Leveraging AI in Human Resources for Enhanced Recruitment

Use Cases & Projects Marie Merveilleux du Vignaux

Human resources (HR) presents a wide variety of AI use cases. Some fall into the most traditional view of AI in which technology takes over functions traditionally reserved for people. In other cases, however, AI’s role is not to replace humans, but to maximize their impact. 

The use of AI and Generative AI in HR is already widespread, and for good reason — hiring directly impacts business goals, and using AI to forward those goals is an obvious win. For example, organizations are leveraging intelligent analytics programs to more effectively recruit and retain high-quality employees. As AI applications become more sophisticated, notably with the rise of Generative AI, their role in these processes will become even greater. This blog post expands on a few high-potential use cases of AI in HR. 

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Recruiting With Greater Precision

Organizations across industries, small and large, can use marketing techniques to target those who are most likely to be ideal job candidates via online and social media advertising. 

Employers used to make educated guesses about where recruiting advertising might be effective: a local newspaper, a national trade journal, a TV broadcast. Much of that was simply based on common sense or reasonable assumptions: An energy firm seeking a top-level executive might place an ad for the position in a national industry journal that is read by industry executives. The next level of analysis would be to the demographic data that TV networks, radio stations, newspapers and magazines gathered on their viewers and readers. Now, however, the targeting has become significantly more precise due to the enormous volume of data generated by online behavior. 

With Generative AI, organizations can create more personalized and targeted job descriptions by analyzing the preferences and characteristics of successful candidates. This can lead to more effective communication and better alignment with the expectations of potential hires. Generative AI can also contribute to the development of a robust candidate relationship management system. This includes maintaining communication with potential candidates over time, providing updates on job opportunities, and nurturing a talent pool for future positions.

Automated Recruiting

In addition to more targeted advertising, employers are increasingly shifting parts of the recruitment process to automated programs. 

Chatbots and AI-driven tools can assist in scheduling interviews, generating interview questions based on job requirements, and even assessing candidate responses. For example, after an algorithm identifies a LinkedIn profile as a prospective candidate, the company may send an automated message to the person inviting them to apply for a position. Subsequent stages of the application process can similarly be automated, including the conducting of personality or skills tests required for the job and the generation of interview questions. This can streamline the interview process and ensure consistency in evaluation.

Generative AI — and NLP — is increasingly playing a significant role in screening applications. This is particularly important for large employers who may receive thousands of applications for a single position. NLP is what allows programs to quickly scan through resumes, interpreting based on the years of experience, the keywords cited and myriad other indicators whether the candidate should be considered. This saves time and effort for HR teams, enabling them to spend more time on more valuable tasks. 

Additionally, with Generative AI, organizations can analyze historical data to identify patterns related to successful hires and process large datasets to provide insights into recruitment trends, candidate preferences, and the effectiveness of different sourcing channels. This information can be used for predictive analytics, helping HR teams make data-driven decisions when selecting candidates.

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Background Verification

Once upon a time, verifying the accuracy of a resume required a number of phone calls to references. That was not only time-consuming, but prone to error: It’s not unheard of for people to have a friend or family member pretend to be a former employer. 

To more fully investigate an employee’s background, including for criminal records that may make them ineligible for the position, companies have traditionally deployed individual employees or hired professional investigators to dig through court records, work that costs substantial time and money.  

AI-enabled solutions enable employers to simultaneously verify multiple resumes in a matter of minutes. In some cases, established background check providers are updating their processes to include AI. In other cases, new AI-centered solutions seek to disrupt the background check industry by making it easy and cheap for employers to take matters into their own hands. 

Understanding Employee Satisfaction

One of the most powerful initiatives that HR can embark on is to use machine learning (ML), predictive analytics and, ultimately, AI systems to gauge their current employees’ attitudes about their work and their place in the organization. Employee attrition programs and other similar programs can analyze all kinds of employee behavior to understand the employee’s level of engagement. With these tools, it is easier for employers to identify the sources of job dissatisfaction and explore ways to improve the situation. 

For instance, by analyzing data across a wide variety of available sources (from vacation and sick days taken to performance reviews, manager performance, and beyond to other workplace activity), an AI-powered program might suggest that employees in a certain unit are bored or unstimulated by their work and find that employees in another unit are overworked and burning out. 

Such insights can help organizations discover problems and strengths that would have otherwise gone undetected. It can serve as a wakeup call to managers that they need to reconsider their leadership approach but it can also highlight employees from entry-level workers to managers  who should be considered for promotions or reassignment. Last but not least, insights into employee engagement help employers reduce turnover and therefore reduce the substantial costs associated with recruiting and training new employees. 

The Potential of Generative AI in HR

According to a McKinsey report,Generative AI deployments are bringing efficiency and new insights to HR functions and are estimated to enhance HR productivity by up to 30%, with examples demonstrating reduced annual budgets and improved efficiency.

With stronger automation and data insights, current GenAI use cases show three times faster content creation and visualization, automation of greater than 50% of tasks in an onboarding journey, and recruiting engagement rates that are twice as high as when personalized messages were written with GenAI.

The Economic Potential of Generative AI, McKinsey

Generative AI has the potential to help HR teams continue to shift their focus from administrative work to helping lead company-wide strategic transformation.

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