(bright music) - Hello, everyone. Welcome to "Tech Innovators Spotlight." Today, I'm twinning with Niranjan. Welcome, Niranjan, to the show.
- Thanks for having me. Happy holidays. - Happy holidays. - It's Merry Christmas, I guess, belated, but we are still wearing the shirts. - (laughs) We are.
We are absolutely playing the part for holidays. So tell us, Niranjan, tell our audience a little bit about you, your role, and Nintex. - Yeah. I lead product and engineering at Nintex. Nintex is a low-code automation platform where you can build full-fledged automation solutions. So imagine loan automation or vendor automation or partner management, et cetera.
So you can build portals, you can build workflows using local technologies. That's what Nintex does. It has a lot of capabilities around the automation realm. We primarily focus on the mid-market, but we have, as any other company, a pretty assorted customer base, 8,000-plus strong, a lot of different use cases, a lot of different industries. I've been here a year and a half. It's an exciting space, with AI sort of infusing all its magic into automation.
And there's a lot of value we can bring to customers and how they improve their business processes using our platform. Great to be here as well. - That's amazing. - Thanks for having me. - Yes. Of course, of course. I was super excited for this session: #NoBias (laughs).
But tell us, because you have automation and low-code, I think AI just helping accelerate that momentum of automation. How do they pair together? - Yeah, the way I think about it is if you look at any business process, let's say you are a citizen portal automating permits or you are a bank automating loan processing, any business process has a set of, I would say, deterministic execution, as well as it has some nondeterministic execution, which is primarily done by humans for the most part. I think the first use case where AI would really transform is that a lot of the nondeterministic or probabilistic use cases where there's a lot of contextual judgment-based decisions, you can train AI models to do it. And that takes automation to a whole different level. So if you think about coverage of automation, AI is just gonna drive a step change in how much coverage you get across each of these business processes.
So that's one, which is the value to the end user. The second value with AI is the acceleration in the build process itself. So how you build...
The value proposition of low-code is really to abstract it, make it easy for somebody to build these solutions. And with AI, that is gonna see a step change as well. Let's say you went from 10 to two with low-code. Now the two now becomes one or maybe 0.75, right?
So just the ability and the efficiency for us to build things, build these solutions faster, also improve. So there's value to the end user, there's value to the builder as well. And so it's super exciting. And every day there's something new coming up, so it's an exciting field to be in. - Yeah, and you touched upon a very important point, which is like reducing time to value for our customers.
So if a workflow with automation historically would be taking a few hours, it's going to reduce it by X amount now with AI coming into play, as well as all of those end customers, the ramp-up to any new product, including Nintex's, becomes much easier, right? - Absolutely. - Because you are conversing in natural language, it just makes that ramp really, really easy. And also, like, tell me if there are also avenues and areas where you were speculating, okay, we should do something like this for our customers, but weren't able to do that because we didn't have AI.
And now that we have AI, you're like, "Oh, we can kind of go solve this problem." Whether it is reducing time to value, whether it is expanding in the markets that you thought, okay, now we can kind of go into a different industry, whatever slice or whatever lens that is. But help me understand that.
- Yeah, there's probably two angles I'll take again. So on the build side, we are primarily a build platform, right? Like, our value prop is you build these solutions faster using our platform. Testing comes to mind.
So a very hard problem when you have a multidomain product. So we have several different products in the automation suite. Workflow is one of them. App creation is another.
Document automation is the third, et cetera, et cetera. When you're stitching multiple things together, the ability to test that entire solution is a hard problem to solve for the builder offering tools and capabilities to do it. But with AI now, which is true in pro-code as well, like, if you ask developers, one of the first use cases that comes to mind to them, using AI in code, is how do you write unit test cases or even functional test cases, right? So it's a similar paradigm, even on our platform. What used to be super hard and probably a gap in the industry itself now can be solved using AI and offering capabilities to automatically test, or at least get a good amount of automated test coverage across the solution. So that's the build side of the equation.
On the end-customer side, there's a lot of bespoke functions. And I'll take my loan processing example again: you can think about a worker function like underwriter, which was typically a very hard problem to solve. We can now offer library or agents that can actually help you be an underwriter. Maybe it's a Tier 1 underwriter and you still need something, a human in the loop for either approval or more complex use cases. But that would not have been possible without AI. The ability for us to build custom models, it's not our core domain, and it's probably something that we would never offer.
But with AI, some of these first-level industry-specific bespoke worker functions becomes possible. And that's a value-add to the end customers who are using it. - And you probably are seeing a lot more excitement from those specific industries now.
- Correct, there's a lot of that really happening, both on the open-source community as well as startups building worker functions using AI, using this exact concept. - That's awesome. When I think of the industry and when I think of sort of your use case, I feel like it spans across all of them, and everybody can kind of adopt, interject, start using it, and kind of from wherever they are and whatever use case it may be: it could be like internal HR, marketing, whatever functions, as well as, you know, solving for the underwriter problem that you said, right? Like, very specific, very niche. So that's sort of the breadth of Nintex's portfolio that goes around. Tell us, if customers wants to understand a few of these products that are using AI from Nintex's portfolio, what would those be? - So we think of our platform in three domain areas.
And so I'll just maybe give an example of each. If you think about building a solution which automates a business process, there's three sort of segments to it. One, you have to identify what is happening today and what is worth automating. That'll give you the ROI. So we call this the identify bucket. And an example there, how we use AI, is we use a fairly sophisticated computer vision, now augmented with LLM models, to understand what is happening on the floor as users are interacting with their machines and with the processes to capture that process and document into a very structured format, which almost becomes a requirement in order for you to then go automate something.
And so that's a very powerful use of AI which has gotten a lot of traction with customers. Because any customer you go to have tons of undocumented processes that they don't understand right now. So that's the identify bucket.
Then we have a middle bucket, which is essentially orchestrating business process. So this could be workflows between systems or humans. This could be tasks which are very repetitive in nature, or it could be documents that need to get automated. And so we offer a host of create functions from these captured requirements in how you can create a templated workflow, a templated document template, a templated RPA script.
Or whatever is automating your task and the coordination layer fairly rapidly versus you having to start with a blank canvas and building that out. And AI makes that possible, because now context is available through the identify phase, and you can translate that into business logic using AI. So that's kind of the second piece. And then the third piece would be... And if you go to our website and read about our products, you'll see a lot of these create functions that helps you get started quickly. Or if you get a trial on our product, you can use them as well.
And the third piece is the app creation portion. So once you automate the business logic, you sort to have some kind of frontend or interaction layer where stakeholders can interact with the business logic, which is generally an app or forms or very quickly, chatbots. And so we have capabilities on our platform to A, create those apps, but also create your own chatbot interactions. So we just launched a capability where you can build your own chatbot, integrate into your custom models, but then expose it into your applications or pages, which uses AI to bolster your engagement with the business logic. So if you think about these three buckets, identifying what problem to solve, orchestrating business logic, and then building pretty powerful interaction capabilities or experiences, each of these areas uses AI to actually enhance what the user gets out of it. - That's pretty expansive. I mean, that's amazing.
It's fun to learn. I have been supporting Nintex for years now, but- - That's too far past to actually talk about it. (host laughs) - Right? This is very apt. I'm learning new things too. So this is great.
This is great information for our users as well, to kind of then think through what am I trying to solve for from these three. It could be all three. it could be start with one and then kind of graduate to another, right? So that's very cool. And everything is available on Nintex's website, as you mentioned. So users can go and kind of play around with it or reach out to the CPO, Niranjan, aka.
- (muffled speaking) me. - Yeah, exactly. Exactly. And get that, you know, the trial going. That's awesome. So tell us like a little bit about your journey with Microsoft. And I know Microsoft provides AI portfolio suite, so does other cloud providers as well.
What's the unique value prop? What's something that differentiated Microsoft for you to kind of feel like, okay, maybe we'll give them a shot. - Yeah, every other place I worked with, I work at, there's been like a conscious choice of like where we are going from an infrastructure provider. The Nintex journey is actually pretty unique, because Nintex started as a Microsoft partner.
So this partnership goes way before cloud was even a thing. And so Nintex, for who's watching, Nintex started as a SharePoint add-on that actually enhanced SharePoint workflow experience on top of many, many, many, many SharePoint deployments. This was the SharePoint on-prem base. So that's the origins of Nintex. And so the partnership with Microsoft has been very solid since way back when.
And so the natural choice when we were moving to the cloud and building a sort of agnostic product that can independently work on the cloud, Azure was the natural choice because of the hooks into the Microsoft ecosystem, which is where our core value proposition started with. So we use Azure very heavily. A lot of our core products are built on the Azure suite. Not only infrastructure, but also the pass layer is used pretty heavily within Azure.
And then as we have sort of gone into the AI realm, because our infrastructure is already pretty closely connected with Azure, that was obviously the first choice. Now, obviously we had to make conscious decision of is the offering competitive or not. And we happen to build a lot of our core products on the OpenAI stack. And Microsoft's partnership with OpenAI has nationally helped there.
And so a lot of our, a lot of our production services go through Azure OpenAI services. We are also testing out some of the other AI offerings like search for, and then different vector databases. Right now we use a third party, but we are evaluating Cosmos DB or any of the other vector database offerings that Microsoft's coming up with.
So that's been the journey with Microsoft. So that's the infrastructure side of the story. I would also say, just from a partnership perspective, we get a lot of support from Microsoft because of the history of the partnership.
There's a lot of joint programs that we are doing right now to get more closely intertwined with the ecosystem, which historically existed with SharePoint. But there's other ecosystems, like Dynamics and Teams, et cetera, where people do their day-to-day work, and there's value to be had by connecting it into the Nintex ecosystem. And the third thing that I would point out is Microsoft also helps us with a lot of educational and training materials as we are going around this curve of AI innovation; which is also very helpful because, as an ecosystem, you see a lot of different use cases being activated. And it's sometimes very hard for us to keep up with what is the latest and greatest. But having that help is super helpful for us to accelerate our journey to what we bring to bear, but also for Microsoft to learn where there might be opportunities to improve the stack and help software providers like us to innovate better.
So I think infrastructure is one, which has been a long story, and it continues to be a good story, but I think the support we get from Microsoft has also been pretty stellar in continuing that partnership. - That's awesome. You touched on all, all of the key value props from Microsoft. So you really understand all of the ecosystem that Microsoft wants to provide their ISVs really well.
And one more thing, probably, that dovetails really well into this, is in addition to what we provide, we would also love our sellers to co-sell your products. So let's say if our seller is talking to the customer, what are some of the key things they would listen and say, "Okay, I think Nintex is the place to go," for solving this customer's ask. - Yeah, look, for customers, because these are mutual customers, I will always start with the value proposition, which is if you're a mid-market customer looking to build a full end-to-end solution in a pretty quick and easy way, Nintex is the platform to come to. Universally, the number one value that we hear from customers and partners is ease of building.
And so that has to be your higher order bit, is this is a platform where you can build a full-fledged solution and it is easy to build. So that's the customer side of the story. Always lead with that. But there's a Microsoft side of the story as well, which is we run on Azure which is mutually beneficial. There's a lot of hooks.
And so we complement and extend the Microsoft ecosystem really well across many different platforms, Power platform inclusive, but also Dynamics, and then SharePoint and then Teams. And so that's the other value proposition. And lastly, I would also say, our mission is somewhat similar, right? Like, I think Satya has always talked about enabling, I can't quote it verbatim, but enabling everybody to do better or achieve more, which is the mission we are on as well.
Like, our fundamental belief is, in the future, with the confluence of low-code as well as AI, you can bring in a lot of automation and drive efficiency for all businesses. And so the mission remains the same. So there's a lot of confluence for sellers to get excited about in the joint partnership between Microsoft and Nintex. - That's awesome. So in summary, it's ease of use of all the Nintex products. Integration is almost seamless between Microsoft and Nintex, right? And empower every person on the planet to achieve more. - Yeah, there you go.
- (laughs) That's fantastic. I better know that. Like 11 years into Microsoft, that needs to happen. But no, that's awesome, because that way our sellers can go and understand that these are the things where we partner really well, and we want to bring in Nintex.
And it's a joint solution for our customers, solving the customer problem as opposed to building something from scratch on customer's platform that requires ton more resources on both sides. And it takes longer time to value, right? And we don't want that. So this is a much easier portfolio to deliver, giving the excellence that the customer is looking for in a shorter time, as well as all of the MAC decrements and all of the other fun stuff that comes with Azure and Nintex's.
- Yes, you think have MAC commitments, like, you can retire those as well. That's also a value proposition. - Yes, exactly, right? So you are not losing. None of the parties are losing anything in this. So it's a win-win overall. So that's fantastic. What I do see from ISV standpoint is there is a lot of excitement, like Nintex kind of jumped on the bandwagon of AI.
There is also a lot of apprehension. What would be your guidance to your peers in the industry to onboard onto AI? What are the things that you have learned through the journey? What are the things that you feel like, okay, this is Moore's law, as Satya talks about. You have to jump on it at some point. Like, what's your take on that? - I think my take is similar.
Look, if you go back, whatever, 50, 100 years, the pursuit of technology has always been to improve productivity for the society, generally speaking, right? And every inflection of technology has come with excitement and apprehension: the advent of computers, cellphone, internet. Like, you look at all the major step function changes have always been met with some level of resistance, apprehension, as well as excitement. And I think AI, fundamentally, drives another step change, in that how do you improve productivity of the society at large at a different level, right? Because it brings in a level of intelligence that we've not had machines deliver in the past.
And so naturally there's apprehension from two sides. One is obviously the bias, the privacy, the security, et cetera side of things, which is what's gonna happen if I connect into an AI system and expose all of that, which is one set of apprehension. The second set of apprehension is what is happening to our jobs? Is it gonna go away? Is a bot gonna do, you know, this podcast, where I'm a bot, you're a bot, and people, they are just conversing. And so I think those two. The latter has always existed.
I think, you go back in time with computers, internet, there's always been talk about our jobs gonna go away, but historically that's not happened, right? New jobs have been created. The same jobs have changed. But that hasn't happened. So I'm like, "This is just another wave." It's a different wave. New things will come up.
The old things will retire as it should be. And I think, for the former, it'll be a mistake not to jump onto it, because there's such a big technology advancement and possibility with AI. But you have to experiment what feels right to you.
Like every company will find its balance. There'll be early adopters, which are less risk-adverse, and there will be others who are more risk-adverse. And that is true even with internet. It is true with cloud.
It's true with every technology that has surfaced in the last 50 years, right? Like, there's early adopters who jumped onto it; there are stragglers who are still not fully there. And that'll be the case even with AI. But it'll be a mistake to overlook the power of what it brings. And so I would encourage anybody who's skeptical to give it a try in whatever risk boundary you are comfortable with, and test out its power in the best way it works for you internally, it works best for your customers.
And it'll evolve and help the evolution. Because it's not a one and done. It's here to stay. It'll evolve into something great like any other technology has. - That's very well put, because I think you have to, your message is you can start small, but start, start somewhere so that way you are not left behind to the date where it's like, okay, it's so hard to kind of come back from it, right? I always think of this camera, Kodak camera, right? Like, they were, sort of the digital one, the adoption was so late for them.
They were a huge presence, and then it was completely eliminated of the industry. - I like that, it's the prized, or Netflix versus Blockbuster. Like, there's so many examples of- - So many examples. - You're skeptical, skeptical, until the point that that is the mainstream.
- Yeah, and then you're too far behind to catch up, right? So start so that way you start building skills, as you said, and that way it's easier to adopt for you and onboard onto it whenever you're ready. That's a great message. Thank you so much, Niranjan. Is there anything before we wrap up that you would like our audience to know? - No, I think we covered most of it. Those who don't know Nintex, please check us out, nintex.com.
And let's talk about what challenges or solutions your business needs and how we can help them build it. I think a lot is possible that was not possible a year or two years ago. And so let's figure out how we drive efficiency together. - That's awesome. Nintex is in for business.
Please reach out. That's amazing. Thank you so much, Niranjan, for joining us. Thanks to the audience for listening to "Tech Innovators Spotlight." Until next time, thank you. See ya. (upbeat music) Thanks for listening to the "Tech Innovators Spotlight" podcast. If you enjoyed today's episode, be sure to subscribe, rate, and review us on your favorite podcast platform.
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2025-01-24 10:31