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What's the impact of accurate and scalable AI within talent acquisition?

 
May 6, 2022

Imagine this scenario: Scrolling through a company’s website, you come upon its career site showing gleaming photos of a diverse workforce in a fun environment. You land on the search function with a sigh of relief because let’s be honest, it can be a lot of work to scan the page looking for what’s relevant to your line of work.  

After entering details in a promising job search and hitting ‘enter,’ you land on a role in your area of expertise! There is only one problem, though, the role is in another country. This is what happens when AI in recruiting software doesn’t work well. 

Two cups of coffee later, you’ve spent time glancing between your resume and webpage to trying to find your skills listed, only to be disappointed when it seems manually scanning the page would have saved you more time.  

An inefficient job search like this can be a frustrating and disengaging experience for candidates and is an example of how some artificial intelligence in recruiting software can surface inaccurate results.  

In this post, we’ll see that all AI engines are not created equal and explore the impact of accurate AI within TA processes. Topics include: 

  • The value of iCIMS Ensemble AI 
  • Types of outputs 
  • Value of scalability and accuracy 

 

The value of patented Ensemble AI in recruiting software

The iCIMS Talent Cloud AI is powered by the newly patented Ensemble AI technology that supports job search results, talent matching, job matching, and more.  

Ensemble AI uses multiple engines with individualized weights and scores to vote on a highly accurate result. Typical algorithms with single-use case AI models have limitations due to various hiring demands across the organization, but this isn’t the case for iCIMS Ensemble AI.   

 

 

(Ensemble AI voting system, iCIMS, 2022)  

Precision is how good your algorithm is at getting only the correct result. Recall is how good your algorithm matches all the best candidates or jobs. True accuracy is finding the right candidate for the use case.  

 

Types of outputs 

There are dozens of use cases for AI within talent acquisition. iCIMS Applied Intelligence methodology takes data from across talent acquisition applications (not just an applicant tracking system) and applies insights to these various use cases. Ensemble AI models further adapt to new data, allowing for scalability and a dynamic approach to recruitment technology.   

Consuming data from across the iCIMS Talent Cloud allows the AI to surface results through the platform (within features listed in parenthesis below) and enable hiring teams to answer questions like: 

  • Which candidates have similar qualities to others in the candidate pool? (Similar talent matching) 
  • Tip: Learn how to find top talent in seconds with Talent Match in the iCIMS Talent Cloud.   
  • Which candidates best align to the company and role? (Leaderboard) 
  • Which job aligns with the candidate’s qualities? (Job to candidate matching) 
  • Which employee aligns to open positions? (Employee to job matching) 
  • Which candidate aligns to open positions? (Candidate to job matching) 
  • As a job seeker, which roles best align with my qualifications? (Job search) 

 

Value of accuracy and scalability 

As we learned in the earlier scenario, limitations within a job search can frustrate candidates. There’s also an impact on the employer. By providing inadequate results to potential candidates, the company increases the overall time to apply and potentially decreases the volume of applicants.  

Improving accuracy through precision and recall, through an ensemble AI approach, allows businesses to:  

 

Continuous evolution 

Beyond use cases for engaging and hiring candidates, innovations for employees are surfacing, for example, within iCIMS Opportunity Marketplace. AI used for internal mobility, like skills-based talent matching, helps create proactive and engaging employee experiences.  

A multi-engine foundation for your AI can significantly impact the results surfaced across talent applications. Talent teams can then use these results to better engage with candidates, hire suitable matches, and advance employees in their careers 

Click here to learn more about iCIMS Applied Intelligence and Machine Learning methodology.  

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