As mentioned in our post about utilizing Artificial Intelligence (A.I.) to support the Human Resources (H.R.) function, clean data results in attracting the most highly-qualified candidates to fill open jobs, which streamlines the hiring process. Remember that a system is only as good as its data!

So what kinds of requirements are needed when filling a vacancy? Foundational elements are a well-written, specific job description, as well as an understanding of the business needs and culture. Hiring managers can’t simply say, “I want someone just like Tom.” While flattering to Tom, this kind of requisition lacks clarity.

Cloning isn’t yet an option, which raises the questions, “What skill sets or personality does Tom have that you seek in a new candidate? What strengths are needed to balance existing teams? What characteristics are important to your corporate culture?” Your existing job descriptions may require tweaking or, in some cases, a complete overhaul.

According to The Economist, investing time to create clear requirements is well worth the effort: by upgrading your job descriptions, you’ll increase by 25% the number of highly-qualified recruits coming through your doors, as well as boost diversity recruitment (e.g., minorities).

In addition to improving clarity and addressing business needs, the new descriptions must be scrubbed to eliminate hiring bias. For example, studies have found that corporate jargon like “stakeholders” and “synergies” tend to drive away certain candidates, especially minorities. Women are less likely to apply for a job that describes “managing” instead of “developing” a team. Men are attracted to phrases like “driven by” and “ambitious goals.”

During the talent acquisition process, A.I. algorithms can be used to identify hiring and retention trends in a company’s data. These algorithms can also identify pay inequalities, racism, and sexual harassment, which managers either unintentionally or consciously overlook. Using A.I. to support H.R. efforts reduces hiring cycle time, eliminating the tedious back-and-forth between H.R. and hiring departments, while increasing the quality and diversity of potential candidates.
This brings us back to the notion that any A.I. solution is only as good as its source data. Here are the benefits of clean historical information and clear job descriptions:

  • Increased Efficiency: reduce hiring cycle times
  • Creates Uniformity: establish common language and concepts
  • Improved Accuracy: identify candidates meet tole requirements and fit the corporate culture
  • Predicts Needs: shift away from reactive hiring practices to proactive searches

Not only can A.I. support filling current employee vacancies, but it will also enable H.R. to predict when and where vacancies will occur. For example, some firms use historical employee data and A.I. algorithms to identify which variables have the highest correlation with employee turnover. Using A.I., H.R. can analyze these variables to identify candidates with a high risk of turnover with 96% accuracy. H.R. can also use this data to inform the development of focused retention strategies. This cultural shift in H.R.’s role escalates their department’s value and invites them to be a bigger part of the strategic planning process. No longer will H.R. be left out of your company’s strategic decision-making process; in fact, you’ll be a key player with “a seat at the table”.

So, what’s next? First things first: clean up your data, organize your statistics, and revise the critical job descriptions. This undertaking is time-consuming, laborious, and tricky. The good news is you don’t have to take this journey alone. MorganFranklin’s Human Capital and Data Analytics Solutions support many firms with these activities. Before heading down the A.I. path, having cleansed data and clear requirements is a non-negotiable. Once this is complete, then you’ll be ready to integrate an A.I. solution to augment H.R. hiring practices.

You’re probably asking, so which variables have the highest correlation with employee turnover? Well, in our next post, we’ll further explore how A.I. can help predict when and where job vacancies will occur.