The buzz surrounding groundbreaking tools like Open AI’s ChatGPT, Google Bard, and Microsoft’s latest Bing upgrades is hard to miss these days.

For those of us in talent acquisition, this exciting new tech wave raises a bunch of questions about AI in recruitment: How will AI advancements tweak the roles we know so well? And what kind of skills will recruiters need as some tasks start to get automated? How can we avoid the pitfalls of AI, like unfair biases in hiring?

While we don’t have all the answers just yet, the Bronson Technical Search team has developed our own AI-enabled talent acquisition process that has seen remarkable success over the past two years.

Based on these experiences, the BTS team has some ideas on where AI is most valuable today and where it might lead us in the next five to ten years.

From streamlining candidate selection to enhancing diversity and inclusion efforts, the advantages of AI in recruitment are abundant. However, it’s crucial to understand the nuances and potential challenges associated with integrating AI into recruitment strategies.

Let’s dive in.

A Brief Definition of AI in Recruitment

AI in recruitment refers to the application of artificial intelligence and machine learning algorithms to automate various stages of the hiring process. From sourcing candidates to assessing their qualifications and predicting performance, AI streamlines recruitment workflows, saving time and resources while improving the quality of hires.

Only 12% of hiring professionals say they use AI in their recruiting and/or talent management processes today.

– Criteria Corp. 2023 Hiring Benchmark Report

History of AI in Recruitment: How Did We Get Here?

The use of AI in recruitment has evolved significantly over the years, transforming traditional hiring practices into streamlined, data-driven processes. Initially, AI in recruitment primarily focused on automating repetitive tasks such as resume screening and candidate sourcing.

Early AI technologies like applicant tracking systems (ATS) emerged in the late 1990s, helping recruiters manage large volumes of applications more efficiently. These systems used keyword matching and simple algorithms to rank resumes based on predefined criteria (such as educational background, titles held previously, and so on).

As technology advanced, AI became more sophisticated, enabling recruiters to leverage machine learning algorithms for predictive analytics and candidate assessment. In the early 2000s, AI-powered tools began to incorporate natural language processing (NLP) and sentiment analysis to analyze candidate communications and social media profiles. This enabled recruiters to gain deeper insights into candidate personalities and cultural fit.

Today, AI in recruitment encompasses a wide range of applications, including virtual interviewing platforms, chatbots for candidate engagement, and predictive analytics for talent forecasting. As AI continues to evolve, its role in recruitment is poised to expand further, driving greater efficiency, fairness, and effectiveness in the hiring process.

While the technology shows great promise, only 12% of hiring professionals say they use AI in their recruiting and/or talent management processes today.

Applications of Artificial Intelligence in Recruiting

Candidate Sourcing

Instead of spending hours sifting through stacks of resumes, AI-powered tools do the heavy lifting for you. These tools scour the internet, job boards, and professional networks to pinpoint candidates who are a perfect fit for your open positions.

For example, platforms like LinkedIn Recruiter use AI algorithms to analyze candidate profiles and recommend top talent, making it easier for recruiters to find needles in the haystack.

Candidate Screening

The majority of talent acquisition leaders cite candidate screening as the most daunting aspect of recruitment, particularly when faced with a sizable applicant pool.

Thanks to AI, gone are the days of manually sorting through hundreds of applications.

With AI, recruiters can set specific criteria, and the algorithms will swiftly identify candidates who meet those qualifications. It’s like having a trusty assistant who knows exactly what you’re looking for and brings you the best candidates on a silver platter. This not only speeds up the screening process but also ensures that no qualified candidate slips through the cracks.

According to recent research, 55% of hiring professionals believe faster candidate screening is one of the biggest benefits of AI.

Interview Processes and Customized Skills Assessments

While AI is seeing more widespread adoption at earlier stages of the talent acquisition process (e.g., writing job descriptions and screening candidates), it can be interesting to imagine how it can transform the later stages (interviews, skill assessments, etc.).

Picture this: You’re conducting a virtual interview using an AI-driven platform.

As candidates respond to your questions, the system analyzes their answers in real-time, flagging any red flags or highlighting standout qualities.

It’s like having a second pair of eyes (or rather, a super-smart AI assistant) to help you assess candidates objectively. Plus, these platforms can standardize the interview process across all candidates, minimizing biases and ensuring fairness.

Benefits of Using AI in Recruitment

1. Improved Efficiency and Productivity

By automating repetitive tasks such as resume screening and scheduling interviews, AI frees up recruiters’ time to focus on strategic activities like building relationships with candidates and stakeholders. This improved efficiency translates into faster hiring cycles and better utilization of resources.

2. Enhanced Candidate Experience

AI-powered chatbots and virtual assistants provide immediate responses to candidate inquiries, offer personalized communication throughout the hiring process, and ensure candidates remain engaged and informed at every stage. A positive candidate experience strengthens the employer brand and attracts top talent.

3. Data-Driven Decision-Making

AI algorithms analyze large datasets to identify trends, patterns, and correlations that human recruiters might overlook. By leveraging data-driven insights, recruiters can make informed decisions about sourcing strategies, candidate selection, and talent pipeline management, leading to better hiring outcomes.

4. Increased Diversity and Inclusion

AI can help mitigate unconscious biases in the recruitment process by focusing solely on candidates’ qualifications and skills. By anonymizing candidate information during the initial screening stages, AI promotes fair and inclusive hiring practices, fostering diversity within the workforce.

Recent research indicates 68% of recruiters think AI will remove unconscious bias from the hiring process.

Why?

While AI has the potential to exacerbate existing human biases depending on how it is deployed, it is, of course, inherently neutral. In fact, many AI firms like OpenAI adhere to design principles for making the technology ethical and fair. One key principle is that all AI programs should be designed so they can be audited, and any bias found can be removed.

5. Predictive Talent Analytics

AI-powered predictive analytics forecast future hiring needs based on historical data, market trends, and business projections. By anticipating talent gaps and turnover risks, organizations can proactively develop recruitment strategies to attract, retain, and develop top talent, ensuring long-term success.

Ethical Considerations When Using AI in Recruitment

1. Potential Bias in Algorithms

Despite advancements, AI algorithms may perpetuate biases present in historical data, leading to unfair treatment of certain demographic groups. Recruiters must regularly audit and refine AI models to minimize bias and ensure equitable hiring practices.

Job advertising platforms have received scrutiny for enabling bias. In 2019, prompted by multiple lawsuits, Facebook Ads made a significant policy change: Employers were no longer permitted to target specific ages, genders, or zip codes when advertising open positions. This decision was just one in a series of steps taken by the platform over recent years aimed at curbing discrimination in advertising.

2. Lack of Transparency: Embracing Explainable AI

A lack of transparency in AI platforms for recruitment is one of the biggest risks of adopting the technology.

The concept of a “black box” refers to the opacity or lack of transparency regarding how an AI system arrives at its decisions or outputs. It essentially means that while the inputs and outputs of the AI system are known, the internal mechanisms or processes by which the system reaches its conclusions are not transparent or understandable to humans. Google Search is a well-known example of a “black box”, because we all know how to use it, but we don’t know how it makes decisions.

With AI recruitment tools, a black box scenario can occur when algorithms make decisions about candidates’ suitability for a job without providing clear explanations for those decisions.

For example, if an AI-powered resume screening tool automatically rejects certain candidates without providing reasons or if a machine learning model ranks candidates without revealing the specific criteria it used. This creates a black box situation that can expose employers to increased risk of bias.

To mitigate the black box problem in AI recruitment, there’s a growing emphasis on developing explainable AI (XAI) systems. These systems aim to provide clear explanations for AI decisions, allowing stakeholders to understand and evaluate the reasoning behind those decisions. By promoting transparency and accountability, XAI can help build trust in AI-driven recruitment processes and reduce the risk of biased outcomes.

3. Privacy and Data Security Concerns

Collecting and analyzing large volumes of candidate data raise privacy concerns and regulatory compliance issues. Employers must adhere to data protection regulations, obtain candidate consent, and implement robust security measures to safeguard sensitive information.

For example, it is now commonplace for employers to review candidate social media profiles in the hiring process. This has long been a grey zone: candidate profiles can reveal information that employers are not allowed to discriminate on, like race, sexual orientation, disability, and so on. On the other hand, social media can present a less curated view of an applicant. Indeed, 71% of hiring managers surveyed in a recent poll said they glean valuable insights from candidate social media profiles.

AI will no doubt make it easier for employers to screen social media profiles at scale, but this could potentially open the door to accusations of bias and ethics violations.

Practical Considerations When Using AI in Recruitment

1. What Makes a Great Candidate?

Even before the emergence of AI in recruitment, this question was widely discussed and debated.

What level of confidence do we need before we bake our “ideal candidate” into an algorithm?

The attributes of a good candidate or employee can be hard to quantify. Typically, metrics we use to evaluate performance might themselves be subject to unfair bias, like performance interviews. When fed into an algorithm, the bias is only exacerbated.

AI tools should absolutely be adopted to facilitate talent acquisition, but they cannot replace fundamentals. Understanding what makes the right “fit” and what qualities matter to your organization cannot be determined by a machine.

As a matter of fact, recent research shows 35% of recruiters are concerned that AI could lead to overlooking unique and unconventional talents.

2. Skill Gaps and Training Needs

Integrating AI into recruitment processes requires upskilling HR teams to effectively leverage new technologies. Providing comprehensive training programs ensures recruiters can maximize the potential of AI tools and adapt to evolving industry trends.

3. Maintaining Human Connection

While AI streamlines many aspects of recruitment, maintaining human connection remains crucial. Candidates value personalized interactions and authentic engagement throughout the hiring process, emphasizing the importance of balancing automation with human touchpoints.

4. Cost and Resource Allocation

Implementing AI-powered recruitment solutions entails upfront investment in technology infrastructure, software licenses, and training. Organizations must assess the long-term ROI of AI adoption and allocate resources strategically to justify the costs.

Another option, to reduce technology management costs, is partnering with a talent acquisition firm that is already mature in its adoption of artificial intelligence.

Using AI in Recruitment — FAQs

Adopting AI in Your Talent Acquisition Strategy

Incorporating AI in recruitment offers a myriad of benefits, including improved efficiency, enhanced candidate experience, and data-driven decision-making. However, it’s essential to navigate potential considerations such as algorithmic bias, privacy concerns, and the need for ongoing training and development.

There is understandable hesitancy around adopting AI in talent acquisition. In fact, 66% of American workers say they would not want to apply for a job if they knew AI was involved in hiring decisions. It is essential for recruiting leaders to understand this, and limit the scope of AI to reducing manual workloads (i.e., resume screening) rather than any serious decision-making.

By understanding the nuances of using AI in recruitment and leveraging its capabilities responsibly, organizations can gain a competitive edge in attracting and retaining top talent.

At Bronson Technical Search, we integrate artificial intelligence with human expertise to help our clients identify qualified technical candidates quickly. Learn more about our search methodology here.