Capterra research reveals nearly all HR leaders say their department will use software and algorithms to reduce labor costs in a 2023 recession, but only half are completely confident their tech will produce unbiased recommendations

Companies are using software and algorithms to make critical business decisions in anticipation of a 2023 recession, according to Capterra’s HR Tech Recession Planning Survey of 300 HR leaders. The survey also revealed that 72% say their employer has already started preparing for a possible recession, while 24% plan to start preparing soon.

Today’s HR department is vastly more tech-enabled than it was 15 years ago. HR software was still in its infancy during the Great Recession (2007-2009), which is why 2023 labor decisions will be more data-driven than ever before. Cutting-edge HR software systems can now aggregate massive amounts of employee information and turn it into actionable insights and recommendations.

While 98% of surveyed HR leaders say they’ll rely on software and algorithms to reduce labor costs in a 2023 recession, only 50% of those leaders are completely confident their tech will make unbiased recommendations. And less than half (47%) are entirely comfortable making layoff decisions based on recommendations from that same tech.

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“A recession will be the biggest test of HR’s data-driven methods so far,” says Brian Westfall, principal HR analyst at Capterra. “Companies need to be careful with not only what data they analyze, but what cost-cutting recommendations they take from software and algorithms at face value. Analytical tools can be incredibly valuable in arriving at the best decisions, but HR departments need to be aware of the biases that potentially influenced that data. A healthy amount of skepticism is a good thing.”

More than a third (35%) of HR leaders will rely mostly or solely on data to come up with recommendations to reduce labor costs in a 2023 recession, compared to 20% that will rely mostly or solely on gut instinct. Businesses are relying on skills data (71%), performance data (70%), work status data (e.g., full-time vs. part-time) (68%) and attendance data (65%) to make recession layoff decisions.

Relying solely on data could miss crucial determinants, including if employees selected for layoffs have a biased manager, lack adequate resources or support to work effectively, or aren’t tracking the right metrics to accurately evaluate employee performance. Algorithms aren’t able to take all of this into account yet, resulting in biased recommendations with potential legal consequences.

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