In 1999 monster.com launched itself with super bowl ads that catapulted it to first place, displacing any other job boards that were trying to emerge at that time. This $4 million investment by monster.com transformed their brand at that time and played a huge role in making it the giant it is today. An established online job board did not exist before 1999, but their advent transformed hiring forever.
Monster’s 1999 ad:
Before job boards, jobs were advertised in print media in local newspapers. Companies like Coca Cola which only received 50 to 100 applications for open positions, suddenly started receiving thousands of applications. A process that had been manageably manual became unmanageable and required additional human resources staff to sort through this large amount of new information. HRISs (Human Resource Information System) though in development since the 1980s were still in their infancy. As technology improved, the decreasing cost of investing in computing hardware gave an impetus for these systems to evolve, making their adoption widespread.
In the 2000s job applications started shifting online large scale. However, though hiring has shifted online, the process of screening these applications, even with the aid of Application Tracking Systems (ATIs), even now, is largely manual. Apart from a few multinational corporations like Google which developed qdroid which is an internal recruitment aiding tool, most organizations haven’t managed to scope in computing to automate and aid hiring internally. The cost implications of developing such a system in-house is prohibitive.
To bridge this gap there has been a surge in recruitment aiding technologies and services that have shifted part of the hiring process online or outsourced them. Organizations providing these technologies and services specialize in specific sections of the hiring process. At the base, there are HR consultancies that basically undertake the manual process of scoping and screening candidates for job openings. Then come assessment platforms that conduct aptitude tests and psychometric tests in siloes. They basically help organizations decide whether the candidate has the skill requirements or the personality for a group of jobs for e.g., jobs that involve selling. These are generally sold as subscription packages to companies that conduct large scale hiring. Companies like Mettl and AMCAT are experienced in this space. PexiScore from Pexitics is also an assessment platform but it has a Unique Selling Proposition (USP). It assesses candidates for job fit, not on the basis of a job group alone, but drills it down to one job, in one organization in one specific industry. This deep level of customization assesses a candidate comprehensively in one phase for skill fit, experience fit, personality fit and culture fit. It’s a do it yourself process, that puts prospective employees at the centre, while giving recruiters the power of an HR Analytics based algorithm that is poised to incorporate machine learning as uptake increases.
After this preliminary screening and shortlisting, the process of hiring becomes largely manual again. This includes the various rounds of interviews and making the offer. There are support organizations that conduct comprehensive background checks before an employee is onboarded. Employee onboarding again has witnessed some automation but not on a large scale. But this is soon going to change with the advent of AI. Google, Apple and Intel have already invested heavily in AI through a string of acquisitions, signalling a shift towards and adoption machine learning and deep learning in business processes, products and services. Cloud computing has made it feasible for even smaller organization to utilize large scale computing power. This computing power when coupled with AI is capable of much more than any highly efficient manual or manual automated hybrid process can achieve in terms of speed, accuracy and adaptation.
Human Resource Management is the most human centric function of any business. Every human is unique with a different set of personality traits. This makes assessing them challenging. This is where Natural language processing (NLP) comes in along with predictive analytics. Predictive analytics in hiring, centres on establishing a data-driven statistical relationship between the goals and initiatives of the job role and the success or failure of the prospective employee in achieving these goals. Predictive Analytics therefore is a cornerstone for HR Analytics as a whole and the catalyst, that will shape the development and programming of AI, for the purpose of hiring. Using NLP in hiring will mean fewer hiring errors in the long run which automation alone cannot achieve. NLP can give computer programmes the ability of analyse human speech, and in the future, ultimately, eliminate the need for a human interviewer to be present. In its current form, NLP can be programmed to be an interview assistive tool that helps measure interview performance from a purely organization centric viewpoint.
It is clear, that AI is transforming the business landscape and can potentially be applied to all business functions. Specifically, in relation to hiring, for now, even with the aid of AI, the decision of who to finally hire, rests on the hiring manager. But what AI will help in, is to shortlist the right people. The list to choose from will definitely be smaller and the decision of whom to choose will be supported by documented facts rather than perception biases. But who knows what AI can achieve if properly harnessed for the future of hiring. With several fictitious accounts of how AI could change the world in the form of books and movies, we have shifted into a time when AI is a reality. The extent of AI’s potential is unclear, but it is vast.