Job recruiters and HR managers around the world have begun integrating artificial intelligence and automation into their talent acquisition practices at a rapid pace, leading some to question what the future of hiring looks like. One reason for the growing interest in AI-driven recruitment tools is the increased difficulty of forecasting future employment trends, as many of today's tech-driven industries depend on versatile candidates who are able to quickly adapt to new workflows and internal processes.
The shifting employment landscape has put a lot of pressure on hiring professionals who must anticipate their companies' staffing needs and attract the most qualified job seekers. According to Development Dimensions International, only 11% of CEOs are confident in their HR team's ability to identify effective talent pipelines and onboard candidates who will add immediate strategic value to their organization. Luckily, AI recruitment tools are helping HR managers refine their talent acquisition processes through advanced applicant filtering and analysis.
How AI streamlines recruitment
Hiring managers spend a lot of time sifting through resumes and cover letters looking for specific work experience and hard skills, which can be exhausting if they're trying to fill a highly competitive position. However, these basic tasks can be automated using AI and machine learning, as these technologies are able to process large amounts of profile data quickly and efficiently. This allows recruiters to evaluate job applications at a much faster rate by removing any unqualified candidates from their queue. Additionally, AI tools can help identify job seekers with specialized or niche skills that might be essential to a specific role that a hiring manager is unfamiliar with. Some common uses for AI in the recruitment process include:
- Applicant sourcing: Reviewing submitted resumes and applications can help hiring managers locate talented candidates, but sourcing is a much more proactive form of recruitment. Filling open positions from a pool of active job seekers doesn't always produce the best results, which is why AI-powered sourcing collects relevant data through several different types of networking platforms, including online job listings, employment search engines and social media sites.
- Candidate assessment: While AI recruitment applications excel at processing large volumes of data, they can also be used to assess the quality of candidate matches. For example, autonomous talent delivery software pulls search criteria directly from job requisitions to identify important qualifications and relevant terms. Once a job profile is created, the AI is able to prune candidates who do not provide adequate or relevant information in their applications.
- Interview scheduling: Onboarding new employees can be time consuming, but waiting too long to schedule an interview may lead to missed opportunities. In fact, a 2018 research report from Talent Board found that 29% of job seekers have withdrawn from a recruiting process due to prolonged delay, demonstrating the value of AI chatbots and self-scheduling tools. Once a qualified applicant is found, an automated recruitment application sends out hiring updates and even provides job seekers with a list of interview times.
AI-driven recruitment is far from perfect
Despite the productivity gains offered by AI hiring tools, there is still a need for significant human involvement. As a mid-April article from Forbes pointed out, AI algorithms are trained to identify qualified candidates based on historical hiring data, which creates a filtering bias that is difficult to ignore. Relying too heavily on automated technologies can lead companies to unknowingly discriminate against talented applicants with diverse backgrounds, as modern AI systems tend to prioritize qualification patterns that overlap with previous hiring trends.
The best way to build a robust and adaptable workforce is to balance the use of machine-driven applications with the insights offered by experienced hiring professionals, as it will likely take decades to progress AI technology to a truly objective state.