When a user needs to combine company number, product number, and product name just to get a unique identifier, the traditional spreadsheet workflow has already failed them. The question isn't whether XLOOKUP can be coerced into handling three criteria, it can, with concatenation or nested logic, but whether that workaround is worth your time. For anyone refreshing a large dataset weekly to track updated amounts and generate variance summaries, the real problem isn't the formula choice. It's that you're spending energy on data retrieval instead of analysis.
Building a helper column that joins those three identifiers, then feeding that into an XLOOKUP, will technically return the updated amount. But technical possibility and practical efficiency are not the same thing. Every week you refresh that data, you need to ensure the concatenation logic remains consistent, that no stray spaces or formatting differences break the match, and that your spreadsheet doesn't slow to a crawl as the lookup range grows. That maintenance overhead is exactly the kind of friction that keeps users from focusing on what matters: understanding why the variance exists and what to do about it.
An AI-native approach changes the equation entirely. Instead of asking which formula can stitch together three identifiers, you can simply describe what you need: "Find the matching row where company number, product number, and product name all match, then return the updated amount." The tool handles the multi-condition logic automatically, without helper columns, without concatenation formulas, and without weekly validation checks. This isn't about replacing XLOOKUP, it's about removing the need to think about XLOOKUP at all. The result is a workflow where your energy goes into interpreting the variance summary, not engineering the lookup.
The practical takeaway is straightforward: if your weekly process requires you to manually combine fields or write nested formulas just to retrieve data, you have room to improve your system. Look for tools that let you express your intent directly. The dataset will still be large. The identifiers will still be complex. But the time you spend wrangling them should approach zero. That's the standard worth aiming for.