How COVID-19 Poked Holes in AI Technology

While people began using the phrase “the new normal” early on in the pandemic, those early months presented a struggle for those looking to find any kind of reliable pattern in people’s behaviors.

Surges in unusual buying behaviors weren’t just occurring in a wave or two. When people could no longer find toilet paper and disinfectant wipes, the frenzy turned to baking staples like flour and yeast.

Any company relying on artificial intelligence (AI) found that their models hadn’t counted on the kinds of events that, in a single week, completely upended even AI ninjas like Amazon and Walmart.

For instance, during the week of April 12, the top ten searched-for items bumped staples like phone cases and phone chargers in favor of COVID-crucial products like toilet paper, N95 masks, Lysol and hand sanitizer. The shift was sudden and impossible to predict by any AI tools.

Experts watching the events unfold tell us now that it was easy to see how the pandemic’s shopping behaviors followed the pattern of the virus. These product searches spiked in the United States and Canada soon after they began to wane just a bit in Italy.

The impact on AI models is obvious. They simply weren’t built with a pandemic in mind, and while analysts know that they can go back to attempting to rebuild AI tools, forecasting past about 30 days is a challenge in these circumstances.

The Human Element

The wrench thrown into AI models tells enterprises what data experts have known for a long time: that AI is simply not something you can put in place and then just walk away from it.

Automated marketing systems, for instance, without both reliable data and insightful, expert analysts won’t produce the results you are looking for. Reliable, clean data ensures that findings and recommendations are based on good information, and an analyst can identify when behaviors represent a true anomaly or are due to an event like a pandemic.

The Good News

The convergence of AI with COVID-19 will serve the technology well in the long run. Analysts have not had a significant disruption at this level to their models before, and it will aid them in developing a set of tools that absorb and adjust to behaviors like an unprecedented surge in toilet paper purchases.

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