In 2018, the annual LinkedIn survey highlighted a crucial shift in Global Recruiting Trends—the use of AI in talent acquisition. Back then, 67% of employers had agreed that leveraging people analytics in recruiting had saved them time with the help of artificial intelligence (AI) and machine learning (ML). The market’s increased volatility has made recruitment more complex, forcing major organizations to rethink their hiring strategies. 52% of Talent Acquisition Leaders now say that the most challenging part in recruiting talent is screening potential candidates from a large pool of applicants—making the case for recruiting with AI even stronger.
Some critics, however, have argued that AI-based resume screening can introduce bias and accused it of being devoid of any transparency and accountability. This was made evident in the 2018 Amazon debacle. Amazon discovered that the company’s internal AI/ML-driven recruitment tool—which was trained on a decade’s worth of hiring patterns and data—discriminated against women. While more people, including lawmakers and Silicon Valley activists, push for regulation and ‘algorithmic fairness,’ AI recruitment continues to grow and dominate the market.
What does AI in recruitment/talent acquisition mean?
AI in recruitment indicates harnessing the powers of data analytics, machine learning, and artificial intelligence in the recruitment process. With technological advancements, leveraging these tools is now possible across any stage of talent acquisition—from shortlisting ideal candidates for an open position to conducting interviews and automating manual tasks like salary negotiation. Application Tracking Systems (ATS) are an excellent example of AI and machine learning in recruitment. Scanning resumes, calls to recruiters, booking interview slots, conducting interviews, and follow-up processes are automated, tracked, and stored using popular AI/ML-based recruitment tools.
How AI and Data Analytics are Used in Recruitment & Talent Acquisition
More recruiters are using data analytics and artificial intelligence in almost every step of the recruitment process. Here are some examples of how:
- AI-based recruiting tools keep track of all open positions in an organization and create targeted advertisements in job portals to attract potential candidates.
- These tools can screen applicant resumes and predict a candidate’s performance based on publicly shared data like skills, location, experience, even their previous contacts with prospective employers.
- Some AI recruitment tools can even forecast when an employee is likely to switch companies or look for another position within their current organization by analyzing their job movements, promotions, and overall fit for the new position at hand.
- Once the predictive analysis concludes, the tools can call selected candidates and start booking interview slots based on their responses. ATS can track and save these responses in a repository that ML algorithms can be trained on later.
- Artificial Intelligence in recruiting, now more increasingly than ever, is including AI Video Interviews. Studies have shown that this can help organizations save a ton of money and effort.
- After a candidate is selected, automation takes over for the next set of processes, including generating offer letters and other documentation, creating an employee profile in the organization, etc.
- Third-party recruitment sites that use AI recruitment tools store this end-to-end data for both candidates and recruiters. They then use predictive algorithms to recommend both open positions and suitable professionals, respectively.
Leveraging Analytics and AI in Talent Acquisition – Tips & Tricks
Many tools from both prominent and outlier organizations are available for employers to harness artificial intelligence in recruiting. The common ways companies can use these in the talent acquisition process are:
Tip 1: Use Chatbots:
AI-Powered Recruiter Chatbots are an excellent way to streamline the initial interviewing process. From screening candidates on a job site to interacting with them to scheduling interviews as per the applicant’s chosen time slots, machine learning and data analytics help these automated bots to make on-the-spot decisions while selecting interview candidates. In addition, linking this to an ATS can further track the status of these candidates.
Tip 2: Use AI to vet the candidate’s character:
AI-driven interview tools use tests and behavioral patterns to analyze and predict a candidate’s likelihood to succeed in a particular role. This has become increasingly prevalent in organizations like Uber, Lyft, and Zomato in selecting trustworthy candidates, increasing the company’s reliability index.
AI recruitment tools also can automate background & reference checks for candidates. This reduces bias by enforcing the same set of rules for everyone and shortens the overall time taken for the process by eliminating the handshake delays between departments.
Tip 3: Use AI-based recruitment tools to diversify hiring & reach hiring goals:
Artificial intelligence in the hiring process is helping eliminate racial and other biases, increasing transparency around the entire talent acquisition process. With algorithms squarely focused on a candidate’s performance, diversified hiring is prioritized by default.
Additionally, organizations can reach their hiring goals by integrating their recruitment KPIs and metrics with AI/ML-based talent acquisition tools.
Tip 4: Automatically generate remuneration and other benefits for candidates using data analytics:
Key steps in a recruitment process for an HR professional include negotiating the salary with selected candidates and determining other benefits like healthcare, joining bonus, paid leaves, overtime pay, etc. These are often time-consuming and can be tricky. AI-based recruiting tools help by analyzing key demographic information for a role via data points like the candidate’s experience, prior job roles, salaries, and compensation offered in similar positions in the city/area of hiring. AI may also generate the optimum salary and associated benefits for a candidate, taking the complexity out of the situation.
Tip 5: Use the predictive analysis for existing employees for internal recruitment:
The scarcity of available talents has made companies look inwards for repositioning existing employees in new roles. LinkedIn’s 2020 Global Talent Trends Report suggests that internal recruitment has increased by 10% since 2015. AI-powered hiring tools can help choose the best candidates for these open internal roles, with data-driven analysis and forecasting based on pointers like promotions, job movement, yearly performance ratings, etc.
Tip 6: Choose the right AI Hiring Software, and don’t be over-reliant on these tools:
Organizations need to choose the right AI recruitment tool that fits their purpose as each feature set varies. An essential step before investing in a tool should be to calculate its long-term ROI.
Another recommendation is to treat an AI/ML-powered tool as a suggestive player and not a decision-maker. This is relevant even more so because AI has been seen, over and again, to gain the same bias as humans (since they are dependent on information that can contain different skews).
How Companies are using AI in Recruitment and Talent Acquisition
Almost all of the world’s largest recruitment firms use some form of AI/ML-based predictive data analytics and recruitment tool for their purpose. Here are some examples:
- LinkedIn has its own AI-based hiring tool called Recruiter that ranks and chooses candidates from its database of millions of users.
- ZipRecruiter also uses similarly driven AI/ML-backed hiring tools.
- HireVue and Arya are behind the world’s two most popular AI recruiting tools with powerful sets of features that many companies like Unilever use to automate their entire talent acquisition process.
- Hilton uses AllyO, a popular AI recruitment software, for its application screening and scheduling interview calls.
- ThredUp uses a mass-texting AI tool called TextRecruit to hire hourly workers. Every month, about 100-200 workers are hired this way.
Even though hiring is a primarily social process, different contributing factors like market instability, lack of candidates, and push towards a better, more transparent, and unbiased recruiting process have strengthened the case of using predictive analytics hiring software alongside artificial intelligence. While many have been quick to adopt, recent revelations on skewed AI caused by biased training data have caused others to remain skeptical about this technology. Nevertheless, AI recruiting tools are here to stay. Rest assured, situations where robots make the final hiring decision are still quite some time away.
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