Copilot in Process Mining ingestion Context: Process mining is crucial for businesses to understand, analyze, and improve their operational workflows. However, one of the most significant challenges in process mining is the ingestion of data from various sources into a format that can be analyzed. This step often involves manual configuration, which is time-consuming, error-prone, and requires deep technical knowledge, slowing down the process of gaining insights from business processes. Solution: MS Copilot in Process Mining Ingestion revolutionizes this step by providing an intuitive, guided experience. It helps users identify and define the process they are analyzing during data ingestion, automatically mapping the data to the necessary schema. This tool leverages AI to understand the context of the data, reducing the need for manual mapping and configuration. It's designed for users of all technical levels, ensuring that even those without extensive data handling experience can quickly prepare their data for analysis. Impact: The introduction of MS Copilot in Process Mining Ingestion can significantly accelerate process analysis by simplifying and speeding up the data preparation phase. This leads to quicker insights into process inefficiencies, thereby enabling faster decision-making and process optimization. It reduces the risk of human error in data mapping, ensuring more accurate analyses. Ultimately, this tool not only enhances productivity but also democratizes process mining, making it accessible to a broader range of business users, which can lead to more widespread adoption and continuous improvement in organizational processes. Previous Item Next Item

Electrolyzer Scheduling Advisor: AI‑Optimized Hydrogen Production Context Hydrogen production requires balancing renewable energy availability, energy prices, and equipment constraints. Static schedules cannot adapt to fluctuating inputs. The Electrolyzer Scheduling Advisor uses an AI‑first design,built through Microsoft Planner Designer to embed intelligence directly into planning, evaluating forecasts and constraints to recommend when to run, pause, or adjust electrolyzer operations. Challenges Planners often work with scattered data, making it difficult to generate optimal schedules or compare alternatives. Manual adjustments lead to inefficiencies, such as running equipment during high‑cost periods or underutilizing renewable energy. Operations teams lack clear guidance, and supervisors struggle to understand trade‑offs behind scheduling decisions. Solution The advisor defines production requirements, roles, and processes in Planner Designer, producing a structured blueprint for AI‑driven scheduling. Copilot analyzes renewable forecasts, energy costs, and equipment health to generate optimized schedules, complete with rationale and scenario comparisons. Teams use the Operator Schedule Companion app to view tasks, recommendations, feedback, and status updates in one place. Automated approvals and continuous monitoring ensure plans are validated and improved over time, creating an adaptive and transparent hydrogen production workflow. Previous Item Next Item

Automated System Monitoring and ServiceNow Ticket Creation Context: Manual monitoring of system parameters is inefficient and may lead to delayed responses during system performance issues. Also, when the values cross threshold, there has to be immediate response and tickets need to be raised and allocated in timely fashion. Manual intervention may take time and prove to be costly. Solution: Using Power Automate, the bot periodically checks CPU utilization through Azure Monitor or custom scripts. If thresholds are exceeded, it automatically creates an incident ticket in ServiceNow using API integration. Impact: Implementing this automated monitoring and incident management system significantly enhances operational efficiency and responsiveness. By continuously checking CPU utilization and automatically creating incident tickets when thresholds are exceeded, it ensures timely identification and resolution of potential issues. This reduces downtime, minimizes manual intervention, and improves overall system reliability. Additionally, it allows IT teams to focus on more strategic tasks, ultimately boosting productivity and maintaining optimal performance. Previous Item Next Item

GitHub Copilot for Power Pages Context: While Github Copilot has various features for different technology stacks for code analysis and code completion, an extension was needed for Power Pages custom code. Solution: The GitHub Copilot Chat integration is achieved by creating chat extensions that use the GitHub Copilot Chat extension API, adding a Chat participant to the VS Code environment. This feature allows @powerpages to be a participant in the Power Platform Tools VS Code extension. Impact: Power Platfrom Engineers can now utilize the Power Pages AI Code capabilities by adding the @powerpages participant within GitHub Copilot chat without leaving their current work context. This integration ensures that users can make the most out of both Copilot capabilities. Previous Item Next Item

AI‑Powered Predictive Maintenance Simulator Context The Predictive Maintenance Simulator transforms how operators interpret equipment health by letting them enter key indicators—temperature, vibration, differential pressure—while Copilot automatically classifies asset conditions as normal, warning, or critical. It also provides clear AI‑generated explanations so supervisors can spot recurring issues early. Challenges Equipment health checks often rely on manual judgment, inconsistent interpretation, and delayed escalation. Operators must navigate scattered data, supervisors struggle to connect repeated symptoms, and reliability teams lack unified insights to identify degradation trends. Safety coordination becomes harder without visibility into permit conflicts and risk triggers. Solution Using an AI‑first design, the system leverages Planner to generate the complete blueprint,users, processes, data tables, and apps. Console operators enter indicators, Copilot classifies conditions with reasoning, supervisors review recurring issues, reliability engineers run trend analysis, and safety teams monitor permit‑to‑work conflicts. With structured data, automated health insights, and role‑based apps, the platform delivers faster decisions, early detection, and safer operations. Previous Item Next Item

Well-Architected Performance Efficiency Checklist and Tradeoffs Context: Without a customizable checklist for ensuring performance efficiency, enterprises may struggle to meet workload demands effectively. This can lead to suboptimal resource utilization, increased operational costs, and potential delays in project delivery. The lack of standardized performance metrics can result in inconsistent outcomes and reduced overall productivity. Ultimately, the absence of such a checklist can hinder the organization's ability to maintain high performance standards and achieve business goals. Solution: The customizable Performance Efficiency checklist, adopted from Well-Architected Framework, presents a set of recommendations to design workload so it can grow and meet workload usage demand. The goal of performance is to maintain the efficiency of every interaction with a healthy system as demand increases. When we design and implement for performance, we focus on the efficiency and effectiveness of cost, complexity, supporting new requirements, technical debt, reporting, and toil. Impact: For every system, there's a limit to how much we can scale it without redesigning, introducing a workaround, or incorporating human involvement. However, if we don't include performance efficiency practices and consider the tradeoffs, our design is potentially at risk. This Checklist helps carefully consider all the points to instill confidence in system's successful performance. Previous Item Next Item

Session Management & HTTP Context: Apps cannot communicate as Sessions and HTTP lifecycle are generally unmanaged Solution: Power Automate can be utilized as to manage session as well as HTTP request-response lifecycle - including Request and Responses (both from and to) Clients and Servers. Impact: All applications can be connected through SSO as well as can hold session context while running complex enterprise-wide automations. This has the power to unleash the multiple permutation combination of applications, working in tandem with each other, just like microservices working across any enterprise system. Industries: Financial Services, Healthcare, Manufacturing, Retail, Media & Communications, Education, Other Functions: IT Offerings: AI Builder & Automations, Web & Mobile Applications Previous Item Next Item

Power Pages AI Form Fill Assistance Context: Entering forms is a cumborsome task for end users and the users have to usually enter multiple fields again and again. The users usually have to match the information with an existing document and the manual process may be error prone. Such errors, may cause delays or even non-completion of tasks and lack of approvals in time. Solution: Microsoft Power Pages features of AI Form Fill Assistance can be configured and customized to extract key information from attachments. Users can attach a file and the AI assistance auto fills the fields by extracting relevant information from the attachments. Users can attach documents (PDFs) and images (JPEG, PNG). Users can always edit the auto filled fields if needed. Impact: Auto fill forms from attachments in Power Pages streamline data entry by automatically extracting and populating information from uploaded documents. This feature enhances efficiency, reduces manual errors, and saves time for users. It also ensures consistency in data entry, improving overall data quality. Additionally, it simplifies the user experience, making form completion faster and more intuitive. Previous Item Next Item

Outlook MeetBuddy – Smart Meeting Scheduling Copilot Agent Context Scheduling meetings often means juggling calendars, sending multiple emails, and manually checking availability. This creates delays, confusion, and wasted time for busy teams. Solution Outlook MeetBuddy is an intelligent Copilot Agent built using Microsoft Copilot Studio , Power Automate , and Outlook . It simplifies meeting scheduling through a conversational interface,no extra clicks, no switching tabs. Core Features: Real-time calendar availability check in Outlook. Instant meeting booking with details like subject, description, attendees, and location. Conversational experience powered by Copilot Studio for ease of use. Secure automation via Power Automate flows for event creation. Impact Outlook MeetBuddy eliminates email ping-pong and manual lookups, making scheduling effortless. Employees can check availability and book meetings in seconds, keeping calendars organized and boosting productivity. Built on Microsoft’s low-code ecosystem, it’s secure, scalable, and easy to deploy. Previous Item Next Item

AI Builder Prebuilt Models Context: For any company or organization, it is necessary to have a customer support model or system to analyze and review customer feedback for better enhancements in the products they deliver and manage. This process enables continuous improvement of products and services, fostering customer satisfaction and loyalty. Solution: Using Microsoft Power Platform's AI Builder and AI Hub, an AI-powered customer support model app was developed on Power Apps. This solution leverages advanced AI capabilities to analyze user and customer feedback for better understanding and decision-making. The app helps organizations identify significant feedback and route it to the appropriate departments for action. Features: AI-driven feedback analysis and categorization Automatic routing of feedback to relevant departments Integration with existing workflows and systems Customizable web and mobile applications Impact: This AI customer support model has significantly improved how organizations handle customer feedback. By addressing key pain points and understanding customer needs, it has led to enhanced customer satisfaction and engagement. Organizations can now resolve issues promptly and make informed decisions for continuous improvement. Previous Item Next Item

Leave Management AI Agent Context: Managing leave can be a bureaucratic nightmare for both employees and HR, with paperwork, manual tracking, and policy confusion often leading to delays, errors, or policy violations. Solution: This AI agent automates the leave process, allowing employees to submit requests, check leave balances, and review company policies with ease, all within familiar platforms like Microsoft Teams or your HR portal, ensuring accuracy and compliance. Impact: It reduces administrative workload, minimizes miscommunication, and enhances employee satisfaction by making leave management straightforward, transparent, and efficient, leading to better work-life balance and HR resource allocation. Previous Item Next Item

AI Sentiment Analysis Context: In various internal and external interactions, the organizations need to understand customer, user, partner sentiments to prioritize focus (and that too in the nick of time). Not understanding sentiments in time has been one of the measure causes of business debacles over years. Solution: Sentiment Analysis AI Model in Power Platform can identify whether the sentiment is good, bad or ugly along with confidence score, that too across many languages such as English, French, Italian, German, Spanish etc. Impact: Such AI Models can be put in a flow to continuously monitor various channels, such as customer support chats, emails and other social media - and help alert in case the sentiments are going to extremes Industries: Financial Services, Healthcare, Manufacturing, Retail, Media & Communications, Education, Others Functions: Marketing, Customer Service Offerings: AI Builder & Automations Demo Link: https://youtu.be/09Qx-klGIRE Previous Item Next Item