--- title: "AI at Work, One Year Later: Copilot, Fabric, and What’s Actually Changed" description: "AI at Work, One Year Later: Copilot, Fabric, and practical lessons on using AI intentionally inside everyday workflows." image: https://blog.systemsengineering.com/hubfs/Hynes%20Thumbnail.png --- [![header-logo](https://blog.systemsengineering.com/hs-fs/hubfs/header-logo.png?width=138&name=header-logo.png)](https://www.systemsengineering.com) 888.624.6737 ![Spyglass](https://www.systemsengineering.com/wp-content/uploads/search-google-e1751908066340.png) - [Careers](https://www.systemsengineering.com/about/careers/) - [Blog](https://blog.systemsengineering.com/blog) - [Events](https://www.systemsengineering.com/news-resources/events/) - [Case Studies](https://www.systemsengineering.com/case-studies/) - [Client Center](https://www.systemsengineering.com/client-center/) - [CONTACT](https://www.systemsengineering.com/about/contact/) - IT Services - [Managed IT](https://www.systemsengineering.com/managed-it/) - 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I’m seeing the best results when AI is **integrated into the systems we already live in** such as Microsoft 365 for day-to-day work, Power BI/Fabric for a “single pane of glass” for operational data, and a few other tools that help with both coding and learning. The other reason I’m paying attention, I recently listened to students describe co-op workplaces that were **AI-heavy**, **AI-available**, and **no-AI**—and the gap was real. In the no-AI environment, the student was doing work they *knew* could be automated. That stuck with me because it’s not just a student issue, it’s potentially a career issue for all of us as well as for companies that are trying to stay competitive. --- **Copilot: Continual Evolution** A few updates have stood out in day-to-day use: --- **Microsoft Fabric: Results From Our Test Drive** If you’re not familiar with **Microsoft Fabric**, think of it as Microsoft’s platform for bringing data, analytics, and reporting together in one ecosystem. We approached it the same way we approach most new platforms: start small, test with limited workloads, and see whether the value shows up in day-to-day use. **The problem we were solving** We have an extensive data warehouse pulling from **6+ different data sources** tied to our **ticketing platform**, **CRM**, plus data from our customers. Individually, each system tells part of the story. The time sink is switching between tools, reconciling what’s “true,” and building a coherent timeline of what changed and when. **What we built: a “single pane of glass” in Power BI** By consolidating these sources into **Power BI**, we were able to give users a single view of client and operational data across systems. The immediate payoff was simple: fewer context switches and far less time spent hunting for related details spread across multiple tools. **Where AI-style features added leverage: Narrative Cards + Copilot Q&A** Once the cross-system view was in place, we added two AI tools on top: **Narrative Visualizations** and **Copilot in Power BI**. The goal for both: get people to “what matters” faster, without having to navigate data in multiple systems. **Narrative Visualizations** are prompt-driven, dynamically generated summaries that turn report context into readable, decision-oriented language. Before we added Power BI Narrative Cards, getting fully oriented on a client often meant an hour of reviewing tickets, time entries, and agreements to try to stitch together a coherent picture. Now, with that data unified and summarized, the “what’s going on here?” question is now answered in seconds. A couple prompts/use cases that have been especially effective: On top of that, **Copilot in Power BI** lets users ask plain-English questions against that same data. When it works well it gives people a shortcut to “query the warehouse” without needing to know where the data lives or how it’s joined. The impact isn’t that any of these replaces the underlying metrics. It’s that it **surfaces what matters first**, and then you can drill into the visuals and source details when needed. **Why this matters** For us, the real win is the combination: It’s not magic, and it doesn’t eliminate the need for good modeling or governance, but it *does* change how quickly people can go from “something happened” to “here’s what changed, here’s why it matters, and here’s where to look next.” --- **Exploring Other AI Tools** While Copilot is the most visible AI tool in the Microsoft ecosystem, we’ve also been exploring tools outside of Copilot—especially where the use case is more **service desk**, **customer support**, and **knowledge-driven automation** than document-centric productivity. **AI assistance for ticketing and customer support** One tool we’ve been evaluating connects into our ticketing workflow and helps reduce friction for both customers and our team. The most promising areas so far: The goal here isn’t “replace support.” It’s to **reduce repetitive work** so the team can focus on higher-value troubleshooting and better customer outcomes. **AI platform to help customers adopt AI** Systems Engineering is evaluating a platform that could help customers adopt **multiple large language models (LLM)** in a practical and controlled way. The reason it’s compelling is flexibility. Different customers have different needs, and we see the need for tools that provide a way to leverage LLMSwithout forcing everything into a single “one size fits all” tool. It’s a platform approach that can support everything from quick productivity gains to more repeatable, packaged workflows. A simple example from my own work: for this blog post, I dictated several brain dumps of notes, brought it into a tool with the LLM option I wanted, and used it to turn raw thoughts into a clean blog-post structure. That saved time and produced a stronger first draft than I would have written from scratch. I still did a manual editing pass for accuracy, tone, and to make sure it reflects what happened. The bigger point is the same thing we tell customers: AI works best when it’s **used intentionally**, with the right guardrails, and with a human still responsible for the final output. **Why we’re looking beyond one tool** Different tools are optimized for different workflows: The bigger takeaway: the AI landscape is widening, and the “best” tool is usually the one that fits the workflow *and* the data constraints. --- **Coding: How I’m Using AI Day-to-Day (Work vs. School)** A quick bit of context: I do software development for work, but I’m also enrolled at Northeastern University’s **Roux Institute** in a **Master of Science in Information Systems program with a focus on AI**. One of the challenges is that my schoolwork has me coding in languages and frameworks that are completely new to me. For this the “AI assist” use case looks a little different than it does at work. **At work: Copilot (real-world constraints, real-world wins)** For work development, I stick to **Copilot**. A lot of the coding I do for work is the same as I’ve been writing for the past 25+ years, but Copilot still helps because I can give it a very specific example of what I want and save myself time. Where it’s been especially useful for me: I still treat the output like I would from a fast junior dev: helpful and quick, but something I verify before it goes anywhere important. **In school: Gemini + Claude as “on-demand tutoring” for new languages** For schoolwork, I’ve leaned heavily on **Gemini (Enterprise)** and **Claude**. Because I’m working in areas that are new to me, the biggest value isn’t just generating code, it’s shortening the learning curve: These tools speed up my learning, but they don’t replace mastery. I still need to understand and own the work. **Claude Code (on my list to try)** One other thing that’s on my radar: I recently saw a demo of **Claude Code** and it looked legitimately powerful. The workflow wasn’t just “explain this code.” I’ve watched people bring in a **spec document**, use Claude to **refine the spec**, and then use Claude Code to **build out a solution** by pulling patterns and code samples from an **existing Visual Studio project**. I haven’t tried it yet, but it’s exactly the kind of “AI inside the workflow” that feels like the next step. **Reality check (because it matters)** AI can be confidently wrong. I validate outputs, test code, and cross-check assumptions. It’s a multiplier, but not a substitute for understanding. Also: for everything AI has helped me with in Python, Java, JavaScript, DAX, and TSQL, it still can’t seem to give me a good curry recipe. No tool is flawless. --- **Lessons Learned** It’s been over a year of using these tools seriously—Copilot in the Microsoft ecosystem, Fabric and Power BI for data/reporting, plus other AI tools for support workflows and learning—and the pattern is clear: **AI is evolving to the point where I can use it as a teammate.** The biggest takeaway is that **integration matters more than hype**. The best results show up when AI is embedded directly in the systems where the work is happening. That said, it still needs to be paired with guardrails and human review.   | ![Hynes](https://blog.systemsengineering.com/hs-fs/hubfs/Hynes.png?width=137&height=137&name=Hynes.png) | Michael Hynes, Software Manager at Systems Engineering and with the company since 2004, has 25+ years of IT experience spanning software development, application architecture, and business solutions. He is also currently enrolled at Northeastern University’s Roux Institute in their Master of Science in Information Systems program with a focus on AI. In addition, he is working with Northeastern University as a Teaching and Program Assistant. 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