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Time is money, and saving recruiters time by using AI to make their tasks more effective and efficient can affect the bottom line. In addition, AI can impact workforce productivity by successfully sourcing, screening and identifying top-tier candidates.
Vendors tout the applications of AI in recruiting as a means to alleviate repetitive tasks, better target pools of suitable candidates and support bias-free interviewing. Building on earlier uses of AI in predicting attrition, many current applications address multiple time-consuming steps in the HR hiring process and subsequent talent acquisition.
Originally, most of the effort addressed challenges faced by corporate HR or staffing companies using recruiting solutions. Increasingly, however, AI is being applied to the sourcing and application processes from the candidate's point of view.
AI's capacity to determine an applicant's required qualifications, skill sets, relevant experience, length of tenure in previous positions -- e.g., the red flag of frequent job changes without evidence of promotion -- and unique characteristics is a valuable tool at any point in the HR hiring process.
However, a potential danger lies in AI's limitation to overcome bias that is inherent in some algorithms, or to recognize the value a diverse or outlier candidate can bring to a team. AI providers recognize this and are proactively addressing bias in their products.
While AI-based talent acquisition software might identify a candidate's qualification, cultural fit remains a bit harder to recognize. Experience and skill matching is easier than ascertaining a candidate's social adaptability and creativity, which may be crucial to successful teamwork and innovation.
Today, recruiters scour the web for the talent they need, as tomorrow's highly skilled workforce is threatened by retiring boomers, declining educational preparation for future jobs and barriers to hiring internationally. AI can help -- something as simple as smart location tools can help companies evaluate the likelihood of a candidate being successful from a large volume of hiring applications based on data from past commuting experiences and the quit rates of previous employees.
AI used properly in the HR hiring process can get past the clone-seeking of yesteryear's recruiting applications that produced a plethora of corporate environments lacking gender, racial and creative diversity. But AI needs data, and it needs to access more information than who was hired in the past and how to get more of him or her.
Smarter applications and smarter applicants
Statistics demonstrate that many potential applicants walk away from the laborious online applications that used to dominate applicant tracking solutions. Companies bemoan the number of applications begun but never finished, which decreases the number of potential qualified candidates in their talent pool.
While vendors who create applicant tracking systems try to make them flexible and customizable, recruiters don't have time to adapt them for different jobs. Applying AI and machine learning to the application itself can offer mutual information for the hiring manager and the applicant.
The manager can learn more about the applicant in terms of the specific position he is seeking and the applicant can get a better understanding of the job to determine his level of interest with relevance to the skill set. The result can be a win-win, saving both parties time and energy, increasing pre-interview knowledge, and ultimately strengthening the industry workforce.
Tools like AllyO and TextRecruit Ari use natural language processing and machine learning to make the application process easy and convenient for candidates by enabling dual-conversation live chat that can give way to intelligent text-based discussions. Applied to the application process, AI technology can help applicants go beyond just applying to a company to actually understand the specific job at hand and tailor the data required for that position in real time.
Candidates tend to apply for jobs similar to their previous one, whether or not they really liked or succeeded in that position. For example, college graduates may take the first job offer they receive and get stuck in a specific professional rut long term.
Machine learning can present different kinds of opportunities to those job seekers, and sourcing tools for recruiters can expand the often narrow requirements in a requisition to find capable candidates. Such creative sourcing would not apply in cases requiring very specific skills -- e.g., neurosurgery or aerospace -- but these cases have unique recruiting parameters and are decidedly the exception.
Candidate selection does not use much of the application data, which can be more effectively collected later in the application process rather than being a potential barrier to getting a candidate into the applicant pipeline. Not only can the application process between person and machine be smarter, but candidates can access AI tools that can assist them in selecting which jobs to look for based on information such as their past experience, education, social media activity and other information available today.
Tomorrow's job interview: Are candidates prepared?
While it may be disconcerting at first for a candidate to interview with a chatbot or some other AI-operated medium, AI can generate useful data from gestures, facial expressions and voice tone that may free recruiters from biases based on accents or regional dialects.
Candidates may find themselves more comfortable with an AI interview, likely on their mobile devices, if the technology is smart enough to ask relevant questions that relate to the position -- something human interviewers often fail to consider. Questions like, "Where do you see yourself in five years?" may not relate at all to one's ability to succeed in the job at hand.
AI tools in HR generally move on a continuum from descriptive to prescriptive as they analyze formatted and unformatted data to get smarter. Nearly all talent acquisition technology providers use AI and machine learning in their recruiting platforms, including Oracle, SAP/SuccessFactors, Workday, Ultimate Software, iCIMS, and younger companies such as Engage HR and Symphony.
Confronted with serious issues, such as the one Amazon faced before scrapping its AI-based recruiting tool when the company realized it discriminated against women, vendors are actively attempting to create bias-free -- or at least bias-neutral -- tools for sourcing, recruiting and hiring tomorrow's talent.
The coupling of AI and human capital management is inevitable. We need to remember, however, that AI is technology; true judgment is human.
Jennifer Boyd, Ph.D., MBA, also contributed to this article. She is a healthcare strategic planning, educational program development and communications specialist.