And while machine learning tools are built to take in new data, they're typically not so robust that they can adapt as dramatically as needed.
For instance, MIT Tech reports that a company that detects credit card fraud needed to step in and tweak its algorithm to account for a surge of interest in gardening equipment and power tools.
An online retailer found that its AI was ordering stock that no longer matched with what was selling. And a firm that uses AI to recommend investments based on sentiment analysis of news stories was confused by the generally negative tone throughout the media.
"The situation is so volatile," Rael Cline, CEO of the algorithmic marketing consulting firm Nozzle, told MIT Tech.
"You're trying to optimize for toilet paper last week, and this week everyone wants to buy puzzles or gym equipment."
While some companies are dedicating more time and resources to manually steering their algorithms, others see this as an opportunity to improve.
"A pandemic like this is a perfect trigger to build better machine-learning models," Sharma said.