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with The International Journal of Human Resource Management

Deadline: 31 January 2020

Artificial Intelligence

Challenges and Opportunities for International HRM

Recent years have witnessed considerable debate on the topic of Artificial Intelligence (AI) and its impact on potential job losses as well as value creation. The verdict of doomsday pundits is quite clear about the adverse effects of AI on humanity and that AI will gradually take over most jobs that currently exist, leading to severe social and economic crises. Proponents of the doomsday thesis believe the adverse effects of AI will become a reality before the turn of this century. As McKinsey Global Institute’s recent report predicts, on a worst-case scenario, globally, as many as 800 million jobs could be lost to automation and AI by as early as 2030 (Manyika et al., 2017; Vincent, 2017). The report produces three scenarios of rate of AI adoption by organisations and potential job losses: slowest case of adoption (up to 10 million job losses), middle case of adoption (up to 400 million job losses) and the fastest case of adoption (up to 800 million job losses). In view of the above, the discourse on contemporary developments in the field of AI and its impact on people and work is dialectic in nature. At the sharp end of the debate, there are doomsday prophesies highlighting the adverse impacts AI and its applications on people, work and employment; whereas at the other end, there is considerable optimism about the opportunities and value AI can create for business, society and people in organisations (Bughin, 2018; Wilson, Daugherty, & Bianzino, 2017).

One way to understand AI and its applications is to think of AI as a system of computer-aided solutions for performing tasks using text, data, numbers, images or sound as inputs for tasks using complex mathematical algorithms to deliver task outputs in the form of decision aids or problems solved (e.g., Akerkar, 2019; von Krogh, 2018). While decision-making typically relies on deductive reasoning and algorithmic logic (Levinthal & Workiewicz, 2018; Puranam, Stieglitz, Osman, & Pillutla, 2015), problem-solving for creating new solutions (Simon, 1973; Von Hippel & von Krogh, 2016) relies more on abductive reasoning and requires individuals to explore a range of novel solutions and knowledge and information from a variety of sources to recombine, triangulate and explore to produce novel hypotheses and solutions that extend beyond the remit of the deductive and algorithmic approaches. This requires learning by both humans as well as machines before any ethical, lawful and relevant AI applications can be delivered (Malik et al., 2019, 2020).  

Indeed, several large IT, healthcare, resources and telecom multinational corporations (MNCs) such as Infosys, Accenture, Hitachi, Microsoft, Samsung, Siemens, and IBM have already started to build AI-based products and solutions to solve or aid their existing business decision-making and problem solving. The declining costs of IT hardware, processing speed and storage capacity as well as emergence of a number of open-source licenses for AI tools and methods has facilitated this increased uptake of AI (von Krogh, 2018). Recent attempts by scholars highlight emerging theories of gradual job replacement (Huang et al., 2018) by AI and how transformational learning theories may help identify the opportunities and challenges that exist in the adoption of AI for efficiencies and innovation in jobs as well as the workplace (Malik et al., 2019; Mezirow, 1994).

While initially the transactional aspects of work or ‘low hanging fruits’ will be gradually replaced by AI tools and applications, there is evidence also of some high impact ‘moon shot’ approaches (Davenport, 2018), wherein, large MNCs are now actively investing in building AI solutions. Large, diversified MNCs with operations spread across geographies have already developed a range of major AI solutions by leveraging their existing digitised assets and databases to optimise decisions and problem-solving and make enormous savings in headcount. There are several ethical and International HRM issues at stake in large global MNCs. Prime among these would be the presence of bias in HRM focussed AI-applications that are developed and designed in one cultural context and are applied at subsidiaries of MNCs as their global best practice tools. There are several examples of hidden cultural and national business system biases in a number of HRM practices, such as biases against certain groups of people, demographic characteristics. For example, gender, age, experience, qualifications, language and ethnicity biases of workers in different cultural settings can cause a number of ethical and moral issues associated in the implementation of AI-based decisions and problem-solving tasks.

Additionally, the adherence-based or diffusion of global HRM best practices by MNCs for their entire workforce population is progressively moving from a generalised to hyper-personalised and individualised employee experience, using AI-based applications. HRM-focused chatbots and digital assistants are being deployed across MNCs’ subsidiaries with business case having a clear focus on outcomes and return on investment of the newly created AI-assets. The twin goals of hyper-personalisation for employee experience on one hand and need for greater coordination, control, and diffusion of global best practices on the other will give rise to issues of consistency, alignment and fit across subsidiary operations. MNCs are deploying AI applications that are using semi-autonomous, and in some cases, even autonomous chatbots, for balancing personalisation for employees while also maintaining a clear line of sight for the business. The adoption and implementation of HRM-focused AI-solutions, often designed at the MNC’s headquarters and rolled out to subsidiaries suffer from a country-of-origin effect and is affected by the inherent biases owing to cultural and institutional boundaries. These AI solutions create ethical dilemmas and can potentially disadvantage certain groups of employees. For example, certain, differently-abled groups of employees may feel technological and social exclusion or a breach of psychological contract as they interact with AI applications.

The key tasks that individuals and managers perform in a workplace setting are: routine and non-routine, rule-based and intuitive, as well as engage and communicate with others through direct and formal modes of communication for developing deeper level critical thinking and strategizing knowledge (Delahaye, 2005; Mezirow, 1994). Developing AI solutions for the latter critical thinking and strategizing knowledge tasks, requires much more than logic and rule-based decision-making. For MNCs there is an added layer of complexity as it requires awareness of potential cultural, institutional, political and deeply held values of people across geographies, which may affect the ability of AI products and tools to propose solutions or decision aids. In such international HRM contexts, the existing theories-in-use will require greater contextualisation and training of the AI engine before ethical, legal and relevant solutions can be delivered.

With global changes in the geo-political landscape such as Trump administration’s trade sanctions, Brexit, India and China’s strengthening political agendas, a major and further reconfiguration and redivision of tasks across borders is likely to change. Larger MNCs have proactively employed on digitisation in selecting their resources for international assignments, re-training and restructuring of work at subsidiary operations. These changes involve a greater focus on digitization, automation and use of AI solutions at headquarter and subsidiary operations. These changes have significant implications for people, work design necessitating major changes in the competencies and skill sets as well as the need for employees to reimagine work, careers and longer-term employment. Several ethical risks need to be addressed before the full potential of AI’s value creation can be realised (Barocas, & Selbst, 2016).

The International Journal of Human Resource Management

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Timeline

  • Submission deadline: 31st January 2020
  • First Revision: 30th September 2020
  • Final Revision: 31st December 2020
  • Target Publication: Early 2021

Guest Editors

Pawan Budhwar, 50th Anniversary Professor of International HRM, Aston Business School and Co-Editor-in-Chief of British Journal of Management.

Ashish Malik, Associate Professor, University of Newcastle, Australia.

For any queries related to this Special Issue:

Email Pawan or Email Ashish

 

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Focus of the Special Issue

Considering the above reported developments and the infancy stage of research on the topic, the core focus (though not exhaustive) of the special issue is both on conceptual and empirical manuscripts which:

  • Highlight the developments in AI, factors contributing to the same and their impact in organisations in general and on the HR function in particular in MNC settings.
  • Examine the role played by the HR function in facilitating the adoption of AI in MNCs and their global supply chains.
  • Present relevant theories and frameworks to draw linkages between AI, HRM and firm performance, AI and job replacement, AI and legal, ethical and moral issues facing leaders, managers and society at large.
  • Reveal the changes needed due to the growth in AI and its adoption, to rationalise work systems and future of HR.
  • Develop appropriate change management strategies for unlocking value by adopting AI in HRM function’s employment life cycle in domestic and global markets.
  • Identify relevant theories such as of social identity, planned behaviour, reasoned action critical in analysing key values and attitudes of employees towards AI adoption and factors affecting a hyper-personalised employee experience of HRM practices.
  • Develop an understanding of the impact of interactions employees will have with humanoids as their co-workers and how this will influence organisational performance.  
  • Undertake observational and experimental designs for developing thick descriptions of AI and human interactions before proposing prescriptions for change.
  • Apply a combination of deductive and abductive logic for advancing future research agenda for identifying how sub-functional areas, such as recruitment and selection, training and development, performance management and rewards that present the greatest opportunities for sustainable and ethical HRM and outlining the doable and limits of HRM-based AI-applications?
  • Identify key employee and managerial knowledge, skills and capabilities necessary for sustainable design and implementation of AI in MNCs.

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References

Akerkar, R. (2019). Introduction to Artificial Intelligence. In Artificial Intelligence for Business (pp. 1-18). Springer, Cham.
Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. Calif. L. Rev., 104, 671.
Bughin, J. (2018). Why AI Isn't the Death of Jobs. MIT Sloan Management Review, 59(4), 42-46.
Davenport, T. (November 2018). Artificial Intelligence: Putting Artificial Intelligence to work. A Webinar presentation by Thomas H. Davenport for MIT, MIT Sloan Management Review
Delahaye, B. L. (2005). Human Resource Development Adult Learning and Knowledge Management. Australia, Qld: John Wiley & Sons. 2nd Edition.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21/2,: 155-172
Levinthal, D. A., & Workiewicz, M. (2018). When two bosses are better than one: Nearly decomposable systems and organizational adaptation. Organization Science, 29(2): 207–224.
Malik, A., Budhwar, P., Srikanth, NR & Varma, A. (2019, forthcoming). May the Bots Be with You! Opportunities and Challenges of Artificial Intelligence for Rethinking Human Resource Management Practices. Paper Accepted for presentation BAM 2019
Malik, A., Srikanth, N.R., & Budhwar, P. (2020, forthcoming). Digitisation, AI AND HRM. In Jonathan Crashaw & Pawan Budhwar (2019 Eds.) Strategic Human Resource Management. UK: Sage Publications
Manyika, J., Lund, S. Chui, M., Bughin, J., Woetzl, J., Batra, P., Ko., R., Sanghvi, S. 2017. Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey & Company: McKinsey Global Institute.
Mezirow, J. 1994. Understanding transformation theory. Adult Education Quarterly, 44/4: 222-232.
Puranam, P., Stieglitz, N., Osman, M., & Pillutla, M. M. 2015. Modelling bounded rationality in organizations: Progress and prospects. The Academy of Management Annals, 9(1): 337–392.
Vincent, J. 2017. Automation threatens 800 million jobs. The Verge.
Von Hippel, E., & von Krogh, G. 2016. Identifying viable “need–solution pairs”: Problem solving without problem formulation. Organization Science, 27(1): 207–221.
von Krogh, G. 2018. Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing. Academy of Management Discoveries, 4/4,: 404-409
Wilson, H. J., Daugherty, P., & Bianzino, N. (2017). The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14.