Architectural: Copilot should detect missing source data, avoid inference, and surface uncertainty.
Users expect the AI to detect when it lacks source data, avoid inference, surface uncertainty, and adapt to environmental constraints like character normalisation. These behaviours materially improve trust and usability. I’ve been working with Copilot on structured data extraction from a PDF and noticed a behaviour that seems like an architectural gap rather than a simple bug. Copilot attempted to infer table structure from a template when it did not have access to the actual source data. It produced confident but incorrect output instead of signalling that the source was unavailable. Additionally, Copilot attempted to output TAB‑delimited data, but the MS365 environment silently normalised TABs to spaces, and Copilot did not detect or adapt to this constraint. Recommendation: Copilot should proactively: detect when it lacks source data avoid inference when accuracy is expected surface uncertainty explicitly detect environment‑specific formatting limitations (e.g., TAB stripping) adapt output formats automatically These behaviours would materially improve trust, reliability, and user experience.Copilot in Excel-5 Minutes to Outperform 90% of Excel Users with AI
Quick note: I'm a native Chinese speaker. This article was translated with AI assistance — but I've personally tested every step in English before publishing. What you see here works exactly as shown. Prerequisites: This tutorial requires the Copilot feature in Excel (Microsoft 365 subscription). Availability may vary by region and may require additional configuration. Following my previous two articles in the Copilot from a User's Perspective series, this is the first article in a new companion series: AI Tutorials. I'll continue updating the previous series — I just think it's important to break up the rhythm with something immediately actionable from time to time. Why did I dare use this title? I'm sure many of you think I'm exaggerating. In 5 minutes, most people can't even explain what a cross-sheet lookup is — but if you follow this tutorial today, I'm confident you'll agree with the title. If you don't believe me, start your timer now. Step 1: Open Excel and Learn the Terminology Before we start, let's make sure we speak the same language: Column — The vertical axis, labeled with letters (e.g., Column A, Column B). Row — The horizontal axis, labeled with numbers (e.g., Row 1, Row 2). Cell — A single coordinate. For example, A3 means Column A, Row 3. Range — A span from one cell to another. For example, B3:B10 means Column B, Rows 3 through 10. B3:D4 includes six cells: B3, C3, D3, B4, C4, D4. Worksheet — The tabs at the bottom of your Excel file (Sheet1, Sheet2, etc.). Each tab is a separate table. Workbook — The Excel file itself. You might be thinking: "You're starting THIS basic? No way you'll deliver on that title!" But here's the thing — if you understand these terms, you already have everything you need to use Copilot in Excel. Step 2: Create a Practice Dataset Create a new Excel file, open Copilot, and enter this prompt. Make sure to click "Allow Edits" when prompted. Create Sheet2 first with these columns: Name, Gender, Student ID, Score, Height, Class, and Commute Method. Randomly generate 30 rows of data. Make sure the Student IDs are NOT sequential numbers. Then create Sheet1: randomly pick 10 Student IDs from Sheet2 and list them in Column A. For both sheets, format the header row with a light gray fill, increase the font size by 1, and center-align. Most tutorials only teach you concepts — they never give you a dataset to practice with. Here, I just had AI generate a ready-made practice dataset so you can follow along with every step below. Now, let's get to work. Step 3: Use AI to Replace VLOOKUP VLOOKUP is the single most searched Excel function on the internet. Give me 30 seconds, and I'll make it irrelevant. With your tables ready, go to Sheet1. In the Copilot sidebar, type: Based on Column A in Sheet1, pull the values from Column D and Column E in Sheet2. That's it. You just accomplished what VLOOKUP does. Now here's where it gets interesting. VLOOKUP has a well-known limitation — it can only pull data from columns to the right of the lookup column, never to the left. Try this: Based on Column A in Sheet1, pull the values from Column A and Column B in Sheet2. If this works — and it will — you've just gone beyond what traditional VLOOKUP can do. And you never had to understand how VLOOKUP works under the hood. The prompts I used above are deliberately bare-bones. You can be much more specific: Based on Column A in Sheet1, pull the values from Column D and Column E in Sheet2. Insert these two columns before Column A in Sheet1, and fill them with a light gray background. The more Excel terminology you know, the more precise your prompts become — and the fewer errors you'll encounter. Did you notice something? Everything you just typed was nouns + logic. That is the core operating principle of generative AI. Let's keep going. Step 4: Multi-Condition Sorting Switch to Sheet2, where we have the full dataset. Sometimes you need complex sorting — Class in ascending order, Score in descending order within each class, and Student ID in ascending order within each score group. I consider myself an upper-intermediate Excel user, and I still couldn't do this manually — it requires nested sort configurations that most people never learn. But just describe what you want. In the Copilot sidebar, type: Sort the data with the following priority: Class ascending, Score descending, Student ID ascending. All three columns are sorted simultaneously, each with its own direction. If you could do this without AI, you'd already be an advanced Excel user. AI just eliminated that skill gap — and it's faster too. You might have noticed I didn't use column letter references (like "Column F") this time. In fact, I didn't need to in Step 3 either. AI can read the headers, think, and identify the right columns on its own. Step 5: Conditional Formatting Still on Sheet2. Sometimes you need visual differentiation — for example, blue highlighting for male students and pink for female students. In the Copilot sidebar, type: Fill the rows of male students with blue, and the rows of female students with pink. Without AI, I'd filter for males, apply the fill, then filter for females and repeat. That two-step process is surprisingly slow for something so simple. Sometimes you need to spot duplicates. Try: Bold the text in cells where Height values are duplicated. Without AI, this requires setting up conditional formatting rules — a skill that already puts you in intermediate-to-advanced territory. Now the sheet looks a bit messy. Let's reset: In Sheet2, reset all cells except the header row to default formatting. A Note on Prompting Style You'll notice that in Step 5, my prompts were almost entirely natural language — no column letters, no technical references. So why didn't I start the tutorial that way? Because I wanted to give you something you could copy-paste and get working immediately — something reliable and reproducible. I use natural language prompts because I've spent enough time with AI to understand its boundaries and behavior. The terminology-based approach from Step 3 is what I call "The Noun Method" — combine domain-specific nouns with natural-language logic to form complete instructions: Based on (logic) Column A (noun) pull (logic) from Sheet2 (noun) Column B (noun) and (logic) Column C (noun) Once you understand The Noun Method, you can effectively operate any generative AI tool. The key is learning the relevant nouns for each domain — and in Excel's case, there are remarkably few to learn. Closing Thoughts If you followed along with every step, the whole process probably took 10–15 minutes. But I believe that the moment you successfully ran the VLOOKUP prompt in Step 3, you stopped doubting the title. If you'd like more Excel + AI tutorials, follow me and leave a comment. I'll keep them coming. Next up: What You Need to Know About TokensCopilot from a User's Perspective #2 — Types of Copilot and How to Choose
I'm a native Chinese speaker, and my English isn't strong enough to write an entire article from scratch. So I had Copilot Tasks translate this piece for me. If you find it reads smoothly — well, that's a testament to what Tasks can do. This is the second article in my Copilot from a User's Perspective series, focusing on the different types of Copilot. After reading the first article, if Copilot caught your interest, you're probably wondering: with so many Copilots everywhere, what's the difference between them? Are they actually useful? Are they really worth your time? By the end of this article, you should have a much clearer picture of how to think about the different Copilot experiences. There are a LOT of Copilot variants out there. I first started using Copilot on March 6th, and since then I've tried virtually every Copilot experience available to me (I'm a Microsoft 365 Premium subscriber). As of May 1st, my conversations have exceeded 9 million Chinese characters（including both my inputs and AI responses across all Copilot surfaces）. So I'll take the liberty of offering my own user-perspective classification of the current Copilot landscape. I believe the AI tools we regularly interact with can be broadly divided into four categories: Chat AI, Tool AI, Search Engine AI, and Agent AI. In my view, AI's core value lies in working alongside humans to boost productivity — and that's the lens through which I built this classification. One important caveat: due to account permissions and the nature of my work, I haven't had the chance to try the Windows system sidebar Copilot, GitHub Copilot, or Copilot Studio. Quick Analogies Before diving in, here's how I think about each type: • Chat AI — A knowledgeable, quick-thinking colleague who's a bit too talkative and not great at actually doing things. Great for brainstorming, but the moment hands-on work is needed, they vanish. • Tool AI — The notebook, sketchpad, and toolbox sitting on your desk. Specialized for specific tasks, with minimal conversation ability. • Search Engine AI — A filing cabinet that organizes your scattered documents so you can find things faster. • Agent AI — The most powerful and practical of all. A knowledgeable, sharp-thinking assistant who doesn't ramble and can actually get things done for you. Chat AI Where you'll find it: Web-based Copilot (copilot.microsoft.com), Edge sidebar Copilot, and the chat panels within M365 apps. What it does: This is the most popular, most accessible, and lowest-barrier type of AI. Chat AI typically can't take action on its own — the most it can do is generate images for you (though M365 Copilot Chat can also create files in Microsoft formats like Word documents and PowerPoint presentations). But don't underestimate it. You can ask it to check the weather, or have it research topics across the web — for example: "What are the most popular conversational AI tools on the market right now, and how are they reviewed?" My take: I've settled on the web-based Copilot as my primary chat AI. In my experience, M365 Copilot feels narrower in its reasoning — its responses are more conservative and contained, while the web version is more open and expansive. You can clearly sense they come from different design philosophies. One notable thing about M365 Copilot is that it integrates your conversation history across all M365 tools, suggesting that all the chat experiences within M365 share the same underlying foundation. Tool AI Where you'll find it: Copilot embedded in Excel, PowerPoint, Word, and other M365 applications. What it does: This type of AI is far more powerful than you'd expect. How much value you get from it depends entirely on how well you understand the underlying tools and how creatively you use the AI within them. With Copilot's help, my Excel productivity has improved by at least 70%. I'll dedicate an upcoming article specifically to using Copilot in Excel. My take: Incredibly powerful and massively underestimated. Stay tuned — I'll be showing you how to use these in future articles. Search Engine AI Where you'll find it: Copilot integrated into Edge's search experience (Bing AI). What it does: Its primary function is summarizing your search results. You might not even notice it's there, because it doesn't present itself as a conversation — it simply provides a summary alongside your results. You think you haven't given it any instructions, but the moment you type something into the search bar and hit Enter, it's already at work. There's not much to choose here — search engine AI is tied directly to your browser. Nobody switches browsers just for an AI summary feature, and the quality of its output depends entirely on what it finds. If the search results are noisy, the summary will be noisy too. So don't overthink this one — and certainly don't abandon a browser you're comfortable with just because a competitor added this feature. My take: The good news is that search engine AI is usually free — it's essentially a feature enhancement that search engines build into their browsers. That said, some AI-native search engines like Perplexity offer a noticeably better experience. Overall, this is a category where we can sit back, let the companies compete, and enjoy the improvements. Agent AI Where you'll find it: Copilot Tasks (on web-based Copilot) and Office Agents (in M365 Copilot). What it does: This type of AI goes far beyond a chat window. It connects to your email, calendar, browser, cloud storage, and other tools. Think of it as an AI that doesn't just talk with you — it takes action. Tell it "Check my meeting schedule for tomorrow and send a reminder email to my colleagues," and it will open your calendar, draft the email, and send it — instead of handing you a block of text and leaving you to do the work yourself. Tasks can even run in the background. Close the page and go about your day — it will notify you when it's done. For example, I've set up Copilot Tasks to automatically compile and send a daily report (with content I define) and to gather competitive analysis based on my requirements. That said, today's agent AI is more like an intern you need to keep an eye on than a seasoned employee you can fully trust. But even so, it's a massive leap forward from chat AI — at least it's willing to roll up its sleeves. My take: Choosing an agent AI is much more complex than choosing a chat AI, because an agent's core value isn't about how well it talks — it's about what it can connect to and what it can do. Agent AI is the category most worth learning about right now. Tool AI excels at specific points; agent AI covers the entire surface (though in certain vertical domains, tool AI may still deliver a better experience). It's the only category that's genuinely changing how humans and AI work together. This category is still young, and the experience isn't fully polished yet. When choosing, don't focus on which one feels the most mature — focus on which one fits your workflow. Even if someone told me Google's AI experience is the best, I still wouldn't abandon my Microsoft ecosystem. Closing Thoughts These are the four types of AI tools as I see them from a user's perspective. Chat AI is the quickest to try. Tool AI gives you the most tangible sense of how AI is changing the way we work. But if you're willing to invest time in learning and adapting, agent AI can deliver productivity gains that the other three categories simply can't match. I'll also be publishing a Tasks guide in the future (assuming you have access to it). Trust me — you'll be amazed at what Tasks can do. Next up: AI Tutorial — Surpass 90% of Excel Users in 5 Minutes
