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AI in practice – use in recruitment

Artificial intelligence is not only relevant for high-tech large corporations, but can be a game changer for different companies of all sizes. Nevertheless, smaller companies in particular do not use artificial intelligence in their value chain, and effective use tends to be rare, especially in the midmarket. Why is that? Often, there is a lack of appropriate know-how on how and in which processes the technology can be used at all. In our blog series AI in practice, we focus on concrete use cases in companies in order to familiarize potentially interested parties with successful use in their own operational processes through practical examples. Today’s blog takes a closer look at the implementation of AI in recruitment.

What is AI and what is its significance?

First of all, a conceptual foundation must be created in order to be able to understand the actual potential of the technology. Artificial intelligence (AI) refers to a software or hardware system designed by humans to achieve a specific and complex goal. A central difference to computer applications, which are based on fixed sets of rules, is that AI is able to recognize patterns and correlations and thus output comparatively significantly better results. A characteristic feature of AI-supported systems is that, in addition to inputting, processing and outputting data, they are also able to draw conclusions and initiate actions to achieve goals without concrete instructions from the developer. AI is highly mutable and in many cases is integrated into a larger technological system, e.g. chatbots, cars and robots. 

A further distinction is made between strong and weak AI. While strong AI is still not very concrete and is merely an abstract goal of scientists and researchers, weak AI can already be found in everyday life. Here, we are talking about the kind of technology that has been trained to perform a specific task and unfolds within a defined framework. Strong AI, on the other hand, refers to a standalone algorithm that is capable of confronting and solving abstract and creative problems. As mentioned above, this type of AI has not yet been developed and the greatest value creation of artificial intelligence is predicted to come from symbiotic collaboration between humans and machines.  

AI technology lends itself to a wide range of applications and experts predict an immense impact on future social coexistence. One reason for this is that AI is becoming increasingly autonomous, as it is able to learn, control itself and constantly develop without human intervention and only on the basis of its own accumulated experience. The algorithms developed can recognize patterns in data and provide predictions for future data sets without having received the corresponding instructions from the programmer to do so. An example of this is AlphaZero, an AI that was ‘fed’ the rules of the board game chess and, after a subsequent self-learning process, was able to deliver better results than “any software developed to date.” This learning process, which enables machines to improve their capabilities, is known as Deep Learning in the machine learning complex. 

Machine Learning and Big Data

Machine learning can be understood as the foundation for AI and describes a technique that is able to learn and develop itself through pattern recognition in large amounts of data. Such practices are becoming increasingly important for companies, since in the context of “Big Data” the availability of enormous amounts of data can make detailed evaluation and further processing profitable. It is precisely this large availability of data, which is generated by all activities in a digitally networked world, that gives rise to various possible applications for AI systems based on machine learning. 

Why should AI be used in recruitment?

Artificial intelligence can also become a real driver of innovation in HR, and many experts in the industry see great potential in the application of the technology. It could have the effect of removing time-consuming and repetitive processes from Ki and allowing HR to focus on more important, strategic issues. The need for such support stems from the consequences of the shortage of skilled workers, and the increasing importance of effective recruitment is creating challenges for companies. By not having a workforce with the right skills, companies can no longer fill the vacant positions that are responsible for value creation and economic growth. The biggest problem areas are nursing and skilled trades, but German companies also lack technical experts. There are perspectives on the problem that see AI as a possible solution to the lack of employees, because the technology is capable of influencing productivity and thus success. 

One potential area of application for AI is recruitment, where there are an increasing number of data sets that are conducive to further processing by artificial intelligence. One example is a digital assistant – a chatbot. This is a self-developing assistant that, through machine learning, is able to analyze input information and provide intelligent responses to it. Known uses of the technology are primarily answering standard requests, customer support or order processing. However, the digital assistants can also be used in the HR departments of companies to communicate automatically with candidates and applicants. It is conceivable to answer simple concerns within the application process such as questions about deadlines, requirements or other formalities. Topics that are too complex can be forwarded directly to the responsible employees by the chatbot. Particularly due to the shortage of skilled workers, companies can increasingly rarely rely on sufficiently qualified applications, with the consequence of having to take action themselves. As part of the active sourcing process of HR departments, in which potential candidates are proactively approached, for example via social media, a matching tool is suitable for pre-selection. In concrete terms, this means that existing data records can be used to match whether a person has the necessary qualifications or the right geographical location. For example, when filling a vacant software developer position, social media (LinkedIn, Kaggle, or XING) can be used to filter for suitable candidates with the appropriate programming language and professional experience. Such methods are also eminently applicable in the HR market, and various platforms exist that can match candidates, companies and applicants based on the data entered. Artificial intelligence can be implemented in many sub-areas of recruitment and complete standard tasks quickly and reliably. The future envisages further specific areas of application and intensive work is being done on the automated development of interview guides in order to increase the informative value of job interviews and to be able to implement differentiated application processes. The application possibilities of AI are manifold and the necessity, due to the shortage of skilled workers, requires a quick rethinking. In order to always find the right AI solution provider, “What can AI do for me?” develops an application that simplifies this process.

Conclusion

The use of artificial intelligence makes recruitment much more efficient, but there are also risks associated with the technology. For example, poorly prepared data sets can lead to discriminatory decisions in the decision-making process, and it is essential to ensure a differentiated learning process in order to guarantee a neutral and suitable applicant process.

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