Hey everyone. I'm, and I'm excited to be here to discuss a topic that's transforming our business operate. The topic is modern ERP as the platform backbone for AI driven supply chain. So we are living in an era where supply chains are no longer linear their dynamic. Interconnected ecosystems driven by data, AI promises to revolution, revolutionize, supply chain planning, execution, and responsiveness. But there's a catch. Most enterprises are still running on outdated ERP systems that were not built for the future. Today's enterprises phase a critical inflection point. On one side we have exponential growth in ai, machine learning, IOT, and predictive analytics. On the other hand, we have legacy RP systems, which are rigid, siloed and are not designed for real time AI power decision making. This mismatch creates inefficiencies like data silos, delayed visibility, and an inability to respond to disruptions quickly. The traditional ERP systems were built for transaction processing and not for intelligence. They're like Ivy built for cars, but now we are trying to run I speed trains on them. Without modern ERP platforms that integrate AI natively, businesses risk falling beyond competitors who can sense, predict, and respond much faster. In this session I will share why modern ERP must serve as backbone for AI driven supply chains and how organizations can transform from reactive, siloed operations to truly intelligent, connected networks. We'll also explore what makes an ERP already with real world use cases and how companies can future proof the supply chain platforms. All right. So now that we've set the stage for why AI driven supply chains are critical let's address the elephant in the room which is our existing ERP systems are holding us back. The promise of AI can only be realized if the foundational systems can support it. And legacy ERP systems simply were not built for this new world. First. Legacy RP implementations are highly fragmented. Over years of customization and bolt on solutions, enterprises have ended up with disconnected processes across planning, logistics, asset management, hr, finance, and sourcing. And this fragmentation creates data silos. Where critical information is locked in isolated systems leading to delays in decision making, duplicated efforts, and an inability to respond quickly to disruptions. Second data, which is the life blood of AI, is often not ai. Already in this systems ERPs may hold vast amount of structured and unstructured data, but much of it is incomplete, inconsistent, and siloed. AI models thrive on clean, standardized, and integrated data across functions like supply chain finance, hr, et cetera. Without this, AI initiatives fail to deliver meaningful insights. The third reason is the rigid architecture of legacy ERP, which makes it extremely challenging to integrate with modern technologies be it cloud-based platforms, machine learning framework, or emerging capabilities like digital twins and generative ai. This lack of flexibility means every integration becomes a costly, time consuming project rather than a seamless capability. This limitations of fragmentation, poor data readiness rigidity are why companies struggle to unlock the full potential of AI in the supply chains. The good news, paradigm is emerging ERP modernization, guided by platform engineering principle, and that's what we'll explore next. So how do we how do we overcome the limitation of legacy ERP we just discussed? The answer lies in rethinking ERP not as a monolithic application, but as platform engineer for agility, scalability, and intelligence. This is where platform engineering principles. Come into play transforming ERP into flexible foundation, capable of supporting AI driven supply chain. Platform engineering focuses on building scalable and maintainable technology ecosystems by treating systems as a set of interoperable services rather than one rigid block. When applied to the enterprise resource planning applications, it allows us to break down the traditional structure into modular composable pieces. That can evolve independently and integrate seamlessly with emerging technologies. The key tenants of platform engineering approach are microservice architecture, A PFS design, even driven architecture microservice architecture instead of a single tightly coupled system, functionality is de delivered through microservices. Self-contained modules, for example, finance, procurement, planning, logistics, et cetera. This gives organizations unprecedented flexibility to scale specific capability rollout updates faster, and adopt innovations without disrupting the entire system. Second is the a p first design. API APIs are the glue that ensures all ERP functionality is easily accessible and integrate with other company or other enterprise systems, be it external partners or internal partners and also AI solutions. A well documented version, API strategy, future proofs the ERP and enables continuous integration of new capabilities. Last but not least, the event driven architecture which moving away from Batch Pro, batch based processes to even driven architecture. Which means real time responsiveness inventory update, demand signals disruption are processed instantly enabling predictive and adaptive supply chain operations. By adapting this platform, engineering principles, microservices, APIs, and even driven architecture, we create an ERP foundation that. That is no longer a barrier to innovation but a launchpad for AI driven transformation. Okay, let's move to the next slide. Awesome. So we've seen our platform's energy platform engineering transforms ERP into a flexible modern platform. But flexibility alone is not enough to truly enable AI driven supply chain. We need unity. A unified ERP architecture that breaks down silos across enterprise. Legacy ERP systems operate as isolated modules. Whereas modern Unified ERP architecture treats every part of the business like planning, logistics, sourcing finance, asset management as intercon interconnected components of a single ecosystem. This shift lays the foundation for AI because AI needs holistic real time data to be effective. End-to-end visibility with unified ERP gain real time access to data across the value chain. This means decisions are informed by what's happening right now, not by outdated reports or delayed updates. Processes processes no longer operate in isolation. For example, maintenance scheduling can automatically factor in production plans, inventory levels and transportation constraints, enabling crew optimized operations unified ERP enforces consistent end-to-end process across the enterprise where it's. Planning, sourcing, or asset management. Everyone follows the same best practices guidelines removing inefficiencies and inconsistencies. Finally, unified, ERP establishes common data model and governance policies across the enterprise with thereby reducing the data quality issues that undermine AI initiatives. Clean, standardized data, as we know, is the bedrock of trustworthy AI insights. Breaking down silos through unified ERP architecture doesn't just improve efficiency. It what it is what makes AI in supply chain truly positive. And so next we'll see how this unified platform enables for intelligent, autonomous, and decision making at scale. Okay, next slide. Alright, so as we saw that unified ERP architecture breaks down silos internally but it truly unlocks air driven value air. Now for that ERP must also integrate seamlessly with the broader enterprise ecosystem. This is where in, integration excellence becomes the key differentiator. Modern ERP can no longer operate in silos or as an island. AI thrives on compressive. Connected data flow from shop floor operations to strategic financial planning. This means moving beyond simple point to point connections to a deeply integrated, intelligent operational network. The core integration pillars in any, in a new era, ERP, like S four is the core backbone. Then on top of that, you have advanced modules like Ariba, extended warehouse management, transportation management, integrated business planning, the financial supply chain management, which use the end-to-end visibility. To the entire supply chain and beyond. And then you have systems like Teamcenter, which are used for product lifecycle management MES systems like camstar or LIMS systems like Lab Vantage, which play a pivotal role in each of their specific areas, but come together in terms of a comprehensive connected data flow. When ERP becomes this deep, deeply integrated up, it transforms from a system of record into a system of intelligence powering predictive analy analytics, automated decision making and AI driven optimization across the enterprise. So the key here is the integration excellence and how do we achieve that. Great. Go to the next slide. Alright, so let's look at some of the examples of of how we can leverage ai. So far we have talked about building the foundation of AI driven supply chain. Now let's look at one of the most powerful real world applications of this approach which is predictive maintenance. This is where the modern ERP IO OT and AI converge to deliver a tangible business value. As you, as you all know, equipment failures are costly unplanned downtime can all production, can dealer shipments and essentially erode customer trust. Predictive maintenance flips the script. Instead of reacting to breakdowns, companies can actually predict and prevent them using AI driven insights and algorithms. IOT sensors IOT sensor network embedded in machines. Feed, continuous stream of data about the equipment be it the temperature, vibration, pressure, and, and many more attributes. Modern ERP systems aggregate and. Con contextualize this data, making it actionable across the enterprise. On top of that, the machine learning algorithm are able to detect subtle patterns and anomalies early signs of way or failure that human operators might miss. These insights are not just descriptive, they're predictive, anticipating problems even before they occur. With AI generated early warnings, maintenance. Maintenance teams can schedule repair during planned downtime. Order parts spare parts in advance and eliminate costly surprises. This moves organizations from reactive fighting. Two proactive. I think the results are very compelling. Reduced downtime, lower maintenance cost, improved asset longevity. And I have customer satisfaction. This is just one example of a modern ERP As an AI enabled platform backbone delivers real competitive advantage. Okay, let's look at another example. As predictive maintenance showed us how AI and RP transformation operations on the supply side. Now let's look at the demand side. AI driven demand forecasting one of the most impactful applications of AI and supply chain management. Accurate demand forecasting has always been the holy grail for supply chain practitioners and AI is finally making it a reality. Traditional forecasting methods struggle with volatility, limited data inputs, and the need and the need for constant manual adjustments. Ai can overcome these limitations by leveraging massive data sets, self learning algorithms and advanced scenario modeling all enabled by modern EIP foundation. The modern AI algorithm tap into vast data sets for multiple sources be it sales history market trends, social sentiments, weather patterns even IOT data to uncover even patterns that traditional methods cannot detect. Unlike static models that degrade over time. Yeah. Algorithm learn continuously. They are just dynamically as data flows in ensuring forecasting remain accurate without constant human intervention. AI also empowers organization with what if capabilities. It can generate multiple forecast scenarios, helping business plan for uncertainties like demand, surge, supply, disruptions, and market share. The result it's it's reduced stockouts, lower inventory costs fast, faster response to market changes, and a significant competitive advantage edge over others. This is our modern ERP enables business to move from guess from guesswork to precision forecasting fueling resilience and profitability. Okay, let's move to the next slide. Alright, so we saw at AWA Transforms operations and forecasting, let's. Now let's address another critical area where modern E-L-P-N-A AI converge contracts management and risk mitigation. This is the area where natural language processing or NLP. Is making a real impact. Contracts are the backbone of business relationships. We all know that covering everything from supplier agreement to customer agreement, to compliance requirements. But in many enterprises, many companies, contracts are still unstructured, manual and very disconnected from operations and NLP. Changes that by turning contracts into actionable intelligence NLP capabilities can can automatically extract and analyze key details from every contract. Like payment terms, what are my delivery obligations? What are the compliance clause and many more items like that. Essentially eliminating manual reviews and reducing errors. Machine learning algorithms. Can scan thousands of contracts to identify hidden risks, such as unfavorable terms concentration of exposures or patterns that could lead to disputes. This proactive insight allows companies to mitigate risk before it becomes expensive and costly. When integrated with ERP systems, this contract intelligence don't just sit. In the legal side or the legal department, it treats directly into operational decisions. One of the examples that I've seen is a supplier, when they miss a critical delivery obligation, ERP can automatically adjust production plans or trigger alternative sourcing. By inputing, NLP, forward contract intelligence into ERP organizations gain better compliance, reduced risk, and faster smarter decision making. This is another step towards making ERP not just a system of record, but a system of intelligence, right? So let's move to the next one. Okay. Forward looking vision. So we've explored our modern ERP supports, today's AI driven supply chain needs. But what's next? Where is this journey heading? The future of ERP is not just a transactional system. It's becoming the launch path for enterprise wide AI innovation. The shift from traditional back office ERP to AI enabled platform is more than an upgrade. It's a paradigm shift. ERP will be the no center of the no center for AI driven decision making across the enterprise. LA large language ma models. The n the LLMs will enhance nearly every happy process from customer service chat bots that under understand complex inquiries to air driven procurement to autonomous planning. This brings intelligence to every interaction that we know of digital twin. The digital twins of entire supply chains will provide real time virtual models of operations powered by the data foundation, ERP System Delivery. This allows company to simulate and optimize operations before making decision in the real world. ERP systems will increase. Will increasingly annual routine decisions automatically like inventory replenishment your supply selection, your production scheduling which will free human experts to focus on strategy and more on the innovation side of things with edge computing. ERP. Connected systems will process and execute decisions at the point of action while staying synchronized with the core ERP enabling instant distributed intelligence. The evolution of ERP into an AI platform backbone is not a distant intuition. It's happening now. Organizations that embrace the shift will lead in resilience, inefficiency, and innovation. The, probably the question for all of us is are we ready to make ERP the foundation of our AI future? Alright, so let's look at implementation strategies. So we, we've seen the vision of ERP as an AI launch pack. But how do we actually get there? This transformation requires disciplined strategic approach, and that's where the implement implementation strategies come in. Implementing an AI Ready ERP platform isn't just a technology project, it's a, it is a business transformation success depends on aligning data architecture people, and the business value. Everything starts with clean, consistent consistent and governed data. Without a trusted data foundation, AI will fail to deliver accurate insights. First thing, first, establish data quality standards, ownership and governance policy from the outset. Avoid the trap of trying to boil the ocean. Focusing on specific business domains like planning, logistics, or maintenance when made modernization will have the highest impact. This approach delivers quick win while bundling and building momentum for broader transformation. Balance innovation with stability, use cutting edge technologies like microservices and APIs, but ensure they instantly and and seamlessly integrate with proven enterprise grade systems. Last technology won't succeed. Without people invest in skill development have a stakeholder buy-in and be ready for the cultural readiness for AI enabled operations. And finally deliver tangible business value early and of and often identify use cases that generate measurable return on investments. This builds the, this builds the executive support and funds further innovation. Let's move to the next slide. Alright. Now that we have covered implementation strategies, let's look at the architectural foundation that makes modern ERP. Truly adaptive composable architecture with the API first design. This is the shift from rigid monolithic ERP system to flexible, more modular platform that can evolve as business need changes in today's dynamic environment business. Requirements change now very rapidly. Traditional ERP upgrades can keep up. They're too slow, they're too expensive, too costly, and too disruptive. A composable architecture enables companies to build, to evolve and integrate capabilities on demand without being constrained by a single inflexible system. The core principle of the composable ERPR, the modular component, the microservices, a PF first design, even driven architecture. We, which we briefly discussed about this architecture turns into leaving evolving system one that can integrate with future innovations without major overalls. And it's the backbone for AI ready intelligent supply chains. Let's go to the next slide. Alright. As we embrace AI driven ERP platforms, there's another critical dimension we cannot overlook which is the security, the governance, and the compliance. AI brings immense power, but it also introduces new risks that, that demand robust strategies with. AI embedded into ERP. We are not just processing business transactions, we are making autonomous decisions, managing very sensitive data and relying on the machine learning models. This raises the stakes for. Trust, accountability and resilience. And the key focus area here is the data governance. We must ensure data lineage, quality and transparency. This includes managing training. For training data for AI models, detecting bias and making algorithms explainable to stakeholders. Model, the lifecycle management AI models can't be set and forget. We need to, we need structured processes for worsening, for performance monitoring and automated retraining. To keep models accurate and reliable. Then there is, privacy production. Protecting personal and sensitive data is, is absolutely non-negotiable. Techniques like differential privacy and federated learning allow us to preserve privacy while leveraging AI at scale. Last but not the least is the AI security. We must defend against advers attacks data poisoning, model extraction attempt, et cetera. Without robust security, even the most advanced AI can be compromised. And building strong security governance and compliance framework ensures that AI enabled ERP systems are not only innovative, but also trustworthy and resilient. This is how we create platform. That businesses and regulators can rely on. Let's move to the next slide. Alright, so we discussed the technology, architecture, and the governance. But here's the truth. Technology alone doesn't transform business. People and processes do, so to fully leverage AI enabled ERP platforms, we must prepare the organization itself for change. AI enabled ERP is not just a new system, it's a new way of operating. Success requires rethinking processes, upskilling of teams, and evolving leadership. To thrive in this environment we must strategically redefine workflows, integrating human expertise with intelligent automation. And this is not. About replacing people. It's about enhancing collaboration between humans and AI driven systems. That's about the process redesign the skills development. Essentially building the right technical and business competencies is very essential. Team needs to understand data. AI and process automation to fully harness this new capability. Then there's the leadership evolution where leaders play a pivotal role. They must adapt to overseeing hybrid teams, balancing automated decision making with human judgment and fostering a culture of innovation and adaptability. The organizational readiness is. Not a one-time checklist. It's an ongoing commitment to change. Companies that embrace this mindset will lead no while those who resist risk falling behind. That's a very simple fact about organizational read. With technology, governance and people all aligned enterprises can truly unlock the power of ERP as the backbone for AI driven supply chain. Let's bring this all together in our final thoughts. Okay. Going to the next slide. So we explore the technology, the architecture, the governance and the people side of transformation. Now let's bring it all together with the big picture. ERP modernization isn't just a IT project, it's a strategic imperative. The world we operate in is increasingly complex, very fast moving, unpredictable. Traditionally, ERP systems ti cannot keep up. They were built for stability but not for adaptability. Modern ERP infused with AI becomes the foundation of intelligent, responsive, and adaptive enterprise. Organizations that embrace this transformation will gain sustainable advantage that go beyond cost saving. They'll be able to anticipate customer needs rather than just re react to them. Optimize resources across the value. Respond proactively to market shifts and disruptions. The urgency of action is very important. The window to establish leadership in this transformation is narrow. Those who act decisively now will create advantage that competitors will struggle to match. Those who do delay risk being left behind in a marketplace that rewards agility, intelligence, and innovation. The future belongs to the enterprise that make ERP the backbone of AI driven operation. The question isn't if this transformation is coming, it's whether you will lead. The transformation or be forced to catch up, now is the time to modernize, innovate, and take the lead. So with that. Thank you for your time and engagement today. I appreciate the opportunity to share insights on how modern ERP can serve as a backbone for AI three one supply chains. We have covered a lot of ground from the challenges of legacy ERP to platform engineering, to integration excellence, AI powered applications, governance and organizational readiness required to succeed. The key takeaway is clear. ERP modernization isn't optional. It's the, it's a foundation of competitive advantage in the ai. I'd be happy to continue this conversation, explore your specific challenges or discuss practical steps to accelerate your EIP MODERNATION journey. As you go back to your organizations, I encourage you to ask are we ready to make the platform that drive intelligence across our enterprise? Those act now will define the future for their industries. Thank you once again. It's been pleasure speaking with you and I look forward to the exciting future. We can build together. Thank you.