GlobalEdgeTalk
GlobalEdgeTalk
Unlocking AI's Potential: Transforming Enterprises and SMEs with Ankit Shah of NetWeb Software
Unlock the secrets of AI in enterprise technology with Ankit Shah, co-founder and Head of Growth at NetWeb Software, who brings 26+ years of entrepreneurial wisdom to the table. Ever wondered how to truly harness the power of agentic AI to revolutionize your business processes? Ankit takes us on a captivating journey through his experiences, discussing the evolution of NetWeb and his own growth in the software engineering industry. With a spotlight on enterprises' challenges in fully utilizing their technological potential, Ankit provides insider insights into the transformative role AI plays across various business sectors. This episode promises a wealth of knowledge on maximizing technology investments to gain a competitive edge.
Transitioning into the world of small businesses, we spotlight the critical considerations for AI implementation, especially when it comes to data privacy and security. Ankit shares valuable strategies for SMEs to effectively leverage large language models (LLMs) and automation in enterprise settings. The conversation highlights how small to medium-sized enterprises can target specific challenges and embrace AI to optimize operations, even with limited resources. By focusing on key use cases and ensuring proper data management, SMEs can navigate the complexities of AI to remain competitive in an ever-evolving market. Tune in to discover how your business can strategically implement AI and transform its operations for success.
Hi everyone, this is Alex Romatovich and welcome to Global Edge Talk. Today is February 14, 2025. My goodness, it's 2025. And we have Ankit Shah of NetWeb Software in our studio. Hi Ankit, how are you?
Speaker 2:Good morning Alex. Yes, my God, it's already February 2025, and we're ending the quarter. I mean well, thank you.
Speaker 1:How are you, I'm doing good. We will have a great discussion today in terms of not only what NetWeb Software is about your path, your journey but also about AI. Now, I know everybody's talking about AI. It's a very hot topic. Obviously, even the president is talking about the AI almost on a daily basis. But let's first, before we jump in, let's first talk about you and why you're on our program.
Speaker 1:Our program is about entrepreneurs, global entrepreneurs, their path, their journey, their successes, their failures and we are very delighted to have you in our studio because you're one of those entrepreneurs successes, their failures, and we are very delighted to have you in our studio because you're one of those entrepreneurs. You're one of those people who really dedicated himself to what you love to do, to your career, to the company, but also with lots of personal values, personal perseverance, and that's what we want to talk about. Tell us a little bit about yourself and why. You are, at NetWeb Software, one of the fastest growing companies in the space and you're at the helm of business development, growth, marketing and so forth. Tell us more how you got there and what were your dreams, and if you realized your dreams out there. Well, thank you, Alex.
Speaker 2:so much. It's a long journey, my God. It looks as if it's yesterday, but it's been 26 years, Alex. And 26 years back, along with Nathan Shukla, a late founder, and my other colleague, Molly, we co-founded NetBeck and, interestingly, I was star of college.
Speaker 1:Wow, 26. You said 26 years. You look like you're 28. How?
Speaker 2:can that be possible? Thank you so much. I was just born and the doctors put me at NetWeb yeah, you're gonna have to tell us about your.
Speaker 1:You're gonna have to tell us about your age, aging remedies that you have or non-aging remedies. But I'm sorry to interrupt you, go ahead.
Speaker 2:No, we have a separate podcast for that. So 26 years back, our late founder Nitin Shukla, my colleague Malik and we founded NetWeb and it was a great start because we were tasked to serve the third largest software company of that time and that needed software engineering, software maintenance, professional service, level one. Level two support the works for a global company supporting clients for 20 countries. So that is how we started our journey and then we learned and we perfected the art of serving global companies across the globe, doing the entire gamut of software engineering as we started, and then that kind of helped us build that as an expertise into our DNA and then we escalated and graduated in serving other customers, other industries and providing a lot of value to our customers since then. So that is in a nutshell what my journey is and in the same way, what NetWeb's journey is so far.
Speaker 1:The world has so many IT services and outsourcing companies out there. It's dizzying how many and how many different solutions they're generating and they are developing for companies. What is your feeling? Do you think that all of this code, all of this software that's being developed, do you think that's being used widely? Do you think that some of it is maybe sitting on the shelf? When I used to work at IBM, we used to joke about the software products that are being developed. At IBM. We used to joke about the software products that are being developed and we would always survey our customers about how much of that software they're actually using and the responses were always 20%, 30%, something like this. So with every piece of software that's being developed, do you think that it's being utilized, that it's being leveraged, used to its fullest potential? And do you think that it's being utilized, that it's being leveraged, used to its fullest potential? And do you think everything that's being developed out there, do you think that is actually getting into production, being used and so forth by the companies?
Speaker 2:That's a very interesting question, alex, and probably not be a right answer to it to it, but yes, there will be a lot of products that have been shelved, maybe while it was being made, maybe after it was being made. For the past three, six years, I have seen so many products being sunset right, but the same time some other products come in. So it is always an organic world and things are being innovated. Some things are being sunset, but it all depends on an enterprise's ability to utilize and leverage the software or solution that they are using. Right, you may have the best of the software, but you may just be utilizing 20 percent of it, which means you're not really getting the best of your investment. So so, getting a software as one, but utilizing to its fullest, is altogether a completely different challenge that a lot of enterprises face, in my opinion.
Speaker 1:Right, and the reason I'm asking this question is because there was an interesting discussion I had with one of the chief information officers, who should remain nameless, and we were talking about artificial intelligence and, specifically, we were talking about artificial intelligence usage within the companies, within the enterprise, because we always talk about AI robots, consumers helping in the kitchen, helping with advice and so forth.
Speaker 1:But this was interesting because there are so many different processes that a large enterprise, even mid-sized enterprise, are involved in their business, be it an e-commerce site, be it a fulfillment or logistics company, a manufacturing company and so forth. And the conversation was around agentic AI, right, those different little agents or big agents that are doing day-to-day tasks, maybe automating some things, and so forth. And when we talk about within the context of the software being used and not being used, he was very hopeful. He was saying that, look, the agentic AI is different in the sense that if you're going to design something into the process, if you're going to automate a certain process, optimize it. The way you should develop it is so that it has the ability to self-optimize and if that's the case, it will never go old or you will always be utilized to the fullest potential, and so forth and so on. What do you think about that?
Speaker 2:It's very interesting and I agree to that sentiment and that thought. Agentic AI has great potential, especially when you think of the agents in a chain all doing different things and working together, and it has great potential. There will be innovation needed once you build your agent. They will be adaptive, they'll be learning things. They will be adaptive, they'll be learning things and we will ensure that the agents are not built with a tunnel vision and they have the feedback blue enabled so that they are able to be more generic and be more adaptive.
Speaker 2:Now, if that part is not well done, that's where you will realize, after a year or after some uses, that it is now repetitive, or after some uses, that it is now repetitive. It's really being able to be adaptive and do more broader things, as all expected it to do. So how you build and deploy the agents would be very crucial. I think in the short term the next one year, one and a half year as we get more agents working for us, we'll see those benefits. But I think in the next couple of years we'll see that they will need to be tweaked or they'll be needed to be more adaptive to do what the CIO, what your friend, thought the real benefit would be Very interesting.
Speaker 1:Let's talk about some of the assets that companies have. Their existing assets have been accumulated over the course of many years and that's data. Right, enterprise data. I don't mean our conversation to become too technical, because this may or may not sit well with some of the audience. However, I think every marketer, every business developer, every operations executive marketer, every business developer, every operations executive, every customer service executive should absolutely be aware of it, be aware of what's going on in the world of AI, how it's going to transform their jobs, how it's going to transform their companies and how it's going to transform, potentially, their careers. When you think about it, right, let's go back to data going to transform potentially their careers. When you think about it, right, let's go back to data.
Speaker 1:There's been a lot of anxiety over what AI is going to do with the enterprise data and how you should integrate it, how you should protect it, how you should handle it and so forth. Give us a very quick overview of what happens inside of an enterprise when you want to leverage the AI superpowers but, at the same time, you're very concerned about data. You're very concerned. You may have even some of the regulatory environments. You're a bank, for example, or you are a healthcare institution or something like that heavily regulated pharmaceutical company, heavily regulated environment, and you now want to automate further, you want to use new technologies and so forth. What should one do? What should they be concerned about and what should they plan?
Speaker 2:There's several dimensions to this question. Right, that's enterprise data. You still have to think of the regulatory, the government effects, the LLMs that you're using, to ensure what type of data privacy and security those LLMs are providing and the type of subscription you have. And that is, I think, the first thing which I believe is very important. A lot of people tend to use the general available LLMs. There are restrictions there, there are clauses of data privacy there.
Speaker 2:So ensuring, when you're utilizing it for your enterprise data, ensuring the subscription that you have for the tools or for the LLMs that you're using, that ensures the privacy. Your data should not be used for training the LLN or for others. Those are some of the basic hygiene. And even while you're training, coupling the sensitive information, the PI information, especially when we are talking of healthcare, how you are training, when you're using it, how you are sending that data, that is also equally important. You don't just generally put all the data. You have to train the element. You clean it up and train it, and then, while you're building it, the software solutions are guided by KIPA and so on. So you have to bring that rigor and compliance in this initiative as well. So if you're doing that it hopefully gives you a good, secure base to start. That's my thought, and obviously, from a technical perspective, there are a lot of other best practices you can follow while you do that, but these are my thoughts on this matter.
Speaker 1:These are very important thoughts, very good ones. We'll actually continue this conversation with different applications of AI in different industries and so forth, so this will be a series of conversations. The other question I have is this All of this stuff is great and a lot of large companies and mid-sized companies can probably afford it. They have the staff, even though there've been a lot of layoffs at the end of last year with different enterprises. Frankly, ai and automation is going to lead to that, no matter what we're saying.
Speaker 1:There are a lot of proponents of the idea that AI, if used properly, will only help us with our current assignments and the industries and so forth. But the reality is such that the private sector will continue to optimize. They will continue to optimize their operations, especially publicly traded companies, right. They want to delight their shareholders, right, and therefore they will look for ways to automate and, let's face it, get rid of humans, right, save cost and so forth. So that's all great. They'll throw all kinds of resources at that because they know what the outcome is going to be. The outcome is going to be positive for them, right?
Speaker 1:But what about the small company? What about the small company? And I'm not suggesting solopreneurs, although that's also a very important piece of the conversation and there are plenty of tools today on the market that will help solopreneurs. It will help very small companies, but I'm talking about smaller companies of a couple of hundred people, companies in the logistics space, companies in the customer service area, even restaurant chains and so forth. How are they going to leverage, or how can they leverage the technologies, the agentic AI components and so forth, cost effectively, first and foremost and secondly, so that they're at least at par, they remain competitive right. Otherwise, it's very possible they're going to lose out on the opportunity because they will be outsmarted, outpaced, outmaneuvered, outsold and so forth and so on. What do you think about this, small guys?
Speaker 2:So I think the way AI, gen AI, the way it is standing out, I think there's room for everyone to benefit out of it, and the small and medium enterprises can also leverage AI agentic AI if they focus on certain problems or certain issues that they want to solve or areas where optimizations can be done either cost or operational efficiencies and by working with the right partners, by ensuring the infrastructure is optimized, there is a great scope for small and medium enterprises to also use a GenTech or generative AI for specific purposes. As they start seeing the benefits, that part can continue to grow. I was in a conversation with another CIO of a small manufacturing company and we were discussing potential use cases as to what can be done in their procurement. How can they implement agents to help do reconciliation, for example, because that is where a lot of human efforts go and that can be done at a reasonably budgeted cost which they can afford, and they can leverage the use of a Gentik AI. So I think there is room for everyone to benefit out of it.
Speaker 1:And that's good to know. It's great to know and it's very encouraging for smaller businesses. But let's talk more about data privacy, the scalability of it and the ability for smaller companies to use, to automate, to optimize their businesses, and so forth. How does one begin? What are some of the steps? To begin to evaluate, to potentially execute on some of these insights, if you will, and to quickly deploy that? Is there such a thing as let's quickly deploy AI Unless?
Speaker 2:you want to generate images or videos with all the available tools and everyone. Whether we know it or not, we're using AI in our daily life. As soon as we switch our phones, there's some AI being used. As soon as we switch our phones, there's some AI being used. But on the enterprise side it's not as easy, nor is it as difficult. Ultimately, the idea of how much data, how well you are training the models to be able to get the right benefit out of it Just because you have data a lot of historical data doesn't mean that you can put AI to use on day zero or day one. So the important thing is for enterprises, the CIOs, to work with the right partners to identify internal use cases, internal problems, and then focus on and figure out how do we implement this. Working with the right partners, working with the right steps and the best practices allows you to deploy it faster rather than stumbling upon reaching the goals. So I think a more focused approach on a particular use case always helps, rather than trying to do it all at once.
Speaker 1:That's amazing, and what should be your advice to some of the kids who are entering, let's say, colleges today, right, who are learning more about this and would love to be better prepared for what's going to happen two years from now, five years from now, 10 years from now? What should be your advice to this younger generation of developers, scientists, journalists and so forth? How should they prepare for the future?
Speaker 2:I think that universal. In my opinion, it's always universal, it's always constant. Right, because you need to be ensuring that you are on top of innovation, so you have an innovative mind, you have the design thinking, whether you're an engineer or whatever field. I think that should be a focus and a passion on solving real-world problems. I think if those two are your primary focus areas, no matter what education or what career path you are, you will succeed, because whether it is AI, whether any other technology, it will ultimately be a means to achieve or to solve a business problem. It may be a big problem for a scientist or very smaller problems for businesses like us or for a manufacturing company, and the graduates or students don't need to be really worried about jobs because as long as they are learning the technology to solve real-world problems, there will be a career for them.
Speaker 1:Appreciate that advice. That's a good one. It's a solid one. Thank you so much for being with us at Global Edge Talk and we wish you a lot of success. You have a lot of experience with this and the network obviously has a lot of experience. You already built a number of different solutions. We're going to continue this conversation as a series of talks, almost to discuss every industry, every use case, every solution. So thank you for being with us.
Speaker 2:Thank you, Alex, so much. It was wonderful talking with you. I look forward to talking to you Great day. Thank you.