The Future of Manufacturing and Mobility in India with AI

Speaker A: You're listening to a, um, Mint podcast brought to you by HD smartcast.

Speaker B: AI isn't emerging. It's already transforming. And it's not just changing the way we work. It's redefining how India builds, moves and manufactures. From predictive maintenance that stops a factory shutdown before it happens, to software defining vehicles that upgrade, like smartphones, artificial intelligence is no longer an add on. It's the engine behind India's next wave of industrial transformation. Welcome to AI Rising. I'm, um, your host, Nelson. John. In this episode, we take you to the front lines of innovation where smart factories are humming with automation, intelligence and possibility. Indian manufacturers and automakers are embracing AI not just to improve efficiency, but to completely reimagine operations. Think AI powered telematics. For real time decision making, Think digital twins that simulate entire systems before a single bolt is turned. And powering much of this is IBM's enterprise ready AI and data platform, WatsonX. Joining me is someone at the center of this evolution, Mr. M. Biswajit Bhattacharya, lead client, partner and automotive industry leader at IBM Consulting India and South Asia. With over two decades of global experience and a seat in IBM's prestigious Industry Academy, Mr. Biswajit helps organizations build smarter products, agile enterprises, and stand out customer experiences. He's also a trusted voice at national industry platforms like CII and AmCham. So whether you're in tech manufacturing or just curious about the future, we've got something for you. Let's dive into the world where AI isn't rising. It's exhilarating. Mr. Biswajit, it's a real pleasure to have you with us on the show. Welcome to AI Rising.

Speaker A: Thanks Nielsen. Thanks for having me here today.

Speaker B: Pleasure is mine as well. So, uh, Biswajit, let's start off. You've been an industry veteran with over 25 years in IBM. Uh, let's start off with a reality check. Indian manufacturers, well, they're trying to compete on a global scale, but many are still operating with aging systems, workforce constraints and supply chain unpredictability. It's a tough landscape to navigate. From your vantage point, how is IBM helping manufacturers modernize with AI and automation to stay ahead of the curve?

Speaker A: Great question to start, Nelson. Uh, see, Indian manufacturing segment has a vast potential, but at the same time, we are grappling with multiple challenges and many of them are very, very core in nature. Whether it's a supply chain, disruptions happening practically every month, there are some issues or the other. There are issues in terms of the quality in our products. And operations. And there are real challenges in terms of the workforce skill gap. And that's really becoming more and more prominent as we progress uh, further. And there are other issues like uh, limited RD spend and um, the importance of meeting the ESG goal going forward. But having said that, see IBM, the way we look at it, we are not just a technology provider, we are the transformation partners. See, our goal is to help the Indian manufacturers embed AI, automation and sustainability into every layer of their operation. Why? Because to compete globally I believe Indian factories need more than the machines. They need a full scale transformation. One that blends cutting edge technology, skilled talent and environmental responsibilities deliver quality and quality at scale. So what does this transformation really look like in action? See through IBM Consulting we are enabling smart and connected factories. Think AI, IoT, robotics, automation, all working together in real time, monitoring the factories and controlling the factories. You use digital twin to simulate factory operations before making any changes in the factory or in the product line. Predictive maintenance, helping manufacturers to anticipate the equipment failures even before it's happening. Cutting down the whole downtime and boosting overall efficiency. Let me give an example. One leading Indian auto parts manufacturer use our AI solution to detect the micro defect which are otherwise invisible to the naked eyes. See the result? It's better quality and lesser or no rework. Let's talk about supply chain because in the last few years have taught us anything that's resilience is everything. Now at IBM, um, we are helping manufacturers to build supply chains that ah, are not just efficient but agile and responsive. How? By using AI to dynamically plan, mitigate risk and manage disruptions. And that's all happening real time. We are also deploying unified data platform that gives manufacturers single view of the truth at source. And that's what m helping them to take decisions smarter and lot faster. And lastly, the sustainability is not just a buzzword. It has its own business imperative and IBM is helping the businesses to walk that talk. IBM is helping the manufacturers to embed the circular economy principles into their operations. That means that AI to optimize the the energy consumptions in their factories and reduce the carbon footprint. So where does AI really shine? It's creating value across the board. See in IBM we have an IBM, uh, institute of Business value. We did a study recently and it was done specifically for auto sector, uh, and we did it for the larger markets including India. And there's something exciting from that 61% of the auto executive we serve. It believed AI will significantly boost revenue in just three years. See we are committed to responsible AI, which means that it's fair, explainable and secure. And with that we believe that we should be able to help our clients or the manufacturing organizations in their work towards Making India Initiative and work towards the Viksheet Bharat Initiative which the government is already propagating.

Speaker B: Right. So Biswajit, so you mentioned that you know there are some automakers using uh, AI to detect minute discrepancies in their manufacturing and all of that. So could uh, you uh, share with us a real world example or a client story where this transformation really made a difference.

Speaker A: So uh, see, let me give uh you an example uh, of really uh, what's happening. See uh, you have operations data which is there and from that standpoint we are able to capture those data from various IoT devices, uh, the SCADA, etc. At a real time level. And with our solution which is what's an X in this particular case, we are able to leverage those data points and see any abnormality vis a vis the uh, expected outcome which is there. And based on that and to aid, to put on top of that is the computer vision what we have. And with that we are able to detect those discrepancies, if I may use that particular word, uh, vis a vis the expected outcome or the designed outcome, what was there. So these are something which are already in place uh in multiple organizations. We are running this kind of solutions and uh, getting a benefit of it. Let me give you another example. Uh, paint shops in automotive industry is typically an area uh, which is also uh, it's a hazardous area for a worker to really work. Now can I use computer vision over here and get the anomalies detected right at source which is otherwise not accessible to a human being or a human worker over there. And this is what we have been seeing, uh, various clients of ours started using it and getting the benefit and able to reduce lot of rework which was needed at a later point of time to remove those anomalies. And in that whole process you are bringing in the whole efficiency, you uh, are bringing in a uh, lot more um, energy saving as also in case of a paint job which is a very high energy uh, uh consumption unit. So there are very clear benefits both in terms of cost, quality and efficiency over here.

Speaker B: Right. So it's clearly not just about plugging new tech, it's about you know, how you can rethink uh, processes, people and potential with AI at the core.

Speaker A: Right, Absolutely.

Speaker B: So you know, uh, switching gears a bit here, I want to talk about mobility A bit more like, you know, since um. So there was a time when you know, I mean um, when people would just walk into a showroom, right they would talk about, you know, they would find out about uh, what's the engine type like you know, what is the horsepower, what's the mileage and all of that. Today you know, when somebody walks into uh, a showroom they ask like you know, what does the car has to offer with related to technology? Is the car connected? Uh, does uh, it have ADAs? If yes, is it level one, two, does it have automation, all of that like you know. So how is IBM supporting Indian manufacturers through this transition to a software defined vehicle from outside of mechanical to a more software driven uh, vehicle.

Speaker A: See automotive industry, which is probably is a 100 year old industry, hasn't really changed much in the last hundred years. But now because the way the consumers are expecting and some of the things, what you talked about, the way people are buying cars today, they are forced to reimagine their overall business models including their products as well as the way they sell their products. And as a part of the reimagining process what they're looking at to bring lot more digital technologies into their product and operation. And along with that the business models and the operating models are also changing from that standpoint. So the shift towards software defined vehicle which you just talked about or autonomous driving adas is not just coming, is already underway whether it's globally or in an Indian market. But having said that uh, these technologies also come at a cost and there are challenges. Essentially we see that there is a significant cost involved in terms of the leaders, the rudders and cameras and the AI chips which needs to get inside the car. There are concerns in terms of uh, infrastructure, road infrastructure, I'm talking about over here, the signage, the lane discipline, uh, and other ecosystems which are needed uh, uh, for this technologies to really work. Regulations is probably work in progress at this point of time from this standpoint and as the vehicle becoming a lot more software driven, cybersecurity risks are also kind of there. And to kind of top it out, while there's a segment of consumer who are really looking for all these technologies, there's a consumer awareness issue which is also there in general. So now having said that these are the, these are the challenges which are very specific to India. The way we, what we see is it needs a uh, collaborative effort with multiple ecosystem players coming together. And that's the approach what IBM is looking at. We're looking at working together with our OEMs, the auto component manufacturer in some of the places, the regulatory bodies, uh, as well as a lot of other tech companies uh, including startups. We're looking at using AI foundation models to power the in vehicle analytics for ADAS infotainment or the driver monitoring systems AIOps to ensure the secure and scalable deployment across the vehicle platform. Looking at IoT and AGI for the capture the vehicle data in real time so that we are able to do the diagnostics at a real time level. We are able to understand the driving behavior and in case of our uh, EV we are able to ensure that smart charging is also happening. This AI powered telematics is also able to provide us uh predictive maintenance capability and a route optimization at a real time level. And in case of an EV we are able to do a better uh battery, manage the battery health a lot better. See the outcome is at the end of the day, better uptime, uh, smarter energy use and more importantly a seamless customer experience. That's where you started off and that's the whole reason why this journey has started in the industry. And we are as I said we are working collaboratively now look at a uh scenario where uh, a AI uh Cobot is offering you a proactive service alert or a uh digital dashboard that's providing you a real time insight, the way you drive the vehicle and give you tips what, what can be done better. Now these are the kind of personalization what we are talking about and this is possible as you bring more software into the vehicle, as you bring more AI capability into the vehicle. We're talking about a frictionless trusted journey across the uh entire vehicle cycle. See and talking about trust, that's everything. And that's why we also when we look at building the solution we ensure that the security and the governance at every layer of the AI stack is maintained and managed. That's the approach, what we are looking at uh, as far as uh, uh adas, uh, and autonomous driving is concerned.

Speaker B: Right. So uh, the road ahead isn't just um, electric, it's going to be intelligent, um, connected and of course privacy first.

Speaker A: Yeah. Just to kind of add uh to your point, see the industry has started a journey, what they call case. It's connected, autonomous, shared and electrified and, and from that standpoint and that's something which has evolved into a software defined vehicle. So we are talking about a completely different experience going forward and that's not far, it's happening and uh, the whole mobility experience is going to be very different in the next couple of years, uh, uh, for all of us.

Speaker B: Right. So you know, getting under the hood a bit. Um, as we all know the IBM has a powerful stack. You mentioned about Watson X, you know from WatsonX to the uh, digital twins, AI powered telematics and uh, the sustainable sustainability tool NVC. All of it, uh, all of these are driving innovations in different ways. How are these technologies actually helping your clients to innovate while also keeping ESG and sustainability goals?

Speaker A: Yeah, let's start with uh, the sustainability or the ESG part of it. See one thing, what I will say Industry 4.0 has really playing a crucial role for uh, organizations, India and globally to really put this sustainability and the ESG goal at the forefront. At ipa we are also helping the organization to meet their ESG goal through a very clear approach which is data driven, AI powered and what we call hybrid by design approach. Let me break it down for you. You talked about WatsonX. So with WatsonX one can build custom AI model that can optimize the energy used in their operation whether it's factory or warehouse or operations and in that process reduce emission and also it can also improve the worker safety. We use the WatsonX data to unify the ESG related data across operations, whether factory supply chain and various other operations of uh, the organization. WatsonX governance ensures transparency, fairness and compliance which is very critical in the whole AI journey for any organization. Let me give you an example again an auto OEM uh used in WatsonX uh model to reduce the CO2 emission across its supply chain just by doing two things, optimizing logistics and recalibrating their production schedule. Now let's move to IBM nvz. That's our sustainability analytics platform. It helps companies to track and report carbon emission, energy uses and any wastage in their operations. So it can pull data from all the IoT sensors, ERP systems or any plant operation system and many more, um, and gives you a unified ESG view. So once again how does it help? I mean let me give another example. A manufacturer is using ESG to monitor uh, their energy uses across their plant and getting that unified view and with that data, point is he's able to identify the area where he can optimize his performance and able to cut emission by 15% in just a year's time. So that's the kind of possibilities which we are able to bring it for our client. Moving on to the AI powered telematics which is, which is uh, specifically for auto OEMs where we have been working with. It's a Game changer for many of them. And I talked about case this is the whole, the connected or the uh, aspect of it. See it monitors the vehicle performance, fuel efficiency and even suggests the best way to drive the vehicle to get your best fuel efficiency for EVs, it can help you to do the smart charging and track your battery health uh, throughout. So there are examples where organizations as well as the consumers fleet operators are able to cut their fuel consumption by around 10 to 15% by leveraging those AI led insights from what they are getting at a real time level. See ESG is not about just compliance. It's about building a better and more responsive business. So what I'll say our IBM solution is helping companies to do three things. A lower emission and design more sustainable products for them. Second, creating a safer and more inclusive workplace. And third, ensure transparency and accountability in their operations, in their governance process.

Speaker B: So you're saying like with all these uh, solutions and all of that, you know sustainability and profitability can go hand in hand with these stacks, right?

Speaker A: Absolutely. It's more than that. See while I mean the example what we were talking about where companies are able to cut down on their energy consumptions, now if you're able to cut down energy consumption, that's add to your bottom line. So it's not just you're not compromising on your profitability or financial performance, you're improving the financial performance while you're meeting your ESG goals. So it's a benefit what one can really look for. Um, and that's the reason it's not about just doing it to meet certain compliance requirement. It's about how to do the business in a better way for your employees, for your consumers and for your partners and the society at a large.

Speaker B: Right. And which uh, of these solutions uh, that you mentioned earlier, uh, are you seeing the most uh, traction for in the Indian market?

Speaker A: Specifically See in Indian market uh, we have been seeing uh, definitely Watson uh suit a product what we call is something which is getting popular in Indian market. Uh definitely. And uh, we are seeing more and more customers really liking it uh, and using it. And in the automotive uh, the OEM space, uh, ivyme is already working with a lot of our customers with our AI powered connected solutions. And this is something which this market is definitely growing for all of us, us uh, uh, collecting right now, talking

Speaker B: about this OEM space itself. You know one area that's generating a lot of buzz right now is data, uh, fabric and edge intelligence. Uh, you know factories are becoming more connected, uh, their need for real time insights and orchestration becomes absolutely critical. Uh could you break down how IBM is using these to bring real uh time insights into traditional factory settings and what kind of impact it is having today?

Speaker A: Yeah, uh good point. See in fact the industry 4.0 or 5.0 uh, we are generating more and more data uh, uh at every factory uh today. Now how do you leverage this data to your benefit is something that's where the whole IBM data fabric approach is kind of built on. See it connects all the data points no matter where it lives and where whatever format it is. So it's enabling the real time access at edge and integrates AI and analytics and ensure the uh data is shared securely. So let me talk about the five things what we are able to get out of uh the data fabric approach or the benefit what uh the customers of ours are getting A the real time access of data at age run AI analytics where the data is getting generated. And that's something which is very important. Make faster and smarter decision based on the data at a real time level. Sharing of data securely across the team within the organization and with the partners and with various ecosystem players which are there as a part of the operations and very importantly scale effortlessly as the data grows. See it's whether it's we are talking about a more resilient operation, faster response time and building a foundation for a continuous uh innovation for the organization. See whether it's AI sustainability or digital transformation, what IBM consulting is looking at is helping client building a smarter factories or vehicle. It's talking about building a smarter future for the organization. And that's the approach what we have been taking in the market.

Speaker B: So with this you know uh, what kind of value do you see manufacturers getting from this shift? Is it uh efficiency, cost savings or is there anything else?

Speaker A: See uh, let me put it this way. Number one essentially is that the, the biggest benefit what uh organization is able to get essentially is to make operation resilient. See today we the each business goes through multiple disruption. I mean if I talk about automotive industry when we had Covid in 2020 once the COVID issues was kind of uh getting managed. We get into a issue of major issue rather of uh semiconductor shortages across industries. Okay. As things were getting kind of normalized. We also hear about the rearrange uh uh um minerals which are becoming a challenge for the EV motors today. So supply chain disruptions which are, which are, which are going to be there various reasons geopolitical um or local issues or environmental issues which will be there now how you make your operation resilient enough is something which is the biggest challenge which each and every board is struggling today. And that's where this approach is really helping uh, the organization. Secondly, as I saying that, uh, as I was saying that the data fabric approaches is able to integrate all sorts of data into one place. And with AI, ah, it's able to create a very clear narrative of the data for the senior leaders to take decisions without involvement of an IT or uh, a data engineer to kind of help them out. So it's, and that's the biggest game changer what I will say for the businesses. And it's a foundation for a continuous innovation for the organization. Now all of them translate into business benefit not just at a short term level. I'm talking about the financial benefits also for a long term level because each of them are very strategic and at a foundation level for an organization.

Speaker B: All right, uh, Biswajit, before we uh, wrap things up, I want to touch on um, something that's at the heart of every AI conversation. And you've mentioned this at the very beginning of our conversation, that's trust. You know, with AI, uh, becoming more embedded in core industrial workflows, um, there's a growing focus on ethics, transparency and compliance. Uh, how is IBM making sure AI is being adopted by responsibly in a way, uh, where it's transparent and trustworthy and also uh, aligned with the evolving regulations?

Speaker A: Yeah, great question Nelson. Uh, see AI is uh, uh, very much there. Each and every board has a mandate to really leverage AI to improve their business operation, improve uh, the experience for their consumers. So from that standpoint, uh, we also need to look at what are the adoption challenges which are there and let me kind of classify into four areas. One is regulatory compliances. Second, addressing bias which evolves out of any AI model and its training process. Third, how do you ensure that the personal data, whether it's of your employee or your consumer or in your partners, how do you protect that and how do you ensure the transparency of the AI decisions which are there Now? The way I see it, it's a balancing act which an organization needs to really look at while they innovate their businesses using AI. These are the challenges also needs to be looked at it. Now IBM's approach for AI ethics is clearly guided by the principle of trans trust and transparency. And we look at four pillars from that standpoint. A IT explainability. So any AI decision should be able to be explained if required at a step by step level. Fairness. It should be devoid of any bias. Third, it needs to be robust and scalable. And fourth, and that's the privacy. How do you maintain the privacy of uh, the various, uh, uh, data points which you have been taking it from various sources. So it's about putting the right guardrail. And that's the approach, what we do when we go and get into the AI journey with our clients. We also advise clients to do a holistic AI strategy before you get into this thing. And that's where we can set up this framework for our clients, specific to their needs, specific to their organization, customer culture, uh, uh, and the people who have been doing it. And we also look at tools which you'll be able to monitor and do the audit trail. Audit is something which is important over here, which actually helps us to relook at the various decisions and if any modification needs to be there, should be able to uh, be carried out so that we ensure that we are compliant, we are not having any bias, our privacy is maintained and overall transparency is there. So that is a very clear approach which IBM has been taking, or rather I'll put it as a cautious approach. What we are taking as we, as we get into the journey with our clients.

Speaker B: Right. You know, I mean, um, talking about compliance and you know, regulations, uh, you know, regulations is evolving, you know, and are there any, uh, regulatory trends globally that India should be paying, uh, close attention to?

Speaker A: See, I think, uh, there are a lot of work which is already happening by the regulatory bodies and there are, uh, many acts which are also on the iron bill. Uh, and IBM is collaborating with, uh, all the right, uh, regulatory bodies to ensure that we have the right, uh, processes and the right, uh, regulations in place, uh, from that standpoint.

Speaker B: Right. But before I let you go here on AI Rising, I've got to ask you one thing. You been spending years helping businesses stay agile, but you know, what keeps you agile? I also understand you're a fitness enthusiast and a marathoner. How does fitness shape your approach to leadership?

Speaker A: See, fitness keeps your mind fresh. And I think, uh, as we are talking about, uh, fairness in AI, See, one of the very, very clear requirement to be a leader is that you need to be fair. Fair to your team, fear to your clients, and, uh, fair to all the partners whom you work as a part of that process. So that's what I'll say. It's kept me, kept my brain, uh, a lot more agile, uh, and fresh. And um, I'm a happy individual and ensure that my client is also happy.

Speaker B: Wonderful. So do you have a calendar marked for the next marathon.

Speaker A: Yeah, I'm, I'm, I'm thinking about it. It needs to be in winter sometime. Wow.

Speaker B: Uh, that's gold. You know, I mean, you know, whether it's transformation, roadmap or marathon, the finish line rewards those who pace smart and stay the course. Leadership is a long distance sport, isn't it?

Speaker A: Absolutely. It's, it's, uh, a, it's a, it's a journey. It's, it's not a, it's not a one day game.

Speaker B: Absolutely. Mr. Biswajit, thank you so much for sharing these insights and giving us a front row seat to the change that's underway.

Speaker A: Thanks. Thanks for, uh, having me today. It has been a pleasure talking to you, Nelson. Thank you.

Speaker B: Pleasure is mine as well. As we've heard today, AI in manufacturing and mobility isn't just about technology. It's about transformation with purpose. From optimizing operations to reimagining vehicles, and from edge intelligence to response, responsible AI, the landscape is evolving fast and India is clearly at the driver's seat to our listeners and viewers. If this episode sparked a new perspective, don't forget to follow AI Rising on your favorite podcast platform. Until next time, stay curious, stay future ready, stay sharp. This is Nelson John, your host, signing

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