I’m a huge proponent of using AI-powered insights and automation in Excel. In fact, I think it’s the only way forward to keep Excel competitive, and in fact, Excel will get stronger from this disorder—antifragile in a way, to borrow from Nassim Taleb. The chaos AI introduces isn’t a weakness; it’s a chance for Excel to evolve, adapting and thriving under pressure. And as AI weaves itself into Excel’s fabric, it forces us to rethink how we work, pushing the tool beyond its old limits into something more dynamic and resilient.

That said, there is a lot of concern in this brave new world of Excel where outputs get generated probabilistically and not always correctly! Outputs aren’t always carved in stone anymore—they’re educated guesses, shaped by algorithms that sometimes miss the mark. So how do we add some sanity checks and safeguards to our AI-powered insights in Excel? That’s precisely what this blog post is about, specifically in light of Python-powered insights with Advanced Analysis:

These are just a few sanity checks and rules of thumb I’ve found useful, but I’d love to hear others in the comments. Let’s get started

Employ the same tactics as before

When it comes to data analysis, whether you’re using AI or not, there are fundamental safeguards and sanity checks that should always be part of your workflow. Too often, AI gets cast as either a magical fix or a villain in a strawman argument. Critics assume it eliminates the need for diligence, picturing a world where we hit a button and perfection spills out, while skeptics imagine users blindly trusting its outputs without doing the foundational work that’s always been required.

Neither is true. The reality is, AI doesn’t replace these steps—it amplifies their importance. It’s like handing a power tool to a carpenter: the cuts are faster, but you still need to measure twice. Here are some key methods to keep your analysis on solid ground.

Profile the data

Before diving into any analysis, take a step back and examine your dataset. What’s the shape of the data? Are there missing values, outliers, or unexpected patterns? Profiling means summarizing key statistics such as means, medians, and ranges while checking for consistency. Are your categories lining up as expected, or is something unexpected sneaking in? This is critical regardless of how you’re processing the data because garbage in equals garbage out—AI or not.

Excel includes a great tool for data profiling right within Power Query which you can learn about here:

If you’re comfortable running Python independent of Python in Excel, the ydata-profiling package is another gem for data profiling, churning out detailed reports on distributions, correlations, and gaps—everything you need to catch trouble early: here’s a guide. Profiling isn’t just prep work; it’s your first line of defense against a dataset trying to trip you up.

Validate data types

Data types—like integers, floats, strings, or dates—dictate how calculations and operations behave. In Excel, a column might look like numbers but be formatted as text, throwing off formulas faster than you can say “#VALUE!” Whether you’re working manually or with AI, you need to confirm that each variable’s type matches its intended use.

This step is non-negotiable because mismatched types lead to silent failures or incorrect outputs—errors that may not be obviously but quietly sabotage your results. With AI-generated Python code, this matters even more: if the AI writes a script assuming a date column is a string, parsing errors or invalid operations could slip through unnoticed, leaving you with a mess you didn’t see coming. Explicitly validating and understanding types keeps your analysis robust, ensuring the foundation holds before you build anything on top.

Audit formulas and logic

Whether it’s an Excel formula or a Python script, you need to double-check the logic behind your calculations. Trace each step: Does it make sense? Are the assumptions valid?

In Excel, this might mean inspecting a particular formula or function, peeling back the layers to see what’s ticking with helper cells or the Formula Auditing tools. With AI-generated Python code, you may need to review function definitions, conditional flows, or pandas operations—steps that can get dense fast.

Fortunately, Copilot often does a great job here, helping you break code down into component parts, adding comments, and so forth. Remember, it’s the code itself that’s still transforming A into B, and Copilot gives you that code directly. Even if the processes to generate that code were a little magical, we still have the actual code we can audit—this part is not probabilistic! It’s tangible, reviewable, and yours to vet.

Sensitivity analysis

Now, sensitivity analysis. Test how changes in your inputs affect your outputs. Tweak key variables and see if the results shift in a reasonable way. Fortunately, Excel makes this genuinely quite easy by letting you add user inputs right to a workbook and try different values to see how the results change. Actually, this kind of tactile previewing and stress testing of the data is probably easier in Excel than in Python, so take advantage of it!

This isn’t just an AI concern; it’s a cornerstone of any analysis to confirm stability and uncover hidden dependencies. When AI generates Python code, sensitivity analysis becomes a lifeline. Did the AI hardcode a value it shouldn’t have? Does the script break with edge cases? By probing these scenarios, you catch brittle logic that might otherwise go undetected.

Replicate independently

Don’t take your first result as gospel—try to reproduce it using a different method. In Excel, this could mean manually calculating a sample or using an alternative formula to cross-check. With Python, it might involve rewriting the AI’s code by hand in extreme scenarios, or cross-checking against Excel’s built-in tools, or even simply asking Copilot to run the analysis again but with a different package, technique, or function. Replication builds confidence and exposes discrepancies, whether from human error or AI missteps. For AI-generated code, this is crucial because subtle bugs can hide in complex scripts.

Visualize early and often

Charts aren’t just for presentations—they’re essential diagnostic tools! Plot your data and results at multiple stages to spot anomalies, trends, or errors that numbers alone might obscure. This applies universally: a bad dataset or flawed calculation looks wrong visually, no matter who—or what—did the work. When AI writes Python code with libraries like matplotlib or seaborn, visualization becomes a quick way to verify its output. Did it group the data correctly? Are the scales off? Seeing the data visually keeps you grounded in reality.

This can really save the day when you’re working with Python in Excel because sometimes, due to the Python in Excel environment, certain Python data outputs just don’t render well in the Excel workbook grid. But with a little creativity, you can ask Copilot to translate those results into a graph and get them added into the workbook relatively easily.

Cross-check on new data

This is similar to sensitivity analysis but more about entire observations or rows changing than one particular input, assumption, or parameter. To sanity-check your results, consider taking that code and applying it to just a few rows of the dataset.

It’s actually quite easy to get a sample of rows from the DataFrame with the sample() function, and it’s often a lot easier to understand the building blocks of your work when you’re dealing with an achievable set of rows to physically monitor yourself. You can eyeball the inputs and outputs, making sure the logic holds up.

Another option is to validate your work against a synthetic dataset, which is a powerful cross-check because it lets you deliberately design a controlled environment to test specific scenarios that might not naturally appear in your real data, ensuring your analysis or AI-generated Python code holds up under diverse conditions. By crafting data with intentional edge cases—like extreme values, missing entries, or type mismatches—you can probe the limits of your script, catching flaws like hardcoded assumptions or unhandled errors that a sample of real rows might miss.

This is especially valuable when validating AI outputs, as it reveals whether the code truly aligns with your intent across a broader spectrum of possibilities, not just the quirks of your current dataset, giving you confidence in its reliability. Fortunately, Python in Excel makes it very easy to whip up such synthetic datasets with the Faker package:

Validating against a sample of rows from your existing dataset offers the advantage of real-world relevance and speed—since it’s a subset of your actual data, it reflects true patterns and quirks, making it a quick, practical test for tools like AI-generated Python code, though it risks missing rare edge cases or introducing sampling bias if not randomized.

In contrast, a new synthetic dataset gives you full control to craft specific scenarios—outliers or errors—and test comprehensively, which is ideal for stress-testing logic and uncovering hidden flaws in AI outputs, but it demands more time to create and may not fully mimic the real data’s complexity, potentially leading to a disconnect between test and reality. A sample is best for fast, context-rich checks, while synthetic data excels at probing robustness—choosing depends on your priorities and resources.

Conclusion

I hope you see that a lot of these steps really aren’t any different from what you’d do to cross-check work created by yourself or another human: perhaps a junior analyst who hasn’t quite mastered the nuances of the craft of data analysis but is smart as a whip with the technical stuff.

Just because AI makes mistakes doesn’t mean you shouldn’t use it, for the same reason that just because humans make mistakes doesn’t mean you shouldn’t use their work. You just need to be even more careful now with your troubleshooting steps, as AI errors can be sneakier and less obvious than what we’re used to with human slip-ups. AI is very, very good at passing off shoddy work as good work and rarely is confident in its inabilities. Treat it like a brilliant but overconfident intern: cultivate its strengths, but double-check its homework.

What questions do you have about cross-checking your Advanced Analysis with Copilot work? Have you implemented any other cross-checks in your work? Let me know in the comments!

The post How to cross-check AI-generated insights in Excel first appeared on Stringfest Analytics.