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How AI Is Increasing Efficiency In Talent Acquisition

By July 30, 2020September 25th, 2020No Comments

Talent Acquisition faces the challenges of time, cost, and human bias. For instance, talent sourcing often involves a large pool of candidate applications. AI recruitment intelligently sources to optimize efficiency. Additionally, data extraction through AI reduces human bias and error. Employers increase time connecting with qualified candidates as a result. Use fewer resources for candidate sourcing and screening. Spend more energy on connecting.

 

1. Talent Acquisition is focused less on formatting and more on content.

Recruiters often evaluate resumes by analyzing the who, what, and why. AI recruitment less often misreads a resume or CV. Often, standard systems will reject a misformatted resume. Additionally, data extraction is intuitive and similar to a human. Sourcing is less likely to overlook an atypical resume format from a creative candidate.Talent Acquisition is more focused on content rather than formatting. Job applicants are sourced based on qualifications and job fit as a result.

 

2. AI recruitment increases the likelihood of selecting a qualified candidate. 

AI in Talent Acquisition alters the landscape of recruitment. AI saves time when sourcing candidates. A big challenge among 61% of recruiters is candidate sourcing. Intelligent sourcing removes the barriers to finding an experienced applicant. The recruitment process uses a global dataset for higher matching potential. Additionally, resumes are matched to company data to source the most relevant candidate. For instance, interpersonal traits and skills are matched with company culture to find the best fit. AI recruitment also sources equally for qualified candidates. Companies receive numerous resumes a year. Even the best candidates are lost in piles of qualified candidates with diverse backgrounds.

 

3. Talent Acquisition is more efficient with NLP

Natural Language Processing (NLP) is used to screen applicants for interpersonal and technical skills.  NLP evaluates the content of language through sentiment analysis. Language parsing is used to categorize the content. Content analysis and categorization will best match a candidate based on education and skill sets.AI screening efficiently streamlines and regulates the screening process. Uniformity in the assessment of candidates is beneficial during the initial screening.

AI-empowered recruitment works to improve the recruitment process from both an HR and a candidate standpoint. The workforce continues to evolve, and recruitment practices evolve with it. 

 

 

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