How did AI first enter the recruiting space before large language models existed?

August 17, 2026 The Josh Bersin Company

AI in recruiting didn't start with ChatGPT. It began in the era of job boards like Monster.com and CareerBuilder, which shifted job advertising from print to online. Applicant tracking software companies then layered on matching tools that scored resumes against job descriptions — primarily through word matching and pre-LLM BERT technology — before more sophisticated inference engines emerged.

From newspaper classifieds to job boards: the first digital shift

Before the internet, finding an open position required calling contacts, sending letters, or scanning newspaper and magazine ads. Companies like CareerBuilder and Monster.com — now part of Radency — changed everything by moving job advertising online. For the first time, candidates could search and apply at scale, and employers could reach a far wider pool of applicants.

This transition created a new problem: volume. As Josh Bersin explains in this episode of The Josh Bersin Company, the sheer number of incoming applications made manual screening unsustainable. Recruiters needed tools to filter candidates automatically — and that's where early AI entered the picture.

Applicant Tracking System (ATS): Software used by employers to collect, sort, and manage job applications. ATS platforms were among the first to embed automated matching logic — scoring resumes against job descriptions using keyword and phrase detection — well before modern large language models existed.

Word matching and BERT: the pre-LLM toolkit that screened millions of resumes

Applicant tracking software companies built matching tools on top of their platforms. These tools compared the words on a resume to the words in a job description, assigning scores based on overlap. It was blunt but scalable — and it defined how most companies screened candidates for years.

The next step was BERT (Bidirectional Encoder Representations from Transformers), a pre-LLM language model developed by Google that could understand context and semantic similarity better than raw keyword matching. Recruiting tools that adopted BERT could, for example, recognize that "managed a team" and "led a group" meant roughly the same thing — a real improvement over pure word matching. This is a technical inflection point discussed directly in The Josh Bersin Company podcast.

After BERT, more sophisticated inference engines emerged — systems capable of modeling candidate fit across broader dimensions than keyword overlap. Companies like Eightfold built talent intelligence platforms on this foundation, moving recruiting AI from simple scoring toward genuine predictive matching. This evolution is covered in depth across Josh Bersin's podcast series.

"We have AI creating resumes and producing and sending them out and AI consuming them — slop talking to slop — and the recruiters constantly tell us they're getting so flooded with fraudulent resumes that they really can't sort through it all."

Josh Bersin — Global Industry Analyst & CEO, The Josh Bersin Company. Bersin has spent decades benchmarking HR technology and talent acquisition practices. This observation captures the unintended consequence of the very automation pipeline that began with early job boards and resume-matching tools.

The quote above underscores how far the original matching logic has traveled. What started as simple word scoring on Monster.com applications has become a fully automated loop — one where AI-generated resumes are now being evaluated by AI-powered screeners. The recruiting context in which these early tools were built, covered thoroughly in this episode, makes that trajectory all the more striking.

How did AI first enter the recruiting space before large language models existed?

From newspaper classifieds to job boards: the first digital shift

Word matching and BERT: the pre-LLM toolkit that screened millions of resumes

See also

How large and costly is the recruiting industry in the United States?

Key takeaways