The first industrial revolution is widely considered to be one of the most transformative periods in our history thanks to the invention of the steam engine among other advancements. Experts argue the upcoming artificial intelligence (AI) revolution will be as impactful, if not greater.
There are similarities between the two revolutions in that, much like the Luddites in 19th Century England, there are critics who believe humans could be out of work thanks to the increased productivity of AI.
Critics have concentrated on how AI will transform jobs. It might be more valuable to look at how the technology could prevent the current record-breaking skills gap leaving 6 million American jobs unfilled. In fact, through solutions such as machine learning and semantic search, it is arguable that AI could lead us on a path where that figure becomes a fat zero.
How AI can improve hiring
Machine learning is already speeding up the recruitment process. Perhaps more importantly, it is also finding those potentially hidden star candidates.
Companies are able to use algorithms to sift through the masses of data from previous hirings in order to recognize the right signals for a specific role. This is in essence what recruiters do but as machine learning thrives on big data it can utilize a much wider amount of information at a faster rate. Doing this provides more robust decisions. If an employer favors a certain career arc, whether that be the major studied at college, previous places of employment or even what they did on their gap year then machine learning can narrow down and identify the best candidates.
Semantic search can improve accuracy when searching for new employees. This is achieved by understanding a searcher’s intent in order to improve search results. Candidates use different terminology to describe job titles or experience which keyword searches will not recognize. Semantic search is able to analyse natural language used by all the candidates to provide a better overview of their suitability.
Glen Cathey is particularly compelling in describing the importance of this by referring to potentially ideal candidates who are using different language as “dark matter” results. These are candidates who are excluded by human recruiters for a variety of reasons. This could be for a lack of time spent filling the position or the type of natural language used by the applicant. This means employers could be inadvertently taking the easy way out by only examining the “best of the easiest candidates to find” rather than the best person for the job which AI solutions are far more likely to detect.
An end to being underpaid
It is not just employers who stand to benefit from AI. The applicants themselves can utilize certain solutions to improve their situation.
Companies such as Paysa are able to analyze more than 35 million data points. This provides an overview of an individual’s market value. An employee can identify if they are being underpaid and if there are far more lucrative jobs available. Consequently, individuals are paid properly for their level of work.
Employers benefit by utilizing the same data to recognize if their top workers are not properly paid. Therefore, they will reduce the chances of losing valuable employees.
Not the end for human recruiters
AI and machine learning will significantly improve the accuracy and speed of filling jobs. Despite this, a human at the end of the process is needed to ensure it reaches maximum productivity.
Summer Husband presents an interesting case for machine learning in recruitment by analyzing its impact on healthcare. Survival analysis is performed using algorithms in order to recognize the likelihood a disease will recur. This same method can be used to predict how long it will take to fill a job based on a number of variables including the number of candidates, company information or the type of role. Armed with this knowledge, recruiters can identify roles which are more difficult to fill and can also manage their client’s expectations.
Machine learning can also use the masses of big data from previous hirings in a market to ensure that recruiters can target people who might be ready for a new role. For example, if a salesperson generally spends 3 years in a role within the telecommunications market, machine learning will predict which candidates might be ready for a change and target them appropriately. Not only are places filled more quickly but workers are happier in a fluid job market. This eliminates the likelihood of stagnation.
Looking forward not backward
It is often cited that AI’s dramatic increase in productivity will actually be to the detriment of humans by rendering their role as useless. Not only is this not necessarily the case, but AI will make everything more productive including the process of finding and filling new jobs.
Rather than signaling the beginning of the end of the human role in the workplace, AI could bring an end to job boredom, stagnation and wage disparity within a market. For this we should embrace, rather than fear, the latest technological revolution.
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