The ongoing artificial intelligence boom has introduced cutting-edge machine learning (ML) processes into the world of talent acquisition. In offering unprecedented automation, qualitative data insights, and powerful screening tools, ML has the potential to revolutionize the reach of businesses when discovering the right hires.
According to a recent Mercer study, only 23% of enterprises have begun using AI in their vetting process to eliminate bias and optimize new hires, making now an excellent time to uncover talent ahead of competitors.
For enterprises seeking to optimize recruitment processes, ML can ramp up their hiring accuracy at scale. For international firms like IBM, the incorporation of AI processes like machine learning has prompted a 30% increase in the quality of candidates shortlisted. With this in mind, let’s take a deeper look at how the technology can help SMEs to improve their talent acquisition:
Strategic Candidate Sourcing
Because machine learning algorithms operate on a heavily data-focused approach to sourcing, the technology can get to grips with historical employee data to identify patterns and traits associated with the role’s most exceptional talent and seek to uncover the same tangible and intangible skills with prospective new hires.
In seeking out successful characteristics, recruiters can source candidates who are more likely to be effective in their respective roles. ML algorithms can pinpoint these candidates to improve the quality of SME hires with a lower probability of churn taking place.
The sheer scale of ML means that this strategic candidate sourcing can expand into global talent pools for recruiting remote employees. Using natural language processing (NLP), algorithms can explore foreign job markets at a scale that surpasses human HR professionals to take talent acquisition global for enterprises.
Predictive Talent Acquisition
Machine learning can also introduce an element of predictive talent acquisition to the recruitment process. This means that the technology can pinpoint potential candidates before they even take their first steps in the job market.
By monitoring their social media activity, online profiles, and studying their given career interests, ML can help recruiters identify passive candidates for opportunities to reach out or to build a talent database that can be monitored for future availability.
Automated Job Advertising
With the help of generative AI, it’s also possible for machine learning to work alongside recruiters to build the most effective job advertising materials in a time-saving, fully autonomous manner.
Machine learning algorithms can tap into existing job advertisements that drive plenty of candidate traffic to analyze key terminology that can attract more applications. This can help to broaden your access to talented candidates and expand your search overseas with adaptive ads that can take into account cultural cues and different nuances that can impact engagement.
Paving the Way for Inclusivity
Crucially, ML can work wonders in eliminating instances of human bias throughout the recruitment process.
According to a Greenhouse survey, 55% of HR professionals acknowledged that candidates who shared a similar background to their own could sway their hiring decisions. This is a particularly worrying statistic for SMEs that are seeking to recruit talented individuals who can help to improve their enterprise on an operational level.
Machine learning can help to eliminate these instances of bias throughout the recruitment process to help prioritize key skill matching and apply some level of objectivity over the more subjective viewpoints of recruiters.
Finding a Collaborative Framework
The prospect of machine learning and other artificial intelligence technologies eventually replacing human recruiters is a major fear among those in the industry. According to SmartRecruiters research, 60% of employees are fearful that AI technology will automate them out of a job in the future.
However, the best implementation of machine learning within the recruitment landscape appears to be more collaborative, with recruiters helping to vet and oversee the accuracy of the framework in discovering the best hiring opportunities.
Relying on algorithms to screen candidates can lead to many qualified applicants being filtered out of the shortlisting process, and this means that maintaining a human eye to oversee proceedings is the best way for SMEs to ensure that they’re accessing the best level of talent at all times.
Finding the right balance between automation and that human touch is vital in driving a fully inclusive hiring process that ensures the best possible level of hires.
Utilizing ML in the Recruitment Process
When uniting ML processes with your existing HR team, it’s possible to access a far greater range of talent on a global scale. From discovering and reaching out to top overseas prospects to accurately vetting candidates on the tangible and intangible qualities that have been a success within your roles in the past, machine learning can add efficiency and accuracy to talent acquisition for SMEs.
With the technology still in its fledgling stages, more enterprises can benefit from outmaneuvering rivals in discovering talents faster, ensuring a more sustainable scaling process and reduced risk of churn.
In adding ML as a collaborative innovation within your recruitment process, it’s possible to reach your potential faster and make the best possible hiring decisions on a consistent basis.
