I'm Kaushik Vangarajalapathy. I welcome you to for this talk on AI and machine learning in workflow automation, where enhancing the efficiency and decision making process in the rapidly evolving landscape of business technology, artificial intelligence and machine learning are revolutionizing the workflow automation. These cutting edge technologies are transforming how organizations streamline their process. making the decisions and human intervention on routine tasks. This presentation explores the profound impact of AI and ML on workflow automation dwelling into these technologies, not only automate repetitive tasks, but also enhance the complex decision making process by learning from data and continuously optimizing the workflow. let's see the introduction of the workflow automation with AI and ML. Before that, let's step back and see what is a traditional, workflow automation. historically the workflow automation focused on streamlining the repetitive task through a rule based system. it like be a business process automation, case management, decision management. It's all derived by the rules, a preset of rules. this system follows, predefined parts and request extensive human oversight for exception. Or complex scenarios while effective for basic process traditional automation lack adaptability and intelligence. this is when the AI, ML enhanced the automation flow, where by introducing an adaptive learning and predictive capabilities, these technologies can analyze the vast amount of data, identify the patterns and make intelligent decisions. AI power systems can handle the complex scenarios, learn from the past experience and continuously improve the performance over time. So the key enhancements, what AI ML can bring into the intelligent task management, we can achieve process optimization and predictive analytics to workflow automation. These enhancements enable the system to prioritize tasks, identify the bottlenecks, and improve performance. forecast, potential issues before they occur, the result is more effective, responsive and intelligent workflow ecosystem. So AI decision powered, making an automated workflow start from the data collection process. AI systems gather and clean relevant data from various sources, ensuring high quality of input for decision making process. This step involves handling large volume of structured and unstructured data efficiently. Often this has been challenged because there is noise and outliers. Getting the right data for, for the, preprocessing is more, and the preprocessing is most important task in any machine learning, in, in, in, Next is the pattern recognition and analysis. machine learning algorithms analyze the historical data to identify the pattern trends and correlations. This insights from the basis for predictive models and decision making frameworks. Then the model is being constructed. AI uses analyzed data to create a predictive models. Then, that can forecast outcome and recommended actions. models. These models continuously learn and improve their accuracy over the time. Finally, the automated decision execution based on the predictive models and predefined criteria, AI can make and execute decisions automatically, reducing the need for human intervention in routine processes. So the industry specific applications of AI decision makings are in various industry and some of the industries heavily used. these type of implementations are the finance, healthcare, manufacturing, and the retail businesses. So in the finance, let's see how, these, APower decision making are helping the finance industry, through a fraud detection. it searches for the transaction patterns and, user behavior and historical data to identify the fraudulent activities in real time. machine learning models can adapt to a new fraud tactic and tactics improving the detection rates and reducing false positives. In the healthcare industry, the predictive diagnostics AI power system analyze the patient data, medical history, and current symptoms to assist in early disease detection and treatment planning. These systems can predict potential health risk and recommend preventive measures in the manufacturing. Supply chain optimization, places and places in a benefit, machine learning algorithms optimize supply chain operations by predicting the demand and identifying the potential disruptions and recommending the inventory levels. This leads to reduced cost, improved efficiency, and enhanced customer satisfaction. In retail industry, a personalized customer experience is an important aspect in the competitive market where AI analyzes the customer behavior, purchase history, and preferences to provide a personalized product, recommendations, and targeted marketing campaigns, and even enhancing the engagement and increasing sales. how the NLP for automated communications, and the NLP for automated communications. we can bring into the workflow management, email triage and routing. as in the, any customer support, we receive, thousands and thousands of emails where an LP algorithms can automatically categorize and prioritize the incoming emails based on the content center and agency. This enables efficient routing of messages, appropriate departments or individuals. Reducing the response time and improving the customer service. The next is the sentiment analysis. AI powered sentiment analysis to tools can process customer feedback and social media posts and support tickers to gauge public opinion and customer satisfaction. This information can be used to trigger automatic responses or alert human operators when intervention is needed. The next is the chat bots and restore assistance. Advanced NLP. enables the creation of intelligent chatbots and virtual assistants that can understand and respond to natural language queries. These AI driven tools can handle a routine customer inquiries, schedule appointments, and even assist with the complex problem solving tasks. Okay, let's see how the NLP, driven task management in workflow. So the first is the content analysis NLP algorithm analyze the incoming communications such as email or support tickets to understand the content, intent and urgency of each messages. This step involves techniques like entity organization, topic modeling and intent classification. The next is a task extraction based on the content of analysis. The system identifies and extracts a specific task or action items. For example, it might recognize a request for a meeting, a deadline for a report, or a need for technical support. And then the third step is the prioritization and assignment. The extracted from the extracted task are automatically prioritized based on the urgency, importance, and available resources. The system then assign the task to the most appropriate team members or departments taking into account workload and expertise. Automator follow ups is the last thing where the AI system monitor task, task progress since an automator reminders and escalates the issue when necessary. It can also generate the status reports and update state stakeholders automatically. Ensuring efficient task completion and communication. Okay, so the workflow is built, but, nothing is perfect in the first place, right? And building an adaptive workflow system with reinforcement learning, is what making the automated workflow, even smarter. At the initial stage, the workflow system starts with a basic set of rules and process. Thank you. Because it has a limited knowledge of optimal strategies for different scenarios Next is the exploration phase. The system begins to explore different actions and decisions within the workflow collecting on the outcomes of each choice learning and adaption using the reinforced Learning algorithms the system analyzes the outcomes of its actions identifying which strategies lead to better results And next is the policy optimization. The system refines its decision making process based on learned experiences, continuously improving its performance over time. And finally, the continues improvement. The adaptive workflow system continues to learn and evolve, handling the new scenarios and adapting to changes in the business environment. So some of the real world examples of adaptive workflow systems Or, smart manufacturing, where in the adaptive systems in the manufacturing use reinforcement learning to optimize the production schedules, adjust to equipment failures and predict maintenance needs. This system can autonomously reconfigure production lines to maximize the efficiency and minimize the downtime. The next is the algorithmic trading. Financial institution employ adaptive AI system for high frequency trading. These systems use reinforcement learning to adapt to market condition in real time, optimizing the trading strategies and managing the risk dynamically. The next is the network management. Telecom company uses the adaptive AI to manage the network traffic and resource allocation. These systems learn from usage patterns to predict the demand, prevent congestion. And optimize the network performance autonomously. And next is the energy grid optimization. Smart grid systems employs adaptive AI to balance energy supply and demand. This system learned from the consumption patterns, weather data, and renewable energy. Availability to optimize the power distribution and reduces the waste. Some of the case studies where AI ML driven workflow automation, the success stories, in a banking setter, AI power fraud detection system, reduces 60 percent of the false positives, and, which resulted in save, 50 million annual savings. In the healthcare industry, ML based, Diagnostic support tool improved 30 percent in early disease detection rates. In the manufacturing sector, AI optimized supply chain management, reduced 25 percent of the inventory cost and improved 15 percent of the delivery time. In the technology sector, an LP driven customer support automation, reduced the, support ticket resolution time by 40 percent and, increased the customer satisfaction by 95%. So the future trend in AI ML workflow automation are the integration with the IoT. because IOT generates a lot amount of data and machine learning models requires a lot of data to perform better. The convergence of AI driven workflow automation and the internet of things, devices will create a highly responsive data rich ecosystem. from the smart sensors and all connected devices, will provide a real time data inputs allowing the AI system to make more informed decisions. Thank you And automate physical processes in addition to the digital workflows. Cloud based AI services, the rise of cloud based AI services will, democratize the access to advanced workflow automation tools. Small and medium sized business will be able to leverage the power of AI capabilities without significant infrastructure investments, leading to widespread of adoption across the industries. of course we, there are some ethical consideration and challenges, where the first thing is always the data privacy and security as the AI systems handle increasingly sensitive data, ensuring the robust data privacy and security measures become crucial. Organization must implement a strong encryption access control and compliance with the regulation like a GDPR to protect the personal and business information. And, in many cases there is an algorithmic biases, that is under fitting or over 15 over fitting model, where a system can inver, you to, or amplify the biases present in the training data. It's essential to regularly audit AI models for fairness and implement the techniques to mitigate the biases. ensuring equitable outcomes across diverse user groups. Next is the transparency and explainability. As AI systems make more critical decisions, the need for transparency and explainability grows. Developing the interpretable AI model and providing a clear explanation for automated decisions will be crucial for building the trust and meeting the regulatory requirements. Thank you for joining us. Finally, the work for, workforce impact. The increasing automation of workflow may lead to, job displacement in some sectors, organization and policy. MA makers must address this challenge by focusing on reskilling and upskilling programs, ensuring, that the workflow workforce can adopt a new roles in an AI driven economy. And, thanks for joining this presentation. hope you learn something. and I'll see you next time. Thank you.