8 strategies HR can use to recruit Gen Z applicant tracking system (ATS)

6 ways AI can improve the talent acquisition process

Machine learning, natural language processing and RPA can source candidates faster, match their skills better and vet them with less bias. An HR technology expert explains how.

Artificial intelligence is the most recent platform play with radical promise for talent acquisition. 

Why a "platform"? Because the technological areas that make up AI -- machine learning, natural language processing (NLP), predictive analytics and robotic process automation (RPA) -- are underlying functions that support a great and growing number of HR business processes. In other words, AI is not an individual application to be purchased; it is an intelligent infrastructure that can underlie many business applications -- HR systems among them.

An AI program analyzes data -- ordinarily an immense amount of it -- then decides or recommends what should happen next to complete a task.  The ability of the application to continually "learn" and adapt from that machine learning is a fundamental aspect of any AI program. Think of AI as a hard-working engine tirelessly supporting your talent acquisition processes.

Here are six important benefits of using AI in talent acquisition

1.     AI can accelerate candidate sourcing

Recruiters cannot be expected to know everything about the nuances of job requisition language, especially new recruiters or recruiters for highly technical positions. This is where an intelligent, AI-based engine comes into play. Starting with the wording of requirements in the job req, it can expand the search beyond the exact wording of that req to related industry-specific language and terms. Unlike a simple word-matching algorithm, AI-enabled sourcing enables a search by intent or meaning.

In addition, through the deep learning that permeates AI systems, the concept of skills adjacency is addressed. If a candidate knows or can do a certain thing, the software can infer the prerequisites of that skill or knowledge and determine the likelihood of the person having a similar but different skill based on analysis of millions of data points. Candidate sourcing is thereby facilitated with a wider, more inclusive pool of candidates that may be missed by humans, and over time can answer such questions as: What are the best sources of high-performing candidates for my firm who will stay on the job for more than two years?

In sourcing candidates or, later, evaluating candidates, AI has the ability to infer skills or interests from, for example, prior jobs held, volunteer positions or education that are not directly stated on the applicant's resume. Because the program is always learning, it retains associations it can then apply in new and different recruitment scenarios. This can increase the possible positions within an organization for which the candidate (or a current employee) may be suited, even if they did not actually apply for them.

2.    AI makes applicant screening more efficient

In screening, as in sourcing, data may be varied and may include past or current employee resumes; competency or skill models that are tied to the specific industry or task; incoming resumes or records of employee success or longevity based on skills, education or even location. All are examples of the kinds of data from which a machine learning program can learn rules and patterns. External data sources such as national or international salary ranges, U.S. Bureau of Labor Statistics job classifications, etc., can also be data sources fed into the "brain" that is AI. Machine learning can then help provide answers to questions such as: What candidate traits best correlate with long-term success in this particular position?

Because candidates often want to apply to a company rather than a specific job, AI can recommend positions that are relevant to the skill set, education or "soft" skills of each candidate. This ability is also useful with current employees for recommending new positions or opportunities based on an employee's skills, interests and aspirations. This is a great advantage to both applicants and hiring companies. If, for example, a college graduate thinks she might want to work for Microsoft, she can submit her resume and the AI-based talent acquisition tools can tell her the jobs or job areas for which she may be a likely candidate. It is a huge time-saver for recruiters and candidates alike.

3.     AI can help eliminate bias

Many vendors employ AI in talent acquisition applications to alleviate bias in hiring and further diversity in future employees rather than perpetuating past homogeneity in race, ethnicity, gender, gender preference, color, etc. In addition, an unbiased software application can help eliminate bias arising from other characteristics that lead to discrimination, such as regional accents, perceived social standing and parental status. An AI tool is not going to be overly impressed with a beautiful resume layout or underwhelmed if a resume has a typo, two things that can unconsciously influence human decisions.

Such smart programs evaluate job requisitions and postings for bias-tinged words and evaluate other prose communications for gender-laden language, for example.

However, not all products purporting to use AI for talent acquisition are the same. If a company only uses its own historical data -- which is by definition limited -- biased output is likely to result. Successful applications that embed AI have access to deep-learning neural networks of external data with which to complement internal data sets.

4.     AI adds intelligence to online interviewing

In online video interviews, AI can catch fine points: subtleties such as gestures and facial expressions that humans might not pay attention to and miss. Similarly, because AI can analyze huge volumes of data without fatigue, patterns may well emerge that would otherwise be overlooked by busy recruiters and hiring managers. It is this ability to make sense or find correlations in masses of information that makes AI such a valuable tool.

5.     AI is the foundation for HR bots

RPA uses bots -- think Alexa or Siri, for example -- to replicate human actions in handling time-consuming questions and tasks. The increasingly widespread use of these natural language chatbots, some of which are capable of understanding and responding to both written and spoken input, is becoming de rigueur in human capital management software, including talent acquisition tools. Today's users are accustomed to bots as sources of information and direction and are used to interacting in natural language with bots in many online and telephone transactions. In HR, bots are noted for their value in handling the repetitive questions employees, especially new employees, tend to ask, such as: What holidays does the company take off? How do I get proof of employment?

The judicious use of bots is even more useful in talent acquisition because bots can respond to a candidate immediately and often in personalized ways. Tools such as AllyO, recently acquired by HireVue, or iCIMS TextRecruit's Ari use NLP and machine learning to enable dual-conversation live chat and initiate intelligent text-based discussions, both of which are easy and convenient for the candidate.  These applications understand people's natural inputs (rather than just programming code) and ultimately improve feedback and predictions by learning over time. Their conversational interfaces can improve candidate engagement, and receiving bot-supplied answers in real time minimizes the need for human intervention in the recruiting process. That saves time for the recruiter, the hiring manager and the candidate.

6.     AI saves money over traditional methods

The efficiency and speed of an AI-enabled talent acquisition application has positive cost benefits in that the AI engine never tires or takes a day off.  CareerBuilder, for one, reported that its recently updated talent acquisition software has reduced the cost per candidate by as much as 50% since the product was rebuilt to employ AI. More expedient hiring, including improved time-to-fill and time-to-hire metrics, brings real cost savings to an organization.  An empty seat is always an expensive proposition.


AI is close to ubiquitous in the new talent acquisition applications to hit the market. That it will make sourcing, screening and hiring easier and more efficient is clear. AI's applicability in HR and in all of business, however, will be widespread, and organizations will be well served to create governance committees to oversee its use. 

Because AI technology is in effect recommending, making choices, and predicting, human vigilance remains necessary to ensure the right data is being fed to the engine and that the output makes sense for the organization. Cathy O'Neil, author and data scientist, makes a compelling point in discussing the use of AI in decision-making: "Algorithms are opinions embedded in code." Those opinions are only good when they relate to the requirements of your business.

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