How it works

A key element of success is awareness of the technologies available to improve pre-screening and initial projections of candidate suitability. Technology, especially machine learning and the development of Large Language Models (LLMs), has made it possible for artificial intelligence (AI) to enhance the assessment and selection process by automating and streamlining various tasks. Reviewing resumes and profiles is a critical but time-consuming process, and algorithms promise to speed it up while being significantly more accurate and unbiased. Experts indicate that there is a “learning curve” for algorithms to improve their accuracy in identifying candidates most likely to meet the required job requirements. As such, plans are required to load multiple resumes for both previously hired and non-hired candidates and to test the system to ensure quality outcomes.

These technologies tend to work best for jobs with a high volume of similar candidates. Applicant Tracking Systems (ATS), Candidate Relationship Management (CRM), and best-of-breed providers have provided functionality that identifies candidates who best match a job's needs. Some systems rank candidates or assign a ranking (for example, A, B, C, or D) to each profile or resume, explaining the factors in the profile or resume that justify its ranking.