This user's frustration is exactly what happens when a tool demands that you adapt to its logic instead of adapting to yours. Excel needs the raw dataset to build a box-and-whisker chart, even when you already have the summary statistics in hand. Power BI offers flexibility, but only if you invest hours in DAX formulas and custom visuals. The real problem isn't a missing feature, it's that both tools treat data preparation and visualization as separate, manual chores. You shouldn't have to reconstruct original data from quartiles just to see a distribution.
The stacked column issue makes the same point from a different angle. When your minimum and Q1 values are negative, the chart breaks because traditional stacking assumes all segments are positive. You correctly calculated the differences between quartiles, but the software still plotted the wrong gaps. This isn't a user error. It's a design limitation: these tools were built for static, positive-only datasets, not for the messy, real-world numbers that financial analysts and data teams actually work with. Every workaround you tried, calculating differences, adjusting stacking order, is a patch on a system that wasn't designed for your task.
What this reveals is a deeper shift in how we should think about building charts. The goal isn't to master formula gymnastics or memorize which visual works with which data shape. The goal is to describe what you want to see and let the tool handle the math. An AI-native spreadsheet doesn't ask for raw data when you already have quartiles. It reads your intent: "Show me a box plot of these five IRR distributions using the min, Q1, median, Q3, and max I've computed." It handles the negative stacking problem by understanding that quartile ranges are intervals, not stacked totals. The human stays focused on the story the data tells, not on the mechanics of rendering it.
For users like this one, the practical takeaway is simple: stop treating chart creation as a formula puzzle. If you're spending time converting summary statistics back into raw rows, or manually adjusting gap sizes to fix stacking errors, you're doing work that a smarter tool should do for you. The next generation of spreadsheet software will interpret your data as you intend it, not as a rigid grid of cells expects it. That's not a promise of future magic, it's a description of what's already possible when you let AI handle the data, so you can focus on the chart.