Greetings, everyone. This is Krishna, and today we are diving into how AI is not just changing, but transforming the product lifecycle management. Think of it less as a technical upgrade and more like giving your PLMA superpower AI break. Systems and lead into a future where innovation is faster, smarter, and more efficient. So, what's PLM? What really it is, it's a story of a product from its first spark of an idea. Retirement as. Is the backbone of product development, integrating people, data processes, and business systems. Imagine you are building a complex machine. PM is the blueprint that guides every step from the initial sketch to the final assembly, and even to how your you service. The aftermarket part of it. Now let's talk about the challenges many of us are dealing with, what I like to call as a legacy. The document here highlights the understanding of legacy a. Let's think it this way to inherit a house with a complex plumbing system, but no one left you a map. You find files are going in all directions, some are disconnected, some are leaking, and you are dealing with a situation, something similar to, legacy PLM, can feel like. The document also mentions the difficulty of tracing an engineering change order. So just think you're trying to recall a change that you have made to a product to a complex machine 10 years back with records scattered across different departments. Some in paper, some in software. It's like trying to piece. Together a puzzle with missing pieces into it. These issues lead to errors, delays, and it's mainly because of the undocumented travel knowledge. Have you ever had a situation where only person who knew how to fix a critical issue. awards on a vacation and no one else had a clue. That's the tribal knowledge that's in action, and it's a huge, big risk, especially in the product development, product management, area. Now let's see how AI shines here. It's like bringing an expert plumber with a digital map. Tools to fix, that complex AI can help us analyze, making them and efficient. It can also help us the, that we providing a. Why, when was it approved? What change was made from one change order region to next region? it's clearly traceable. It can capture the tribal knowledge turning into accessible and searchable data. Now let's talk about some of the benefits. Benefits of, the artificial intelligence, the real world wind. That's what we call document mentions. Here, the AI powered PLN solutions are projected to reduce time to market by 30% while improving the product quality metrics by up to 35%. Lemme give you a reliable example. Imagine you are developing a new smartphone. Traditionally, you have teams working in silos, passing information back and forth. With ai, you can have a centralized system that and allows the data from all stages of development identify the potential issues earlier. you can catch design flaws before they reach to manufacturing, saving time and money and effort. Another, relatable example is the predictive maintenance. Imagine a wind turbine with sensors connected to an AI powered system. The AI analyze data predict when. It's likely to gonna fail alarming or proactive maintenance and preventing the cost of downtime and any unwanted, loss of efforts and applications. no. Let's talk about, let's talk about the implementation strategies. How do we bring these vision in life? Start with the small and focused projects. Identify the areas where AI can have the biggest impact and build from there. Remember, it is not about technology. It's about people. In training and chain management to ensure everyone is on board, essentially addressing the organizational transformation or chain management, challenges here, and of course the data security is most important. It's paramount. Implement the robust data protection frameworks and. And conduct regular security, audits. Also, the emphasis is on maintaining the ethical standards. And of course, the data security comes along with that. So let's talk about, how. Some of the key studies that we have here, for example, Boeing, one of the leading aircraft manufacturer maintenance company through AI driven, PLM, the process around two terabytes of data from their sensors across more than thousand aircraft flake. And, with that, they achieved a 35% reduction in unplanned maintenance and also the decrease in the part replacement cough because, upfront, proactively, what's gonna happen, where things can go wrong, where things can fail, and the proactive maintenance helps. to reduce the maintenance and component. Similarly, Tesla, they, they use, AI power. Is reducing the downtime line by 40% and cost and a. No, definitely the future of AI is, is very strong, is very prosperous, with ai, you. So these air power digital pins, you can simulate the product behavior, with the 99% accuracy. for example, when you manufacture a car, an airplane, you create, the models, you do some tests and see how it works. But with these. Digital pins, you don't need to do that. You can create the virtual models, try do some tests, validations, and which are like 99%, accurate. Accurate. And also this AI powered. Solutions will are giving you operative maintenance and automated design that is helping you to optimize and make your supply chain more intelligent and more, more powerful. And at the same time, there are some widespread challenges we have seen in the industry, like data quality and integration. Have legacy systems. Data model that you have for the legacy systems may be different from the modern systems. For example, the mismatch between bill of materials formats, the data models between your PLM systems and say for example, your ER PSAP system can lead to a, decrease in, accuracy and even, it'll the cross function collaboration between engineering and procurement. Another important factor is a skill gap. We need a person who understands PLM and as well as the artificial inclusion AI technologies. This gap is evident. It's difficult to find such professionals, and that's one of the challenge in adopting AI for PLM. The other factor is the implementation cost. Implementation cost is very high. It varies between $500 to $5 million. and of, and often justifying that initial investment in infrastructure and licenses, is always a, challenge. Another thing is the perceived complex. May not. Recommended, options, AI recommended, opportunities while making a critical decision. So these are some of the challenges, you need to keep in mind while adopting AI in, in, in, in p and m in general, the future looks bright. AI is not just a tool, it's catalyst. Break free from the limitations of the past and build a future where innovation is faster, smarter, and more sustainable. So with this, future is definitely bright and, looking for your comment and feedback. Thank.