Artificial intelligence in recruitment - AI manages the candidate database.
AI brings revolution to recruitment, particularly to searching resumes stored in candidate databases.
The word revolution is not an overstatement here. As the first technology, artificial intelligence allows to immediately, using a minimum amount of human labor, use the candidate database’s full potential. Something that has never been possible before.
Candidate’s resumes are stored in various ways: e-mail boxes, laptops, cloud resources. Sometimes dedicated systems are used to collect and manage resumes, particularly recruitment systems, commonly qualified as applicant tracking systems (ATS).
Regardless of the method of storage and size of the candidate database, we cannot use its full potential without the involvement of human labor. The size of this involvement is directly proportional to the size of the candidate base.
The classic model of searching the candidate database
Imagine that you work for an engineering company that has a database of 10 thousand candidates. These are various candidates that we have gathered in the course of numerous recruitments for engineering positions. Let’s also assume that we are looking for a candidate who:
- has four years of experience as an engineering pre-sales;
- has managed the sales department for at least two years;
- speaks English at C2 level;
- lives in London.
What would the process of finding such a candidate in our database looks like? So far, we have had two possibilities:
- The first possibility is that we store resumes in a place or system that does not allow us to search the documents’ contents. In such a case, it is necessary to review each resume personally. It is the most expensive scenario. The cost increases with each new resume uploaded to the candidate database. With a database of 10 thousand candidates, it is the cost of checking each of these 10 thousand resumes by a human.
- The second possibility is that we have an ATS system, which allows searching the resumes’ content. In such a case, we can explore the database with any sequence of characters, often using logical operators (boolean search). So if we type in”engineer” in the search engine, the system will find all the resumes that contain the word “engineer”. It is not an ideal solution because we still have to look through each resume and check the context in which the candidate used the word. The more candidates we find, the more resumes we have to analyze on our own.
In both cases, the work related to resume analysis is usually done manually upon receiving it. The result of this manual analysis can be a note or a tag added to the resume. It is a beneficial solution, although only as good as these notes and tags are. Unfortunately, accurate labeling and detailed notes require extra time and, therefore, cost.
Searching resumes with Artificial Intelligence
To solve our task as effectively as possible, we need to use a system that can read the resumes’ content (example no. 2 above) and parse this content. Parsing resumes’ content is the next stage of developing ATS systems, that revolutionizes the way we work with candidate databases. What does it mean to parse the content of a resume? We will explain it with an example.
A few seconds after the candidate applied, parsing capable ATS reads the CV content. It reads character by character and saves the content of the whole document. Then the process of parsing the content begins. Resume parsing is usually based on AI algorithms making an independent decision about the meaning of the resume content’s particular elements. In other words, an ATS first reads the content of the resume and then interprets what elements of that resume stand for:
- first and last name
- residence
- employer’s name
- job position
- job description
- start and end dates of work
- language knowledge levels
- skills
- certificates
- training
Therefore, if AI encounters the phrase “John Smith” in the resume, it will autonomously decide that this phrase is, in fact, the candidate’s name. If it encounters the term “Sales Engineer”, it will determine the candidate’s job position. AI takes many factors into account when making its decisions. The location of the word or phrase in the resume is one of the most critical factors. After analyzing the entire resume, ATS creates a structured profile of the candidate. Thanks to this structure, ATS knows the candidate’s employment and educational history or what language skills the candidate declares.
Artificial intelligence algorithms can parse hundreds of resumes in fractions of seconds and create structured candidate profiles from them. Thus, a structured database of candidates is created with now human labor. In such a database finding a candidate with a specific professional history is a matter of seconds.
Three classes of applicant tracking systems
Some ATS systems available on the market save resumes without reading their content. When searching for candidates, the user of such a recruitment system can only look through each resume individually and determine whether the document’s content contains the desired information. This model is already archaic and renders searching for candidates very inefficient.
The vast majority of ATS save resumes and allow searching their content. Users of such ATS do not know in what context the phrased searched for was used in the resume. Therefore, after finding the resume, a user has to check its content to see if it meets specific requirements. In such ATS, the only way to make resumes search more efficient is to add notes or tags to each resume.
Finally, some ATS (including Element) go a step further and parse the resume’s content. It is done with machine learning algorithms that are fundamental for every AI. Such recruitment systems automatically turn the entire candidate’s database, regardless of its size, into a structured database of information, wherein a precise and fast search for resumes is possible, without the need for manual, human work.
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Maciej Michalewski
CEO @ Element. Recruitment Automation Software.
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