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In the world of talent acquisition, situations will always exist where no amount of computational sophistication can take the place of human intuition. Hiring managers and human resources departments will probably always need a human touch to perform their duties well.
Even so, we're already seeing the many potential benefits of bringing data analysis and artificial intelligence into the hiring process. Broadly speaking, companies large and small are using advanced technologies to help them:
- Source candidates more successfully
- Analyze applicants' talents and hard/soft skills based on language and keywords
- Make better matches between applicants and open positions
- Predict the behavior of new hires and how long they're likely to remain with the company
In other words, hiring can be much more strategic now that human beings have the aid of impartial and vastly more observant AI tools to help make our staffing decisions. Here's a look at how big data and AI are shaking up hiring just about everywhere.
Employee Retention
In 2018, Global Recruiting Trends polled professionals involved in the hiring processes at their companies. Approximately 50 percent said big data was, or would soon be, a significant part of their hiring processes and strategies. Additionally, 56 percent indicated big data was just as important in employee retention. We don't need to remind you how costly turnover can be — onboarding and training a new hire to take over a vacated position can cost 50 percent or more of the outgoing employee's annual salary.
So, how can big data and even artificial intelligence help out?
The use of data analytics in workforce management — particularly pattern recognition — can help companies broadly. Hiring managers specifically leverage historical data from their organizations to create better-informed hiring decisions based on how long new hires are likely to "stick it out." The results could be better matches between talent and open positions, more personalized job offers and potential savings for employers of billions of dollars per year.
Selective Automation of the Hiring Process
We've made a point to note how important the human element is in hiring today and is likely to remain. Nevertheless, even a quickly growing mid-sized company can find itself scaling up faster than it might've imagined. This doesn't mean cutting corners in the hiring process, but it might mean turning to artificial intelligence to do some of the heavy lifting for you.
Employers these days are using artificial intelligence more and more often to selectively automate portions of the talent acquisition process. For the most part, this isn't "last-mile" stuff — it's using algorithms to automatically winnow down reams of potentially qualified candidates to just those who fit the profile you're looking for.
Artificial intelligence is already helping hiring managers automatically reject applications and resumes with typos or grammatical errors, as well as those that lack relevant keywords. Moreover, automation can help flag candidates with the right mixture of hard and soft skills or even draw particular attention to candidates with a specific, sought-after background or set of traits. Pre-screening can save companies as much as 70 percent of the time usually required to fill a vacant position, and automation fueled by AI could slash those numbers even further.
Responding Better to Seasonal Fluctuation and Staffing Shortages
Plenty of industries out there have been flying by the seat of their pants for years as far as hiring is concerned. This "eleventh-hour" approach to filling staffing shortages just in time — or responding to seasonal fluctuations without the benefit of data — isn't sustainable for most companies. That makes the inclusion of big data, analytics and artificial intelligence particularly relevant for organizations with non-standard or variable staffing needs.
One example? Staffing managers can use organizational data coupled with AI to make informed estimates of future demand and plot staffing requirements further in advance. Having more lead time for screening candidates to fill vacancies means companies can be more selective about hiring, make fewer impulsive staffing decisions and generally enjoy longer employee longevity within the company thanks to a better cultural fit.
Examples of How Companies Apply Big Data to Hiring Decisions
Companies large and small are beginning to bring artificial intelligence and big data analytics into their organizations. Hiring and human resources departments are already benefiting from high-tech ways to complement human instincts during the new hire onboarding process. Here are two real case studies:
- IBM: When IBM wanted to figure out which types of applicants would be the best fit for salesperson positions within the company, they decided to purchase an analytics firm called Kenexa, which had occupational data on some 40 million professionals in the workforce. By applying artificial intelligence and heuristics to their newly acquired data, IBM was able to discover which phrases, keywords and traits seemed to indicate individuals with an aptitude for salesmanship.
- Xerox: Faced with a stubborn attrition rate among employees in its call centers, Xerox set out to use data analytics to help them cut down on employee departures — and the cost of hiring and training replacements. In similar fashion to IBM, Xerox used existing hiring data to find out which employee traits seemed to signal longevity within the organization. Decision-makers at Xerox identified "inquisitive" and "creative-minded" individuals as the most likely to remain with the company.
Digging into the data uncovered additional persistent sources of employee dissatisfaction — and ultimate departure — at Xerox: long commutes to the workplace and a lack of reliable personal or public transportation. Thanks to this information, Xerox was able to cut the departure rate for employees in this division by 20 percent. The lesson to learn here is that no variable is too insignificant to take note of when you're trying to solve a stubborn or long-running problem.
Examples like these are only just getting the ball rolling. In the coming couple of years, there's every reason to believe even more hiring departments at even more companies will be leveraging advanced data tools to help make the best fit between position and candidate. Not all the robots are taking our jobs — some of them actually want to get us hired. And thankfully, it looks like "wowing" the algorithm will still take a measure of talent and charisma.
