Technological advancements aim to improve our daily life and facilitate the way we manage our work and respond to the complex world around us. Human Resource (HR) management is amongst many other fields of practice which are becoming more and more dependent on technology and Artificial Intelligence (AI). To the extent that, all firms today are using at least one type of technology to manage their business and employees.
There is no doubt that technology is imperative for HR to effectively manage the high number of employees, plan for their future and development, and respond to the complexity of the organization. For example, a company such as Google cannot manage and keep track of over 100,000 employees without technology and AI. Besides, daily HR practices in such firms will produce a massive amount of data that
would be worthless without powerful tools capable of processing, analyzing the raw data, and making decision faster than ever.
The key to success and survival of organizations, according to Diclaudio, (2019) is the quick reaction to employees’ evolving preferences and provide them with the most effective solutions. AI and machine learning have provided companies with tools that can manage and predict employees’ behaviors and fulfill employee’s expectation.
Moreover, technology granted organizations with access to diverse talent pools around the globe which saves them a remarkable time and provides them many opportunities to choose from. This piece of technology is especially vital for global organizations around the world as the environment and demographics of the countries they operate in has a direct effect on their recruitment and talent acquisition.
Artificial intelligence, according to Kaplan and Haenlein, (2019) consists of three generations of Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). First generation ANI consists of Analytical AI is characterized by cognitive intelligence, use learning based on experience to inform future decisions. Most HR technologies used by firms today fall into this group.
Technologies that are available today are talent acquisition platforms that provide application tracking, candidate assessment, referral, video interviewing, and onboarding. Smart Recruiter, for instance, is a popular software in this category.
Core HR and payroll systems such as Workday are also available to manage employees’ wages; track and analyze employees’ communications; provide platforms for information sharing and employees’ interactions; manage employee records and history; workforce management to track time and attendance; and employee experience portals and chatbots that offer knowledge management, communication and information sharing platforms.
Performance management systems are integrated into the flow of work and provide tools for goal setting, succession planning, team management, feedback management, managerial assessment and coaching, leadership development, and reward management.
In addition to these commonly used systems, there are more platforms available that separately offer compensation, benefit, and rewards management; wellbeing management systems; corporate learning systems; workplace productivity to manage collaboration and project management; analytics and planning; transparency, diversity and inclusion tools; and engagement and culture platforms for culture assessment and behavior change.
The second and third generation of AI will be available and common in the far future of HR. Second generation AGI consists of Human-Inspired AI, which is characterized by cognitive and emotional intelligence, can understand human emotions and consider them in their decision making (Kaplan & Haenlein, 2019). Affectiva, for instance, is an AGI which uses advanced vision systems to recognize emotions at the same level as humans and can be used to recognize the applicants’ emotions in digital interviews.
Third generation ASI consists of humanized AI that shows characteristics of cognitive, emotional, and social intelligence. Such systems, which would be able to be self-conscious and self-aware are not available yet (Kaplan & Haenlein, 2019).
In 10 years from now, we will witness the emergence of new solutions that converge all these HR solutions in one unique product capable of analyzing massive data and providing unprecedented analyses for HR decision makings. This evolution will remarkably enhance HR functions, reduce human errors and biases, fulfill the expectations of employees, and achieve organizational goals and visions. However, the mechanical processing systems of AI tools may hinder ethical concerns, disrupt the work-life balance, not much eliminate biases, and be costly if not administered properly.
Machines (yet) are incapable of sensing emotions, and programmers must introduce the ethical dilemmas to the system to avoid unethical decision makings. Otherwise, as Haenlein and Kaplan, (2019) argue, AI will result in unique ethical, legal, and philosophical challenges such as the Trolley Problem- a thought experiment in ethics and philosophy that deals with ethical decision making. Indeed, there will be some sensitive areas that would fall into blind spots of future machines and result in unfair and unethical decisions made by AI tools. However, training human programmers and developing new regulations will minimize such chances (Haenlein, & Kaplan, 2019).
Consumers mindset and expectations are some drivers of demands for technological advancement. In the HR field, employees expect to choose how and when they work, get feedback, learn, and develop their skills. AI self- service feature can fulfill these expectations and improve their work-life balance. However, using these features at home and at any time, if not managed carefully, can have the opposite effect. That is, employees may work for longer hours and dissociate their sense of resting at home and performing at work which would result in burn out and affect their performance and engagement.
One of the advantages of integrating AI with HR processes is the elimination of individual’s biases. Conversely, technology by itself cannot eliminate biases, but create them as these machines look into past data to predict future behavior. Thus, as stated by Haenlein and Kaplan (2019), any bias present in the input data and used to train an AI system will persist and may even be amplified. Therefore, the data fed to the system must be carefully selected,
and programmers must code the machines in a way that prevent such errors.
Finally, any AI
platform must be a fit and aligned with organizational culture, strategy, and goals. They must be easy to use, appealing to employees, and ensure the confidentiality and security of the users’ information. Otherwise, organizations will waste a significant amount of time, energy, and financial resources to implement a new system which cannot deliver its purpose and would be resisted by employees.
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Diclaudio, M.(2019). People analytics and the rise of HR: How data, analytics and emerging
technology can transform human resources (HR) into a profit center. Strategic HR Review, 18(2), 42-46.
Haenlein, M., &
Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4),514. https://doiorg.proxy.library.georgetown.edu/10.1177/0008125619864925
Kaplan, A., & Haenlein, M. (2019). Siri, siri, in my hand:
Who’s the fairest in the land? on the interpretations, illustrations, and implications of artificial intelligence. Business Horizons; Business Horizons, 62(1), 15-25. doi:10.1016/j.bushor.2018.08.004EditAI and HR