Many Human Resources companies have already adopted Artificial Intelligence (AI). 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 handy tool for recruiters because, on its own, it brings a wealth of benefits, but it also enhances the tools, specifically the candidate database, that recruiters are already using. Let’s understand how Artificial Intelligence can improve the CV database and help the recruiter search 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. With the candidate’s consent, these CVs can be perused for future searches. This is one of the most common reasons recruiters start using software such as Inrecruiting.
A centralised database provides recruiters with a solid talent pool, but sometimes, that is not enough to find the right person for the job search. However, when a layer of AI is added, 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 actively engaged in the search for a job, who frequent portals and sites dedicated to the professional world and who visit and register for open job offers. Their applications are collected within the company database and represent the starting point of the selection process. The recruiter hopes to find the candidate that most closely matches these CVs’ 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 qualify the CVs in the company database while simultaneously speeding up the screening activity. An intelligent CV Parsing system, such as the one from Inda (Intelligent Data Analysis), is needed. 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 and evaluate specific skills based on a weighting system: this is a decisive factor in making the selection process objective. By orienting the search on candidates’ skills (technical and transversal), AI is committed to minimizing any possible cognitive bias. The result is a shortlist of candidates ordered based on objective scores, allowing recruiters to quickly and easily identify the profile most compatible with the job search.
The most common recruitment approach is to publish a job advertisement and review the CVs received in response to the ad. Sometimes, however, if there is time in the selection process or when particular skills are sought, the recruiter has to go hunting for the candidate.
The passive candidate is a 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 currently employed but may be interested in a change, or someone 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, so searching for them on social media, professional networks and in the company database is necessary. Again, artificial intelligence provides the recruiter with a tool that significantly simplifies and speeds up this operation: semantic search.
When the recruiter searches the company database using semantic search, the scope of the 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 candidate’s skills.
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; 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 helpful tool for transforming passive candidates into active ones is the Job Alert feature, which sends the candidate a notification (typically an email) with a list of new job advertisements that may interest them. In traditional systems, the candidate sets the parameters to receive a notification (therefore, the qualities an advertisement must have). Thanks to Artificial Intelligence, it is possible to use Job Matching, which automatically analyses new ads, recognizes critical 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 needing 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.