AI in Hiring, Use Case #2: Auto-Filter Candidates at the Assessment Stage

January 8, 2024

min read


Bhargav Brahmbhatt

Imagine you have 1,000 applicants for an open job. AI can rapidly sort, filter out, and identify the top 20 to interview. Sub-par assessment techniques generally result in weak signal, poor filtering, and prolonged time-to-fill. But a combination of AI and behavioral science allows teams to bring predictive, holistic digital assessments into their hiring process.

According to Forbes, simply sorting through applications in an ATS can take up to 40% of a recruiter’s time despite up to 88% of applicants being unqualified.

The problems with candidate assessments are many and varied:

  • They occur too late in the hiring process 
  • They’re biased toward positivity and embellishment
  • They’re inconsistent by assessor
  • Feedback is siloed
  • They’re tedious and inefficient—it might take hours to schedule a call, have the conversation, and take useful notes 
  • Traditional assessments don’t quantify skills or behaviors: they give an incomplete view of candidates’ strengths, growth areas, and work motivations—and they can’t speak to candidates’ alignment with your organization’s culture

Supercharge Assessments

AI transforms the game by supercharging assessments. Gold-standard templates are sent to candidates’ former colleagues and managers and the candidates themselves. Incorporating self-assessments gives organizations a more comprehensive perspective of the talent in their pipeline, because they gather the significant behavioral data points that resumes and interviews overlook or omit. 

Assessments are gathered on Searchlight before on-site interviews. The platform automates a three hour manual process with the click of a button, and hiring teams obtain the data in less than 36 hours on average. AI like TRACY enables highly-predictive assessments—precisely because it automatically customizes the questions to assess for the very criteria it learns will lead to top performance in the organization.

It’s important to stress that this is predictive feedback on data-backed, predictive attributes. With AI, hiring teams can assess candidates on behavioral, attitudinal, and cultural alignment (the more precise indicators of success), transforming their entire assessment process. And because the assessment data is consistently structured, teams see less “interpreter” variation—and less bias.

“Recruiters and hiring managers were surprised by the honesty and depth of Searchlight’s reference checks. Many of them had never had a negative reference call in their life, so Searchlight’s reports were remarkable for their sound credibility.”
— Cara Brennan Allamano, former SVP of People @ Udemy
“Searchlight not only fixes the broken reference process, it also educates my managers to make faster, data-driven decisions. Everyone that I’ve talked to loves having Searchlight’s information.”
— Elizabeth Shober-Smith, former VP of Talent Acquisition @ Udemy

Find out more

As you learned in our earlier blog, Searchlight can help you eliminate a whole interview from your hiring process. Today, you learned you can filter candidates more efficiently and save even more time by leveraging AI earlier in the process. And we haven’t even gotten to the best part yet. You’ll have to read the eBook—or wait for the next blogs—to find out more!

In the meantime, book your demo today to learn more about Searchlight’s AI-powered hiring software.

Bhargav Brahmbhatt

Head of Marketing

Bhargav, our Head of Marketing, blends storytelling and customer empathy to drive business growth. Special flair for bringing new products to market.

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