5 Ways To Use AI in Recruitment

In a fiercely competitive talent market where graduate talent is becoming harder to find, it’s no surprise that recruiters and HR professionals are turning to AI as an innovative way to streamline their hiring processes and find the best talent for their organisations.

Regardless of your organisation’s stance on AI adoption in the workplace, there’s no denying that it  presents a critical juncture for recruiters and HR professionals who are responsible for finding the right talent for their organisation. In fact, 69% of large companies have already adopted at least one form of AI​, and 86% of CEOs believe AI will be a mainstay in their offices in the years to come.

Even in today’s early stages of AI development, we are able to see how it is beginning to reshape the landscape of recruitment by automating repetitive tasks, improving candidate experiences, and enhancing decision-making to eliminate bias.

5 Ways To Use AI in Recruitment

With AI emerging as a potential game-changer in the world of recruitment, here are five ways your organisation can practically implement AI to change the way you identify, attract, and hire top candidates, supported by real examples of AI-powered recruitment in practice.

1. Resume Screening and Candidate Matching

Studies have found that Hiring Managers look at CVs for an average of six to seven seconds. With so many applications to sift through and little time to read them thoroughly, your organisation could benefit from AI-powered resume screening tools that saving time and improve the quality of your candidate matches.

While automated job matching has been around for a while (particularly on LinkedIn), today’s innovations in AI tools use natural language processing (NLP) and machine learning algorithms to scan resumes and identify the most qualified candidates based on specific criteria, such as candidates’ personality traits, skills, and salary preferences to automatically assess candidates against the job requirements.

In practice: Research into AI-powered screening has found that it can reduce the time spent reviewing CVs and cover letters by 75%, ultimately resulting in a 3x increase in the number of qualified candidates identified.

Amazon is one organisation making strides in this area: their AI-powered candidate matching system flags when incoming Amazon interns and new college grads are interested in full-time roles, and automatically recommends roles that match their interests, skills, and experience. These candidates have skills that could apply to a number of roles, but Amazon’s AI identifies interests based on natural language processing of the candidates’ CVs.

2. Chatbots for Candidate Engagement

Chatbots are an excellent way to engage with candidates 24/7, answering their questions and guiding them through the application process. AI-powered chatbots can provide immediate responses, schedule interviews, and even conduct preliminary assessments, all while providing a human-feeling experience that wont alienate your prospective hires.

In practice: Unilever integrated an AI chatbot named “Unabot” into their recruitment process, which Unilever claims has resulted in a reduction of around 70,000 person-hours of interviewing and assessing candidates.

In addition to its role as a screening tool, the chatbot is also used to improve the Unilever candidate experience post-acceptance: “Unabot doesn’t only answer HR questions, questions about anything that affects employees should be answered by Unabot, and it is now the front face for any employee question – they might ask it about IT systems, or about their allowances – so we are learning about what matters to employees in real time” Unilever’s former Chief Human Resources Officer told Forbes.

3. Predictive Analytics for Talent Sourcing

According to research by Deloitte, companies that use analytics in recruitment have two to three times better results in the quality of hire, time to hire, and employee turnover.

Predictive analytics leverages AI to identify the most promising talent sources and channels. It analyses historical hiring data, market trends, and candidate behaviour to predict which sources are most likely to yield high-quality hires for your organisation.

In practice: Innovations in the AI-powered HR platform space have resulted in algorithms called “More Likely to Move” to identify candidates who have a 30% likelihood of switching jobs in the next 90 days, allowing hiring managers to focus their time on candidates that are more likely to switch jobs in the near future.

Even without AI-driven predictive analytics, using data to underpin your recruitment strategy is an excellent way to begin optimising Early Careers in your organisation, drive better-informed decisions, and hone your strategic focus for long-term success.

4. Video Interviews

AI-driven video interview analysis tools assess candidate responses, body language, and facial expressions during video interviews, providing valuable insights into your candidate’s personality traits and suitability for your organisation.

In practice: bp implemented an AI-powered video interviewing platform in their graduate interview process, allowing them to tap into a global graduate pool that they may not have had access to previously due to time zones.

With the addition of competency and cognitive reports, the platform’s AI analysis also helped bp identify top talent more effectively while freeing up time, as candidates were able to take their interviews on-demand, enabling hiring managers to review and watch the candidates’ responses at a time that suits them.

5. Diversity, Equity, Inclusion and Belonging (DEIB)

AI can help reduce bias in your recruiting by standardising candidate evaluation and removing unconscious bias from your hiring process. It can anonymise resumes, use predictive analytics to suggest diverse candidate pools, and flag potential bias in job postings.

In practice: A study published in Harvard Business Review found that when AI was used to anonymise resumes during the screening process, the likelihood of female candidates advancing to the interview stage increased by 23%. This demonstrates the potential for AI to contribute to greater diversity and inclusion in hiring, though critics have warned AI is not yet perfect in this regard.

The caveat to AI in recruitment:

Although the AI innovations shown here are excellent tools for organisations looking to improve their approach to recruitment, it’s important to remember that hiring is fundamentally a human process, and that these tools should be used to bolster your human approach to hiring – not replace it.

There is particular room for error with AI tools used in the recruiting space, where AI models have been demonstrated to show bias based on the flawed data they have been trained on. When utilising these AI tools, try to remember that AI “neural networks” are only as perfect as the data sets they are trained on, and in today’s relatively early stages of AI development, they are still capable of egregious mistakes that will need human review.