AI and machine learning offer substantial benefits to recruitment in terms of productivity, decision-making, candidate experience, sourcing and evaluation, and employer branding.
AI tools, when combined with skilled recruiters, create a potent force in building talent pools and attracting high-quality hires.
AI's role in recruitment is continually evolving, offering new opportunities for automation and smarter decision-making as technology advances.
Artificial intelligence and machine learning have become hot topics in almost every facet of business and life. In fact, they have completely transformed the way people create and consume content and navigate big data. These technologies are also making an impact in recruitment, as organizations discover how to use new AI-powered tools for increased automation, better candidate engagement, and smarter decision-making.
According to 2022 SHRM research, 79% of organizations have already incorporated AI and/or automation into some part of the recruitment process. However, many may only be at the tip of the AI iceberg. AI technology continues to evolve and create exciting new opportunities for organizations and recruitment teams to automate processes and recruit with greater effectiveness.
Artificial Intelligence vs. Machine Learning: What’s the Difference?
AI and machine learning are often used indiscriminately, as if they are interchangeable. However, though closely related, they are two separate and distinct concepts.
In simple terms, AI is a system of technologies that attempts to mimic human reasoning, responses, and behaviors. Conversely, machine learning is one type of artificial intelligence (other types include robotics and language processing). It creates algorithms for analyzing and processing large amounts of data. When talking about using data for better decision-making, machine learning is often the vehicle for making this happen.
Note: The team at Recruitics, for example, has been using machine learning to inform numerous algorithms since inception. The team didn’t call it that when they started doing it – nor they didn’t call their rule-based functionality “programmatic” initially, but that’s what they were doing. This is to say that recruitment teams have been utilizing machine learning programs for quite some time now.
Another term commonly used when discussing AI and machine learning is generative AI. Generative AI is another application of artificial intelligence; it enables the creation of new information and content based on large amounts of patterns and data. A great example of generative AI in recruiting is a career page chatbot.
5 Ways AI and Machine Learning Boost Recruitment Success
One of the challenges related to AI, machine learning, and other related technologies is their complexity. For many organizations and recruitment teams, understanding the universe of possible use cases can be head-scratching, leaving recruitment teams wondering where to start. However, with the support of a trusted recruitment partner with deep knowledge and experience in AI-powered recruitment tools, talent acquisition teams can identify and adopt the solutions that fit their unique needs.
Here are several ways AI and machine learning can elevate recruitment effectiveness in any organization:
1. Increases Recruitment Team Productivity
Companies are severely understaffed for many roles they pursue, no matter who or what HR/TA role teams seek to fulfill. There is hardly a role in talent acquisition where hiring professionals say they’re staffed appropriately. With this being the case, figuring out how to augment responsibilities to generate more outcomes for a company’s work is essential.
Recruitment professionals are often stretched in many directions. At the same time they must manage interactions with applicants at all stages of the candidate journey, they must also track critical recruitment metrics and keep branding content up to speed. AI-driven solutions allow recruitment teams to keep pace with their key priorities by working alongside them. These new technologies can screen applicants, flag best-fit candidates, and engage candidates in the talent pool, whether they’re actively interviewing for an open position or not. By helping to automate these tasks, AI doesn’t replace recruiters; it enhances their efforts so they can be more productive and ensure continuity in the hiring process.
2. Improves Data-Driven Decision-Making
Machine learning helps recruitment teams make sense of the massive amounts of hiring data generated every day. It can combine internal recruitment and external job market data to help organizations identify what actions are required to attract talent across different geographies and job titles.
Note: Recruitics has always done this, and the team’s goal is to use data to inform every decision. Now, with AI, the team can make even greater use of disparate datasets to inform the instant decisions the algorithms make.
Here are just a few examples of how organizations can use machine learning to improve decision-making in recruitment:
Predict future hiring needs based on company goals, changing market trends, and competitor activity
Identify the candidate characteristics and skills most common in successful hires
Determine which job posting formats are most successful in attracting applicants Analyze patterns in the candidate journey to identify areas of friction and candidate drop-off
Tip: Making smart decisions about where to invest recruitment dollars and where to pull back is at the core of a well-run talent acquisition program. Programmatic job advertising promotes better spending decisions by using machine learning algorithms to promote job ads on the best-performing sites.
3. Improves the Candidate Experience
From chatbots that answer candidate questions to messaging tools that provide candidate feedback, AI helps talent acquisition teams keep candidates informed and engaged. As a result, it’s much easier to maintain candidates’ interest while also building their awareness about the organization.
In a Robert Half survey, 62% of job seekers said they lose interest if a company doesn’t reply within two weeks of expressing interest. AI allows organizations to be proactive and reduce the occurrence of slow and inconsistent candidate communications. For example, generative AI can help maximize candidate feedback by providing customized responses to pipeline candidates. AI can also recognize which candidate skills and experiences match company needs, and make recommendations to candidates in the talent pool. As a result, companies increase the chances of generating applicants who are a better fit for open positions.
Finding candidates and determining their suitability for open positions leaves most recruiters with their hands full. Every day, there are job ad campaigns to launch, applications to review, and candidates to interview and assess. AI can make those activities run more smoothly so they happen with a consistent level of quality and help generate a healthy recruitment ROI.
Here are the many ways recruitment teams can use AI and machine learning to enable smarter candidate sourcing and evaluation:
Identify similarities in candidate applications and use that data to screen and filter candidates according to their suitability for open positions
Address bias concerns by parsing resumes and extracting specific features from resumes
Generate personalized job descriptions and job title alternatives that will enhance recruiters’ ability to reach a broader candidate audience
Democratize candidate assessment scoring so it’s more consistent across roles
5. Enhances Employer Branding
Today’s job seekers carefully consider where they want to work, often conducting extensive research on a company and its brand. According to LinkedIn research, 75% of job seekers consider an employer’s brand before even applying for a job. As a result, organizations must ensure their recruitment messaging aligns with the brand image they want to portray.
AI tools improve branding by evaluating career page content and other messaging. They can offer recommendations to optimize the content and make it consistent across branding channels. Recruitment marketing teams can also use AI to conduct AB tests on branding content, to ensure candidate messaging effectively communicates the employee value proposition and attracts high-quality talent.
Tip: The company career site is a powerful vehicle for engaging prospective visitors and encouraging them to apply. If it’s been a while since the last refresh, consider how a newly optimized career site can drive more candidate engagement and conversion.
6. Faster Processing with Predictive Analytics
Importantly, AI can help (no matter the role a company has) to support recruitment teams by facilitating faster processing of candidate acceptance and onboarding. It can and should reduce bias, if done right, and ideally can speed up the entire hiring process.
The biggest avenue recruiting teams are pursuing with AI is whether they can use it to predict talent acquisition expenses for tomorrow. The team at Recruitics believes it can do so. Recruitics has extensive experience using historical data to generate bids or target CPA goals, and the potential to forecast application acquisition is becoming more tangible with the integration of AI into the acquisition funnels and data input for algorithms.
Harness the Power of AI in Recruitment
Company recruitment will always benefit from skilled recruiters who use their expertise, empathy, and judgment to attract quality talent. And when recruiters have the added support of artificial intelligence and machine learning tools, they can do even more to help companies compete for talent. These technologies accelerate data-driven decisions that result in robust talent pools, a smoother candidate journey, and ultimately, more quality hires.
For help building a recruitment strategy that incorporates AI and machine learning for better hiring results, the team at Recruitics is here to help! The Recruitics team can provide strategic support and customized AI-powered solutions to help organizations reach more candidates and hire more efficiently.