Top of Mind Thursday – April 23, 2026: Brain Drain

It’s so tempting to let all the great technology and systems available to us manage our lives and businesses.

But what happens when we let the systems do the thinking, rather than allowing the people involved apply their knowledge and expertise?

For the last two months, my bill from a mail order pharmacy was significantly higher than it should have been. The error was the same both times: my order for two bottles of a medication was double charged, so that I was charged for 2 x 2 bottles (4 total), rather than just two.

There was also an extra charge on a test for our dog at the vet that needed to be shipped to an outside lab. The vet’s computer system was pre-set to include the shipping charge when the test was ordered. The problem was this had been programmed to include both a regular shipping fee and an express charge—when the test only had to be shipped once.

In both instances, the people involved had relied on the system set up for them. They were not getting paid to think about any of these details—the thinking was being done for them. The charges were pre-entered; they just had to add them to the order. And, in both instances, because the information entered was incorrect, everything from that point forward was wrong.

Both of these situations were straightforward and simple to fix. But what happens when we are talking about an AI-based system that compares mammogram results to thousands of others in a database, or one that reviews medical databases to diagnose a hard-to-identify illness associated with a specific list of systems?

Even the best, most advanced AI systems are only as good as the information they’ve got available to them. When the data entered is incorrect, or the models the AI tool is relying upon have an inherent bias or use improper assumptions, the results are therefore off as well.

Automating tedious processes sounds terrific—except we run the risk of getting more bad results faster, rather than better quality results. Garbage in, garbage out.

In the rush to AI this and AI that, let’s not throw our experience and expertise out with the trash.

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