BotCast: Exploring The World Of AI In The Enterprise Space

Mohan Dhamodaran:

Sure.

Manasa Kavuri:

So another thing is we should not close the recording until it is 100% loaded from both our ends. That

Mohan Dhamodaran:

Hmm.

Manasa Kavuri:

is another segment. Okay, let me start. Okay, one, two, three. Hello and welcome to broadcast, the show where we explore the fascinating world of AI and it's backed on various industries. I'm your host Mansa, and today we have Mr. Mohan Damodaran with us. He's the senior director in global supply chain planning. at Maxwell Technology. Welcome, Mohan.

Mohan Dhamodaran:

Thank you, Mansa. Yeah, great to be with you and share some of my experiences with AI.

Manasa Kavuri:

Thanks Mohan. Can you walk us through a bit of your background and give our audience a little bit of introduction about yourself?

Mohan Dhamodaran:

Yeah, sure. And let me start off with my background with education being done in India, primarily engineering graduate. And I did my master's from, master's from NITI Mumbai, industrial engineering. And then my experiences vary with a lot of consulting work throughout the globe. I did work in many larger scale, ITech industry companies in Asia, and then moved on to being in the US. And then I've been with consulting till 2014, and primarily moved to operations role. So I've been in operations role for past 10 years. So my role has been not being more of advisory and then slowly move into an operations role.

Manasa Kavuri:

Thank you Mohan. Now let's kick things off by exploring AI's role in supply chain operations. How

Mohan Dhamodaran:

Hmm.

Manasa Kavuri:

do you perceive AI's impact on changing supply chain operations, especially in your current role?

Mohan Dhamodaran:

So let me actually in if you look at our industry, we've been using deep learning and mission learning for past many years with the ramp of AI. With the ramp of AI recently, it's changed the landscape of using large scale language models, large scale language models like give me a minute. Sorry Mansa, I had to reset my family saying that I'm on a video call. So can I get started again? Can you hear me?

Manasa Kavuri:

Sorry, I was talking on mute. We can totally edit it. Start now.

Mohan Dhamodaran:

Yeah. Yeah, so let me actually start off with. Let me start. Okay, so we have been using machine learning for past many years in supply chain. So primarily such as examples in forecasting, demand forecasting, on clustering, segmentation, and prediction models. So it's been very handy, and we have refined the models to do a great job. which we previously were not without those tools. And similarly, the same case with inventory planning on clustering side and creating a segmented supply chain. And supply chain planning had many optimization tools. So we used to use a lot more optimization tools, linear optimization, and so on and so forth. What's exciting? is with the ramp of generative models, large language models, has given a platform for us to be more interactive. Actually, now with the interactive models, with we can go upscale our interaction with us, upscale our capability through large language models, for example, using like a companion or for detection model or for prediction models. So it's becoming more interactive in nature. And I see large language models as a wrapper around our machine learning that it can interact with. problems, detect problems, and predict our solutions to help solve the problems.

Manasa Kavuri:

fascinating insights, Mohan. To our audience, where, sorry. Okay. Our viewers are always keen to learn about AI use cases. Could you elaborate on the top use case for AI in supply chain, such as, could be forecasting, quality control, and balancing cost and speed. But our- there any specific AI tools or platforms or technologies you find particularly promising for supply chain optimization.

Mohan Dhamodaran:

Yeah, let me give my own example of a couple of years back, we developed an inventory planning tool from scratch up. Basically, we took advantage of the mission learning models to develop inventory planning. It was from scratch. It helped us create those models. Like for example, we used network flows, created a segmented supply chain for differentiated service levels. And this segmented supply chain change offered us with differentiated services. With differentiated services, we can extract a different... value proposition from our stakeholders, our customers, or our suppliers. But with the large language models, now with Ramp, what has happened is it has encapsulated those models with interactive model, like we talked about. It could be a companion model, for example. It's some could say, in the inventory planning scenario itself, a different level. And one could ask for what those levels are for different customers, for different products, and you don't have to wait for a planning team to come back to reply and respond to them. It's kind of an interactive model can respond to those queries, right? So that's a very basic fundamental I'm looking at. where the interaction becomes more online, synchronous basis. And the second level I'm thinking about is what happens if an inventory level is below certain limits for certain customers. Today, there are many planners going to look into those inventory levels and trigger those actions to get to a level. But with the AI model, the detection could already be asynchronous as it goes down. And we already have past historical data, how we kind of address those supply issues. And it could already trigger our actions to fulfill those things. So those were like very concrete use cases from an inventory planning alone, right? And now if you imagine logistics side, I'm thinking about, let's say there is a shipment that's supposed to reach a customer. And for some reason, there is a delay in the shipment. So instead of waiting for somebody to trigger, that's a delay. with this model, you could already identify those delays and take those actions with alternate options. Options could be reroute them, it could be a refill from some other DCs. So many of them, like in those logistics cases. Similarly, I can keep going on for many areas of supply chain. Those exist. And that's why I categorized this large language models to be more interactive and really working on those three basic fundamental areas that I categorize them, being a companion model, being a detection model, and coming up with an action and really looking at going further along, not just showing multiple options to action them, but even implementing those actions. I don't see, I really foresee them happening quickly and come up with the actions. So what does this mean to overall supply chain? The overall supply chain performance would be synchronous based today to act on those actions, either we are a day delayed, or some companies are even week delayed to respond to those. supply chain issues or disruptions. So with this very synchronous calls, we can respond to the customers quickly. That means responses within hours. And you capture those customer excitement and customer value quickly. That's what I pretty much see as the important KPI we all can measure against.

Manasa Kavuri:

On that note, tracking the effectiveness of AI is crucial. So what in your opinion are some of the key performance indicators that businesses should monitor when assessing the success of AI implementation in their sub-large chain ops?

Mohan Dhamodaran:

Right. I think we touched upon briefly today, if I were to see one KPI that we all can measure against is customer success factors. And today, if you look at not many companies respond on a weekly cadence or some companies with monthly cadence, and some matured supply chains can respond within a day. But think about this and we all can get to a state where they can be in an hour's time. If you look at stretch that idea how this can be implemented as well. So today, if you were to respond to a customer, they have to go multiple hoops to get to that response. And imagine we create a large language model and that is within the... company large language model, but we can have a language model which is on the hedge. So when I call language model on the edge, those language models can be exposed to the external stakeholders outside of our companies. So you're breaking the barriers and those language models can really interact with customers. So today customers can go back and ask, hey, what's my shipment? Where does my shipment look like? when can I expect that to dock in my dock? That could be readily available for the customers to see and real time track that on those shipments, right? Similarly, where vendor and looking at from the vendor side as well, they can see what's my request coming along and they can respond quickly to those requests. And those requests go along now on a real-time basis, and they can look at it on real-time and respond to them. So interactive, if you look at that, it will become more interactive supply chains and reduce the lag in the supply chain much narrower. And that's what I see with. with this large language models helping us too.

Manasa Kavuri:

It is at Mohan and I would like to take the focus on the skills expertise and the implementation of. So let's discuss knowledge, I mean the skills and knowledge professionals and supply chain field should develop to effectively leverage this AI solutions. And also when it comes to AI implementation, do you think building in-house solutions or purchasing them is a preferable choice?

Mohan Dhamodaran:

Yeah, actually, it's very interesting that you bring up this skills from a supply chain resource standpoint. I think with AI development with large language models, actually, what it means is you train the language models to be a supply chain expert, right? Like now, actually, we don't need many experts to be developed. So we have expert already developed. So now the skill would the requirement of the skill would not be that high bar that we all need to run supply chain. It would already reduce because most of the things will be done by a large language model. So it will understand your language, it understands the problem statements, it understands to where to how to decipher the whole problem statement, give you the solution to you, and get to that state of answering. So it becomes a low-skilled profession that everybody can be a supply chain professional, right? I think it's kind of two factor in it. One is the very expertise in developing those models in-house. or models, a generic model, a domain industry domain specific model. And we can subscribe to industry specific domain model by each company, and then develop or train the models to data specific to the company. Once you have it, so that's the expert sitting there. So like for example, somebody looking for it for a question from an expert. need not wait for the expert. They can ask the AI model, and it should be able to answer those questions, right? So that's why I would say the skill requirement would kind of become more, less of a need, because AI already would cover most of the expertise, and we could fulfill a lot of the supply chain. needs with the genetic skills.

Manasa Kavuri:

Now coming to our last segment for today, which is mitigating risks on AI reliance. So in your view, what are the potential risks associated with over reliance on AI and supply chain management and how can they be mitigated?

Mohan Dhamodaran:

I think supply chain has a kind of evolved and effect based. I think we have less risk being a biased model because when you look at how this will be implemented, it will not be a public domain model. It has to be a model which is generic for domain specific. And it has to be a once you have a domain specific, then everybody subscribe to the domain specific and they have to train the model with their own data. And I hope at least we have large historical data that can help us to train the model. And the challenge would be training the model itself. I think the challenge would be, do we have enough? data to train the model, do we have captured all the use cases that we have. So I think that will take a while for them to get to the mature state. But it will get to the state, how fast we can get to that and train the model and the resources we can utilize them doing it. So that will be a challenge. And I think, I think It creates an opportunity for companies with mature supply chain with large scale data that can be publicly available. Could also sell the services to companies who have less maturity in the supply chain and less skill set in developing those AI model and become utilize them as a service for companies, those companies. I think that could be a business model coming along to help everybody in the industry. But the fact would be like, it will be more of an unbiased or less biased than what it is if you look at the public domain large scale models for generic use that we all are used to. I think I will see a bright future. It will take for us for a while to get us get that to a trained and to a matured state for us to be more productive and the vision that I foresee that it can deliver.

Manasa Kavuri:

Thank you Mohan for sharing your expertise and insights on the transformative power of AI and supply chain management. It's been a pleasure having you on podcast.

Mohan Dhamodaran:

Oh, thank you Mansa. Great talking to you and really catching up with you. Thank you.

Manasa Kavuri:

That's wrap up today's episode of podcast. We hope you found this discussion enlightening. If you have enjoyed the episode, please subscribe, leave a review and share it within your network. Stay tuned for more exciting episodes where we continue to explore the world of AI. Until next time, I'm Mansa signing off. Let's wait until it is 100% uploaded on the top and then we can stop. So, I'm stopping the recording now.

Rasna:

Thanks for watching!

Manasa Kavuri:

Hello everyone and welcome to the podcast. I am your host Mansa and today we have a special guest, Rasnaas Rani, vice president of digital as a service at Waterford. Thank you. I'm so sorry. I'm really sorry. I just, I got a message and I just like, yeah. Hello everyone and welcome to broadcast. I'm your host Mansa. Today we have a special guest, Rasna Srani, Vice President of Digital Asset Services Board of Home. Here to discuss generative AI and its impact on business. Welcome to the show, I trust now.

Rasna:

Thank you, thanks so much Mansa for taking out the time and inviting me to actually share my thoughts over here. A little bit about me, been working now since 21 years, I would say. Started my journey right at the base level in the call center. But then after that, I think my inclination was towards quality, towards process, towards technology. And that is where I switched within a couple of years, right, and worked my way up. About 12 years, I have been completely into digital, where I worked with various technologies right from chatbot to automation, robotics, mining, you name it. And it's been a very interesting journey so far. And currently I'm leading a team where I'm managing a center of excellence, managing these different technologies and implementation in Vodafone. So thank you for that, Manju.

Manasa Kavuri:

Thank you, Rasna, for giving us, you know, sharing your journey. And I think it has been quite an incredible trajectory in your career growth, where you are currently placed. So no further ado, let's dive right in. Rasna, in the ever evolving world of generative AI, what recent developments have particularly piqued your interest and why?

Rasna:

I mean, that's a good question. I would say, see, generative AI has progressed over the last few years, and I'll still not say it's the end. There is so much more to really unlock because we are not completely there yet. You know, while I speak about all the good things of AI from a productivity, work efficiency point of view, I think for me, personally, what really picked my interest is, you know, from a... creativity point of view. If anybody who is very creative and is into art, right, if you know an AI tool called Dali, it was a surprise, right? Earlier this tool you could generate maybe art only with a few inputs. But now if you realize generative AI can help you with real time animation, music, audio for various use cases, and you will For me, I would say you would see this as a continuous growth, right? Over a period of years, enabling musicians, songwriters, art creators, sound effect professionals and any normal user, right? Like you and me. If you have an inclination towards art, you can actually get the full potential of generative AI tools and express your creativity. So I found that really interesting, a big change apart from only focusing on the, let's say the work related front. I thought I'll share that with everybody.

Manasa Kavuri:

Fascinating. Now, I understand you had some experience in implementing GenAI in various sectors. Could you share an instance where generative AI or AI in general played a pivotal role in transforming operations within the sectors you have worked on?

Rasna:

Yeah, I think I'll give a little bit first of background. Now, one of the biggest plus that I see in operations or in our industries where it has helped us is that AIs can help out with a high level of personalization. Now, how this happens is that there is a huge amount of data feed that goes into the data analysis, data generation, data prediction capabilities. In that way, you are able to really personalize what an individual really wants. I think today all of you experience some amount of it, right? If you look at Netflix and you go into it and you realize that, oh, the content that you have watched previously, somebody is again, you know, only the similar type of content is getting recommended for you. Similarly, if I can say from the telecom industry, when we are looking at driving sales, we are looking at, you know, identifying the right target. audience right from a marketing point of view. So I think AI driven personalization, I think for us to draft the content from a marketing propaganda, using personalized information of you know, people and customer behavior, analyzing their demands to increase the sales, I think is something that you know, we continuously use. Also, what is the best product? right? What are the best plans that we offer as a consumer or you know, as Vodafone that we go ahead and offer, you're able to do, you can predict that using data, right? And can we offer something more what the customer is looking for and that itself is going to help, you know, I think help with our overall revenue generation. That's the top line. I think that is something I would say is how we are using it in our industry. The second thing is, you know, telecom is purely from a services point of view, right? And when you're looking at services, everybody wants a personalized service. We have something called as chatbot, you all may be very familiar with. Now, if you look at a few years back, a chatbot was very similar to like a robot, right? What you feed in, you can get some answers that I have a query today, right? My network is giving a problem. You will type in something, you will reach a chatbot and the chatbot will try saying do some troubleshooting steps. With generative AI, I think it's gone one step further, where you can actually have a conversation with the bot. The bot can create its own training content based on the experience and the data that we look at feeding in. And in that way, let's say from a productivity point of view, if I don't need a human to actually answer that query. And for a consumer, if you can get an answer 24 by 7 without being dependent on a person, I mean, how amazing can that be, right? So we have been using that extensively, I would say, a lot of innovation in those areas, in using chatbot, using other technologies, even process mining, right? Huge amount of data, no longer, right, that you need to go ahead and do a one by one that you need to... you know, look at drawing a process map, data throws out, right? What is the process map for you? So I think in various ways, in terms of identifying trends, being more predictive, right, being more personalized and preventing then, I think, moving things into a human and being, let's say being done by the bot itself using the generative AI technology, I think is where. I have seen this benefiting the business, both the top line as well as the bottom line.

Manasa Kavuri:

That sounds like a game changer definitely. But as businesses increasingly...

Rasna:

Absolutely.

Manasa Kavuri:

I'm sorry?

Rasna:

Yeah, I was just saying absolutely there's a game changer. So.

Manasa Kavuri:

I have to repeat it. Sorry. So I thought like there was some glitch or something. So I'm sorry I couldn't hear it properly. Sorry.

Rasna:

of the learning.

Manasa Kavuri:

Sounds like a game changer as businesses increasingly embrace generative AI. How do you foresee it impacting the traditional process assessment? and improvement methodologies.

Rasna:

So I'll give you a very good example. I currently am working on a process mining technology. And if I can give an example, because I come from a lean Six Sigma background as a master black belt, what we used to do is go on a whiteboard, draw out a process, understand and go into step-by-step, do a data collection, and then see what is the baseline of a process. and then work towards the improvements and see what needs to be done to change, move it from a point A to a point B, from a customer success point of view. However, now with technologies, let's say something like a process mining, now you no longer need to draw out a process map. You are actually taking data from the source system, you're feeding it into a platform, you are having already coded, let's say a low code, no code. outcomes where you don't spend a whole amount of time because there are apps which are built using AI on what is the best practices. And you already get a visual of what is your map, how much variance is there, what is the standard process that people are following, how much of deviation is there from that standard process, how many different paths that process is taking. And with a little bit of analytical knowledge, you are able to figure out. what is wrong and why it is having maybe a thousand different parts instead of a few parts that a process should take. So I would say, I mean, look at it, right? Where we used to spend days in terms of doing traditionally process mapping, process understanding. Now you use solutions, right? Which are AI driven, can just give you that output. Of course, analysis is something which is still always going to be with the human, but this just helps. speed up the process, right? And the time to value just increases. So I think for me, improve decision-making, innovation, right? That we are able to bring some products and new processes and changes into the processes so fast. It just fosters innovation, right? And it helps the team stay ahead of the curve. So, I mean, these are some of the ways that I would say Generative AI is really... taking over, right? And this is just one part of it. If you've seen from a software engineering point of view, the QA side of things, right? Where you do a quality check today in terms of building what should go into a product, build off the test data right from creating user stories, what should be your checks in a product development, everything is being provided by generative AI models, right? So for an engineer, let's say a software engineer having a gen AI. is only going to complement what he or she is doing and even just help speed up the process which today everybody wants right you don't want to wait for years for something to come alive.

Manasa Kavuri:

Certainly the integration of generative AI into existing systems is a critical consideration. Now coming to the last question of our segment, what obstacles do you foresee in this integration and how can businesses address them effectively?

Rasna:

Yeah. See, I think one thing we all need to pay it to is that it is a, it's a growing technology, right? Generative AI. So people who know about chat GPT, it's just revolutionized the whole world, right? Where you have everything accessible. Chat GPT is a very good example of generative AI. However, I think few things which I can say that we should keep in mind one, it is a very Because we are not as mature, it is a very resource-intensive, I would say, model, which means that training a generative AI is extremely complex process, which requires time and resource. Even after that, ensuring a high accuracy in its output is not easy. So we have to be cognizant of that fact. Also, the results at time. you know, it relies on a very high quality data. We all need to bear that in mind that anything related to generative AI is dependent on data and what you feed into it, right? To make accurate predictions. So if your data is incomplete, if it is inaccurate, if it is outdated, if it is biased, right? Because of the different demographics of the people and what they have input into that data, it can obviously then lead into unreliable results. So that is something, you know, is another area to be mindful of. I think for large companies though, the biggest, I would say area or, you know, an opportunity is the security part of it, right? So many organizations, if I can say, you know, I've argued on the user data to train the AI models. So in terms of regarding security of the user data, the privacy and the accessibility. I would say these are certain things that big organizations are always concerned about and there is an opportunity on how this can be streamlined further because generative AI also builds its own test data. How are we able to use that? How do you again correlate it back to the original data because it goes on creating new information is something for us to still I would say explore a little bit more and then take more. So I mean, I would say maybe resource intensive, unreliable results, security related threats, which are there, I would say as a three things from a disadvantage point of view, that big companies, organizations will be mindful of.

Manasa Kavuri:

That wraps up another episode of podcast. A big thanks to Rasna Asrani for sharing her insights with us today. Rasna, you have any parting words for our viewers?

Rasna:

Oh, no, thanks. I would say just I think it's been a very interesting topic, I would say. You know, there is always so much more that one is able to talk about. I have only been able to share based on my experience, but I'm sure, you know, chat GPT, as you guys know, have changed the world, right? The way we look at things, the way we look at information. But one thing I would say we all are very mindful about is that while technology is there, I think the element that humans add to making that a success is always critical. And we should always keep a good blend between both, human as well as technology, and use it in the best way possible. I think

Manasa Kavuri:

Thank you so much,

Rasna:

I'll

Manasa Kavuri:

Asna.

Rasna:

close on that note once.

Manasa Kavuri:

Thank you so much Azrani.

Rasna:

Thank

Manasa Kavuri:

And

Rasna:

you.

Manasa Kavuri:

if you have enjoyed this episode, don't forget to subscribe and stay tuned for more engaging conversations. Thanks for viewing and until next time, take care. I will wait until this

Rasna:

Take

Manasa Kavuri:

upload

Rasna:

care.

Manasa Kavuri:

100% but thank you so much, Asna.

Manasa Kavuri:

Welcome back to podcast powered by SmartBot. I'm your host Manza and today we are diving into the fascinating world of generative AI and its implications for business. With us today, we have a distinguished guest, KC Ramakrishna, co-founder of Advantage Circle. Thank you for being here.

KC:

Thank you Mansa.

Manasa Kavuri:

Wonderful. Let's. Okay, let's jump right into our discussion. Ramakrishna, some experts have likened the emergence of chat GPT and similar technologies to the iPhone movement for AI. What's your take on this? Is generative AI really the turning point for the field?

KC:

So Yeah, so before I dive into that question I would like to give a bit of background, you know, first of all, thank you so much for having me on your platform It's a pleasure To talk on these topics because this is something very close to my heart I've been wanting to do ML and AI for a very long time and it's only in the last eight to ten years that The hardware has caught up you know, the mathematics and the science itself, you know, there were a fair amount of research happening over the last 30 years or 40 years in places like MIT, Stanford, and, you know, Harvard and all of these places. But we did not have the processing power required to really bring out practical applications of this AI in such a wide application set. And with the recent emergence of graphics cards, high-powered CPUs, fast storage, fast network, emergence of technologies in data science. So a lot of these technologies came together to make AI commonly applicable. And to the specific point of your question, is this the iPhone moment? Yes. So the parable here is for iPhone to happen, a lot of other technologies had to come into place. You had to have the camera technology, you need to have display technology, you need to have small batteries, you needed to have high bandwidth for your phone to the tower, you needed to have low-powered chips, you needed to have low-power consumption storage. So all of these technologies had to come together. for iPhone to be possible. Of course, it is Steve Jobs' genius which sort of put all of that together. And not only did he make it together, his genius also was the fact that he made it affordable by tying up with the telecom providers to make it kind of an EMI kind of an approach rather than you put all that money upfront. So there is a lot of development which happened for iPhone. And yes, You are right. I think chat GPT is an iPhone movement. And it was enabled because of all of these developments in other technology fields. So yes, I think it is there. And everybody knew AI will change the world. We never knew when. And I think today is the time. And I would say, I don't know. I did a lot of science fiction. So I would say science fiction is now. It's not in the future. It is now. That's how I would. See you.

Manasa Kavuri:

There's a lot of buzzer on generative AI right now. Do you think we are at the peak of hype cycle or is there more to come? What trends do you foresee in the near future?

KC:

So I would see a phenomenal amount of application for AI in general. So I would see a scenario where most of our mundane daily tasks are completely automated, not just in terms of our office work, even like, for example, self-driving cars. So tomorrow morning, you could have self-driven. Robots which are going to do the cleaning for you. So all your household stuff is done by somebody else you could have 100% automation of agriculture, right and Once you have 100% out of agriculture, then you know a lot of people so a big chunk of what defines us as humans is That we have to work to feed ourselves if that becomes free, you know, what is How are we going to evolve? if we do not, if our survival is not at stake, right? So I think AI is going to make inroads in every single aspect of our life, including making our survival pretty much guaranteed, whether you work or not or whatever. At least the technology will be developed enough to feed everybody on earth. Again, now what do we do? So many people are employed in agriculture or employed in producing so many things. If all of that, the need for all of that is taken away. right then what is it that I'm going to do in the morning? I don't know you know so when you're looking at AI in that aspect generative AI is only one small part of it generative AI is a cool tool it looks good and you know it's fascinating but I think generative AI is only a small part of the whole AI and ML landscape and from a generative AI landscape I can obviously the applications are already out in the market you know, like from legal advice to, you know, to looking at history, to creating jokes, to creating artwork, to impersonating people, to creating fake personalities, you know, for customer support or for, you know, a lot of fraud which happens, you know, which can happen because of generative AI. So generative AI is fantastic, but it's not the only part of AI. It's the one which looks cool, but there are a lot of stuff, you know, which, you know, done. So if you look at the use cases of generative AI, which I have seen myself, there are one or two things. So the first thing is, you know, that it is cross-functional. It's a cross-functional expert to a certain sense. So if I want to develop a product, which is most of the time, that's what I end up doing, we develop product. So for developing a product, I need expert inputs on various technologies. You know, so just like to put together an iPhone, you needed to have camera technology, you needed to have battery, and you need to have microposter technology. So sometimes when you're creating a product, you need inputs from all of these different fields. And in the past, you needed to have all of these engineers sitting around a table for you to do brainstorming. So you would have a ton of these brainstorming sessions because I would ask for a certain feature, and that particular expert would say, hey, you know what? That's not going to be possible. But then I will have to dig back into him and say, hey, is this possible? Is this possible? In this particular context. So I needed to be having these 10 engineers sitting around the table or four or five experts sitting around the table, and it would be a brainstorming session which lasts a couple of days, right? Now, all of that, I can ask just one generative here. So recently I tested it out with ChatGPT for one particular automation that I was trying to do. And it was cross domain, I needed to understand the protocols of that particular domain, I needed to understand the legal. guidelines in that domain. I needed to understand the ethics. And then I wanted to put the technologies, four or five technologies together to bring out the solution. And as I was starting to work with the generative AI, I could see that it was definitely answering a lot of my questions, right? To at least a certain level that enabled me to go forward. It doesn't replace the need for experts because the native AI always hits a wall. You know, after two or three questions, it hits a wall, it doesn't know enough. Or I need to know enough to ask the right questions. So it could either be a limitation of the generative AI that it doesn't know. Sometimes it may be that I don't know how to ask the right question. And which is where you hear about prompt engineering and stuff like that. But yeah, it definitely democratizes creation. It definitely democratizes information more than internet. I could ask a very specific legal query, and Generative AI will give you a fairly decent answer. For a more deeper understanding, you still need to go to a lawyer. But the Generative AI will at least go. If 100 is your perfect answer, the Generative AI can take you to 20 or 30 at least. So there's a lot of groundwork you can do. before you go talk to an expert. And as generative AI becomes better and better and more niche, you know, specific to a particular field, I think, you know, you can get to like a 60% of the answer before you have to talk to a human being and get the full answer. So I think generative AI is incredibly important for creative people, you know, and product managers and people who are creating the next set of solutions for the world.

Manasa Kavuri:

I think you have covered the next segment of question, but still I would like to put it across. Generative AI is being explored across industries. And in your opinion, what are the most promising use cases of generative AI in the enterprise context?

KC:

So most promising, you could look at it in terms of impact, or you can look at it in terms of low-hanging fruit. So from a low-hanging fruit perspective, marketing and sales comes into picture, and customer support comes into picture, which has been happening all over the place. Now, if you're looking at impact, Like I said, you know, creative people, okay, people who are product managers. Let us say I'm an electronics engineer and I'm working on a solution which requires not just electronics know-how, you also need mechanical know-how and you need to know maybe aerospace know-how and you need to have a civil engineering know-how. Now, no single person is going to have all of these, you know, all of this knowledge. And that is where I think generative AI will start becoming more and more cross-domain, cross-functional kind of Oracle. It will give you answers across multiple domains. To a fairly reasonable extent, one person can make significant progress before you need to bring in the rest of the experts with the field. So I think product managers, engineers, medical field, pharmacists. Today is the day of hyper-specialization of doctors. Sometimes some particular symptom may not be related to one particular organ of the body. It may be because of a complex interplay between three or four organs. So those are the kind of things which I think AI in general will be able to give insights to help the doctors make the right diagnosis. Genitivir will still not make the diagnosis, but we can't rely on it. But I think physicians, surgeons, research people, they will get a lot of input from other fields which today is cumbersome to get. That is how I would characterize Genitivir.

Manasa Kavuri:

I see that you've been actively involved in integrating generative AI into your services. Can you share an example of how it's impacted or could impact your client's operations?

KC:

So in our field, we're just scratching the surface. We are using a very common and general use case of generative AI. So one of the things that we do is on our platform, employees can appreciate each other. So if you helped me, like Mansa helped me achieve a particular task in my project, or they gave me some mentorship, so I can type out. appreciation saying that hi Mansa, thank you for mentoring me in this particular topic and it was not easy for me and you know your mentoring helped me achieve this particular objective and help in my career. So our generative AI helps you polish this message because this is something which goes into the company building board. And there is a wall of these messages for organizations in Vante Circle. Sorry, I don't know if I gave you a context for Vante Circle. So Vante Circle is one of India's largest employee rewards and recognition platform. And we are also growing very, very fast in the US and globally. And we have very, very marquee customers, like Infosys, Wipro, Capgemini, and a lot of companies that use our product. And so one of the big use cases of our product apart from rewards and rewards catalog is that we enable this kind of a social setting for the employees of large corporates. So you can appreciate your colleagues. You can give them kudos. Managers can encourage their colleagues to all their team members in a certain set direction. And in those places, a lot of that communication is the key. So when people type out messages, we sort of, our generative AI sort of understands what they're trying to convey and see if we can give them suggestions on how to convey it better. So that is where we use a generative AI to improve the message which the employees try to communicate to each other. And when you're looking at thousands of messages per day, you know. It's quite useful to the employees because it's not just the food you eat, it is also how it is presented. So it's not just the appreciation, but it's also how you are presenting that appreciation. So generative AI helps in presenting that appreciation. So that's where we use generative AI.

Manasa Kavuri:

And we come to the last segment of our conversation today, which is ethical considerations. As we move forward with AI integration, ethical concerns become paramount. How do you approach ethical considerations, particularly when dealing with sensitive enterprise data?

KC:

So that's a philosophical question. I'm not as much a philosopher as an engineer. But ethics, so there is like a huge field of ethics, right from the most common example which we know. It's a train going down a track. And on one track, there is five people walking who are not aware of the danger. a side track, you know, which has just one person. So would you let the train go in that direction or will you change the direction? Right? So that's just one aspect of ethics. And in this case, AI is a train. AI and ML is the train which is bearing down on humanity. So ethics is going to be super critical in how we use it. And it's also going to be extremely difficult because a lot of our ethical frameworks come from our culture and come from religion to some extent. Right. So we have these constitutions which try to remove the basis of your ethical and moral questions from religion and culture. And they're trying to make it culture agnostic or religion agnostic. And there will be similar debates for ethics of AI. You know, for example, the most common thing which we take for granted, which is equality of the genders, right? But not every country in the world believes in equality of gender, right? Not every country in the world believes in equality of religion. Not every country in the world has consensus on abortion rights, for example. Right? So a lot of these questions apply equally to ML and AI. We ourselves haven't been able to solve these or at least come to a consensus. These are still evolving. So I think it will be the same with machine learning and AI. It will keep evolving. We will have to, and that I think will hopefully a lot of human beings are freed up from their mundane work, so that human beings can actually debate on these ethics and make sure that these ethics are implemented in ML and AI. So that is how I would look at it. I think that's going to be the most challenging part of AI and ML, because the technology parts, the mathematics, all of those things are, you know, they can be done pretty easily. You know, they say it either works or it doesn't work. But ethics is not like that. Ethics is very, very subjective. So I think there's going to be a lot of debate and a lot of back and forth on ethics and AI. And it will never be solved. It will just keep going. It will just keep making progress and steps. That's how I would characterize ethics in AI.

Manasa Kavuri:

KC Ramakrishna, thank you for sharing your insights on the future of generative AI and its impact on businesses. It's been truly a thought provoking conversation. Do you have any

KC:

Thank

Manasa Kavuri:

parting

KC:

you so

Manasa Kavuri:

words

KC:

much, Manaswar.

Manasa Kavuri:

for our listeners?

KC:

Thanks

Manasa Kavuri:

Sorry.

KC:

so much Manusa, it's always a pleasure. As much as I would like to do things, you also definitely need to think about how this affects other people. And talking like this helps to crystallize your thoughts and to sort of structure your thoughts. These thoughts are in my mind, but I never put it in a structure. And thanks for asking the questions, which will help me sort of structure the thoughts and share my views with. with your platform and with the world.

Manasa Kavuri:

Thank you once again. And to our listeners, thank you for tuning in to another enlightening episode of podcast. Stay tuned for more engaging discussions at the intersection of technology and business. Until next time. So thank you,

KC:

That will be done.

Manasa Kavuri:

Casey. We just need to wait until this is 100% loaded, and then

KC:

Oh.

Manasa Kavuri:

we can stop the recording. I think I can stop. And then.

Manasa Kavuri:

Hello everyone and welcome to broadcast. I'm your host Mansa and today I have a pleasure of sitting down with Suman Sourav, a transformational enthusiast and a seasoned leader in the industry. Vice president of technology services currently, sorry. Can I start with currently Samy or like can I... Okay. Hello everyone and welcome to the broadcast. I'm your host Mansa and today I have the pleasure of sitting down with Suman Saurabh, a transformation enthusiast and a seasoned leader in the industry. Currently working as a vice president of technology services at Genpact. Suman it's great to have you here.

SUMAN:

Thank you, thank you Mansa. Thank you for inviting me here and it's wonderful to be on your show. So just to give a brief introduction about myself, I am a B.Tech passed out from IT Kanpur and post that I have around 13 years of experience in various domains and industries like supply chain, FinTech, EdTech, these kinds of stuff. And thereafter, I pivoted my career into management. I did my MBA from Indian School of Business. And currently, I'm the Vice President of Technology Services in Genpact. I'm looking after various strategic and transformational level initiatives in one of their verticals and trying to make my mark there.

Manasa Kavuri:

to have you here Suman. My first question to you is regarding your leadership and strategy role that you have. Sorry. So my first question to you is around leadership and strategy. So Suman you have had an impressive career in leadership and strategy. As a seasoned leader, could you share some key strategies you believe have been essential in driving business transformation and enhancing productivity?

SUMAN:

Yeah, sure. So thanks for the question. And because I had this opportunity to drive various initiatives in my earlier organization and current organization as well. I think in my own humble experience, I would like to point out in terms of three major factors when it comes to leadership and strategy. I think I'd like to put it as PPT, not exactly your Microsoft PPT, but it is people processing technology. So when it comes to designing some strategies as a leader for some business transformation, people are your strengths. So basically as a leader, you need to have that empathy and emotional intelligence. to deal with people, not only to leverage their strengths, but also grow them as the future leader. So anything and everything you do has to have a people-centric attitude, whether it is your own people or whether it is customers. The second point is about the processes. So whatever transformation or whatever business value addition you plan to do in any of the actually need to start with challenging the status quo. So you need to make those fundamental assumptions. You need to actually think from the very first principle and basically work upon the smallest of the smallest changes in the processes. So that's about my second point. And the third word, as all of us know in these changing times, Technology is a great enabling factor. So which stands? T stands for technology. So I primarily am very, very supportive of using technology as the enabler to solve the problems for our business, for our people. So yeah, that is I'd like to put it.

Manasa Kavuri:

And I've noticed you have been involved in discussions around AI and generative AI extensively. How do you see the integration of AI and generative AI shaping the future of industries now? And what role do you envision in playing and driving sustainable growth?

SUMAN:

So Mansal, these are one of the very unprecedented times where in technologies are changing pretty fast. This is, we all are witnessing one of the fastest changing times in the history of mankind, I believe. And AI and GenAI, so as to say, is one of the pivotal pillars as we see it in transforming various businesses, whether it is health, whether it is supply chain, whether it is entertainment or education. So it has a kind of all-pervasive impact as I see it. But the only hitch being that we need to actually carve out strategies in a way to design our business value that enhances customer centricity, which is very much focused for the people. and which has its various unfair element like your biases, the overfitting issues of gen.ai or AI as such, or the ethical compliances which we all know. So these are some of the caveats I would like to put in, which could be taken along so as to actually use or leverage this particular technology. for the various sectors. And as the second part of your question goes around sustainable growth. So I think using or leveraging this AI and deep learning algorithms, large language models, one of the instance, I think we can make the best use of it to actually provide a sustainable growth to any of the functions, taking along the environment as well. So maybe you can design some effluent treatment plant, which kind of caters to less of pollution and more of inclusiveness of the environment and things like that. So I think taking this particular technology in the right spirit. making all these precautions, I think in the long run we have a great future with this technology impacting the industries at a sustainably large level.

Manasa Kavuri:

Moving on to the next industry segment which is supply chain innovation. Your expertise in supply chain management is quite impressive, Suman. What emerging technologies do you believe hold the most promise in revolutionizing supply chain processes?

SUMAN:

So yeah, thanks for that question, because I have been into this industry for considerably longer period. I was there for 10 years with ONGC designing and working with all these supply chain processes, right from sourcing strategies to logistics. And I think given my background of business, as far as I see supply chain at this stage is at a cusp. wherein we are talking about something called resiliency in supply chain. We are talking about sustainability in supply chain. We are talking about integration in supply chain. So supply chain design flexibility is something which many organizations are putting their emphasis upon. So in that context, I believe there are ample technologies available. right now, be it your blockchain, which kind of helps in knowing the provenance of the products, which is very, very essential for the supply chain manager. Besides that, we have this generative AI, which is very, very important for all the stakeholders to actually bringing in the right value and products to the customers in real time. So when we talk about supply chain, it is all about your real time inventory management. It is all about managing the largest SKUs without having any glut or without having any stock out. So basically avoiding these, your supply chain bottlenecks. So in that case, I think all these technologies like block change and the TVI, Internet of Things, Metaverse, all these technologies have a great future for carving out an altogether different scenario of integrated and flexible design for the supply chain.

Manasa Kavuri:

It's clear that technology is shaping the business world, but how can companies strike the right balance between leveraging advanced technology and still valuing human expertise?

SUMAN:

Yeah, so see, at the end of the day, I think AI is, or for that matter, any technology, is one of the tools to solve the problems for the larger human race, right? So, human in the loop, as we call it, we have to have a human intelligence associated with your technologies like generative AI to minimize all these adverse consequences, right? So even if you see, so just for your viewers, if you talk about GPT, so what is GPT? GPT is Generative Pre-trained Transformers, right? Transformer is nothing but a kind of your deep learning algorithms, neural networks basically. And when you talk about these generative adversarial networks, and when you talk about these generative and discriminative, then basically somewhere or the other human comes into the picture, right? So even if you train for the large amounts of data, even if you have the higher cloud computing power, you need a human to actually assess the ethics of this technology, whether it is infringing some human rights, whether it is going against some IPR thing, right? And human touch, even if you come to any of the sectors, I think human touch is very, very essential. So you need these humans. So as someone rightly said that probably AI will not replace you in the long run, but a human who knows AI might. So in that case, I think human is a very, very integral part of the whole ecosystem. And the. the future era is not about the IT innovation, it is about the ecosystem innovation. So in that case, I think even if all these technologies are integrated, whatever I've just uttered in the last question, we still need human in the loop to actually come up with all these creative ideas, all these unstructured data. See, technology can do very well with... with your particular domain, it can learn one particular process from the scratch and maybe even beat human in that. But technology cannot think. So maybe with all these trainings and everything on the for the large language models, we are actually trying to bridge that gap. But eventually we are trying to foresee another where then a general purpose technology would evolve that would need human monitoring as something which would lead to a larger business value for the people.

Manasa Kavuri:

Now moving on to our last question. Simone, how can generative AI be utilized to create personalized and engaging marketing campaigns that truly resonate with customers?

SUMAN:

OK, so as I said, generative AI has its impact in almost all the domains as I foresee it. But talking particularly about marketing, I think at every stage, right from generating your leads to collection of the data. around people and then actually customizing your product to reach your target segment, making some compelling visual campaigns for the people, doing some dynamic kind of an advertising which actually takes the sponsors from the people and then adjusts itself to cater to the to the particular segment as we know that the marketing fundamentals is all about STP, the segmentation, targeting and positioning. So I think generative AI has a capability to actually reach to the targeted segment, whether it is your email marketing, whether it is making blogs, visuals, etc. Getting to the roots. So we are talking about in these days all these click through date and all these parameters and metrics, right? So we, gone are the days wherein we used to spend a lot on the billboards and then we could get some customers. Today is the age of predictive marketing. Today is the age of targeted marketing. Today is the age of converting all these three Vs around the data, the volume, velocity, right? Variety into the value for the customer. So I think... the future of marketing is great with all these technologies.

Manasa Kavuri:

Thank you so much, Suman, for sharing your valuable insights on all the topics. It's been an enlightening conversation. In fact, you're the first person I had got onto podcast who has an eclectic experience and various functionalities and also in most of the industry sectors. Do you have any parting words for our audience?

SUMAN:

So thank you, thank you Mansa again for inviting me and giving me this opportunity to share whatever small experience I've had. I just would like to congratulate you guys for doing this wonderful work of bringing the awareness and technology to the larger segment. I think as I mentioned earlier as well, we are living in an age wherein we are we are seeing disruptions everywhere, right? So we need to be a bit cautious. I would use the term just for the lack of a better word, but I think this disruption, you won't know where it's gonna be. So, I mean, just an example comes to my mind that when computer was first used, so actually the mathematical, cost got cheaper and then this computer was used to disrupt your photographic industry. All of us know the story of Kodak, which has nothing to do with mathematics, right? So it's just for us to see and be vigilant about how this technology, how can we adapt to this technology, grow our own capability, understand our business objective and align our capability to add value to the lives of people. So Yeah, that's about it. And thank you again for having me out here.

Manasa Kavuri:

Thank you. And thank you so much for the kind words. And to all our viewers, thank you for joining us on this episode of podcast. Remember to stay tuned for more conversations that matter until next time. Summon, I...

Manasa Kavuri:

Hello everyone, welcome to the podcast part by Smartbots, where we talk about all things enterprise AI. I'm your host Mansa and today we have Samrat Guglani as our guest speaker. Samrat is the Director of Growth at Kubashen Consulting. His impressive background comes with his expertise and devising international revenue strategies and building robust sales and marketing organizations. Plays a vital role in guiding the firm's expansion across industries. It's great to have you on board with us today, Samra.

Samrat Guglani:

Thank you Manso, thank you for having me here.

Manasa Kavuri:

Thanks for being with us today. We're eager to dive into the exciting world of AI and how it's shaping the business landscape. Today, we'll be discussing the opportunities and challenges of AI in consulting industry.

Samrat Guglani:

Yeah, I mean, see, AI has been around for a while and the shape and form now we have seen AI has transformed in last few years, but actually we have been using those models for a while. And the core of the model lies in the data fabric which our customers have. So once we have those fabrics in place, we create models for our customers and then the role of artificial intelligence and training those models comes in place. Once we train those models, there is a intelligence which is brought in. and that decision intelligence will actually help create value for our customer. So we see immense potential of using generative AI in creating real value for our customers, whether it is optimizing their operations, whether it is generating more sales, or whether it is just reducing the cost by doing work. which could be trained and outsourced to an automated bot, like collecting feedback from their customers or analyzing large volume, large scales of data. So there is definitely a lot of potential in leveraging AI for our customers.

Manasa Kavuri:

rightly said with the increasing adoption of AI technologies and various industries and the opportunities and challenges, can we do this question again? I'm sorry. Yeah.

Samrat Guglani:

Really?

Manasa Kavuri:

No, I mean, not for you, from my end. You did well, you really did well.

Samrat Guglani:

So you

Manasa Kavuri:

Not

Samrat Guglani:

can

Manasa Kavuri:

you.

Samrat Guglani:

now focus on the challenges, right? Let's just look at what are the key challenges because I've talked about opportunities but I did not talk about challenges. So, you know, that could

Manasa Kavuri:

So

Samrat Guglani:

be a follow up question.

Manasa Kavuri:

yeah, then I can just say like challenges can be the next question. Right. So,

Samrat Guglani:

Yeah, you

Manasa Kavuri:

okay.

Samrat Guglani:

can say, okay, so thanks for the opportunities, but what kind of challenges you are seeing? And then we can talk about a few of the challenges and then from there on, we can move to the next question.

Manasa Kavuri:

sure. So can say like okay thank you for So, talking about the options. Okay, so we'll do one thing. So with the increasing adoption of AI technologies in various industry, I think you have covered the opportunities that these technologies have been in. Why am I doing this? I'm sorry. So with the increasing adoption of AI technologies in various industries, you have covered the opportunities. Now coming to the challenges. What do you foresee for such consulting organizations like yours, in fact, in leveraging AI to deliver value to clients?

Samrat Guglani:

Yeah, I mean, there are quite a few challenges, Mansa. Clearly, data privacy and security is a challenge. We are using client data and using an outsourced model or an artificial model, which is kind of black box. So data privacy issues would be there. Then the biggest other big challenge is actually how do we train these models, right? We need large volumes of data to train the models. And some of that data is coming from web scraping. So there is an issue there. There is privacy issues there as well. The regulatory compliances around AI are not very clear right now. So that seems to be a key challenge as well in how we regulate the framework in itself. And we need to make sure that we create a model which is not biased and it's not discriminating in a particular manner. challenge always remains. So there are quite a few challenges, but I think industry is working towards solving those.

Manasa Kavuri:

Can you share any examples or success stories where AI or conversation AI solutions have been instrumentally driving significant business outcomes for your clients or organizations that you have worked with?

Samrat Guglani:

Yeah, I mean, see some of the basic use case which we have used for conversational AI is been around implementing chatbots or improving the customer experience in general for our clients and they reaching out to their customers collecting feedback in a much faster manner than analyzing that feedback very easily. So that remains a very basic use case which I think everybody's using. But one of the more advanced use cases when we are now creating digital twins for our customers, we are actually building digital twins for their operations and that those operations and the optimization using the intelligence there is actually creating real value for our customers where they can now optimize their entire operations just looking at the digital twin. So I give you an example, like if you have an airport and you have... models for the parking and you have models for the check-in and if you have models for how the flights are going to run. So just by looking at how many cars are there in the parking, you would now know how many people would be there at the check-in and probably you can reduce the bottlenecks there. So you can train the models to run all those kind of use cases, right? And then actual capacity can be increased. So we obviously are seeing a lot of bottlenecks with our customer operations, which we can use and solve. using EA.

Manasa Kavuri:

interesting ops example that you have taken. In your opinion, what are some key skills and competencies that professionals in the consulting industry should develop to effectively navigate the evolving landscape of AI and its automation?

Samrat Guglani:

Okay, can we take a pause there? Let me just.

Manasa Kavuri:

Yeah.

Manasa Kavuri:

Hello, everyone. Welcome back to another exciting episode of podcast. We dwell deep into the realm of generative AI and its real world applications. I'm your host Mansa. And today, we've got our distinguished guest speaker on board. Sai Nikleshwar. Sai is a senior operations manager at ADP with extensive experience in engineering, operations strategy, and complex problem solving. So Sai, it's a pleasure to have you here. Could you please introduce yourself to our audience?

sayee Nikheleshwar:

Thank you Mansa, thanks a lot. First of all thanks to the broadcast for giving me this opportunity to present myself. And I am Sai Nikleshwar. I come from a merchant navy background. I have an extensive experience of close to 12.5 years in the Marchant Navy, where I was heading power plant operations, engineering operations, day-to-day activities, logistics, maintenance, propulsion machinery, everything in different phases of my career, eventually ended up becoming Chief Engineer in 2021. And post that, once I was Chief Engineer, eventually I transformed myself to a corporate, to incorporate a state of work life by doing an MBA from ISB class of 23. Then eventually I've joined ADP this year April 2023 and I'm part of ADP in its wage garnishment function. I'm a senior operations manager there. So this is roughly about my journey from merchant maybe to the corporate setup. Yeah.

Manasa Kavuri:

Must tell you Sai, it's quite an enticing journey, I must say, and it's pleasure to have you here. But, alright, let's write, dive into the broadcast today and Sai, thanks for being with us today, here. And we're eager to dive into the exciting world of AI and how it's shaping the business landscape. So my first question to you is, as an ex-chief engineer in Merchant Navy, you must have witnessed some exciting advancements and integration of AI. Could you shed some light on how you see the role of AI in streamlining and improving the maritime industries?

sayee Nikheleshwar:

Perfect, definitely. So to begin with, I'll just give you a glimpse of how maritime industry has transformed. Possibly, you know, a few decades back, shipping industry was more reactive in its approach. In fact, it was like more into a kind of breakdown maintenance whenever required and, you know, it used to move on. From then, it eventually transformed into a conventional proactive kind of an approach where we started having periodic maintenance or periodic activities that are being conducted as per the insights from the OEMs or called the engine manufacturers and the subject matter experts. They used to give us insights and we used to follow those guidelines on a regular basis in a periodic way making sure we do not waste time, we do not waste our manas, resources and for optimizing operations. Then eventually in the last decade if you see slowly the transformation has started happening. with respect to so-called artificial intelligence and bringing in various components related to generative AI in the entire industry. If you see maritime industry as such, slowly we are transforming in such a way where we are able to look into various trends, stats, the way things are moving around and try to compute it along with a variety of inputs and getting in... various images, sounds, animation models, types of data, integrating everything in such a way that we want to optimize our operations, the logistics part of it, the human resource part of it, and most importantly, the overall revenue that is involved in the entire picture. So to give you a small glimpse of what is happening in the last five to six years and in coming times, there is a rapid change that is happening with respect to specifically involvement of AI in the

Manasa Kavuri:

It

sayee Nikheleshwar:

entire

Manasa Kavuri:

stopped.

sayee Nikheleshwar:

maritime operations and this is helping us to be more proactive in our operations and thereby saving time,

Manasa Kavuri:

One second, one

sayee Nikheleshwar:

saving

Manasa Kavuri:

second Sai, it

sayee Nikheleshwar:

man

Manasa Kavuri:

stopped,

sayee Nikheleshwar:

hours

Manasa Kavuri:

it

sayee Nikheleshwar:

and

Manasa Kavuri:

stopped.

sayee Nikheleshwar:

eventually saving money.

Manasa Kavuri:

Sai we can't hear you. There's a... I there's a

sayee Nikheleshwar:

It's very.

Manasa Kavuri:

there's a glitch sigh. I'm sorry. Yeah, there's

sayee Nikheleshwar:

What

Manasa Kavuri:

a

sayee Nikheleshwar:

kind

Manasa Kavuri:

glitch.

sayee Nikheleshwar:

of

Manasa Kavuri:

We your can you can you go back to the response like where you have started like to two three lines before this conversation and we'll start rerecording some of that work. Okay.

sayee Nikheleshwar:

But was it something from my end that created a problem?

Manasa Kavuri:

Yeah.

sayee Nikheleshwar:

Okay.

Manasa Kavuri:

Should I go through the whole conversation that I've made right now or can he just start responding to the question? Yeah. So I just lost that question. Can you just like repeat?

sayee Nikheleshwar:

No, no, no problem. You want me to start from the beginning? The question? Okay, and stack. It shows 31% on my screen. Do you think there's some problem with my-

Manasa Kavuri:

Welcome to a special episode of the podcast, where we delve into the remarkable journeys of our inspiring leaders. Today we have the privilege of speaking with us Pratima, a prominent reader in hospitality in the ad tech space. Join us as we explore her path to leadership, the obstacles she encountered and the valuable lessons she learned along the way. Welcome Pratima.

Pratima Dantuluri:

Thank you Mansa, happy to be here.

Manasa Kavuri:

Prathima, in your remarkable journey to leadership, what obstacles did you face along the way? How did you handle them?

Pratima Dantuluri:

I think for me Mansa, whatever obstacles I faced was very specific to, like, it was almost like an outcome of my choices. And as an individual, I'm extremely headstrong and then stubborn in what I want to do. So I think most of the obstacles that I faced were like, you know, making choices of not sticking to environments which inhibits growth or which does not. allow me to flourish despite putting in 100% of my work. And I also stood up for, you know, not being treated how I felt I should have been treated. But during this entire time, I also knew that my actions or my choices would have consequences, right? So those are essentially the obstacles that I faced that I believe in putting my foot down and... and speaking my mind. My moral compass always points north when it comes to the professional space. So I think that often I meet obstacles in that sense. As to how do I overcome them, I think it's very important to be completely secure about yourself as a professional. That's the most important thing I feel. Also to realize that if you are not what you bring to the table, it is not a place that you should be in. Right? That is one thing. Second thing is, I think also to realize and sympathize as to why things are happening. Like what usually happens is, you know, if you are being treated a particular way, you will not like it, but that also sort of seeps into how you are treating people. So when it comes to, you know, like even casual sexism, it's always important to be very cognizant of the fact that you do not perpetuate it further down. So I think to sort of also, for example, if I think that I was in a toxic environment or something like that, I need to make sure that in the future, if I have a team, I'm ensuring that... you know, the work culture is something that they flourish in. So I think obstacles are a great way of also teaching you what you should imbibe in your leadership journey and what you shouldn't. So yeah, that has been very, very specific to who I am as a person. So these would be a few.

Manasa Kavuri:

It's truly inspiring and now looking at inspiring women who strive for leadership positions, what advice would you give them and how can they better prepare for the challenges they will inevitably face?

Pratima Dantuluri:

I think not just for women leaders but for everyone who's trying to climb that ladder. Moreover for women because it's a fact that women are lesser in the workforce and as you go higher up the ladder in the leadership position the percentage decreases. I think the first and foremost thing is to constantly upskill yourself, learn new things, understand the space that you are in. Know that there will be biases. purely because not necessarily that people are doing it consciously but also unconsciously and ensure that the steps that you take are overcoming those right so for example when I came into the ad tech industry like I just pivoted into the ad tech industry before that I wanted to ensure that I get some sense of the industry right so I looked into courses I tried to understand the jargons that I used in the industry so I think whenever someone is trying to climb that ladder it's extremely important to upskill Secondly, I think this may sound very cliche but my biggest advice would be to just be yourself. I often hear because I'm extremely headstrong and you know I speak my mind, I'm always, like it always attributes to like you know masculine like you know features. or thoughts and things like that. But you don't have to prove to people as to why you're behaving a certain way. If it's you, just go for it. And I think when people see germinity is when they accept you better as well. So don't try to be someone else. For example, if you see your boss emulating a particular personality, don't try to emulate that into your leadership skills. Just be yourself. And lastly, I think it's extremely important to have a community or a group of women that you can reach out to for professional and personal help. It will help you leaps and bounds because at the end of the day they can relate to what you are going through in your professional and personal space and sort of give you advice. It can, you know, you can all huddle, discuss things and it makes you feel very normal and um... very okay with what's happening. It sort of gives that sense of togetherness. So yeah, I think these do help in sort of climbing the ladder.

Manasa Kavuri:

Thank you Pratima for sharing your remarkable journey and valuable insights. And

Pratima Dantuluri:

Thank

Manasa Kavuri:

truly

Pratima Dantuluri:

you.

Manasa Kavuri:

your advice and experiences are undoubtedly inspiring leaders like many of us like me, especially women who aspire to make their mark in respective fields. And join us next time for another insightful episode of podcast. Until then, stay motivated and continue researching. Sorry. Okay, I think we can stop with the episode of podcast.

Pratima Dantuluri:

Yeah. Should you want me to repeat? I don't know if I was blabbering a lot of nonsense.

Manasa Kavuri:

No, yes, you're phenomenal because whatever you said, I think me and Samia completely resonate with it.

Pratima Dantuluri:

I'm sorry.

Manasa Kavuri:

I'm sure she must be clapping there because

Pratima Dantuluri:

No.

Manasa Kavuri:

we work in the same team together. We sort of reflect the same ideology. In fact, when we started this, we thought... It's a great way and we want to bring in more women, you know, being the actual feminist in my core feminist. It's just not, I know, like, like you said, it's just not women, everyone. But like for men to get successful and get into a leadership role, Pratima, it takes a whole family. A lot of support is being there. But for the women to grow to that stage, the lot of like unseen. intangible hurdles that you cannot even put it across.

Pratima Dantuluri:

Absolutely, also I think with women leaders, they always, like I was, when I had read Indra Nooyi's book, right, I mean, she's phenomenal, she's reached great heights, but also in her book, she credits her husband so much for actually, you know, giving her that space to grow. But if you read a man, like a leader, like who's a man, their autobiography, they would not give as much credit to family. Like, you know, women just feel like they have to justify the fact that Okay, because people allowed me to do it, I grew, you know. So,

Manasa Kavuri:

No,

Pratima Dantuluri:

yeah.

Manasa Kavuri:

it also matters because I'll tell you what, if you see the numbers, the differentiative numbers when it comes to women, it is quite frugal, like very minuscule

Pratima Dantuluri:

Yeah.

Manasa Kavuri:

because you compromise on your family situation, somebody is sick, somebody has something to do with it, you always take a step back, right?

Pratima Dantuluri:

Yes,

Manasa Kavuri:

Men

Pratima Dantuluri:

we also.

Manasa Kavuri:

inevitably they feel that it is taken care of.

Pratima Dantuluri:

Yeah, yeah, agreed, agreed.

Manasa Kavuri:

So it's just been passed on with the generations. And I feel women don't support women. That is an

Pratima Dantuluri:

Yeah,

Manasa Kavuri:

other thing.

Pratima Dantuluri:

that's true.

Manasa Kavuri:

Yeah, and especially because when you see the larger percentage of women who are in leadership role, they would have gone through so much because

Pratima Dantuluri:

Yeah.

Manasa Kavuri:

they were the first ones to get their

Pratima Dantuluri:

Thank

Manasa Kavuri:

first

Pratima Dantuluri:

you.

Manasa Kavuri:

generation, right? So they would want to give the tough time to the younger ones. So I'll

Pratima Dantuluri:

Yeah,

Manasa Kavuri:

show

Pratima Dantuluri:

that's what I said.

Manasa Kavuri:

you.

Pratima Dantuluri:

It's very important to like to fight against conditioning like even

Manasa Kavuri:

Right.

Pratima Dantuluri:

if I am a woman I should make sure that I am not doing it for like, you know, the community in general also

Manasa Kavuri:

Yeah.

Pratima Dantuluri:

I think like when we are stepping up to like leadership positions, especially married woman There is always a thought or guilt in head of like, oh my god. I'm working I need to go back and you know, take care of me. I think you're made to feel guilty or conditioned that way but

Manasa Kavuri:

Yeah.

Pratima Dantuluri:

men Men would never have thought, oh should I sit at home because I need to take care of my family, you know?

Manasa Kavuri:

No, it's always treated as, oh my god, poor thing is working so hard. But like, you know, you're programmed to think that you have to come back, take it off the house. Even if the maid is

Pratima Dantuluri:

Yeah,

Manasa Kavuri:

not coming,

Pratima Dantuluri:

yeah.

Manasa Kavuri:

the kid is sick.

Pratima Dantuluri:

Yeah.

Manasa Kavuri:

You know, I'm a single mother, so I literally, yeah, so I know how it goes. And I feel like, you know, most of the women who are like successful, either they have a supportive family or they don't have a family. There's no in between.

Pratima Dantuluri:

But also like you know sometimes when the household chosen etc are being divided by like the man and the woman, women are very like you know like oh I get support we divide it like 50% but that should be a given. Like you shouldn't

Manasa Kavuri:

Yes.

Pratima Dantuluri:

be thankful for it also right.

Manasa Kavuri:

True.

Pratima Dantuluri:

Like I mean but yeah like I think that's asking for too much but like yeah we'll wait when

Manasa Kavuri:

Thank you.

Pratima Dantuluri:

like society teaches that place.

Manasa Kavuri:

I know. Samia, is the recording done? It's still uploading, right? Oh shit, I have to stop, I guess.

Manasa Kavuri:

Hi, I welcome you all to Vodcast, powered by smart bots, where we talk about all things enterprise AI. For this series, we are discussing the role of conversational AI and generative AI in enterprise solutions. And this month, we will be highlighting the role of women in leadership. This episode, we discuss how AI powered conversational AI virtual assistants enhance. customer experience by providing instant, accurate responses and resolving issues and offering personalized interactions 24 by seven. I'm delighted to announce our first ever guest speaker, Shantel Love, the global VP of customer success at Pearson. A quick introduction, Shantel, a transformational leader hailing from Detroit, currently based in Nevada. has a strong background in consultative sales and customer experience. A stellar 20 years of career in various industries such as telecommunications, technology, hospitality and education inspires many. Shantel's journey from an entry-level position to her current leadership role while championing and adopting the latest technologies is quite exemplary and roadmap for aspiring leaders. She's also a member of CHIEF, a select group of senior women executives. A warm welcome to broadcast Chantel.

Shantel Love:

Thank you so much. I'm so thrilled to be here. And I'm so excited that you all have this platform to share experiences in the customer experience and customer success space for women in leadership. Can't be happier.

Manasa Kavuri:

Thank you so much. With no further ado, let's get into the topic of AI and customer success. And we've seen that AI is transforming the world of customer success and support by automating repetitive tasks, allowing agents to focus on complex inquiries and delivering a high level of service. AI-powered chatbots and virtual assistants offer 24 by 7 support and resolving. queries efficiently and reducing customer wait times. So in your role as a global VP of customer success, you must be at the forefront of driving this adoption of AI in your organization. What is your vision for deploying AI-powered solutions in your organization?

Shantel Love:

And that's a really great question. And I will say that I may not be at the forefront. I know I am running alongside with the transformation of AI. This ideology around AI and improvement of the overall customer experience began during the pandemic, actually. I stepped into my role as global vice president of our customer success organization in 2021. at the heart of the pandemic and myself, like many other citizens across the world was in lockdown. I was building an organization from the ground up and I did it all while my then two-year-old was sitting on my lap trying to juggle what life meant and what work looked like at that time. And I never imagined that I would be leading an organization. While my husband worked in one part of the house and I worked in one part of the house and my son was on my lap, that wasn't anything that I imagined that life would be like, like many other people across the world. But what that experience made me realize is that customers, regardless if they're in this space that I work in or other spaces, they wanna connect with companies just like they would their family or their friends. They want people to meet them where they are. They want a friendly demeanor. They want a frictionless experience, and they want to get things easily done. And with the introduction of AI, it made me think of how we can reimagine our customers' experience. All of us at some iteration of our life is a consumer or a customer, and we want things done quickly, painlessly, and fast. So as I was looking at those repeatable tasks across our organization, or those things that were barriers to entry, due to the fact that our organization is well over 100 years old. You know, we have those legacy tech issues that may be creating those barriers to entry. We looked at those repeatable tasks and those ways that we can make it easier for our internal agents to support our customers easier. And then also ways to support our customers from any entry point, be it on our chat, live chat or chat bot or calling in. doing a self-service, whatever modality they utilize to get to us. At the end of the day, in order to have customer success, you have to think about their experience, that end-to-end process and what that looks like for them, and how to make it easier for them to do business with your company. With AI, I look at it as a supplemental resource to the overall customer experience. and how we can optimize our efficiencies as an organization to provide the most exceptional service to our customers.

Manasa Kavuri:

Absolutely, I completely resonate with every experience that you have shared. An entire world has seen that in the last couple of years. And AI has enhanced the customer experience by enabling seamless omni-channel support, like you said, enabling customers to consistently engage with business through their preferred channels. So how is your team or organization preparing for such adoption? adopting AI-based solutions.

Shantel Love:

Yeah, I honestly think that many organizations, and mine is not exempt from that, may have had a plan to prepare, right? Typically when you're planning, you do a three-year plan, a five-year plan, a 10-year plan. But most of those plans got truncated as a function of the pandemic. You learned and you realized that the plan that typically or historically took three, five, or 10 years, needed to happen in the matter of a few weeks or a month at the most, right? So we took those plans from that period of time, the three-year, five-year plan, and we became very agile. We worked across the aisles. So there wasn't a, hey, this group does this. It was, hey, we need to get this done. What are the resources that we have across our organization? And really leaning shoulder to shoulder with... finance and operations, marketing, sales, technology to create the most optimal experience for our organization as well as our company. And I say this because while we've historically had a plan in the wake of the pandemic, many of us didn't have a plan of how to move an entire organization to work in a remote environment, but our company, like many others, figured it out. And from the perspective of figuring it out, it wasn't just you do it to get it done, you do it, you assess how it's going, you calibrate, you recalibrate, you do these tweaks to make it a bit more better than you anticipated it to be. The things that didn't go well, you take those as lessons learned and you calibrate there as well. But with those plans, as I mentioned, we did have a three-year plan on what that would look like, our technology enhancements, what that would look like next. But most of that accelerated as a function of the pandemic because as you know, there was a point in time, you woke up, you can remember the night before, no one was talking about chat GPT, no one was really talking about AI. And then, you know, you go to the day when it came out and every second, every newsfeed, every... message that you saw on social media was related to chat, GPT, or AI. And it basically showed, hey, this isn't going anywhere. And either you're going to get with it, or you're going to get left behind. And we were of that mind frame that we didn't necessarily want to get left behind. We wanted to educate ourselves on how to make this work within our organization, how we wanted to work for. our customer experience and how it fit within what we currently had. And then honestly, if it didn't fit within what we currently had, we needed to figure out how to make it work for that next phase of innovation that we were looking to accomplish.

Manasa Kavuri:

I think every aspect of your word definitely resonates with where we are currently. And my next question is more damaging towards that. So it's not AI that is going to take your job.

Shantel Love:

You're

Manasa Kavuri:

But

Shantel Love:

right.

Manasa Kavuri:

someone who knows how to use AI might. It's a quote from the 2020 World Economic Forum's growth summit.

Shantel Love:

Yes.

Manasa Kavuri:

So is there a generative AI or conversational AI, the technology behind the chat and voice box strategy in your organization or broadly in the customer success function that you can share with us?

Shantel Love:

Yeah, you know, I've read articles on organizations moving to all AI agents, or they use terms like cyborgs, where they don't have humans actually interacting or engaging with the customers. And I think that's pretty phenomenal if there is an organization that has that level of sophistication where they can remove the human element and still provide a superior customer experience for their customers. However, in my industry, we're not at that point where we want to say, hey, AI is going to take over everything and we're removing the human element because the human element is the key differentiator. And I would say it is a value add for our organization of having individuals who understand our products and services inside and out. Individuals who understand our customer's persona in their pain point. and helping to usher them and guide them through the pre-purchase process and the post-purchase process as well. I believe that that is mission critical. But as we're looking at AI and the things that we are utilizing AI for, it's to one, when a customer calls in, we know how often they've called in, what cases they have associated with their previous experiences. If they have anything that's on back order. We also wanted to use AI from a proactive standpoint. So if we have customers that typically call in for an invoice, we make them aware, hey, you don't necessarily have to give us a call for an invoice and it looks like you've called us several times for this invoice. Let me guide you to the steps that you can take in regards to receiving that invoice. That does a couple of things for us. What that does is one, that saves our customers time from having to wait on hold to get an answer. That also saves us time in regards to having to explain to the customer how you can support yourself. And that also saves us money overall because it reduces the overall call volume that comes in if customers have better channels and ways and means to support themselves. I know that's just one example, but there are so many phenomenal ways that we use AI. like predictive chats, predictive conversation. So if a customer is calling in for checking on the status of an order, right? If we're communicating back to this customer instead of spending five minutes typing up an email, AI formulates that email for us as a human. We may want to proof and tweak it and put our little spin or twist on it, but that's saving time for our company instead of... typing up a new email each time a customer comes in. There's so many ways that we found that utilizing AI as a compliment to the amazing humans that we hire within our organization, but also how it contributes to employee engagement and employee satisfaction when you're bringing in these supplemental resources to help them do their job effectively and efficiently.

Manasa Kavuri:

I think you have answered my next question already, but still I would want to put it across as the last question for the broadcast today is what are the main key criteria for any organization who would be inclining or would be considering for selecting any AI solution for specific to customer success?

Shantel Love:

Yes, absolutely. I know that it's so easy to go along to get along. And what I mean by that is AI is the buzz. You're hearing it everywhere. And it's like, if I don't get it, will I be left behind? And the reality is AI may not necessarily work for every organization. I know for me, since I'm in the space of customer success and customer experience, I look at things from the intersectionality of customer obsession and employee obsession. And what does this do for my overall employee experience? And what does this do for the overall customer experience? And once I have identified the impact, positive impact that it will make on my employee experience as well as the customer experience, I began to do an evaluation on, is this an immediate ROI or is this something that will continue for years to come? And how does this look? for the evolution of our overall business in our company. So I look at it in those buckets, starting with the employee experience and then looking at the overall customer experience and then tie that into our customers or rather our overall business strategy and what that means for us. So as we evaluate AI or any other technology, it shouldn't necessarily be a function of, hey, this is the hot. thing right now, but it should be tied to your overall operational and your business goals. How is this going to increase operational efficiency? How is this going to increase productivity? How is this going to improve overall customer satisfaction as well as employee satisfaction? And those are the things that I typically look at as I'm evaluating, introducing new technology or implementing something that is in that vein of innovation.

Manasa Kavuri:

We thank you so much, Shantel, and we'd like to express our sincere gratitude for sharing your expertise and insights with us today. And stay tuned for future episodes of podcasts where we continue to explore exciting topics in the world of enterprise AI. Thank you for listening, and until next time.