Recruitment in the Age of AI and Automation

Nov 30, 2017
6 Min Read
Recruitment in the Age of AI and Automation

Recruitment in the Age of AI and Automation


Talent acquisition practices have evolved as a result of artificial intelligence and automation. Here’s what you need to know today.

In the human resources world, automation and artificial intelligence caught fire in the recruitment domain first, and the elimination of many manual and subjective processes has led to greater efficiency and accuracy in attracting the best talent. There are countless of examples of innovative strategies in action, but let’s talk specifically about a few of my favorites: recruitment segmentation, predictive analytics, digital talent networks, and candidate bias elimination.


Recruitment Segmentation

Identifying segments and custom-creating messages to reach each one is a strategy that has been used by marketers for decades.

Do you have a HRIS (human resource information system)? If so, you likely have a great deal of data at your disposal. Examine it and note any large groups of candidates being hired. Candidate pools can be segmented in a variety of ways, including by geographic location, division, experience level, role, skill, and background.

For example, if your company hired over 100 college graduates last year but far fewer in other age categories, college graduates might be one of your target segments. Or, if you’re based in Philadelphia and have had greater success hiring from the Big Apple versus Washington D.C., you might create a segment for New Yorkers.

Once you have defined your segments, you can use your HRIS to deliver messages for frequent and consistent touchpoints that resonate specifically with each group and encourage recruits to keep in touch.


Predictive Analytics

Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends.

As recruiters, predictive analytics can tell you, for example, exactly how and why your recruitment efforts are hitting the target with a particular audience of candidates. Already, platforms such as LinkedIn now offer the ability to mine data to show the number of candidates in a segment (e.g. IT engineers in the USA) based on the number of job listings in each city.

Analyzing data like this further will allow you to automatically adjust your recruitment experience to ensure you reach the talent you are trying to hire. At VMWare, a cloud solutions company, diversity hiring – especially among women – is a major initiative. The company is using predictive analytics software to assess the relationship between Net Promoter Scores and reviews on Glassdoor and perform root cause analysis to identify areas of the diverse candidate experience that need improvement.


 Digital Talent Networks

Sodexo USA has simplified and automated candidate relationship management by creating digital talent networks, which serve as a common place where past, present and future Sodexo employees and industry professionals can network with trusted and like-minded friends.

Using these networks, companies encourage recruits to become ambassadors and provide them with specific actions to do so (create videos, post in fan pages, etc.). In other words, once active, the network communities run them rather than HR staff. The networks are optimized for mobile devices, providing candidates the ability to interact with recruiters and available positions on-the-go.


Candidate Bias Elimination

An unconscious bias is an automatic attitude about gender, age, race, etc. that we are unaware we have and act upon. Unconscious biases are hard-wired into humans. The ability to quickly categorize people helped our ancestors distinguish friends from enemies, and assists us in sorting through billions of pieces of information every day.

But unconscious biases sometimes prevent us from seeing recruits’ true abilities, and this is where artificial intelligence can come in handy. Combat it by using software like Textio to phrase your job descriptions with neutral language. Remove names from resumes so evaluators don’t make assumptions about gender, race, or ethnicity and leverage tools like, a Google Chrome extension that removes faces and names from LinkedIn profiles. You can also automate your interview process, structuring the process so all candidates are asked the same questions.


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