By By Brien Posey05/04/2026
Publication Date: 2026-05-04 00:00:00
How-To
Reduce the Risk Associated with AI-Based Data Analytics
It’s a well-established fact that large language models can hallucinate and produce answers that sound confident, but are completely wrong. What is talked about far less often, however, is a similar phenomenon that can occur when AI is used for data analytics.
AI-based data analytics is all about finding patterns in business data and then trying to derive business insight from those patterns. The risk here isn’t that AI finds patterns in the data, but rather that AI feels compelled to explain those patterns, even when the patterns are nothing more than a coincidence. This can cause the AI to overreach, rationalize, and ultimately give bad advice. So what can you do about it?
Before I get into a discussion of mitigation techniques, I want to give you an example of a potentially problematic dataset. For the sake of illustration, let’s pretend that a company sells widgets and that the company’s sales data points to two trends.
The first of these trends is that every year, around the holidays, sales increase by 20%. For the sake of this discussion, we will assume that this is a “real” trend.
The other trend points to the idea that on the second Tuesday of the month, the company sells more blue widgets…