Five AI tools to enhance recruitment

How much human involvement there should be in the recruitment process given the capabilities of AI is a matter for debate. Recent evidence has emerged that candidates are not happy with AI-driven processes, but here, Terry Terhark presents the case for businesses to deploy the five main types of AI in their efforts to attract the best talent.

According to the Society for Human Resource Management in the US, 40% of companies are already using AI to screen and assess candidates, 41% use AI chatbots to engage with job seekers, and 44% found AI can help identify talent from social media profiles.

Given this, it is likely many talent acquisition professionals have been left wondering whether their organisations understand why the “human” skills they use every day are critical to successful recruitment.

The good news is that most leaders understand the importance of the human touch in recruiting and talent acquisition  activities. However, the fact remains that there are some tasks AI will no doubt monopolise.

Recruiting is a combination of transactions (screening, communications, and data) and strategic activities (interviewing, evaluating, and match fitting). Blending technology and human skills at the right touch points will make sure candidates have an elegant and professional experience, as well as an efficient one. The recruiting teams that excel during this time of change will be those that integrate AI into their workflows early and identify the technologies that support productivity and improvement rather than replace recruiters.

Five AI tools for recruiters

Learning how to use emerging AI and machine learning technologies will help recruitment teams save time and money by streamlining tedious administrative tasks, resume sorting, candidate outreach, and more. They can also support more engaging candidate experiences.

There are five AI tools that recruiters and TA teams may want to consider:

Generative AI

The first application of AI recruiters can leverage is generative AI, made popular most recently by ChatGPT. Generative AI relies on existing data, such as images and online content, to create original writing and other media. It can be an extremely useful tool for busy talent acquisition teams, with a report from McKinsey finding that using generative AI strategically can boost workers’ productivity by up to 70%.

Recruiters can use generative AI to draft job descriptions, source plans, recruitment ads, employer branding elements, outreach emails, and social media posts, allowing them to focus on other parts of the recruiting life cycle. It can also help recruiters identify patterns and trends in given sectors, provided that the program has access to up-to-date information. However, it’s important to remember that ChatGPT’s open nature means that any information fed into the program becomes accessible to other users. Generative AI programs such as ChatGPT often learn and retain all data entered, making it essential to exercise caution when using it.

My tip: Save time and effort by letting tech take care of the “busy work”. Using generative AI, such as ChatGPT, can help you draft recruiting content faster and so you have more time to devote to candidate evaluations.

Natural language processing

Recruitment teams often struggle to engage candidates in today’s tight labour market. A report by Jobvite found that less than half of job seekers find communications from recruiters to be effective. Natural language processing (NLP) can help with that. It enables computers to comprehend, interpret, and manipulate human language (think the iPhone’s Siri). In the recruiting process, NLP-powered chatbots can help talent acquisition teams better interpret interactions with potential candidates, thereby expediting the application and screening processes.

Recruiters can also use NLP to screen, interview, and hire candidates across multiple communication channels such as email, text, and social media. Moreover, NLP can play a significant role in scanning for content that may contribute to conscious or unconscious bias in the candidate screening process.

My tip: Use NLP to speak the language of your best candidates. Communicating with purpose helps keep candidates engaged and fosters trust between applicants and recruiters.

Sentiment analysis

Sentiment analysis goes hand in hand with NLP, as it analyses and categorises emotion within text to identify if a given correspondence is positive, neutral, or negative. This can help recruitment teams gain a deeper understanding of candidate responses, especially to predefined question sets in applications. Keep in mind that sentiment analysis should not be used to qualify or disqualify candidates. Rather, its value is in translating candidate sentiments to provide recruiters with valuable information for well-informed decision-making.

My tip: Developing a list of screening questions and using sentiment analysis can help you get a better understanding of candidates’ attitudes, allowing you to tailor processes to meet their needs.

Machine learning

Another helpful tool for recruiters is machine learning (ML), commonly referred to as conversational AI or “generative AI with guard rails.” These systems observe patterns in datasets and apply this knowledge where needed, proving particularly useful for recruiters who learn to work with the system to automatically match open roles with the right candidates.

ML systems consider factors such as career site activity, professional experience, and personal interests when evaluating candidates. By offloading the burden of research to a ML system, recruiters can devote their time to engaging with high-quality candidates. Furthermore, ML can enhance these interactions by delivering supportive messages at the appropriate time and through the most suitable communication channels, supporting ongoing engagement. It is important to keep in mind that ML systems can only work with the direction they’re given. That means candidates that use language the system might not expect (like “content specialist” instead of “writer,” for example) may be omitted from results despite being a good fit for the position.

My tip: As HR analyst Josh Bersin says: “AI models can handle 10,000 applicants just as easily as 1,000.” Using ML lets you teach the system the patterns you’ve identified in potential candidates to sort applications based on your experiences, helping save time without sacrificing the benefits of the human touch.

Optical character recognition

Despite its relatively low profile, optical character recognition (OCR) is a valuable tool for recruiters. OCR helps to convert images of text into digital processing formats. It sounds complex, but people use it every day to e-deposit checks and scan receipts and it’s been a staple in recruiting for some time now. In talent acquisition, OCR allows job seekers to scan their resumes and other necessary documents eliminating the need for manual input of work history and other information into the recruitment portal. By integrating OCR into recruiting platforms, a simple snap-and-apply feature is created, streamlining the entire process.

My tip: OCR is also great for improving the candidate experience. Gone are the days of repeatedly inputting resumes, which many applicants find rightfully frustrating.

While AI can help recruiters find higher-quality candidates faster, its value and efficacy will rely on HR professionals’ insight. Although the average recruitment team is not made up of AI experts, with the right training and a little bit of practice, recruiters can become well-informed technologists. Those who start training their AI systems now and familiarising themselves with emerging tech will not only have a hand in the future of the industry but will find themselves with more accurate, effective, and helpful tools today.

Latest HR job opportunities on Personnel Today

Browse more human resources jobs