot of complicated concepts that generative AI is based on. So I, you know, I struggle with it every day when I'm learning how to use these. So I always wish I had a lot more understanding of the underlying concepts. So you know, that would be, that would be good to have. But I also think that even if you did get all the concepts in a book, I do enjoy the process of learning and having the aha moment myself, taking the time and actually understanding what the book is trying to say, and then follow up on the reference and then try to dig through. So I know it's kind of my own, the way I read books is kind of like, you know, go off on a tangent and research a topic that the book refers and come back. So it's kind of, uh, I, I actually like that. So I'm not sure if I want to get all of the joy of reading the book taken away, just plug it in.

I mean, it's all nice sometimes just to play around with something and understand what I think.

And sometimes the book was instructions to, to deal with all the knowledge you would take from another book. So yeah. Sometimes quite tricky to have lots of knowledge if you don't know how to apply it. But the thing is, is that this is a very fascinating thing these days because we have so much knowledge out there. So whatever you think of, you can get, but we are sort of, I heard this phrase is like, we like knowledge giants, but we are, so we are, we are, we, we are not getting into actions, although we have all these massive amount of information. It's about people getting action, get started with stuff, or sometimes they're now as well overexcited about AI and they have all the knowledge, but they're not getting started with it. Just do a tiny project to see how it goes and then take it from there. So they always think, oh, I need to have like a huge impact project. No, no, this, we talk about this a lot, isn't it?

I don't know.

It's this tiny step, be excited about it, and then just do it all over again. Yeah.

So be prepared to fail fast as well, because I think if you, if you see what doesn't work, then at least, you know, if you need to go to a book or a reference point, what not to focus your effort and energy on. That's why we like use cases. I think use cases really provides two things. First of all, it provides an example to explain to somebody in very simple terms what a piece of technology can do. And sometimes if we read a book, it could be really hard for a book to explain that to you because I have seen some books that has got sort of use case driven examples, but usually it's reference based, isn't it?

I mean, you really want to touch it, so via an explanation.

Yeah, exactly. Sort of learn by example. And the other thing is like, like I said, if you fail fast, you can understand, well, you know, if it's AI concept and it's to do with linear regression, then maybe you need clustering. So at least you know which one not to go read about. So yeah, no, thanks for that. That's, that's very interesting.

All right. So the, the, the next question is what's the best advice you have ever received?

Ever received. Okay. That's a, that's a That's a dumb question. I think something about career comes to mind. Like, you know, when I first started in my career, I sort of felt like, okay, I go— if I go join a startup, then I know I'll learn a lot. Whereas if I join a different company that is not a startup, I may not learn a lot. But what I've been, I think, learned, or, you know, somebody might have told me, is that It's not really like a zero-sum like that. You always, you know, when you progress, when you work in your career, you build relationships that expand. And you get opportunities because of the relationships that you built and the trust that you built. It's not really something that you give or somebody takes away. It's not really a zero-sum game. So that's one of the things that stayed true with me. And I have seen it happen in my own career. Now, when I came back to work with the guys who found Wavemaker, but this is the second time I'm working in my career with them. So it's kind of good to know that the folks that you worked with earlier in your career reach out to you and seek you out to work with you. So that's something that rings true with me.

That's great.

Right, so I'll tackle the next one. If you can, you could be an Olympic athlete, what sport would you choose?

I'll go with tennis.

What a surprise.

Doubles again, and then having Roger in your team, right?

I might go with singles, but yeah, it's fun because I do play it often. So, you know, that's at least— I don't think I'll have a shot at it, but Yeah, that would be good.

Yeah, it's always good to see good tennis games. I'm looking forward to the Olympic Games later this year.

Yeah, it'd be on clay, I think. That would be really good surface.

Yeah, brilliant. So yeah, thanks for going through the questions. So yeah, it was a great pleasure to have you on the podcast today. I think one thing at the end is always the, the question for what would be the best way for our listeners to reach out to you? Because I can imagine after that episode, a few people will reach out to you, want to reach out to you. So what would be the best way to do that?

Uh, they can reach out to me on LinkedIn. Um, on LinkedIn they can find me Prashant Reddy at Wavemaker. Uh, that's, that's the easiest way to too.

Oh yeah, that's great. So yeah, we'll put that into the show notes. If you have any other things you want us to put in there, so obviously put the URL to Wavemaker and then to your profile. And yeah, that will go into the show notes. And yeah, with that, we are coming to the end of the episode. Thank you, Prashant, for joining us today. Yeah, a big thank you as well to all our listeners out there, as always. We will be back with another episode very soon. And until then, let's automate it.