Many Human Resources companies have already adopted Artificial Intelligence (AI) into their operations. This is because they have clearly understood the advantages and potential of AI in improving their search and selection process. Artificial Intelligence can be a really useful tool for recruiters because on its own it brings a wealth of benefits but it also enhances the tools, specifically the candidate database, that are already being used by recruiters. Let’s try to understand how Artificial Intelligence can enhance the CV database and help the recruiter in searching for active and passive candidates.
The company database is a prime source for finding candidates because it collects candidate applications (CVs) in one place: both spontaneous applications and applications relating to a particular vacancy. These CVs can be perused – with the consent of the candidate – for future searches. This is one of the most common reasons that recruiters choose to start using recruitment software such as In-recruiting.
Having a centralised database definitely provides recruiters with a strong existing talent pool, but sometimes that is not enough to find the right person for the job search. When a layer of AI is added however, it is possible to strengthen the power of the candidate database, enhancing its potential when searching for active and passive candidates, old and new.
When we talk about active candidates we mean all those people who are actively engaged in the search for a job, who frequent portals and sites dedicated to the professional worlds, and visit and register for the open job offers. Their applications are collected within the company database and represent the starting point of the selection process. Among these CVs, the recruiter hopes to find the candidate that most closely matches the job requirements. This process of CV analysis and screening can take quite a long time: it is the moment in which the recruiter compares the skills of the individual candidates with those specific to the ad, looking for the most compatible profile.
Artificial Intelligence helps the recruiter to qualify the CVs in the company database, at the same time speeding up the screening activity. To do this an intelligent CV Parsing system is needed, such as the one from Inda (Intelligent Data Analysis). The CV parsing activity allows CVs to be read and the information in the CVs to be analysed much more quickly than with manual methods. The system also makes it possible to recognise specific skills and also evaluate them based on a weighting system: this is a decisive factor in making the selection process truly objective. By orienting the search on the skills (technical and transversal) of candidates, AI is committed to minimizing any possible cognitive bias. The result is a shortlist of candidates ordered on the basis of objective scores, allowing recruiters to quickly and easily identify the profile that is most compatible with the job search.
The most common recruitment approach is to publish a job advertisement and review the CVs that are received in response to the ad. Sometimes, however, if there is time in the selection process, or when there are particular skills that are sought, it is the recruiter who has to go hunting for the candidate.
The passive candidate is that person who is not actively engaged in a job search, does not visit sites and portals dedicated to the working world and, therefore, is not necessarily interested in responding to a particular advertisement. Typically, the passive candidate is also someone who is currently employed, but may be interested in a change; or someone who is already in the company database because they had submitted their application in the past; or a business user with career ambitions, and so on.
The passive candidate could be anywhere and for this reason it is necessary to search for them on social media, professional networks and in the company database. Again, artificial intelligence provides the recruiter with a tool that greatly simplifies and speeds up this operation: semantic search.
When the recruiter searches the company database using semantic search, the scope of te analysis is so broad that it includes active and passive candidates, old and new. There is no difference between “dormant” candidates and those who have made a specific application. The semantic search on the candidate database aims to identify exactly what we’re looking for: the skills of the candidate.
This is a keyword-based search. The recruiter enters a word (AI solutions such as Inda also allow you to assign a weight to each search key) and, unlike traditional search, obtains all profiles semantically close to the word used. This means that a CV does not need to contain the exact keyword entered by the recruiter: a semantic proximity is sufficient for that Cv to be included in the list of suggested candidates.
This universal action on the company database and its speed make semantic search an indispensable resource for anyone who wants to raise the quality of their selection processes.
Another useful tool for transforming passive candidates into active ones is the Job Alert feature which consists of sending the candidate a notification (typically an email) with a list of new job advertisements that may be of interest to them. In traditional systems, it is the candidate who sets the parameters to receive a notification (therefore the qualities that an advertisement must have). Thanks to Artificial Intelligence, it is possible to use Job Matching which automatically analyses new ads, recognizes key information (e.g. required qualification, skills, location, etc.) and at the same time identifies which individuals in the candidate database have a CV online. After obtaining the candidate’s consent, a communication will be sent to them, without the need for a manual alert.
Ultimately, if the recruiter leads the recruitment activity with the help of artificial intelligence they can refine their work and more easily reach their Talent Acquisition goals.