AI-based scoring solution can play a pivotal role in hiring the right talent and streamlining recruitment process.
It has predicted scores help identify the best candidates for further screening, saving enormous time and effort of recruiters.
Thus, AI has the potential to make the recruitment processes more effective by reducing the overall time-to-hire.
With the job market opening up fast post-COVID-19 slowdown, recruiters and companies are inundated with many applicants.
Selecting the best-fit candidate from many highly skilled applicants has become a problem for recruiters. They also need to have the functional expertise to screen and select the best candidates, and this takes a lot of time and effort. Research says that as high as 40% of a recruiters’ time is spent on screening resumes and data entry operations. This time could have been better utilized in engaging with the candidates to understand them better, thereby enhancing recruiters’ experience.
According to a study, only 10% of organizations are currently streamlining recruitment using artificial intelligence (AI), while 36% of the organizations are keen to implement artificial intelligence and machine learning (AI/ML) into their recruitment processes in the near future. The need is an AI-driven applicant-scoring solution that can address this drawback by providing purely AI data-driven relevance scores for each candidate for the job they’ve applied for. This scoring mechanism can be applied for both internal as well as external recruitment. The solution should automate the simultaneous screening of several candidates and identifies top candidates having the right job fitment which makes the recruitment drive more effective, less manual effort-intensive, less time consuming, and reduce the overall time-to-hire.
To build such an AI/ML solution, we can use NLP-based text classification, leveraging organization’s historical recruitment data, onboarded employee profiles, etc. Through this approach, it is possible to generate different clusters with respect to various job profiles, skills, management levels, etc., subject to different business needs.
When a candidate applies for a job, the solution will fetch the candidate details such as total experience and skills along with the applied job details as per business requirements. This data is fed into prebuilt AI models to generate relevance scores for the candidate against the job they have applied.
This gives recruiters an idea of how much a candidate’s profile matches to the job description of that organization. As indicated earlier, the solution will ease the efforts of the recruiters by providing a primary level of processing and sorting the candidate profiles in the decreasing order of relevance. It will significantly speed up the processing of applicants, allowing recruiters to process more applications. The solution must have an innovative engineering feature to deal with imbalanced datasets and use the ensemble technique to combine multiple AI models and get more accurate and generalized predictions.
AI-based solutions have great potential in revolutionizing the recruitment industry. We believe that switching to AI-based processes will enable top recruiters to process candidates’ profiles much faster, reduce manual efforts, reduce the impact of bias, and help the organization become more data-centric. It will enable firms to work more efficiently and provide better services to their clients by filling the job openings quicker with the best-fit candidates.