Effective human resource management techniques constitute critical factors for organizational success in the contemporary business world. Businesses utilize different approaches to ensure that the activities of their members of staff support the achievement of the organization’s strategic objectives. However, due to various emerging issues in the job market, business organizations face an unceasing need to upgrade their human resource management approaches and remain competitive in the prevailing market. For example, continuous technological advancement necessitates the establishment of new forms of job specialization, which necessitate the utilization of advanced human resource management techniques. Also, intergenerational differences often emerge in the workplace, which forces human resource managers to regularly adjust their management strategies to accommodate the needs and interests of their members of staff. Such adjustments often involve assessing the organization’s production process to identify areas that require greater specialization. As a result, businesses conduct regular recruitment processes to develop an efficient workforce. An ineffective recruitment method often results in multiple workplace challenges such as lack of adequate specialization and the development of negative organizational cultures, which result in an increased risk of declining productivity. Therefore, organizations need to develop innovative recruitment techniques to achieve greater production efficiency and remain competitive in the prevailing market.
Contemporary workplace dynamics necessitate the utilization of an automated human resource management strategy. Human resource managers have experienced an increase in the number of tasks that they complete. The human resource department has evolved tremendously over the years and shifted its focus from personnel management and administrative tasks to managing employee engagement and strengthening culture (Pandita and Ray 2018). Human resource managers focus their activities on organizational culture to create long-lasting team bonds, manage talents to ensure compatibility between employees’ capabilities and tasks assigned to them, and engage employees to stimulate motivation and increase productivity. Consequently, many business organizations require strategies that could reduce human resource managers’ role in employee recruitment and administrative tasks (Fenech, Baguant, and Ivanov 2019). Also, as service automation technologies gain popularity in many industries, many tasks in the human resource department are still completed using traditional methods. Therefore, many businesses are in search of innovative ways to automate some human resource management tasks to increase efficiency, reduce their running costs, and increase their competitiveness.
Due to rapid technological advancement in the contemporary world, many people in the job market appreciate the use of internet-sustained job application processes. Technological advancement has reduced the world into a global village in which people from different geographical locations can easily communicate through the use of digital devices. As a result, many people in the job market depending on the internet as a reliable source of information regarding the existing employment opportunities (Lorincová, Ližbetinová and Brodský 2018). Various social media networks, such as LinkedIn, also allow users to update information regarding their professional qualifications to allow for easier scrutiny by potential employers. Therefore, many job seekers appreciate the opportunity to submit their job applications via the internet. As a result, business organizations are looking for new recruitment strategies that allow for the rapid assessment of many applicants’ qualifications to identify individuals with the highest level of the desired skills and experience. Also, with the rapid increase in the proportion of millennials among organizations’ workforce, businesses are looking for ways to automate certain human resource management functions to ensure alignment between their operational strategies and employees’ preferences. Therefore, many businesses could appreciate an opportunity to automate certain tasks within their human resource departments.
Moreover, the continued adoption of big data analytics necessitates the utilization of human resource techniques that allow for the collection and analysis of workforce-related data. Big data analytics technologies have been implemented in business to refine various operational and managerial processes. They involve analyzing large volumes of data to establish an effective approach that could increase a company’s capacity to achieve strategic goals (Pappas et al. 2018, p. 479). Companies leverage automation technologies to effectively use big data analytics to collect, store, and process data into useful information depicting areas that require improvement. While effective human resource management remains a critical success factor in business, many organizations are looking for ways through which they can implement big data analytics to refine their human resource management strategies and build a strong workforce to gain a competitive edge. Therefore, business organizations could appreciate an innovative idea that could create an opportunity for the use of big data analytics in human resource management.
Business organizations could implement artificial intelligence (AI) technologies to refine their recruitment techniques. The strategy could involve the implementation of an automated recruitment process through which companies could perform recruitment and onboarding functions. AI-driven recruitment could allow companies to create job adverts, develop targeted placement of job adverts, and interact with potential candidates through the use of service automation technology (Rąb-Kettler and Lehnervp 2019, p. 105). Augmented writing technology could make it possible for human resource managers to predict whether a job advert they post could bring about the desired outcomes. An AI-driven recruitment technology could use machine learning and predictive analytics to establish whether a job advert aimed at a certain type of candidate will appeal to the available candidates with the desired set of skills. The second component of AI-driven recruitment technology could focus on sourcing. The technology could include an automated and targeted placement of job adverts through which human resource managers can specify the exact characteristics, skills, and demographics they are looking for in a candidate. The technology could ensure that the job advert is only shown to people with the desired characteristics, at the right time and on the preferred platforms. It could also provide an opportunity for the preselection of candidates by comparing their characteristics with the desired attributes specified by the human resource manager. Eventually, the company could manage to recruit the rightful candidates for the existing positions.
The onboarding feature of the AI-driven recruitment technology could virtually organize everything related to the arrival of a recruit into the organization’s workforce. The technology would automatically use the contact information provided by a candidate when submitting their job application to provide them with information regarding the organization’s values, their specific duties and responsibilities, and other details related to their new positions in the organization. The technology would also connect the new employees’ data with their respective payroll, expense, and asset management systems during the onboarding process. Eventually, the automated onboarding processes could help to remove much of the manual labour, saving time and resources in the long run. The technology could also onboard the new employees into the organization’s talent management, learning and development, and performance management systems to facilitate the continuous assessment of their contribution to the company’s productivity. For example, an integration between the AI-driven recruiting technology and the organization’s performance management system could allow for automated analysis of a new employee’s progress while in the company’s workforce to provide valuable insight concerning their strengths and weaknesses. Therefore, an AI-driven recruiting technology could be implemented to refine a company’s employee onboarding process.
As illustrated in the storyboard above, the technology will perform two main human resource functions: recruitment and onboarding. During recruitment, the digital solution will automate the development of job adverts that are capable of attracting the desired talents into the organization. It will also automate the targeted placement of job adverts and perform preselection activities to identify the most qualified candidates. The proposed solution will also automate the employee onboarding process by including the candidates’ information in the payroll and performance management systems. Therefore, the proposed solution is intended to automate the recruitment process to increase efficiency in human resource management.
Potential users’ feedback regarding the effectiveness of the proposed AI-driven recruitment solution indicates that the technology could develop issues with accuracy and reliability, lacks the nuance of human judgment, and may overlook qualified candidates. Artificial intelligence technology may lack accuracy and can be easily confused by formatting methods used in the applicants’ documents. For example, the technology may fail to qualify a candidate due to irrelevant factors such as using unorthodox styles of bullets in resumes and application letters. It also excludes critical factors such as diversity and inclusion in the hiring process. Also, the proposed solution may fail to follow up with someone who could be well-suited to a role, despite not having as much relevant experience. My second assignment will involve identifying ways through which the technology could be improved to alleviate the identified biases and increase its efficiency.
The technology was also blamed for encouraging misconceptions regarding a candidate’s personality characteristics based on their professional qualifications. AI-based tools use algorithms to predict future data patterns. Using such technology in the employee recruitment process may generalise a candidate’s personality, experience, and identity, which may not always be true. As a result, the organization’s recruitment criteria prioritize professional qualifications while overlooking the possibility of a mismatch between an applicant’s personality characteristics and organizational culture. The technology may also be prone to malpractice. Applicants who know how applicant tracking systems work may include or omit certain keywords in their resumes and application letters to earn a higher score based on the program’s algorithm. Eventually, an organization may recruit incompetent employees, leading to serious implications on productivity. Therefore, I will research possible ways to make AI-driven recruitment techniques more efficient.
Pandita, D. and Ray, S., 2018. Talent management and employee engagement–a meta-analysis of their impact on talent retention. Industrial and Commercial Training. https://www.emerald.com/insight/content/doi/10.1108/ICT-09-2017-0073/full/html
Fenech, R., Baguant, P. and Ivanov, D., 2019. The Changing Role Of Human Resource Management In An Era Of Digital Transformation. Journal of Management Information & Decision Sciences, 22(2). http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=15247252&AN=141174731&h=PwRxRB805krW8Fuo3Tmy%2Fy2fTXf6pgoL4%2F56G6TK39BI8xJT%2Fdk2FwNy2RGMPrTOyqLzthaWzHZHqc2FTV40Mg%3D%3D&crl=c
Lorincová, S., Ližbetinová, L. and Brodský, Z., 2018. Social networks as a tool for job search. Scientific papers of the University of Pardubice. Series D, Faculty of Economics and Administration. 42/2018. https://dk.upce.cz/handle/10195/70518
Pappas, I.O., Mikalef, P., Giannakos, M.N., Krogstie, J. and Lekakos, G., 2018. Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16(3), pp.479-491. https://link.springer.com/article/10.1007/s10257-018-0377-z
Rąb-Kettler, K. and Lehnervp, B., 2019. Recruitment in the times of machine learning. Management Systems in Production Engineering, 27(2), pp.105-109. https://sciendo.com/pdf/10.1515/mspe-2019-0018
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Published On: 01-01-1970
