We've seen this story before. A pricing table that once felt manageable has become a sprawling monument to workarounds, held together by one person's tolerance for complexity and a legacy tool's stubborn limits. The user here is the company's "Excel guy", someone skilled enough to build a Power Query pipeline, honest enough to admit he knows what he doesn't know, and exhausted by a structure that was never designed for the granularity the business now demands. Our take is straightforward: this isn't a data entry problem. It's a structural one, and the fix isn't another column or a second workbook. It's a fundamentally different way of thinking about how pricing data is organized.
The core issue is that this team is treating discounts as columns when they should be treated as data. Every new level of granularity, state, city, zip code, currently requires its own column, which multiplies blank cells and creates a file that is both enormous and brittle. The proposed solution of a second workbook only kicks the problem down the road, introducing duplicate product lists and manual synchronization. That's not a solution; it's a tax on future productivity. What this situation calls for is a normalized data model: a single table where every row is a unique combination of product, geography, and discount. Instead of 2,000 columns with 95% emptiness, you get a lean, queryable dataset that can be filtered, aggregated, and updated without structural chaos.
This is exactly where AI-native spreadsheet tools shine. They don't force you to choose between human readability and machine efficiency. A well-designed AI spreadsheet can ingest the current messy table, recognize the pattern of product rows and discount columns, and reshape it into a clean, normalized structure. The person who likes the current format can still enter data in a familiar layout, because the tool can translate between views. The Power Query pipeline the user already built becomes simpler, not more complicated, because the source data is clean. And when the business inevitably asks for city-level discounts, the model absorbs it without a single new column.
The practical takeaway is this: stop expanding the spreadsheet. Start expanding how you think about the spreadsheet. The user's instinct to reach for a second workbook is understandable, but it's a sign that the current tool has reached its limit. The real opportunity here is to adopt a system that separates data storage from data presentation. Let the person who loves the wide table keep their view. Give the business the granularity it needs. And free the company's Excel guy from being the bottleneck who has to manually reconcile two workbooks. That's the kind of transformation that makes a pricing table feel like a tool again, not a trap.