Hi everyone. Thanks for joining this conference, 42 Mission Learning Event 2025. I'm excited today to share this mission learning for the advanced document management systems. In this session we will see how we can leverage this mission learning for the advanced document management system, revolutionizing the efficiency, compiles and automation in today's ATE business environment. Organization face increasing challenges in managing the. Vast amounts of document data and whether it's invoices, contract, the insurance claims, or the regulatory documents efficiently. Extracting and analyzing this data is very crucial for operational efficiency and compliance. And Microsoft Machine Learning offers a powerful platform to address these challenges by integrating AI and machine learning technologies with the document management systems, enabling the automation and data driven insights, and streamline the workflows across the industries, including the life sciences, the healthcare insurance, and general purpose operations. And my name is re I'm a Microsoft certified professional, a technology specialist, and also play like a Microsoft 365 Cloud architect. And I play a technical manager. So for the various projects which I work on through my 17 years of IT industry experience coming to this, machine learning. For the document management system, we will see the various extraction methodologies or the models and how we can leverage this machine learning for our document system into the various industries. When coming to this topic, automated data extraction on the classification, we see like how it reduce the manual effort, the cost reduction, and the intelligent class. Applications when it comes to the manual effort, the Azure Machine Learning automates the data extraction from the various document types. It's significantly reducing the manual data entry and the associated human errors. When it comes to the cost reduction, the company is using this AI powered document processing. See up to 30 to 50% reduction in operational costs related to data entries, operational costs related to data entry task, and the intelligent classification. Intelligent classification is like a machine learning. Azure Mission Learning classifies model classifies and tag the documents based on the content, categorization them into the relevant groups of. Relevant groups for efficient Val Azure AI document AI document intelligence, which is formally form, like form recognition. It extracts all the. Structure data such as the dates, amount, the names, and the product details, and the ID card details from the various documents like the invoices, contracts, and regulatory forms, and streamlining the inform and streamlining the information management process. When it comes to the when it comes to the search capabilities, when it comes to the search capabilities that in the document management system, such capabilities plays a critical role because, like for the business users way, they search for the particular document in the traditional approach, they use the keyboard to search for the document and where, how we are enhancing the security. Like how about this, how to differentiate between the general documents and the. Sensitive document and for showing for one department, the document should not be shown and one for document for one department it has to be shown like the for HR document cannot be shown to the employees and the employees. Documents to business documents cannot be shown to the customers. So in this way, like how we enhance this capital, this machine learning, we will see how this machine learning enhances the such capabilities when it comes to the traditional approach, semantic analysis and improved results in the traditional approach is nothing but the keyboard based methods often fall with the short, ambiguous terms. When it comes next to the semantic analysis, Azure artificial intelligence analyzes the context and the intent behind the. Queries improved results. Improved results. This results it re, it returns the relevant documents even with the in incomplete search tab. Azure ai, Azure Artificial Intelligence search. Enhance enhances the document tool by understanding the meeting behind the search queries rather than just the keyboards. What this is particularly valuable for the large document repositories where the traditional search models often fall in written more relevant results. And coming to the, one of the key area for the mission learning for document management system is like automating document workflows, how we can automate the document workflows from the manual approach to the traditional approach to the advanced workflow systems. When the whenever the business upload the document, either into the Microsoft SharePoint, there's a repository or to the blog storage repository. What, and when the document is uploaded, what is artificial intelligence? What it'll do, it'll analyze this. The help of this machine learning, Azure machine learning. It analyzes, classifies and extracts the key data of the document and it transforms into the workflow trigger at a workflow trigger. Here the Microsoft uses the advanced workflow systems like a Microsoft Power Automate, and Azure Logical Apps undo Azure function apps, which are built on top of this Microsoft Cloud. So the with workflow trigger system indicates the notifications, updates or the approval process parallel up towards it has all three facilities to connect with the external connectors, like a business to business B2B integrations, and to perform these automated documents workflows and transforming to the next completion status. The process it process the completes with a minimal human interventions. We use something like here, Azure Azure Logical Apps and Azure functions enable the automation of the document workflows significantly reducing the manual intervention and accelerating the decision making. According to the recent, according to the recent data, automating the workflows can lead up to the 40% of the operational efficiency. This is this, document workflow place, automating the document workflows in this IT industry for the various of the domains like insurance, in insurance, the life senses, health, pharma, and retail. Like these are all the IT domain, which are heavily dependent on the document management system and the enterprise content management system and the metadata. Is very crucial here. And it also, the classifications and between the sensitive data and the non-sensitive data and the record retentions, and these are all, that plays a very critical role, even with from one process when it, when the workflow processing from one department, the another department, and this is how the Azure machine learning helps helps the business. Help the business users through this document management system process. And when it comes to this compliance and security monitoring, this compliance and security monitoring is one of, is also one of the three key critical area for the. Azure Machine learning, the document management system, the compliance and security monitoring, how we do this, like document analysis compliance verification, issue detection, and automated deduction. Automated reduction is nothing but a hiding or removal of the certain certain information. The document analysis, machine learning models scan for regulatory compliance. And compiles verification. Final check again, the industry regulations issue detection, system, flag sensitive, and non-complaint information. Automated reduction. LNLP models. Identify and redact P 11 three and compile is a major concern for the your IT industries, like the health industries healthcare, finances and the life. S Azure Mission Learning helps address these challenges by analyzing the documents for compiling the industry regulations such as like hipaa. Ft federal regulations requirements. Research shows that automated compliance monitoring, using artificial intelligence can reduce the likelihood of regulatory violations by up to 70% of mitigating the risk of costly data breaches and penalities. When it comes to the industry specific applications that when it comes to industry specific applications, like the insurance and the healthcare and the life. S automated claims auto automates the claims processing and the fraud detection by analyzing the data, patents and the historical claims. By reducing by reducing the processing time, by reducing the processing time, and enhancing the customer satisfaction. And when it comes to the healthcare area, it processes. It processes the patient, record patient records information while ensuring the HIP compiles, improvising the care coordination, improvising the care coordination, and reducing the administrative burden. The life sensors. When it comes to the life sensors it automates the quality management system processing, ensuring the ensuring the SOP based on audit reports com compiling with the regulatory standards while tracking the document revisions. Azure Machine Learning provides a tailored solutions for different industries addressing their unique document management challenges. By evaluating the documents quickly and efficiently, organizations can also streamline the operations while maintaining the compiles with the industry specific regulations. This is all about the industry specific applications when it comes to the data analytics and visualization. It's not about always the how we how the rate raw data in the document management system, and it is helping the business. It's also in the modern world of this one, the data can be converted through the data analytics and the visualization for the base of easy understanding, the easy calculation, and for the decision making. When it comes to the interactive interactive dashboards, the trend analysis and compile tracking, and when it comes to the interactive dashboard, the custom power BI dashboards visualizes extracted document data, and it's enabling the stakeholders to track the key metrics at a glance. When it comes to the trend analysis, the visual representation of a document, processing the trends helps the identifying of the patents. And the opportunities improvement, compliance tracking. Compliance tracking is all about the real time Visualization of compliance metrics enables the proactive management of regulatory requirements. Azure's integration with the inter Azure's integration with the tools like the Power ba transforms the extracted doctor in into the actionable insights in quality management systems. Users can visualize. Audit findings like audit findings we can say recertifications of the systems under the risk assessment, under the document status, whether it's in like draft status, approved status approval, pending up rejection status, and enabling the informed, the decision making based on that real time data. Scalability and security. In this scalability and security measures, you will see how the enterprise grade security happened, the cloud native infrastructure, how the native infrastructure is happening, and how the role-based access controls is helping under data encryption, enterprise grade security, comprehensive protection for the comprehensive protection for sensitive documents. It just like how we are making the documents like the. Once the document is once the document is uploaded and it, when it passed, the certain stages of the approval passes, that the document has to get log his state by making the read through the record management of the. Part of the document management system, how the cloud native infrastructure, not only always with the security of these external tools and how the infrastructure also backed by backed by this system and how it is helping this document management system. It is through this back by the Azure Kubernetes sub service you can also call at is a Kubernetes as a K eight service. And the role-based access controls in the real world. In the real world particularly in the documents document management system. The role-based access controls are very very crucial between the departments, between the users, from the, for example, for the insurance documents. It can be from the policies, the claims. The re policies casualities department, personal property, and various kinds of various kinds of insurance things between the department. The document should not be shown how we choose this. It is achieved through the rule-based access control. It means adding granular permission management, like a unique permission to only see the document, who are eligible to see the. Document not the other not the other business customers or the customers or any department. Data encryption. Data encryption is crucial. Like it's, it has to be like end-to-end production. End-to-end production. It's not to be the, it's not to be exposed to the third one, right? So end-to-end production, but it's like a one-to-one kind of thing. And mission learning provides Azure robust security features, ensuring. That sensitive information remains protected. As the organizations scale their. Scaled. The operations assured machine learning infrastructure ensures that mission learning models can be deployed at a deployed at a scale of the real time in inference without compromis without compromising the performance or the security when it comes to this. Implementation benefits. We should also see that when, whenever free or implementing a system, whenever we are utilizing some, technology, the models, the artificial intelligence, whether it can be a cloud or enterprise or talk, whatever the system, we need to see whether it is a, whether that the cost reduction is there or not. Whether we are gaining the efficiency gain is there or not. Whether it is part of the. Compiles is improving or not? Here when it comes to the implementation benefit, there is a 50% of cost reduction, decrease in the operational cost related to the document processing and reducing the paper thing, reducing the paper thing, reducing the manual thing, reducing increasing the efficiency. And it's like efficiency. It's like a 40% of efficiency gain. In improvement in improvement in the operation, implement in the operational efficiencies and through this workflow automation. So workflow automations plays a key role in eliminating the manual process and the manual approaches, and 70% of the compiles improvement is not only about the automation or the cost reduction. It also improves the compiles improvement by, in reducing the likelihood of this regulatory violations. Organizations implementing the machine learning for the document management see a significant measurable benefits. These improvements translate to better resourcing allocation, allowing the teams to focus on strategy task instead of manually administrating work while maintaining a high compelling standard. Coming to this coming to this roadmap. Coming to this roadmap when it comes to the Azure machine learning models or methodology. So we will see how the RA roadmap will be there. The one is like the assessment and planning. It'll, any implementation roadmap, whether it's machine learning, whether it's artificial intelligence, or it's the cloud or the standard waterfall or any process, any project we start, it goes with the plan. It's like assessment and planning model development and training, integration and deployment and optimization under the expansion. So when it comes to the first one, the assessment and planning. So what it does like in an assessment and planning, we evaluate the current document process, identify the pain points, and define specific objective goals for the mission learning implementation. Determine which document types and the number of the workflow, the type of the workflows will benefit from the automation. It can be workflows when I mentioned like the type of the workflow, whether it's automated or it's a diamond or it should based or it's trigger based on other action. Or it can be like a business B2B workflows, or it can be like a customized workflows or connecting workflows triggered based on another workflow. So this kind of the automation benefit this kind of workflows which will benefit for most of the automation from the most, from the automation. When it comes to the model development and training. So in this model development and training this is the next sta this is next stage to the assessment and the planning. In this model development, we we start with what we gather the, and what we plan in the first to phase of the assessment and planning. In this model, we completely concentrate on developing and training Azure methodology models. Specific to your document types and the business requirements here. So here we don't do, again analysis. We will just do an improvement in case of any assessment according to the development. But all the assessment will happen at the fastest stage, and the planning will happen at the fastest stage. So in this development we just train the Azure methodology model specific to the document types and the business requirement. This includes the data extraction models. And the classification systems and the compiling verification tools. And next comes to the, once the development, once assessment and the planning is completed, and we once we complete the development and training, then what? Then it's like the integration and we have to deploy some where what we develop, right? So the develop integration deployment, integrate the Azure machine learning solution with the existing document management systems and the workflows that has been developed in the. Second stage of the model development and training, deploy those models to the production environments or from the lower environments to the upper environments with appropriate monitoring and the security controls. Once this deployment is completed, we will go to the next process of the step like optimization and how we can expand this process. Like that, what we have done from the assessment and planning and building the development and after the integration and develop deployment. So we have to see where we can expand this kind of solutions into the other system. Like continuously optimization. What is an optimization? It's like continuously improvise the model accuracy and expand implementation to additional document types and business processes based on measured results. Conclusion. Conclusion and revolutionizing the document management. It is enhanced operational in revolutionizing the document management. Here in this section, you'll see how the enhanced operational efficiency improved compile, and we discuss and data driven insights and superior customer experience. The customer experience is the most important thing for all the work we have done through this mission. Learning for the document management system. So for anything the customer experience is the most top priority in the project. So enhanced operational efficiency in the significant improvements in the processing time and the cost savings through the AI powered automation improved compiles. How the better the adherence to regulatory requirements with the automated monitoring and the verification data driven insight, transformation of a document data into actionable. Business intelligence, superior customer experience, the faster response time, faster response times, and more accurate information delivery. So integrating we can achieve all these things by integrating the machine learning, integrating the machine learning with with the document management system. The document management system which offers un. Precedent opportunities to optimize the document processing to ensure, compile and improve data accessibility and automated workflows in the real world. Results in the real world. Results speak for themselves with the business, seeing a significant improvements across all the operational metrics. And we'll coming to the final section of this section, and we have seen how. How the mission learning for the document management is revolutionizing the complexities and helping the business, and helping the business insurance, the life sensors. And here we'll also see in the real time how it is getting benefited to the industries. We will see we'll see. We'll take an example for the insurance life sensors and how the AI and Microsoft for the document management is helping because the Share Pan Micro Microsoft Share Pan is the leading software for the document management system in the IT industry. And we will see how the machine learning impact on the real business. So the insurance, the machine learning significantly transforming the insurance industry with the integration of the yay and the pro. Under the process of this claims management has become much faster and efficient. The AA algorithms are now capable of analyzing the historical claims data, identifying the patents that can be used to predict what we can predict the fraud, or assess the risk levels. In addition to this, the claims processing time is reduced as yay. Automate, such as the administrative work, providing the faster claim resolutions. Mission learning also improves the customer satisfaction by automating the responses and offering the personalized recommendations here in this life sciences. When it comes to the life sciences, when to the real business, in this life sciences sector, the AI and the mission learnings are used to automate the quality management systems. It's called the, in the life s we mostly called it as the terms, like the QMS, the IR information research, and the QMS is like a quality management systems and in the commercial systems and the patient care. And these are all the things which we use during this life sensors like standard operational procedures, which are called the S four piece and the call pass. Like a corrective actions and the pre preventive actions, ensuring the compiles and the regulatory standards by tracking the document revisions, auditing the reports, and ensuring that standard operation, standard operating. Procedures, SOPs are followed. A artificial intelligence ensures that all processing, all processes meet the industry standards. This this particularly important in industries where the compiles is critical, such as the FTA or other regulatory authorities. Additionally, the data analytics, the data nalytics, powered by this mission learning. It helps the researchers. It helps the researchers are who are performing the. CTM is like a clinical trials management systems, and it makes them more discoveries quickly improving the overall research and development. Yaya in share, yay in SharePoint document management system. In the case of the, in the case of the Microsoft SharePoint. Machine learning has improved the document management systems by automating the document classification and the analysis using Azure Machine Learning. The documents are categorized, the documents are categorized. Into the documents are carrying into the relevant groups based on their content, which improves the efficiency of the document ritual. Furthermore, the workflow automation powered by a can trigger the actions such as the notifications, updates or scheduling the documents for the migrations. Archival, archival, this reducing the manual effort. Required for the compiles and sensitive industries like the healthcare or the insurance. The A models scan for regulatory compiling, ensuring that all documents adhere to necessary industry standards. And this is how, and this is how we all discuss about the mission learning for the document management systems. We have covered what are the automated solutions for the workflows we have covered for the compliance and security. We have covered the agent models. We have covered about how the, it is impacting the real business. And we have covered about the process flow of the. Mission learning in the document management systems. And thank you very much. Thank you very much. And thank you very much for patience for joining the live event of the conference 42 mission learning event. And thanks and thanks for the CONFI 42 team, for providing me an opportunity to showcase to showcase and present the deep drive analysis. And deep drive investigation things through the mission learning for the document management systems. Hope you enjoyed this event. And one second. Thanks for joining this event.