Guest Speaker:
Transcripts
generative AI, let's talk about it today. My name is Neerja and this is tech lyceum. Now as we all know, generative AI is one of the buzzwords heard everywhere these days seems to be there in multiple discussions and there are multiple ways of looking at this technology. So let's demystify this tech jargon, and you know, really understand what it means get to the bottom of it, we have somebody to help us with that. Ajit Singh Chawla joins us senior vice president Global Head of the digital business unit at Birlasoft. With nearly 20 plus years of experience in the technology space, Ajit has been a driving force for many accomplishments and projects. Hi, Ajit. Welcome to the podcast. Thank you for joining us.
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Speaker – Ajit - 00:59
Hi Neerja,Thank you for having me.
Hi Neerja,Thank you for having me.
Speaker – Neerja - 01:01
So I've read everyone's talking about generative AI, it is a distinctive and revolutionary macroscale tech that can potentially change everything, right? And every organization is racing to be a part of this revolution. There's a lot of hype around it. What is your take on this?
So I've read everyone's talking about generative AI, it is a distinctive and revolutionary macroscale tech that can potentially change everything, right? And every organization is racing to be a part of this revolution. There's a lot of hype around it. What is your take on this?
Speaker – Ajit - 01:21
Thank you for this question. Neerja. And I've been asked this question a lot of times by our customers, as well see, Generative AI is once in a generation technology. And as with any new tech, it's natural to have its share of excitement, confusion, as well as fear, okay. And that's what we see, you know, across the board now, but at the same time, it has established its credentials, and enough to be seen as a, you know, a real force for productivity experience and operational gains. And that's what No, that's how it's revolutionizing different sectors and functions. So, you know, as we start looking at, how do we plan for generative AI adoption, I think it's super critical to have a sustainable strategy, which is, you know, manageable from a long run perspective, but at the same time dynamic enough, you know, to deal with rapid developments as they are happening on a day to day basis, because the technology itself is also emerging on a day to day basis, right. And as I need some customers, often times, you know, my advice to them is that do not look at this as just a technology, but as an opportunity for a 360 degree transformation. And therefore, what's super critical, again, is to have the organization readiness as the number one, you know, lever to consider when you look for generative AI adoption, and my organization grittiness. It's about people, it's about processes, and it's about technology, and technology, you know, I'm purposely putting at the end, because people and processes have to be ready to adopt the technology. And that's the larger organization change management that the customers need to look into. The second aspect that's also important to look into is the larger data strategy. Because at the end of the day, whatever generative AI produces, it's important to have the downstream data strategy, that's, you know, ensuring that the outcome is reliable. So that data strategy in terms of quality in terms of data pipeline, etc, is the other very, very important aspect. And the third aspect is, let's not get carried away. Because you know, we can only sound relevant if we only when we're talking about generative AI. Not everything requires generative AI. So let's look at the problems with an open mind. Let's look at what is the right solution for the problem. And then if generative AI is one, then let's look at the cost benefit analysis and many other factors, and then decide if or not to liberate generative AI for that. So those three aspects right organization readiness, a robust data strategy, and the robust problem analysis and assessment are important aspects to consider while going for any such program.
Thank you for this question. Neerja. And I've been asked this question a lot of times by our customers, as well see, Generative AI is once in a generation technology. And as with any new tech, it's natural to have its share of excitement, confusion, as well as fear, okay. And that's what we see, you know, across the board now, but at the same time, it has established its credentials, and enough to be seen as a, you know, a real force for productivity experience and operational gains. And that's what No, that's how it's revolutionizing different sectors and functions. So, you know, as we start looking at, how do we plan for generative AI adoption, I think it's super critical to have a sustainable strategy, which is, you know, manageable from a long run perspective, but at the same time dynamic enough, you know, to deal with rapid developments as they are happening on a day to day basis, because the technology itself is also emerging on a day to day basis, right. And as I need some customers, often times, you know, my advice to them is that do not look at this as just a technology, but as an opportunity for a 360 degree transformation. And therefore, what's super critical, again, is to have the organization readiness as the number one, you know, lever to consider when you look for generative AI adoption, and my organization grittiness. It's about people, it's about processes, and it's about technology, and technology, you know, I'm purposely putting at the end, because people and processes have to be ready to adopt the technology. And that's the larger organization change management that the customers need to look into. The second aspect that's also important to look into is the larger data strategy. Because at the end of the day, whatever generative AI produces, it's important to have the downstream data strategy, that's, you know, ensuring that the outcome is reliable. So that data strategy in terms of quality in terms of data pipeline, etc, is the other very, very important aspect. And the third aspect is, let's not get carried away. Because you know, we can only sound relevant if we only when we're talking about generative AI. Not everything requires generative AI. So let's look at the problems with an open mind. Let's look at what is the right solution for the problem. And then if generative AI is one, then let's look at the cost benefit analysis and many other factors, and then decide if or not to liberate generative AI for that. So those three aspects right organization readiness, a robust data strategy, and the robust problem analysis and assessment are important aspects to consider while going for any such program.
Speaker – Neerja - 04:01
Right, Ajit, thank you for taking us through that lots of moving parts there. And we also keep hearing that generative AI is going to change the world as we know it, you know, right from pushing the boundaries of creativity and productivity to augmented human machine partnerships. I mean, that day that we grew up on the seeing in movies is now fast approaching in reality, what do you think are the key areas of impact? What do you visualize?
Right, Ajit, thank you for taking us through that lots of moving parts there. And we also keep hearing that generative AI is going to change the world as we know it, you know, right from pushing the boundaries of creativity and productivity to augmented human machine partnerships. I mean, that day that we grew up on the seeing in movies is now fast approaching in reality, what do you think are the key areas of impact? What do you visualize?
Speaker – Ajit - 04:28
See from my study, and you know, I've been studying both a lot of consulting organizations like flex McKinsey, Deloitte, BCG, etc. Some of their reports, as well as how our customers are starting to adopt generative AI. There are two macro themes that emerge out in terms of areas of impact. Number one is cost driven, which is essentially productivity efficiency, operational excellence, etc. And the second theme is growth driven, which is sales and marketing R&D, Engineering. and customer experience if you met, right. In the early days, as you know, one would expect most of the adoption is driven on cost related themes. But as the technology would stabilize, and as people will learn more about it, I do expect it to pivot towards some of the growth themes as well. And some customers have already started doing some pilots. You know, one of the McKinsey studies that I recently looked into actually anticipate that about 75% of the value that generative AI use cases would deliver will actually fall into customer operations, sales, marketing, engineering, and R&D. And I kind of, you know, resonate with that. But still, you know, as I mentioned earlier, the early part of the adoption, I see the impact is largely going to be on cost driven use cases, and the likes of productivity efficiencies and operational improvements that I spoke about.
See from my study, and you know, I've been studying both a lot of consulting organizations like flex McKinsey, Deloitte, BCG, etc. Some of their reports, as well as how our customers are starting to adopt generative AI. There are two macro themes that emerge out in terms of areas of impact. Number one is cost driven, which is essentially productivity efficiency, operational excellence, etc. And the second theme is growth driven, which is sales and marketing R&D, Engineering. and customer experience if you met, right. In the early days, as you know, one would expect most of the adoption is driven on cost related themes. But as the technology would stabilize, and as people will learn more about it, I do expect it to pivot towards some of the growth themes as well. And some customers have already started doing some pilots. You know, one of the McKinsey studies that I recently looked into actually anticipate that about 75% of the value that generative AI use cases would deliver will actually fall into customer operations, sales, marketing, engineering, and R&D. And I kind of, you know, resonate with that. But still, you know, as I mentioned earlier, the early part of the adoption, I see the impact is largely going to be on cost driven use cases, and the likes of productivity efficiencies and operational improvements that I spoke about.
Speaker – Neerja - 05:48
The AI revolution has also brought in a set of risks that organizations are still dealing with,
Q: What are the potential risks associated with generative AI? And how can organizations mitigate these risks in this, you know, waves for trust.
The AI revolution has also brought in a set of risks that organizations are still dealing with,
Q: What are the potential risks associated with generative AI? And how can organizations mitigate these risks in this, you know, waves for trust.
Speaker – Ajit - 06:06
So classify the risks in two key categories, right. One is the technology associated or technology induced risks, you know, aspects such as privacy and security, and our managing hallucinations and biases, transparency and traceability of the reserves, intellectual property ownerships and equal access, right. And these are very important risks to mitigate. They were as it is important in the larger world of artificial intelligence. But with generative AI and the speed of adoption, they are super, super important for any organization to, you know, a clear risk management strategy in their adoption of generative AI. But there is an equally critical risk, which is about people. And when I talk about people, it's about people management, and also expectation management. Because as I said earlier, right, generative AI has the potential to actually disrupt and transform an organization and the core to any organization. And the key to success for any such transformative initiative for any organization is managing the people and managing their expectations for good and for bad, right. So that's a very important risk for any organization to manage. So the way we advise our customers and partners to look into is first and foremost, this must start a risk management strategy. Right at the very beginning, as they start looking into adoption of generative AI, they must work with the different partners, whether they are hyper scalar partners, technology partners, and so on to look at their security, the privacy, guardrails, etc, that are in play, they must also look at their enterprise data as well, because a lot of them have started to build custom generative AI models on their enterprise data. So therefore, it's important to look at the security posture in their data, their data pipelines, and so on and so forth. And then on the on the people side, they must start investing upfront on proper upskilling of people, workforce planning, the change management initiatives, the right communication, the right messaging to the people that this is not necessarily going to take their jobs away, but will give them an opportunity to actually leverage their skills on more value driven initiatives and be more valuable to their customers and their businesses. So that investment on people management is also important as much as it is on the other areas of risk management.
So classify the risks in two key categories, right. One is the technology associated or technology induced risks, you know, aspects such as privacy and security, and our managing hallucinations and biases, transparency and traceability of the reserves, intellectual property ownerships and equal access, right. And these are very important risks to mitigate. They were as it is important in the larger world of artificial intelligence. But with generative AI and the speed of adoption, they are super, super important for any organization to, you know, a clear risk management strategy in their adoption of generative AI. But there is an equally critical risk, which is about people. And when I talk about people, it's about people management, and also expectation management. Because as I said earlier, right, generative AI has the potential to actually disrupt and transform an organization and the core to any organization. And the key to success for any such transformative initiative for any organization is managing the people and managing their expectations for good and for bad, right. So that's a very important risk for any organization to manage. So the way we advise our customers and partners to look into is first and foremost, this must start a risk management strategy. Right at the very beginning, as they start looking into adoption of generative AI, they must work with the different partners, whether they are hyper scalar partners, technology partners, and so on to look at their security, the privacy, guardrails, etc, that are in play, they must also look at their enterprise data as well, because a lot of them have started to build custom generative AI models on their enterprise data. So therefore, it's important to look at the security posture in their data, their data pipelines, and so on and so forth. And then on the on the people side, they must start investing upfront on proper upskilling of people, workforce planning, the change management initiatives, the right communication, the right messaging to the people that this is not necessarily going to take their jobs away, but will give them an opportunity to actually leverage their skills on more value driven initiatives and be more valuable to their customers and their businesses. So that investment on people management is also important as much as it is on the other areas of risk management.
Speaker – Neerja - 08:27
Right. Many things to consider in risk management when it comes to generative AI as organizations across the globe gear up to put generative AI front and center in their approach. There are two areas of concern. One is how can they differentiate themselves in this race? And the second is how do organizations at varying stages of adoption unlock value from generative AI?
Right. Many things to consider in risk management when it comes to generative AI as organizations across the globe gear up to put generative AI front and center in their approach. There are two areas of concern. One is how can they differentiate themselves in this race? And the second is how do organizations at varying stages of adoption unlock value from generative AI?
Speaker – Ajit - 08:55
I recently read a report from Deloitte and I quite liked the way they had summarized it, that the competitive advantage for any organization looking at adopting Gen AI will be driven by two factors or two primary factors, a buy access to the proprietary data sets, you know, and you know how well the data ecosystem and data pipeline is defined. And second is the access to the right talent. And, you know, I do believe that the latter is practically going to be a showstopper for organizations in their transformation journey. So talent clearly is a very important aspect that's going to drive competitive advantage for organizations. So as you know, as organizations start their journey for unlocking the potential from generative AI, as I mentioned earlier, they need to have a sustainable and an inclusive strategy that they need to start embarking on. They need to look at how they can drive collaborations, cross cross disciplinary teams, so that it's inclusive, that will include their ICT At the people HR team, or you know, their product teams, and so on and so forth. And it's important to start laying out a clear technology strategy, including data engineering and data pipelines, you know, ml ops, the AI ready talent and all of that, because the more inclusive they are, and the more integrated they are, from the very beginning, I think the chances of them succeeding in this journey are going to be that much higher.
I recently read a report from Deloitte and I quite liked the way they had summarized it, that the competitive advantage for any organization looking at adopting Gen AI will be driven by two factors or two primary factors, a buy access to the proprietary data sets, you know, and you know how well the data ecosystem and data pipeline is defined. And second is the access to the right talent. And, you know, I do believe that the latter is practically going to be a showstopper for organizations in their transformation journey. So talent clearly is a very important aspect that's going to drive competitive advantage for organizations. So as you know, as organizations start their journey for unlocking the potential from generative AI, as I mentioned earlier, they need to have a sustainable and an inclusive strategy that they need to start embarking on. They need to look at how they can drive collaborations, cross cross disciplinary teams, so that it's inclusive, that will include their ICT At the people HR team, or you know, their product teams, and so on and so forth. And it's important to start laying out a clear technology strategy, including data engineering and data pipelines, you know, ml ops, the AI ready talent and all of that, because the more inclusive they are, and the more integrated they are, from the very beginning, I think the chances of them succeeding in this journey are going to be that much higher.
Speaker – Neerja - 10:26
Ajit, as we wrap up, I just want to ask you, amidst all the generative AI hype, organizations need to identify a partner of choice to support them in this journey. So
Q: Could you tell us about how Birlasoft engages with customers on this?
Ajit, as we wrap up, I just want to ask you, amidst all the generative AI hype, organizations need to identify a partner of choice to support them in this journey. So
Q: Could you tell us about how Birlasoft engages with customers on this?
Speaker – Ajit- 10:42
Well, the first thing I tell our customers is that we don't have all the answers. Okay, you know, we don't profess to know everything, because quite frankly, nobody does yet. And therefore, it is very important to have a grounded approach as we embark on this journey. And we help our customers determine, you know, some of the quick wins, that allow them to understand both the potential of the technology or Art of Possibility thereof, but also address some of the teething issues that any search adoption brings in. The third aspect, and I can't speak enough about it is that you know, focusing on processes, people and technology at the same time, technology is just a means to the end. But processes and people are super critical to be focused on and looking at, you know, the larger organization change management, you know, in an inclusive manner. I think those are the ways that Birlasoft engages with our customers nature,
Well, the first thing I tell our customers is that we don't have all the answers. Okay, you know, we don't profess to know everything, because quite frankly, nobody does yet. And therefore, it is very important to have a grounded approach as we embark on this journey. And we help our customers determine, you know, some of the quick wins, that allow them to understand both the potential of the technology or Art of Possibility thereof, but also address some of the teething issues that any search adoption brings in. The third aspect, and I can't speak enough about it is that you know, focusing on processes, people and technology at the same time, technology is just a means to the end. But processes and people are super critical to be focused on and looking at, you know, the larger organization change management, you know, in an inclusive manner. I think those are the ways that Birlasoft engages with our customers nature,
Speaker – Neerja - 11:37
All the makings of a great partner or Ajit, this was a wonderful conversation, and we've gone a little past the surface, but I'm sure there's a lot more to dig into. So please promise us you will be back for another chapter.
All the makings of a great partner or Ajit, this was a wonderful conversation, and we've gone a little past the surface, but I'm sure there's a lot more to dig into. So please promise us you will be back for another chapter.
Speaker – Ajit - 11:52
Anytime you want. Neerja, thank you so much.
Anytime you want. Neerja, thank you so much.
Speaker – Neerja - 11:54
So just to summarize our Ajit’s points. Number one, there is no expert for generative AI, as most organizations are still figuring out its applications. Number two, in order to have a better outcome, change management in an organization should be a focus. Also integration of processes, people and technology should be a priority. And finally, number three, the best investment to make is in your employees. Organizations must create awareness and make required trainings available to their employees. That's the takeaway from this episode. I hope you had a great time. I will catch you on another one of these until then, it's bye for now.
So just to summarize our Ajit’s points. Number one, there is no expert for generative AI, as most organizations are still figuring out its applications. Number two, in order to have a better outcome, change management in an organization should be a focus. Also integration of processes, people and technology should be a priority. And finally, number three, the best investment to make is in your employees. Organizations must create awareness and make required trainings available to their employees. That's the takeaway from this episode. I hope you had a great time. I will catch you on another one of these until then, it's bye for now.
You were listening to tech Lyceum, a podcast by Birlasoft.
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