The article “Artificial Intelligence in Human Resources Management: Challenges and a Path Forward” is well-researched and efficiently put together to provide information regarding the application of Artificial Intelligence in HR management and related tasks and the challenges faced thereof. Authored by Prasanna Tambe, Peter Cappelli and Valery Yakubovich, it presents a succinct report on the influence, requirement and reactions to the usage of AI focused in the HR sector, duly verified by a special workshop conducted to analyze and verify the issues associated with the subject matter. In the current times, with a massive shift towards technological advancement globally, the article presents relevant issues and arguments in favor of AI development for betterment of the sector.
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The write-up is well-structured and begins by a brief mention of the Artificial Intelligence technology and the need for its integration in HR management systems. Describing the dependence of the HR sector on AI, which is minimal as mentioned in the article, it also outlines the challenges faced in the application of AI in HR processes, corroborated with relevant examples.
For an enhanced study of the challenges of integrating data science-based algorithms in the HR systems of organizations, the authors organized a special workshop with the data science faculty and heads of workforce analytics of 20 major U.S. corporations. The issues and ideas as discussed in the workshop and its further analysis has been shared throughout in the article.
On the basis of the study, the article determines four major challenges in incorporating data science techniques in the field of HR, namely, the complex nature of HR processes, limitations faced as a result of availability of small data sets, issues related to fairness of decisions regarding hiring and dismissals among others, and other constraints involving ethics and legal issues and the reaction of clients to the dependence of such decisions on algorithms based on data.
It further explores the effect of these challenges and attempts to propose course of action to resolve them as they appear or are encountered in the AI lifecycle enumerated as Operation, Data Generation, Machine Learning and Decision Making.
The majority of the article focuses on enunciating the AI lifecycle with examples and subsequently addressing a detailed analysis of the challenges faced, as mentioned above, in the usage of the technology in correlation to the stages of the lifecycle. Thereafter it discusses the ways in which the AI based algorithmic systems can help in the evolution and further growth of HR management systems with increased acceptance among employers as well as employees.
Beginning with a brief summary of the issues faced in the application of AI Techniques, the report illustrates the complexity of HR operations as one of the major challenges faced by the application of AI technology due to difficulty in arriving at a consensus for the approved measure of the vast variety of decision-making criteria for each function of the processes.
Additionally, for small offices or for rare operations, the data available is limited. Basing and developing algorithms on small data sets cannot yield efficient predictability nor profitability.
Another crucial issue is the fairness of HR decisions and its ethical and legal purview. The decisions based on AI algorithms need to be in alignment with the ethical and legal framework of current times to prevent discrimination in an organizations HR policy, and further to prevent legal action against the company. In addition to this, the difficulty in the “explain ability” of the algorithms and its outcomes due to its complex nature is also a major source of concern.
Further, the article explains the importance of the acceptance and reaction of the employers and employees, the end users of the technology. The decisions made by the algorithms affect their psychological well-being and hence it should be ensured to enhance the predictability of the outcomes.
Thereafter the report goes on to provide an overview of the AI lifecycle comprising of four stages, namely, operations, data generation, machine learning stage and decision-making stage. The article then goes on to discuss in detail the challenges discussed previously in the context of the AI lifecycle stages. Having identified the HR operations and the subsequent prediction tasks in a tabular format, the analysis begins with the data generation stage, where the elaborate and complex nature of HR systems is underlined through various arguments.
The article, with the help of the study group derives the fact that due to the non-availability of a quantifiable metric for several HR tasks, there is an absence of uniform variables that can enable organizations to standardize the best practices in analytics for HR sector. The tasks include measure of attributes such as performance and behavior, the context of which varies considerably for every company which invariably lead to biased and un-informed decisions. Additionally, not all organizations track activities of their employees or applicants, or retain their information.
Another issue adding to the complexity of generating data is the absence of the willingness to share the inferences drawn with the help of various systems of different vendors, due to inter department rivalry. The authors stress on the importance and need of increased cooperation in the organizational departments internally to enable data sharing for creation of effective data sets that can be utilized to generate efficient algorithmic systems.
The article also mentions that the selection of HR tasks that need to be researched or which require further collection of data is also an often unregulated operation. The authors suggest to adopt a more systematic approach to truly fulfill the actual knowledge base for research. In addition, they also list measures that can be adopted to improve data generation taking guidance from other fields of management.
The second challenge of small data sets is another issue faced in data generation. With most organizations not enabled to tackle digital advancement, extracting usable data is a task. Also, smaller firms with lesser number of employees also are not able to generate sufficient data for the creation of algorithms. Lesser data will provide reduced learnings from data analytics.
Furthermore, the current data sets based on biases and past assumptions are misguiding and render AI findings to predict unfruitful conclusions. In addition to the relationship among variables, causation needs to be developed to provide accuracy in the AI predictions. The article describes, hiring vendors as one of the solutions to this, as the larger data sets or different organizations available to the vendor can be utilized to generate algorithms. However, due to the lack of standardized units of measures for the behavioral attributes, the effectiveness of the predictions of these AI systems is an obstacle.
The increased variation in the methods employed for gathering data of the employees by organizations poses another challenge in the data generation, as the article informs. Trying to track the employee attitudes and behavior by tracking their social media and mail accounts is a frequently used method, raising increased privacy concerns and causing legal issues. The article cautions on the use of such methods, iterating the need for regulation of the system as per the respective government regulations.
The article also raises questions on the authenticity of the employee data obtained from tracking social media, also pointing out that if the employees will change their entries once they realize that their digital activities are being monitored.
Moving to the machine learning stage, the article points out that a machine learning algorithm can provide better predictions with regard to employee performance if the training data includes additional non-traditional choices as well. However, the authors also caution that feeding and operating the algorithm on only specific data based on the objective criteria can make it restrictive. They suggest that to turn off the usage of the algorithm to check the performance of non-conforming criteria.
Emphasizing further on the complexity of HR tasks, the article elaborates that further problems may arise due to “range restriction” and the inability of the algorithm to differentiate on a variety of input data differing in race, gender and other groups, if the training data did not include the information.
The following decision-making stage is faced with the concerns of fairness and ethical and legal issues. The report articulates the duality of the algorithms explaining that the training data is based on past decisions, which involves bias, leading to similar outcomes; subsequently, the algorithms also can reduce the bias, by the removal of discriminatory criteria like race and gender. However, the legal issues arise because of easy identification of the bias and the greater repercussions as a result of it.
The article proposed two alternatives as a solution, the first being the establishment of a cause for the prediction and the second being allowing random outcomes, or as stated earlier, switching off the algorithm to provide alternative outcomes which can be studied and later fed into the program to reduce the bias over time.
Another issue affecting the decision-making stage is the “explain ability” of the outcome. The authors inform that although more complex algorithm can provide better outcome, they are not easily understandable, and therefore not easily acceptable. To make AI technology more favorable, it is important to make is more comprehensive.
Finally, the article explains the contrasting reactions of the employees to the AI based decisions. On one hand the decisions are just simply conveyed, without development of any personal reaction towards the employees, which may lead to a feeling of dissatisfaction eventually. On the other hand, sometimes the decisions made by a random selection of an algorithm seem fairer and easily acceptable, especially ones having negative consequences.
The article thereafter proceeds to discuss the possible solutions to address the challenges raised in the report. The importance in development of machine learning based algorithms resulting from causation rather than correlation is stressed upon. This requires expert input and the report suggests two ways to resolve the issue.
The first method involves formation of a council to decide on the assumptions, data and ethical parameters to be considered. The second method is, as also mentioned previously, randomization, not only of input but also in the outcome. The article suggests another solution to make the application of AI in HR systems more appealing; the involvement of employees is identified as an important step for increased acceptance of AI.
The article provides in depth analysis of the issues faced in the integration of the AI technology in the HR management systems, raising valid discussion points. The identification and discussion of the challenges faced in the advancement of the technology is succinct and eye opening. Additionally, the inferences drawn from the workshop conducted for the purpose of study for the article also provides justified arguments to aid in conducting further research in the field and further rationalizing the usage of the technology in the HR sector.
However, as also mentioned in the article, the study group represents the perspective of a smaller percentage, which may differ from the general consensus of the larger sector. Additionally, the methods suggested for wider and improved application of the AI systems in the field of HR, though important, still represent a theoretical solution. The technology, as mentioned above, is fairly new and underutilized in the sector. When the application of the AI systems itself is quite limited at the moment, the workability of the solutions and the resolution of further issues that might be raised as a result will require time to present itself.
Providing an interesting study of the development of the AI technology in HR sector, the article puts forth valid issues that need to be resolved for furtherance not only of the technology in the domain, but also the advancement of the HR management system itself. The article provides reasonable possible solution to tackle HR related issues and more, effectively justifying the question raised by the article. However, as recommended by the authors, the successful integration of the AI technology in the HR domain will result when we better prepare and align ourselves to its increased usage.
This is a study of the Human Resource management systems employed in various organizations, encompassing the policies and methodologies adopted to tackle tasks such as recruitment, training and performance growth of employees among others, based on a comparative analysis of the case study of two major multinational retailers, Tesco and Walmart, to evaluate the differences in the systems and the related effects thereof. The objective of the paper is to derive inferences from the case study also making recommendations for improvements in management.
Human Resource Management involves practices employed by an organization to manage, utilize, motivate, develop and retain its employees, helping in the successful management of the organizational processes contributing to its progress. Recruitment is one of the most fundamental tasks of HR in any organization as it deals with the selection and hiring of employees. If the hired candidates are selected accurately as per the requirements and perform well, the productivity of the organization increases furthering its growth (Marwan mustafa Shammot, 2014).
HR handles the management of the workforce of an organization. Besides recruitment and dismissal, it involves ensuring proper discharge of responsibilities and consistent performance of the employees for the progress of the organization. This involves organizing learning and training programs for the continued development of the employees.
A British multinational founded in 1919, Tesco is one the world’s largest general merchandise and groceries retailer. It is the largest retail chain in the UK, third largest in the world, with its operations spread across 14 countries. The retail giant has grown from humble beginnings as a stall selling war -surplus groceries to 6,800 stores with revenues to the tune of £ 63.911 billion
With a work force of 450,000 and operations spread over vast geographic network, Tesco ensures sound HR practices based on respect, trust, team work and supportive work culture for its employees. Tesco has a decentralized functional organizational structure. In this type, the organization is divided as per functions with the power of decision making with the functional heads. This allows the lower level functional managers to undertake measures as per the needs and demands of the situation. It helps in empowering the employees, building trust and motivation and enhancing their productivity (Mollah, 2016).
Focusing on linking its HR management to its business strategies, the key HR tasks involve employee training, simplification of work, performance management and effective utilization of core competencies of the employees.
Realizing the relevance and importance of training, Tesco ensures on job and off job guidance training programs for all employees. Front office employees are provided integration training while the employees working at back end offices are provided periodic skill updating programs.
To keep its workforce motivated and to ensure performance standards are met, the retailer has devised an effective compensation and reward system. Specific targets are assigned to the employees, and on completion the associated rewards and benefits are awarded (Julie, 2015).
Established in 1962 at Arkansas, USA, Walmart is a multinational retailer with 11,484 stores and clubs across 27 countries under 56 different names. With a business strategy of “everyday low prices”, Walmart began operations as discount store and displayed exponential growth over the years. World’s largest company by revenue, Walmart’s annual earnings are close to $514.405 billion.
Faced with market saturation and intense competition from rivals like Amazon, Target and Costco, Walmart needs to develop a business strategy to overcome the challenges. Enhancement of its HR management can help in improving processes greatly, increasing productivity.
Although Walmart operations are spread all over globally, it follows a centralized hierarchal functional structure with strict rules and policies. In a hierarchal structure every employee has a direct supervisor except the CEO. Every decision or mandate approved at the top level are implemented throughout the levels of workforce down to the rank and file employees. The advantage of such a structure is to effectively to monitor and control its workforce, ensuring correct execution of directives, maintaining the business standards all over.
However, the downside for Walmart is that there is no room for flexibility. Being the largest retail chain with branches all over, it is difficult and time consuming to communicate all issues at every point for decision from the core management team (ANDREW, 2019).
Walmart utilizes AI technology in the HR realm to guide the management of its resources, adjusting its employees as per need as predicted by the AI systems. The company has a constant recruitment and training program in place for new employees. This activity combined with AI predictions ensures that vacancies are filled immediately, without affecting business operations.
Walmart’s recruitment strategy is decentralized, with methods including direct recruitment locally, online recruitment, recruitment through universities and referrals.
For training, the company employs need analysis to determine the employees needs and the business needs. The results of the analysis are communicated to the corporate managers who then determine the training program as per requirement, which include online programs as well. Further feedback on the effectiveness of the program is determined through performance, supervisors, customers and employees.
Management of performance at Walmart is conducted by a performance appraisal system. The company provides regular feedback to the employees of their performance with information on how to improve their productivity.
With the case study of the two multinational retail giants above we can see that their approach to HR management is vividly contrasting, despite belonging to the same sector, having similar business characteristics and achieving great success.
Tesco has a decentralized organizational which enables the vastly spread network of the retail chain to take immediate action as per requirement, without having to follow the cumbersome communication for every issue that may arise, strengthening the company’s relationship with its workforce.
Walmart on the other hand has used centralized organizational structure with equal financial success, proving that monitoring and control of operations relate to generation in revenues. However, its employee relationship has not developed adequately as a result. Even though Walmart provides employee benefits and frequent feedback programs, its employee grievance redressal system is lacking and is often criticized.
The adoption of AI systems in HR management by Walmart is a progressive move towards advancement. Tesco can look at employing the upcoming AI technologies effectively for its recruitment and management HR systems.
Following measures can be undertaken to improve is efficiency:
Looking beyond recruitment: Whenever a job vacancy arises, instead of looking at recruitment options for the appropriate candidate, the HR team should first analyze within the organization to channelize and utilize its internal resources more effectively. This can be both time saving and cost effective.
Investment in the most suitable technology: With rapid advancement in technologies, AI systems seem to be the new way forward for HR management. Adoption of these automated predictive technologies is worth investing in. However, the correct usage and application of the system is necessary to leverage maximum benefits for the growth of the business.
Development of transparent and effective communication: The HR team needs to ensure efficient communication with the organizational workforce to correctly convey the procedures and policies and any changes therein that must be adhered to while also being open to receive feedback and take action on it to develop trust and a positive work environment for increased productivity.
Efficient Recruitment Strategy: The HR management should ensure to provide explicit details for the job vacancy requirements and provide an appropriate package as per the position. They should also maintain proactive and responsive communication with potential candidates for recruitment.
As established through the case studies above, efficient and proactive HR management is crucial for the success of an organization. HR management ensures the hiring of suitable candidates creating an effective talent pool, monitoring and control of the workforce of the organization, maintenance of a productive and healthy work environment, focused on continued development of skills, building trust and motivation among the employees. A well-managed HR system not only generates sustained growth of an organization but also the consistent advancement of the workforce of the organization.
ANDREW, T. (2019). Walmart’s Human Resource Management. Panmore Institute.
Deepak, S. (2012). ROLES FOR HR PROFESSIONALS. Integral Review - A Journal of Management Volume 5, No. 2, 1-11.
Julie, H.-M. (2015). BUSINESS STRATEGY AND THE ENVIRONMENT: TESCO PLC’S DECLINING FINANCIAL PERFORMANCE AND UNDERLYING ISSUES. Review of Business & Finance Studies Vol. 6, No. 3, 91-103.
Kelley, W. (2017). The Importance of Training and Development in Employee Performance and Evaluation. Northcentral University.
Kelley, W. (2017). The Importance of Training and Development in Employee Performance and Evaluation. Northcentral University.
Marwan mustafa Shammot. (2014). The Role of Human Resources Management Practices Represented by Employee’s Recruitment and Training and Motivating in Realization Competitive Advantag. International Business Research 7(4).
Mollah, M. (2016). The role of Strategic Human Resource Management within Tesco plc. The International Journal of Human Resource Management.
Mwaniki, R. (2015). Role of Human Resource Management Functions OnOrganizational Performance with reference to Kenya Power & Lighting Company – Nairobi West Region. International Journal of Academic Research in Business and Social Sciences Vol. 5, No. 4.
Stella, O. (2016). Importance of Training and Development on Performance of Public Water Utilities in Tanzania. Kisii University.
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