A lot has changed since The Monster Board, arguably the first-ever internet job board, went live in 1994. Between then and now, the massive adoption of job boards, search engines, job aggregators, LinkedIn, social media, and online job applications have radically transformed how candidates find and land jobs.
Along the way, HR and recruiting teams dealt with their own deluge of new technologies that have revolutionized how employers attract, engage, 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.
More recently, the evolution of machine learning and the availability of significant data sets (a.k.a., big data) have helped to streamline recruiting processes even further, making it easier than ever to identify and recruit hard-to-find talent.
This is about more than HR, though. Pick up any Fortune 2000 company’s annual report and you’ll see that corporations across industries recognize hiring and retaining talent is a business imperative. PwC’s 2017 CEO Survey describes this imperative as “the talent challenge.” According to the survey, 52 percent of CEOs plan to hire more employees, but 77 percent of CEOs worry about the effect skills shortages could have on their businesses.
PwC recognizes the role tech may play in addressing the talent challenge, writing,
“Automation and the advent of artificial intelligence (A.I.) brings the promise of increased efficiency, productivity, and profitability within reach, if CEOs can work out how to best access their potential.”
What Is Machine Learning and What Can It Do For Recruitment?
As defined by computing pioneer Arthur Samuel in 1959, machine learning is “the field of study that gives computers the ability to learn without being explicitly programmed.” Today, ubiquitous computer usage, robust yet inexpensive cloud-based infrastructure, and high-quality data have made machine learning more available and valuable than ever before.
The many ways machine learning is revolutionizing recruitment include:
- Handling repetitive and time-consuming tasks including sourcing, resume screening, and candidate short-listing
- Simplifying the scheduling of phone, video, and in-person interviews
- Analyzing resumes and identifying data patterns against set criteria to find strong candidates
- Addressing 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
- Using automated video interviews to screen candidates by comparing their vocabularies, intonations, and body languages with those of existing top-performers
- Eliminating the unfair and unconscious bias that factors into many hiring decisions
Beyond its benefit to in-house recruiters, machine learning also helps employers identify, evaluate, and engage with external recruiters.
A key component of the strategic talent acquisition mix, external search firms are valuable resources for recruiting. They can streamline the sourcing process by presenting to employers only pre-qualified candidates who meet the role’s criteria. Because of their extensive knowledge of both the job specs and the candidate pool, external recruiters can often source these candidates much more quickly than the employer could on their own.
Machine Learning Plus Recruitment Marketplaces: A Powerful Combination for Employers
According to the American Staffing Association, there are about 20,000 staffing and recruiting companies in the U.S. today. 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 one-on-one approach to recruiting, which limits employers and search firm recruiters to working only with the people they know directly, new marketplace recruiting solutions combined with machine learning capabilities can help employers harness the power of data to pinpoint the best agency recruiters.
Machine learning tools built into marketplace recruiting solutions can use cutting-edge natural language processing, latent semantic analysis, and deep neural networks to understand the unstructured text of job descriptions. Then, associated performance-based matching algorithms can identify which search firm recruiters have proven to be most successful in delivering high-quality applicants to meet relevant specs like skill sets, geography, industries, etc. By and large, these top-performing recruiters are “specialty recruiters” – that is, recruiters who focus exclusively on filling the same kinds of roles.
Additionally, the marketplace recruiting approach lets employers cast a wider net 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 can be quite impressive.
Where Will A.I. and Machine Learning Take HR in the Future?
With businesses paying increasingly more attention to performance metrics and accountability, more HR departments are integrating people and process analytics into their recruiting processes. If talent acquisition teams don’t have data on the productivity and effectiveness of their recruiters, both internal and external, they can’t improve their methods and will lose out to competitors.
Beyond recruiting, smart HR professionals and hiring managers will increasingly use data analytics throughout the entire employee life cycle. Data capture and analysis will hold up a mirror to company-wide employee performance and suggest improvements for onboarding, career development, employee satisfaction, and even termination and offboarding.
As Google’s former Senior Vice President of People Operations Laszlo Bock writes in Work Rules!, “Don’t trust your gut. Use data to predict and shape the future.” Bock’s new company, Humu, takes this directive to heart. Its mission is one all employers should aim for in today’s age: to make work better “through science, machine learning, and a little bit of love.”
While A.I. and machine learning will make recruiting smarter, easier, faster, and cheaper, the contributions of human recruiters will continue to be essential. As Michael Tresca, director of global talent acquisition communications at GE, writes,
“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.”
Both powerful machine learning algorithms and great recruiters – with their experience, relationships, and emotional intelligence – are needed for recruiting success. 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.
People aren’t robots. Let machine learning do what it does best, which is finding the very best specialty recruiters. Then, let the human specialty recruiters do what they do best which is delivering the first-rate talent your company needs.
Ken Lazarus, Ph.D., is CEO of Scout Exchange.