The potential of AI in human resource management

Artificial intelligence (AI) is unquestionably a powerful tool. Its economic value is increasing tremendously and transforming numerous industries such as manufacturing, fintech, healthcare and automobile. Workers in finance and marketing have much success using AI technologies, whereas HR practitioners find it rather hard to integrate these into their daily practices.

Prasanna Tambe, Peter Cappelli and Valery Yakubovich state in their research: “there are systemic and structural differences for HR that do make it harder, when you are building an AI-based system.” [1]. Due to the fact that the quality and explanatory power of big data and AI are limited they are still considered unconventional in the fields of HR and employment. To have a better understanding of this matter, we need to consider the AI problems in terms of data science in human resource management (HRM).

There are three main challenges in HR practices when it comes to data science. The first issue is the lack of consistency in HR process measurement throughout the employee life cycle. For example, when determining which candidate to hire or choosing whom to promote, it is crucial to consistently record and analyze which criteria and skills were the decisive factors in the previous hiring process.

The second problem with HR practices is the limitation of data sets produced in HRM. Unlike some fields such as marketing and finance, where a lot of data are generated and easily gathered, data collection in HRM faces big challenges in terms of quantity and quality. Moreover, data in HRM is oftentimes unstructured (on paper, in excel or PDF) and consequently, difficult for a computer to process.

The last difficulty concerns ethical issues related to data processing. The results of HR decisions can have a significant impact on someone’s career. Therefore, it is imperative to think about how fairness and transparency can be achieved. Furthermore, it is also crucial to know how employees react to the results that are solely based on data-driven algorithms. As Morgan Hampton from Tesla declared, “recruitment should be automated as much as possible, hiring should remain human.”

Taking these three issues into consideration when searching for AI solutions, HR managers should focus on the following aspects in order to utilize AI more effectively. Firstly, HR managers need to create the right HR process that is ready both for the digital age and AI technology.

Currently, AI technologies are separately, for example in recruiting and talent acquisition, payroll management and self-service transactions. However, they lack a mechanism to generate data that can assist the whole AI process in HR practices.

HR managers often merely keep the applications that they are interested in and don’t retain those that are screened out. This leads to a one-dimensional analysis and conclusion [1]. All these criteria should be gathered in data collection and eventually, be evaluated to facilitate the development of big data models and AI processes.

Furthermore, it is also imperative to generate data in a sustainable way. For instance, there are AI applications that can predict which workers are about to quit their jobs, and some even track data points from employees’ social media or emails [2]. If employees were aware of such a system, they would probably change their behavior and deliberately produce misleading data.

Last year, the story about the Amazon AI recruiting tool being biased against women was proof that machine learning can mimic human attitudes. Gender, nonetheless, is not the only aspect that is reason for discrimination. Others such as age, nationality or ethnicity could also have a negative impact, keeping companies from inclusive and diverse hiring. HR managers should carefully collect data samples that are representative and look for explainable AI solutions. The complex neural networks in deep learning is far from self-explanatory.

Up until today, the standard data criteria that HR managers should respect throughout the HR practice cycle does not exist yet. This means that HR managers have to team up with their company’s internal IT department or with external AI vendors to determine what data to trace and how to measure those data, in order to establish the best practices for AI within their companies.

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[1]Prasanna Tambe, Peter Cappellli and Valery Yakubovich. 2019. Artificial intelligence in human resources management: Challenges and a path forward. URL: [2019.10.20]

[2] Samantha Mclaren. 2019. Here’s how IBM predicts 95% of its turnover using data. URL:[2019.10.20]

Artificial intelligence revolutionizing job search in Paraguay thanks to the help of the Inter-American Development Bank BID and

The IADB helped Paraguay create the “Labour Intermediation Support Programme System“(Sistema del Programa de Apoyo a la Intermediación Laboral – SIPAIL), a job search tool that, among other things, supports the change from paper records to digital records in employment management offices, facilitates the analysis of online CVs and encourages the publication of vacancies throughout the country.

Sex segregation in the workplace

The segregation of people in the workplace according to their biological sex is partly due to different preferences and aptitude for specific occupations. Traditionally, jobs with the highest concentration of women are to be found in teaching, nursing and other care-related service work. The majority of male workers, conversely, holds blue-collar jobs, for instance in construction, equipment operation or repairing.

Furthermore, since occupation fields dominated by female workers have a lower compensation in comparison, it is more common for women to change into a profession that is male dominated than vice-versa. This is accompanied by recent developments such as the #MeToo movement and the introduction of women’s quota. However, this change is one-sided: occupations with a disproportional amount of men such as childcare remain largely unchanged.

In 1996, the European Commission Network on Childcare set the strategic goal to increase male employment in childcare to 20% by 2020. With a few months to go before the deadline, the employment rate of men in this domain is still far below the target number.

In Germany currently 6 in 100 pre-school childcare workers are male. In the UK and Ireland, the numbers are even lower than 2 out of 100. Norway, which is considered the global leader when it comes to gender equality, has the highest rate – with 9 in 100 men working in early childcare. This score is nevertheless far below the target of 20%. Outside of Europe the situation remains fairly unchanged. In the United States and Australia, the number of men working in early childcare amount to 4% and 2%, respectively.

Interestingly, things are quite different in a childcare center situated in Stuttgart, Germany. This center employs 12 people, half of which are male; moreover, it keeps receiving applications from more men. What is the secret of this center that appears to be so attractive for male childcare workers?

Mr. Nöth, the center’s founder and a former childcare worker explains that, unlike in traditional childcare centers with fixed groups, in his center children are presented with seven different activity groups, including ones for painting, handcraft and gymnastics. Each group activity takes place in a separate, specially equipped room and the children can attend a different group every day. Nöth notes that this way of operating offers opportunities for the childcare employees to work creatively, which includes individually designing and developing the activities. Such liberties, in turn, are said to be highly valued by many men. [1]

Gender stereotyping in the workplace like the one pertaining to men in early childcare is still present and it can create a hostile environment for male employees. In addition, work life can also be isolating when most co-workers are female. If there is little camaraderie and social activities among male coworkers, this might impel the few men to quit. At least, this is suggested by the fact that several of the male childcare workers from the Stuttgart center report to feel more comfortable among the relatively high number of male coworkers.

Such special cases can give important indications for changing the workplace situation where sex segregation poses a significant challenge. One point is concerned with creating new functions for roles that are dominated by one particular sex.

This is already happening in some blue-collar professions. With the advent of new technologies some of the jobs are shifting their focus from intense physical tasks towards sophisticated machine-involving activities. This in turn attracts an increasing number of women to the field.

Another point involves the achievement of gender balance in the workplace. If one sex is in the minority it is likelier to have unpleasant experiences at work. Statistics indicate that for women such unpleasantness increases tremendously in occupations with over 90% male employees.

In Norway, the education system actively directs men towards certain teacher colleges in order to prevent unbalanced concentration. This way, it encourages more men to apply and contributes to higher shares of male workers in early childcare. [2]

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[1] Philipp Awounou. 2019. Kann Mann machen. URL: [2019.10.03]

[2] Jack Graham. 2018. Men don’t feel welcome in early childhood. Here’s how to change that. URL: [2019.10.03]