A lot has changed since job seekers first could search a job database with a web browser (when the original Monster Board went live in 1994). Since then, the massive adoption of job boards, search engines, job aggregators, LinkedIn, social media and online job applications have radically transformed how candidates seek, apply and are selected for jobs.

At the same time, an onslaught of HR Tech solutions has revolutionized how employers attract, engage, recruit and hire employees. From applicant tracking systems (ATSs) to social and mobile recruiting, HR departments now work with a dizzying selection of tools and strategies.

Machine learning and big data are transforming talent acquisition

More recently, the evolution of machine learning tools, and the availability and ability to process significant data sets ("Big Data") is helping to streamline and improve how search firms and employers identify and recruit 'hard to find' talent, which is an essential contributor to business success. Pick up any Fortune 2000 company's annual report and you’ll see that growing corporations recognize that hiring and retaining talent is a business imperative.

The PwC 2017 CEO Survey (of almost 1,400 CEOs) describes this imperative as "The Talent Challenge," and notes that while 52% of CEOs surveyed plan to hire more employees, 77% of CEOs worry that skills shortages could impair their company’s growth.

"The challenge isn't necessarily about finding new and innovative ways of accessing the talent market," PwC wrote, "it's about using the full range of HR expertise and tools to identify skills gaps, anticipate needs, spot potential and build the workforce for the future." Both automation and the advent of artificial intelligence (AI) "brings the promise of increased efficiency, productivity and profitability within reach, if CEOs can work out how to best access their potential," PwC said.

What is machine learning and what can it do for recruitment?

Coined by computer pioneer Arthur Samuel in 1959, machine learning gives "computers the ability to learn without being explicitly programed."

Fast-forward to the present day when advanced machine learning based on pattern-matching machine algorithms now coincides with ubiquitous computer usage, robust yet inexpensive cloud-based infrastructure, and high data quality.

Among the many ways machine learning is revolutionizing the recruitment marketplace is its ability to help in-house recruiters to:

• Handle repetitive and time-consuming tasks including sourcing, resume screening and candidate shortlisting.
• Simplify and reduce the tedium of scheduling phone, video and in-person interviews.
• Find strong candidates by analyzing resumes, identifying data patterns that produce the best results (according to set criteria).
• Address semantic issues common in sourcing (e.g. going beyond keyword matching to identify candidates whose resumes contain work experience and skills relevant to the job opening).
• Use automated video interviews for initial candidate screening. HireVue and similar offerings use voice and face recognition software to compare applicants’ choice of words, tone of voice, and facial expressions with the vocabularies, intonations and body language of their best performing hires. Data captured through the video interviews is analyzed through machine learning algorithms to help recruiters rank and engage with "best fit" prospective employees.
• Computer algorithms can eliminate unfair bias that humans may have in hiring decisions (e.g. discrimination based on age, gender or race).

In his 2011 best-selling book, Thinking, Fast and Slow, Daniel Kahneman summarized research conducted over decades which found that humans are inherently biased and place too much confidence in their judgment. Whereas many HR pros and hiring managers may favor applicants who share things in common with them (hobbies, causes, schools and hometowns), machines are impartial — serving up those candidates who best meet job-related criteria.

Marketplace recruiting helps find the right external recruiters

According to the American Staffing Association, there are about 20,000 staffing and recruiting companies operating around 39,000 offices in the U.S.  With so many to choose from, employers face the massive challenge of finding and engaging the right search firms that can deliver the best candidates as quickly and affordably as possible.

As opposed to the traditional "1:1" recruiting approach, which limits employers and search firm recruiters to work with only those "whom they know," new marketplace recruiting solutions (powered by machine learning) can help employers harness the power of data to pinpoint the best agency recruiters to fill each and every job.

Machine learning tools built into marketplace recruiting use cutting-edge Natural Language Processing (NLP), latent semantic analysis and deep neural networks to understand the unstructured text of job descriptions. Then, associated performance-based machine learning matching algorithms identify which search firm recruiters have proven to be most successful in delivering a high-quality pool of applicants to meet relevant specs (e.g. skill sets, geography, industries, company culture, prior employer relationships, etc.).

Additionally, the marketplace recruiting approach lets employers cast a wider net, not only by enabling them to find the best specialists for each job, but also by allowing them to work with multiple specialty recruiters simultaneously. By increasing the number of external search specialists working on any given job opening, companies can significantly improve their time-to-fill while reducing costs. The results are impressive. Employers using machine learning algorithms in a marketplace recruiting setting have consistently lowered their time to fill by over 30%, increased their pool of qualified applicants and fill rates by up to 30%, and at the same time, reduced their cost per hire by an average of 30%.

Where will AI and machine learning take HR in the future?

With businesses paying more attention to performance metrics and accountability, more HR departments are integrating people and process analytics into their recruiting processes. If talent acquisition doesn’t have data on the productivity and effectiveness of their recruiters, both internal and external, they can’t improve and are going to lose out to their competition. Likewise, if they cannot measure, benchmark and improve their processes, they do not have a chance to consistently hire the best talent.

Beyond recruiting, smart HR professionals and hiring managers will increasingly use data analytics throughout the entire employee life cycle. Data capture and analysis will help hold up a mirror and suggest improvements for onboarding, career development, employee satisfaction/recognition/retainment, even in termination and offboarding.

A recent report issued by industry analyst John Sumser explores the impact of machine learning, NLP, big data and neural nets on the HR technology industry. In the research, Sumser observes a wide swath of "intelligent technology being applied to a variety of problems across the HR spectrum" and predicts that "huge benefit will accrue to companies that are willing to be early customers [of this intelligent technology]."

Conclusion

While AI and machine learning will make recruitment smarter, easier, faster and cheaper, the human contribution that recruiters make will continue to be essential and valuable. In an 2017 article, Michael Tresca, director, global talent acquisition communications at GE gave his perspective on the question, "Will robots replace recruiters?", writing: "it seems unlikely recruiters will be replaced by robots anytime soon. But with artificial intelligence and automation at their fingertips, recruiters will be able to engage more candidates in a personalized way more than ever before."

Therefore, both powerful machine-learning algorithms as well as great recruiters, with their experience, relationships, and emotional intelligence (EQ), are needed for recruiting success.  As such, the role of niche recruiters will continue to be crucial in hard-to-fill, high demand and critical job types, especially in high-growth sectors such as healthcare, banking and finance.  Finding the best specialty recruiters not only enables employers to find great candidates, but also helps them drive the engagement needed (interacting with them one-on-one to assess their interest, feeling out hidden concerns, putting together compelling compensation packages, and helping companies negotiate job offers that land top talent) for successful hires.

The bottom line: People aren't robots — let machine learning do what it does best, which is finding the very best specialty recruiters.  And let the human specialty recruiters do what they do best, which is delivering the first-rate talent your company wants and needs.