Catching the eyes of a potential hire is the antecedent need in the recruitment process. Attracting the attention of a candidate and driving the traffic towards a company’s hiring page is one place where an AI can and is seeing a widespread use. Many professional and job-finding sites such as LinkedIn, Glassdoor and Indeed employ machine learning algorithms to provide job recommendations to their users.
Using data acquired from a user’s activity such as posts, search history, clicks, list of connections, and other such criteria, these algorithms help recruiters by enabling attraction and diversion of talent to companies.
Screening Tons Of Resumes:
The recruitment wing of any HR department is weighed with the task of scanning, screening and filtering countless resumes received from applicants. The existing Applicant tracking systems (ATS) have played a large role in reducing the burden by enabling the electronic engagement in the process. The easy migration of data from one platform to another, and partnership between various companies and job boards with these softwares have taken us light years ahead in the journey.
However, being criteria-based (duration of schooling and institutions attended, skill sets, past employers, experience and so on), these systems are limited by their lack of spontaneous flexibility that the addition of AI can do away with. With the ability to learn, every hiring cycle helps the system develop a better resume-matching capacity.
An example of a provider of such an AI feature to the screening process is Ideal. Ideal’s AI service screens thousands of resumes across the third party candidate providers that company’s use and add only best suited candidates to their ATS.
Reduction Of Bias And Promotion Of Diversity:
One of the most common pitfalls even in the most stringent of screening processes is bias. Bias based on age, gender, race and religion are the most common ones that seep into the final decision making process and selection of resumes. What if there was a system that provided a pool of candidates based entirely on merit and company needs, while the same time making sure all forms of diversity are maintained? AI can play the role of an all-inclusive filter that makes sure that diversity and talent go hand in hand.
HiringSolved is an AI-powered recruitment tool that enables diversity during selection. While it has many features, its experimental Rai chatbot tool can be considered as a highlight. It employs hundreds of data points to spot diverse candidates. It then applies an inhouse statistical model that permits users to boost search relevance through the platform’s ethnic and gender diversity models.
Responding To Applications And Answering Candidate Queries:
A company is nothing short of a valuable brand. According to a study, over 22 percent of millennials await a response within 10 minutes of getting in touch with a consumer brand. The importance of reaching out to customers, in this case applicants, within the shortest duration is of utmost importance these days, as it can be a decisive factor in losing a good candidate to a highly responsive competitor. Also, applicants having queries about their application and other information related to it may seek quick responses. Thus, AI can step into this role and expedite this task and reduce the burden on the HR department.
Mya, an AI recruitment tool very similar to Ideal, accomplishes this task in real time. The chatbot answers questions, furnishes candidates with current updates, provides feedback and support during every step of the hiring process.
Detect Attrition Patterns:
Of late, AI is being used to predict the outcomes within various aspects of an organisation’s functioning. Some examples of AI used in the predictive function by companies include, Walmart for supply chain optimisation, Hopper for forecasting price trends and Under Armour for extension of audience base, among others.
Nothing can prepare the immediate superior for his teammates’ ‘I quit’ bomb. The use of AI for the evaluation of attrition tendencies may help do away with the possibility of attrition altogether.
IBM Watson is working towards building such a predictive model for companies. Simply put, Watson can detect the most typical reasons contributing to employee attrition by simply analysing a structured data file fed into it. It then generates a score for every employee based on the calculated probability of them quitting their jobs. This predictive model can play a critical role in alleviating the attrition woes of of an organisation’s HR unit.
Sifting through calendars and organisers to find the ideal slot for an appointment is both a tedious and time consuming process even for a personal assistant (PA) who is hired specifically for the job. Scheduling meetings with candidates,training sessions and other HR activities is another area where AI can help improve efficiency. By stepping into the role of an advisor, AI can help streamlining the entire process of scheduling on an organisational level.
Take the example of Amy and Andrew Ingram, the autonomous AI assistants who handle scheduling. They were designed by x.ai, a hardcore technology company, with the purpose of democratising personal assistants. After linking one’s calendar to the AI assistantants and updating preferences, the PAs take over scheduling. From suggesting timings to helping rescheduling meetings, the Ingrams help in effective schedule management, all the while reducing unnecessary email correspondence