We have been studying the role of AI in HR for many years. Today we are announcing our brand new AI in HR Certificate Course, built on our years of research and client interviews in all areas of HR. (And a 10% discount if you sign up before the end of March! Coupon code: JBPREDICTIONS10).
AI in HR Has A Rich History
Starting in the early days of applicant tracking systems (ATS), vendors have been using inference models to scan resumes, match them to job descriptions, and try to assess “fit” or “match” between a job seeker and a position. (This all started with Monster.com and Taleo in the late 1990s.)
As recruiting vendors grew, companies started to build “sourcing tools.” These systems (popularized by LinkedIn) used machine learning to scan millions of employee profiles and identify skills, job history, and other information, all in a goal to match job seekers to jobs.
While this was going on, corporate training vendors were building AI-based systems to recommend training content, promote learning paths, and even promote career journeys (Saba built this in the early 2000s). This led to the market for Learning Experience Platforms (LXP) which used machine learning to identify skills topics within training content and then associate skills with people, by keeping track of what courses they searched for.
Simultaneously with these two paths, in the mid 2000s companies started to look at work, performance, and talent data to build predictive models for hiring, retention, and other internal uses. These People Analytics teams built large data sets and tried to predict which job candidates would fit, who was likely to quit (and why), and even look at predictions and signals that would predict fraud, accidents, or harassment.
(I remember one of the executives at Enron told me he now had a data model that would have predicted the financial fraud in the company simply by reading the tone, pace, and words in emails – which inferred stress, worry, and fear.)
Since then we have seen AI enter the world of career management (talent marketplaces), leadership assessment (Heidrick uses AI to identify hidden leaders), highly regulated job candidates (Seekout can find rare technical proficiencies in healthcare), and even identify people who are most likely to excel in oncology (Draup is working on this).
And there’s much more to come. Not only can machine learning be used to solve a myriad of HR problems and make People Analytics easier, we can now use Generative AI to transform the employee experience (eventually making it possible to do away with complex corporate employee portals), record and notify people of meetings, automatically generate courses and tests, and even decide when and how to send an employee a message based on their preferences (Firstup). And tools like Vee (Visier) can turn integrate complex people data and turn People Analytics into a simple conversation with a chatbot.