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Artificial Intelligence & Screening Candidates

Updated: Oct 29, 2020

A.I. SCREENING CANDIDATES


Machine Learning (ML), Artificial Intelligence (A.I) and BOT technology are now becoming more streamlined in the screening of resumes and applicants. Given that most of these A.I. firms are in their infancy, the majority are partnering or building out data science teams who also have a strong foundation in predictive analytics in order to tie everything back to performance and retention.


Terms like predictive analytics and A.I. have been "buzzing around" in the talent acquisition space for a while and are becoming more widely adopted. In the early stages of using A.I. in recruiting it was important for it to gather enough information to learn (i.e., by processing 1000's of applicants) and make a better decision on candidates who should move to the next phase. As a matter of fact, A.I. has made such significant improvements to the screening process that it can actually process more applicants at the front end, and it can do it more quickly by “burning fewer calories” for the recruiter and candidate. However, the key to success using this advanced technology is to be able to validate it to performance and retention to improve the accuracy of screening quality candidates to generate a shortlist of potential hires.


While I am a huge advocate having efficiencies in recruitment, it is far more important for organizations that they have a well-protected selection and screening practice abiding by human rights as well as predicting on the job performance. You can’t just simply be screening candidates based on hire rates, but rather be able to predict performance and retention! At the end of the day, talent acquisition core existence is based on two key metrics:

  • That everyone is treated fairly in the recruitment process; and

  • Performance & retention of employees.


TWO TYPES OF SCREENING ERRORS


It is important to note that in the screening phase, there are potentially two types of errors that can occur such as:

  • Missing someone good; and/or

  • Hiring in someone undesirable into your company.

More often than not most recruiters are trying to do their due diligence to ensure they are not missing someone good. However, the greater cost to the organization is when you let someone bad into your system. So if you are not using analytics to your advantage and monitoring your data at the screening phase you might end up making a few costly mistakes along the way.


It is fundamental even if using A.I. to monitor your data by comparing both your top to your bottom performers and the characteristics that are predictive. Many organizations a lot of times just focus on their top-performing people and this is a major mistake. The reason is, if your top and bottom have the exact same characteristics then you are not predicting anything other than they are identical. Your goal is to be able to properly differentiate your top from your bottom and what are those characteristics especially as it relates to on the job performance and retention. 


I think A.I. is a wonderful addition to the landscape of talent acquisition, but don't treat it as the magic ingredient just to save you time. Treat it as a gift that needs to be nurtured and monitored just like you always have done in the past.




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