Getting Started with Copilot Studio: Your PAA & FAQ Guide
What is Microsoft Copilot Studio? Microsoft Copilot Studio is a low-code, graphical tool within the Power Platform used for building and conversational bots. It empowers users, even those without extensive technical backgrounds, to create sophisticated logic and connect to various data sources and services using prebuilt or custom plugins. Is Copilot Studio easy to use for beginners? Yes Copilot Studio is designed to be easy for beginners. You only need to describe the agent you want in plain language to start creating it. The platform uses a graphical, low-code interface that streamlines the process of defining instructions, knowledge sources (like documents), and conversation triggers, making it accessible to most users. What is the difference between Microsoft 365 Copilot and Copilot Studio? Microsoft 365 Copilot is an AI assistant that integrates across Microsoft 365 apps (Word, Excel, Teams, etc.) to enhance productivity. Copilot Studio, conversely, is a development platform used to build customised AI agents that are tailored to specific business goals or data sources. Copilot is the agent you use; Copilot Studio is the tool you use to build or extend agents. Do end-users need a specific license to use a Copilot I create? Yes, licensing for end-users depends on how and where the custom copilot is deployed. While development often requires a Power Platform or Azure subscription deploying the bot across an organization may require specific Copilot licenses for the end-users accessing the agent. Check the official Microsoft licensing documentation for your specific scenario. How can I add SharePoint data as a knowledge source for my Copilot? You can connect your Copilot agent to SharePoint data using the generative answers feature in Copilot Studio. The agent can search documents stored in a SharePoint document library. Be aware that there can sometimes be nuances with how attachments versus core document libraries are indexed, which the community is actively discussing in the forums. We hope this formatted FAQ helps you quickly find the information you need! If you have more questions, please use the discussion board to connect with the community.Microsoft Ignite 2025: The Dawn of the AI-Agent Era
Microsoft Ignite 2025 wasn’t just another tech conference — it was a redefinition of the modern workplace. Instead of incremental upgrades and predictable product updates, Microsoft used the event to signal the beginning of a new era: an agent-driven, AI-first workplace where intelligent copilots, governed automation, and deeply connected data systems form the backbone of how organizations operate. From massive transformations in Microsoft 365 to the emergence of fully governed AI agents with their own identities, Ignite 2025 delivered a bold roadmap. In this blog, we unpack the biggest announcements and explore why they matter — not just for IT leaders or developers, but for anyone who uses technology to get work done. https://dellenny.com/microsoft-ignite-2025-the-dawn-of-the-ai-agent-era/Analyzing Sales Data with Simple Natural Language Prompts in Copilot Excel
In today’s fast-paced business world, being able to efficiently analyze sales data is crucial for making informed decisions. Traditionally, this required advanced spreadsheet skills, experience with formulas, or even specialized data analysis tools. But with the introduction of Copilot in Excel, anyone—from beginner to expert—can now analyze data using simple natural language prompts. Instead of writing complex formulas or constructing pivot tables manually, users can simply type a question like, “Show me total revenue by region,” and Copilot does the heavy lifting. This transformation signals a major shift in how organizations and individuals work with spreadsheets. It gives non-technical users the ability to uncover insights that were previously either time-consuming or out of reach. In this blog, we’ll explore how Copilot in Excel works, how to use it for sales analysis, and why this new approach is a game-changer for data-driven decision making. https://dellenny.com/analyzing-sales-data-with-simple-natural-language-prompts-in-copilot-excel/How to Generate Formulas Using Copilot in Excel
In recent years, artificial intelligence has transformed the way we work in productivity tools, and Microsoft Excel is no exception. With the introduction of Copilot in Microsoft 365, users can now generate formulas, automate workflows, and analyze data with natural language commands. Instead of struggling to remember complex syntax or nested functions, Copilot acts as your intelligent assistant—interpreting your requests and building formulas for you. Whether you’re a beginner who always forgets how VLOOKUP works or a power user wanting to prototype formulas faster, Copilot offers an intuitive way to work smarter in Excel. In this guide, we’ll walk through how to generate formulas using Copilot in Excel, step-by-step, along with examples and tips on getting the best results. https://dellenny.com/how-to-generate-formulas-using-copilot-in-excel/Step-by-Step: Your First Data Analysis with Copilot in Excel
Data analysis has traditionally required a combination of technical skills, domain knowledge, and familiarity with complex Excel formulas. Today, Microsoft Copilot in Excel makes the process faster, more intuitive, and more approachable—even for beginners. Copilot works as an AI-powered assistant within Excel, helping you analyze spreadsheets, clean messy data, create insights, and build visualizations using natural language prompts. Whether you are new to data analysis or looking for smarter ways to explore information, this guide walks you through performing your first analysis using Copilot in Excel, step by step. https://dellenny.com/step-by-step-your-first-data-analysis-with-copilot-in-excel/Goodbye, Hours of Chart-Making: Hello, Copilot
Taking a neat table of numbers and turning it into a beautiful, insightful chart can feel like a chore. You know, clicking through menus, tweaking axis labels, and hoping you picked the right type of chart to tell your data’s story. It’s time-consuming, and for many people, it’s a stumbling block on the road to getting real value from their data. What if you could skip all that manual work? What if you could simply talk to your data tool and have it instantly whip up the perfect bar graph, line chart, or scatter plot? It’s a powerful change in how we interact with spreadsheets and data analysis platforms. This blog post will dive into how Copilot can automatically generate charts and graphs, making you a data visualization superhero with almost zero effort. https://dellenny.com/goodbye-hours-of-chart-making-hello-copilot/Stop Guessing Your Guide to Asking Copilot Questions About Your Spreadsheets
Let’s be honest—how many times have you opened an enormous Excel file and instantly felt your brain shut down a little? You know the insights are in there somewhere… the trend that matters, the weird outlier, the formula that will finally make the numbers make sense. But digging them out can feel like you need a data science degree—and two quiet hours with no interruptions. I’ve been there too. I’ve spent more mornings than I’d like to admit wrestling with complex VLOOKUPs or manually sorting through hundreds of comments. Exhausting, right? https://dellenny.com/stop-guessing-your-guide-to-asking-copilot-questions-about-your-spreadsheets/How to Measure ROI from Copilot Deployment
“So, you’ve rolled out Copilot. Now what?” That’s the question every IT leader and business owner asks after the initial excitement fades. Sure, Copilot feels like magic—automating tasks, summarizing meetings, and even drafting emails—but how do you prove it’s more than a shiny new tool? How do you measure its real return on investment (ROI)? Let’s break it down in a way that’s practical, relatable, and yes, human. https://dellenny.com/how-to-measure-roi-from-copilot-deployment/Beyond the Code: Setting Up Alerts for Unusual GitHub Copilot Activity (and Why You Need To)
It’s 3 AM. You’re sound asleep. But somewhere, a developer’s Copilot instance is working overtime, not on a feature, but potentially on a security breach. GitHub Copilot is a game-changer. It’s the closest thing we have to a genuine, tireless code-whisperer, boosting productivity and making the mundane parts of development vanish. But with great power comes great responsibility—and significant new security challenges. When an AI is operating within your codebase, often with the same access as the human developer, it becomes a crucial new endpoint to monitor. Ignoring Copilot security isn’t an option. Its contextual awareness—its superpower—is also its biggest vulnerability. If an attacker gains control of a user’s session or if a vulnerability is exploited (as has happened in the past), Copilot can become an unwitting accomplice in data exfiltration or the silent injection of malicious code. The solution? We need to treat Copilot not just as a developer tool, but as a privileged system user. We need GitHub Copilot alerts for unusual activity. https://dellenny.com/setting-up-alerts-for-unusual-github-copilot-activity/How to Continuously Optimize Data Quality for Better AI Output
If you’re running a modern business, you’re probably already in on the secret: Data is the new oil. But if data is the oil, then Data Quality is the refinery. You wouldn’t put crude, unfiltered oil in a precision-engineered race car engine, right? So why would you feed your cutting-edge Artificial Intelligence (AI) models raw, messy, or incomplete data? The truth is, many organizations treat their data pipeline like a one-off cleanup project. They do a big purge before a new AI initiative, dust their hands off, and expect everything to run perfectly forever. But data—like the real world it represents—is a living, breathing, constantly shifting entity. It degrades. It drifts. New sources introduce new inconsistencies. The old adage “Garbage In, Garbage Out” (GIGO) has never been more relevant than in the age of AI. A model trained on flawed data won’t just give you slightly off results; it can learn and amplify those flaws, leading to biased outcomes, catastrophic business decisions, and a loss of customer trust. The solution isn’t a one-time scrub; it’s a commitment to Continuous Data Quality Optimization. It’s about building a robust, ‘always-on’ system that ensures your AI is running on the cleanest, most reliable fuel possible. https://dellenny.com/continuous-data-quality-optimization-for-ai-the-essential-guide/
