As 2023 rang in, so did the hype over artificial intelligence, particularly with generative AI (such as ChatGPT). Seemingly overnight, the long-gathering buzz surrounding AI’s applicability not only to the world of work, but also to business and everything else, became nearly deafening. AI has become seemingly pertinent to just about everything — and it probably is.
As more industries begin to realize the opportunities this rapidly developing technology presents to their operations and bottom lines, questions abound. Front-and-center among them is: where to start? In human capital management (HCM), the answer is, just about anywhere. We see vendors applying AI to their solutions for payroll, scheduling, employee self-service, performance management, benefits administration — the list goes on. Name a subdomain of HCM, and there’s likely an angle that makes sense for AI.
Coming into focus with this growing and fertile ground for the use of AI will be an ever-growing necessity to rethink how we measure and benchmark success for the deployment of the latest innovations in software for HCM.
For an analogy, let’s look back to the days, decades ago, when automation of operational HR had just barely become a possibility. Before this, during what 3Sixty Insights calls the Paper-Lithic Age, success in HCM often amounted to measuring how much administrative work the team could complete manually over the course of an hour, day or week. Enter automation, the technological innovation du jour of its day, and the aforementioned measurement of productivity suddenly became meaningless: an indication of inefficiency no matter how fast the team could work. To an ever greater and more fundamental degree, AI will upend many, many key performance indicators that leaders in HCM have for decades relied on as objective measures of success. These KPIs simply won’t equate to success anymore.
AI and Traditional Metrics
Over 10 years ago, the Center for American Progress found that the cost of losing a seasoned professional can be as high as 213 percent of the lost person’s salary. The true cost of voluntary employee turnover is estimated around $1 trillion annually. Meanwhile, the U.S. Bureau of Labor Statistics’ latest Job Openings and Labor Turnover Survey estimates nearly 10 million job openings across the U.S., but with the hire rate at only 4 percent. Translation: much money is probably being wasted on external hiring. It’s good business sense, when a role opens, to investigate whether an internal candidate has the skills or could develop the skills to meet that new need.
As a concept, retention as a cost savings is nothing new to any industry. Neither is the innate efficiency, the further cost savings that internal talent acquisition brings. And, when AI improves retention and helps reduce external hiring, measures of success are straightforward — fewer dollars spent backfilling roles suddenly left open by otherwise avoidable attrition, external hiring costs decreasing because it’s only done when the organization absolutely knows it’s necessary.
These are the evergreen KPIs that make sense to traditionalists. You can be effective in showing the value of AI in HCM to organizational leadership by sharing these KPIs — to a point. It’s in getting from there to the future of work, however — where AI plays a definitive role — when things begin to fall apart for the uninitiated organization stuck in measuring success in only the old ways.
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