Hello, everyone. Welcome to my talk on transforming workflow automation, leveraging AI and machine learning for enhanced efficiency and digital decision making. In this talk, we are going to explore how these technologies are driving efficiency, intelligent decision making and adaptive process across the industry. Let's get into it. The evolution of workflow automation from task to intelligence. we have the traditional automation, and the now AI power automation. So the traditional automation, centered on mechanically automating repeatability tasks through the rigid and script driven process, where, people, used to write Java, NET, the typical software development, characterized by, manual configuration, predefined workflows, and limited flexibilities. This system operate on static rule-based logic with minimal contextual understanding. So once it is set, every time there is, as a change happens or a new, SOP, comes into the picture, somebody have to open the code and then, change the configuration or rewrite the, the workflow, stages, and then how to redeploy release the late, the next version of the software. Which is the type consuming and expensive on the long run. coming to the AI powered automation. so the transcending, basic task execution, AI driven, driven workflows leverage advanced machine learning algorithms to dynamically analyze the complex data set, generate the intelligent insight and, autonomously adopt to evolving operational environments. This sophisticated approach enables continuous learning, predictive optimization and unprecedented operational efficiency. So in, in the, the world of, BPM and C-M-N-D-M-N, the workflow stages are going to be driven by the data, and create the task or create the approvals. based on, based on the data and the, the ever changing, the business processes. So what is the benefit of the AI driven, automation, approach, unlocking the efficiency and insight through, AI and ML? 50 percent of intelligent, task optimization, AI driven workflows dramatically minimize the manual intervention. empowering teams to redirect human creativity to towards the strategic high value organizational objectives. And 90 percent of operative intelligence, advanced machine learning algorithms transform historical data into precise predictive models, enabling organizations to anticipate the train, mitigate the risk, and make data driven decision with unprecedented accuracy. AAML in action, the real world applications, it is across all the, sectors, in finance, advanced AI algorithm detect fraudulent transactions with 95 percent accuracy, proactively safeguarding financial institutions, reducing potential losses by millions annually. The healthcare industry, the machine learning models in the predictive diagnostic, and identify the potential health risk up to 18 months in advance. transforming the healthcare from reactive treatment to proactive prevention. The manufacturing, AI driven predictive maintenance reduces the unexpected equipment failures up to 70 percent minimizing the production downtime and generating substantial cost saving across industry operations. The power of national, language processing, in a couple of use cases here, automated email management, leveraging the advanced NLP algorithms. Organizations can now, automatically parse, prioritize, and respond to the email with unprecedented accuracy, dramatically reducing the administrative overhead and accelerating the communication workflow. in the case of intelligent chatbots, our next generation age and was powered by sophisticated NLP technology can comprehend context, intent and nuances, delivering the personalized real time support that seamlessly mimics human interaction and dramatically enhances the customer engagement. So adapting to the change, the reinforcement learning for dynamic workflow the first step, the reinforcement learning algorithm leverage the sophisticated reward based mechanism to enable the workflow that, autonomously adapt and optimize performance in complex and rapid changing environment. Through the advanced computational models, this intelligence system iteratively learn from each interaction, progressively refining the decisions, making strategies, and minimizing, performance errors. In critical domains like supply chain management, reinforcement learning can dynamically recalibrate routing, inventory, and logistic protocols, delivering a significant operational efficiencies and cost reductions. The future of workflow automation, is, starts with, the bottom level, with the robotics where advanced robotic system that, intelligently collaborates with the human teams. Thank you Automating complex, physical tasks with the precision, adaptability, and expanding the boundaries of human productivity. As an next upper layer, cloud computing, where the elastic, globally distributed infrastructure provides scalable computational power, enabling seamless integration of advanced technologies and support complex intelligent workflow architecture. Going higher, IOT, hyper connected sensor network, that captures real time granular data across physical and digital environment, providing rich insights for predictive and proactive strategic planning. And at the top, AIML transformative intelligence that, dynamically learns, predicts, and autonomously optimizes the workflows. enabling unprecedented adaptive decision making and continuous organizational evolution. The actionable strategies for implementing AIML in the workflow. The first step is a strategic process assessment, conduct a comprehensive audit to pinpoint the specific workflow bottlenecks and opportunities where AIML can deliver transformative operational improvements. and the next is the robust, data infrastructure, develop a strategic data management, framework that ensures high quality, clean and diverse datasets necessary for training, accurate and reliable AI models. And as a next step, iterative implementation approach, deploy the targeted pilot project with a clear metrics, enabling controlled experiments, learning, progressive scaling of AI model solution. across organizational functions. So the benefits of AI driven workflow automation are enhanced efficiency, where, streamline the operation, reduce the manual effort and free up valuable resources and, the improved decision making, leverage the data driven insight to make more informal and strategic decisions. Increased agility, adapt quickly to changing the conditions and market demand. improving the responsiveness and the competitive advantage on gain a competitive advantage by optimizing the process and deliver exceptional customer experiences. Reimagining the work on the powerful synergy of human creativity and AI intelligence. A ML driven workflow automation represents a transformative partnership where intelligent technologies amplify human potential. By automating the routine task and providing advanced insights AIM powers professionals To elevate their work enabling deeper, strategic thinking Unleashing creative problem solving and fostering more meaningful customer connections that derive genuine innovation So the key takeaways and next steps are from this talk are AIML power workflow automation is transforming the way we work offering the unprecedented opportunities for efficiency, intelligence, and agility. Embrace this advancement, explore the potential of AI, and prepare your organization for a future where human and AI collaboration derives, rise to innovation and success. Thank you for joining my talk. See you next time.