AI in Talent Acquisition

Spoiler alert: We don't think AI will replace recruiters anytime soon.

But we do believe that the role description of the recruiter is bound to change.

Because when you think about the buzz around AI in recruitment, one word cuts through the noise and rises above the rest: efficiency.

This efficiency manifests in how quickly recruiters write emails, take notes, improve outreach, and decide which candidates to screen.

Everyone can agree that AI is a lifesaver when it comes to automating the more mundane administrative tasks that pop up in a recruiter's day-to-day.

But administrative work becoming more and more efficient is just the first step to understanding AI's role in recruitment. Step two is to figure out what you will do with the gained efficiency and the time and headspace that comes with it.

Do you use the additional time to improve the quality of candidate interaction? Do you use it to reduce the size of your recruitment team? Do you use it to focus and invest in current employees?

How you spend your gained efficiency will depend on your team's strengths and weaknesses, the degree to which you incorporate AI in your recruitment process, and your awareness that efficiency at all costs isn't always the correct answer. Because the truth is, quality and human touch might triumph over your ability to automate a task in the grand scheme.

So, with that said, let's jump into how you can incorporate AI into your recruitment process and see how efficiency you can unlock with these tools at your disposal.

How to Incorporate AI into Recruitment

There are obvious and non-obvious ways to incorporate AI into your recruitment process (both of which we will get into).

The first step of AI will be to automate administrative and more "boring" tasks - and human interaction is

1. Automate resume reviews. One of the most significant benefits of using AI in recruitment is automated screening. AI can read through applications and compare resumes to the job at hand to quickly identify candidates that meet the required criteria. Language model AI can even read between the lines of resumes and cover letters to make a recommendation. Using AI to narrow down top-of-funnel applicants allows recruiters to efficiently go through hundreds or thousands of applicants in a fraction of the time. The time gained can be channeled into more personalized communication or other human-centered tasks that take a back seat in processes with a high volume of applicants.
2. Chatbots. Chatbots have been a longstanding tool for improving customer experience, and it's about time they are given a chance to make the same impact on candidate experience. Chatbots can help improve the candidate experience by answering candidates' questions about the company, job openings, and the recruitment processes. They can also schedule interviews and send reminders to candidates, freeing up admin work from the recruitment team and ensuring candidates don't fall through the cracks. However, keep in mind that these sorts of automated responses are still best suited for top-of-funnel candidates. For candidates deeper into your recruitment processes or who have already met team members, it's best to keep that communication high-touch and personal rather than from a chatbot.
3. Transcribed interviews. Interview fatigue is real if you're a recruiter or even a hiring manager. It's even more real when you're scrambling to write down every bit of important information you uncover during these interviews. And while you can always record each interview and rewatch those a-ha moments, this is time-consuming and can even feel invasive for some candidates. Therefore, using AI to transcribe the interview is a more effective and less invasive way to capture those a-ha moments - and easier to find them using Control+F later.
4. Employee referrals. AI can improve employee referral programs by analyzing data from employee referrals and identifying patterns that lead to successful hires. You can then use this information to refine the referral program and increase the likelihood of successful hires.
5. Structure pipeline data. It's not uncommon for recruitment teams to use spreadsheets to help them keep a granular overview of their pipeline. However, not everyone is an Excel wizard, which is where AI-powered spreadsheet tools, such as Sheet+, can come in handy. These tools help take the technicalities out of formula writing and do the heavy lifting when formatting and correcting data entry.
6. Predictive analytics. AI can also be used for predictive analytics in recruitment. By analyzing data from past recruitment processes, AI can identify patterns and predict which candidates are most likely to succeed in a particular role. This can help talent teams make more informed decisions about which candidates to hire.
7. Candidate Retention. Following the same thread at predictive analytics, AI can be used to improve retention rates by identifying candidates who are likely to drop out of the interview process. AI can identify those at risk of leaving by analyzing data such as candidate engagement and provide targeted interventions to retain them.
8. Skills matching. AI can be used to analyze job descriptions, and candidate resumes to identify the specific skills and qualifications needed for a role. This analysis can then match candidates to the job more accurately, improving the quality of the candidate pool. However, someone who is well-versed in logic tests might score disproportionally high.
9. Cultural fit. AI can identify candidates whose values and personality traits align with the company by analyzing data such as social media activity, online behavior, and communication patterns. However, a serious consideration when using AI to determine cultural fit is the risk of falling into the "like me" bias. This bias is when you are predisposed to hire people who are similar to you - specifically for the sake of them being a "cultural fit" - which can have serious consequences on diversity. In short, when you ask AI to find the most suitable candidate from a "culture" perspective, its job is to find candidates who look, sound, and think like the rest of the team.
10. Video interview recognition. One of the more controversial uses for AI in recruitment is using the technology to analyze candidates' facial expressions, tone of voice, and body language - all of which can provide insights into their personality, character traits, or even truthfulness. However, this is controversial for obvious reasons and should be treated carefully (or avoided altogether) by talent acquisition teams.

Real-life Examples

Now that you have an overview let's look at how companies like Hilton and Pepsi use it to enhance recruitment.

1. Unilever uses AI-powered video interviews to screen job candidates, which helps reduce bias by evaluating candidates more efficiently and objectively.
2. Hilton uses AI to screen candidates based on their job requirements and qualifications. They also use chatbots to communicate with candidates and answer common questions.
3. IBM has developed an AI-powered tool called Watson Recruitment that analyzes job postings and identifies the most critical skills and qualifications for a given role.
4. PwC uses AI-powered chatbots to screen and engage with candidates and AI algorithms to analyze candidate data and make hiring decisions.
5. PepsiCo has developed an AI-powered recruiting platform called Talent Acquisition, which helps them identify candidates with the right skills and experience.

Ethical Considerations 

1. Avoiding bias. One of the most significant ethical concerns with AI in recruitment is the potential for bias. AI algorithms can inadvertently perpetuate discrimination if trained on biased data or programmed with biased decision-making criteria. It's important to ensure that AI algorithms are regularly audited for bias and that diversity and inclusion are prioritized throughout the recruitment process.
2. Transparency. Another ethical consideration is transparency. Candidates have the right to know employers use AI in the recruitment process, what data they collect, and how they intend to us it to make hiring decisions. Companies should be up-front, detailed, and transparent about their AI recruitment processes and ensure that candidates know how their data is being used.
3. Respect for privacy. AI-powered recruitment tools can collect and analyze vast amounts of personal data about candidates. It's vital to respect candidates' privacy rights and ensure their data is collected and used lawfully and ethically. This includes ensuring that data is stored securely and only used for the purposes for which it was collected.
4. Human oversight. While AI can automate many aspects of the recruitment process, it's important to ensure that human oversight is still involved in decision-making. Human intervention can help catch errors or biases in AI algorithms and ensure that ethical considerations are considered.
5. Fairness. Finally, ethical considerations in AI recruitment include fairness. Companies should strive to ensure that all candidates are evaluated fairly and objectively, regardless of their demographic characteristics. This can involve using a diverse set of criteria for evaluating candidates, ensuring that algorithms are regularly audited for bias, and allowing candidates to contest automated decisions.

To wrap up, yes, AI can be a valuable tool for talent teams in recruitment - and not using it will leave you and your team behind the competition.

From automated screening to predictive analytics, AI can help streamline the recruitment process, save time, reduce bias, and even help improve the candidate experience. However, the lack of transparency, privacy, and fairness that comes with using AI in such a people-centric domain like recruitment is something you have to consider before jumping on every bandwagon that rolls by.

After all, in a world obsessed with automation and efficiency, your ability to slow down and bring humanity into your work might just be what sets you apart from the rest.