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

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

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

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

Copilot Studio inline Base64-encoded images Context: Displaying images in conversational AI interfaces like Microsoft Copilot Studio can be challenging due to system sanitization of HTML tags. While rich media enhances user experience, embedding images directly into messages often fails due to platform restrictions. Developers and makers need a reliable workaround to present visual content dynamically within chat flows. Solution: A clever method to display images in Copilot Studio is by using inline Base64-encoded images via the data URI scheme . This approach involves encoding the image into Base64 format, embedding it within an tag, and assigning the tag to a variable. Instead of placing the tag directly in the Send message node (which gets sanitized), you store it in a variable as shown in the image. Then, assign this variable to the Send message node. This technique allows any image—charts, icons, screenshots—to be displayed as long as it’s Base64-encoded. Impact: This method unlocks a new level of visual interactivity in Copilot Studio, enabling makers to enrich conversations with dynamic imagery. Whether it's showing a product preview, a data visualization, or a user guide, this approach enhances clarity and engagement. It’s a simple yet powerful trick that expands the creative possibilities of Copilot Studio without compromising platform integrity Previous Item Next Item

AI generated multi-step Power Pages form with Copilot Context: Rapid development of Power Pages is essential for businesses needing dynamic, user-friendly web applications. Creating multi-step forms can be complex and time-consuming, often requiring significant coding expertise and resources. Solution: Using Copilot for rapid Power Pages development simplifies the creation of multi-step forms. Copilot's AI-driven capabilities streamline the process, reducing the need for extensive coding and allowing for quicker, more accurate form creation. This enhances productivity, reduces costs, and improves the overall quality of web applications. Impact: The complexity of developing multi-step forms can lead to delays, increased costs, and potential errors. This can hinder the overall efficiency and effectiveness of web applications, impacting user experience and business operations. Previous Item Next Item

Copilot integration with To Do and Planner Context: Managing tasks across multiple platforms like Microsoft To Do and Planner can be challenging, especially when trying to prioritize them by due dates. Solution: Copilot integration with Microsoft To Do and Planner, fetches all tasks and automatically sorts them in ascending order of their due dates. This ensures a clear, organized and hyper personalized view of upcoming responsibilities. Impact: By providing a prioritized list of tasks, this tool helps everyone focus on what needs to be done first, enhancing productivity and reducing the risk of missing deadlines. It streamlines task management, making it easier to stay on top of workload. Previous Item Next Item

Feedstock Sourcing Intelligence: AI‑Driven Cost & Carbon‑Aware Procurement Context Sustainable aviation fuel (SAF) producers depend on the right feedstock mix—balancing cost, carbon intensity, availability, certification validity, and overall compliance. Feedstock decisions directly influence CI scores, production economics, and regulatory eligibility. Feedstock Sourcing Intelligence introduces an AI‑first sourcing advisor that evaluates suppliers using structured cost and carbon profiles. It reviews available options and recommends the most efficient choice, adding an intelligence layer without requiring external data. Challenges Procurement teams often manage supplier data manually—spread across spreadsheets, emails, and disconnected systems. Understanding cost differences, carbon profiles, certification status, and sustainability risks becomes time‑consuming and error‑prone. Sustainability teams must verify attributes and certifications, while compliance teams check validity dates and evidence completeness. Finance and executives lack real‑time visibility into trade‑offs and sourcing rationale. Without a unified model or automated evaluation, selecting the optimal feedstock mix becomes inconsistent and slow. Solution Feedstock Sourcing Intelligence establishes a structured, AI‑ready sourcing workflow using Planner Designer. It begins by capturing user requirements across procurement, sustainability, compliance, finance, and executives, then generates a detailed blueprint of processes, data models, and technology components. Supplier data, feedstock costs, carbon values, certifications, expiration dates, and decision logs are stored in a unified model. A model‑driven Sourcing Console enables comparisons, while the Sourcing Decision Copilot becomes the intelligence centerpiece—evaluating all feedstock options, recommending the optimal choice, explaining trade‑offs, highlighting risks, and retrieving evidence or certificate status instantly. Automated alerts flag expired certificates, and dashboards summarize sourcing rationale for review. The result is a transparent, consistent, and AI‑assisted decision system that improves procurement confidence and sustainability performance. Previous Item Next Item

Reinventing Business Workflows with Dynamics 365 and Power Platform Sidecar Apps Context The modernization initiative focuses on moving away from a heavily customized SAP ECC system toward a clean‑core model using Dynamics 365 Finance and Power Platform applications. Most business processes have already shifted to the modern platform, reducing risk and eliminating parallel systems as SAP ECC is retired. A core principle guides this approach: no ERP customizations, neither in the old system nor the new one. Challenges Legacy SAP ECC contained many embedded customizations,such as pipeline inspection tools and coded procurement workflows—making changes slow, costly, and dependent on ABAP development cycles. Traditional SAP workflows were rigid, not mobile-friendly, and difficult to adapt. Any future ERP migration would force a complete rebuild of custom logic. There was also a need for cloud-based approvals, field-ready apps, and AI-assisted audit support without reintroducing ERP technical debt. Solution A clean‑core, sidecar architecture was adopted, rebuilding all custom workflows outside the ERP using Power Apps, Power Automate, and Copilot Studio , while Dynamics 365 Finance becomes the financial system of record. Custom SAP tools were recreated as independent cloud apps, procurement approvals moved to Power Automate with Teams-based interaction, and a digital‑twin strategy ensured workflows remain reusable across any future ERP system. Supervisors gained unified tracking surfaces, operators received mobile‑friendly apps, and an AI agent now supports audit and workflow insights. This modern approach cuts technical debt, accelerates change, and enables long-term upgrade flexibility. Previous Item Next Item