Robotics and Artificial Intelligence in Organizations

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Table of Contents

Introduction
Scientific Perspective on Robotics and AI
McKinsey 7s Model
70:20:10 Model
Hofstede’s Cultural Dimensions Theory
Big Five Trait Theory and Emotional Intelligence Theory
Recommendations
Conclusion
References

Introduction

Robotics and AI are widely used today in many companies around the world. Every day, technologies are becoming more and more sophisticated, making it possible to simplify tasks for personnel and increase processes’ efficiency. However, there is a downside to technological perfection – many people fear that AI and robots will supplant white-collar workers, as happened with the decline in blue-collar jobs during the Industrial Revolution. The question of AI’s influence on wider society is often discussed in terms of potential benefits or harms of the broader adoption of technologies. This essay hopes to reach an agreement on a presented question by examining people and technology’s interaction through the lens of organizational theories. In particular, it is proposed to analyze the possible opportunities of introducing technologies for workers and organizations, applying various approaches. Based on the analysis results, it will be possible to conclude how robotics and AI impact society.

Critically demonstrate critical awareness of knowledge issues related to the rise of Robots, and Artificial Intelligence (AI) on employees, organization and wider society.

Scientific Perspective on Robotics and AI

Scientists have different opinions on the positive and negative consequences of the widespread adoption of robotics and AI, but they adhere to one general direction in conducting research. In particular, Li et al. (2019) note the significant impact of employee awareness of AI on turnover intentions. Scientists emphasize that such intentions can be reduced by providing organizational support and creating a competitive psychological climate. Then, Wirtz (2018) examined the interactions of human employees and robotics in the service industry. The scientist concluded that AI services would likely be perceived by customers as commodities, mentioning the example of ATMs. According to the scientist, service robots are also unlikely to become the companies’ primary competitive advantage.

This conclusion suggests that the human-focused principle that considers employees as the main competitive advantage will be preserved. Simultaneously, AI data processing and outstanding AI training practices can become another factor in the company’s success. Interestingly, Wirtz (2018) acknowledges that “robots will master cognitive and analytical tasks of unprecedented complexity and will be able to mimic surface acting-type emotions” (p. 7). Therefore, the scientist believes that “cognitive and analytical tasks with low emotional or social complexity will increasingly be performed by service robots” (Wirtz, 2018, p. 7). Besides, services, mainly emotional or social, like psychologists’, nurses, or social workers, will be mostly delivered by humans. Otherwise, cognitively complex tasks and those demanding emotional intelligence will be performed by humans, with the support of robotics and AI. Therefore, an organization will need to learn how to balance AI- and human-performed jobs by implementing ambidextrous management approaches and practices.

The need to establish interaction will inevitably arise, as robots will begin to implement tasks previously performed by humans. For example, the scientist notes that in one Chinese bank, it was decided to cut 1,000 jobs, since the work performed by low-skilled call-center employees began to be completed by chatbots (Wirtz, 2018). The scientist predicts a sharp decrease in the need for low-skilled human labor in the service sector and increased requirements for remaining employees’ who will deal with exceptional cases. The scholar supposes that HR departments will develop personnel training practices, including work in robot-human teams, and ambidextrous management and organizational practices.

Further, Lazanyi (2018), after examining adolescent attitudes towards AI, concludes that humans did not have enough time to adapt and embrace AI. The scholar also notes that trust is a significant factor in increasing readiness for change, and acknowledges that people are not ready for robotic co-workers. Scientists Galloway & Swiatek (2018) drew attention to the fact that today AI and robotics are more often viewed in the context of their potential in task automation. However, scholars believe AI has broader applications, particularly in public relations and other industries.

Then, Erdélyi & Goldsmith (2018) took a broader perspective on the implementation of AI and robotics in many areas of modern life and proposed creating an international AI regulatory agency to regulate AI technologies and help develop national and international AI policies across the world. The need to make such an agency is dictated by new technology challenges, such as threats to personal data security, which leads to financial or political risks, or the proliferation of new AI-controlled weapons.

Further, Yawalkar (2019) describes the role of artificial intelligence in HR management. According to the scientist, AI helps to carry out many HR tasks, mainly when sorting of job-applications, analyzing speech patterns during the job interview, through digital software interviews, scheduling interviews, and work meetings. Besides, AI can reduce discrimination and favoritism and increase transparency in hiring. It also enhances employee-friendly practices such as the organization’s training, increasing the efficiency at the workplace. The scientist believes that if the HR department introduces AI into their everyday routines, this will increase the level of trust in AI among other employees. Then, Brougham & Haar (2018) note that futurists predict that by 2025, a third of existing jobs could be replaced with smart technology, artificial intelligence, robotics, and algorithms. According to the study, with greater AI awareness, employees showed lower commitment and satisfaction (Brougham & Haar, 2018). Therefore, this study speaks of the importance of employee trust in AI and organization.

Siau & Wang (2018) discussed the importance of trust, presenting the sequence of steps required to build initial trust and develop continuous confidence in AI. In particular, the initially built trust requires going through the stages of AI representation, changing AI image and perception, getting reviews from other users, ensuring transparency and ‘explainability,’ and continuous access to AI. Further, to develop ongoing trust in AI, it is necessary to provide AI usability and reliability, humans’ collaboration and communication with AI, social activities involving AI, and privacy protection. After these steps, the company can proceed to job replacement and AI’s goals congruence with the humans’ ones.

Critically evaluate the pros and cons of using Robots and AI based on your knowledge, experience, and concepts/theories learned in class.

McKinsey 7s Model

The McKinsey 7s Model describes how companies act in a coordinated and synchronized manner based on the organizational structure. Wirtz (2019) mentions: “An additional area of interest is the effective management of people-robot teams and what type of soft and hard skills will be required when AI becomes an integral part of decision-making processes” (p. 8). The McKinsey 7s Model implies the existence of seven indicators that characterize organizational processes: structure, strategy, skills, staff, style, systems, and shared values.

All of these elements are interconnected and grouped around the central piece of shared values. ‘Systems’ include a company’s business and technical infrastructure that establishes work processes and a decision-making chain (What is the McKinsey 7S Model, n.d.). Therefore, this model gives technology an equal role in business decision-making. Simultaneously, ‘skills’ reflect the company’s capabilities and competencies that allow employees to achieve their goals. This formulation implies that technologies are the company’s capabilities or competencies that employees will use to increase efficiency. In general, the McKinsey 7s Model characterizes a company’s internal processes and is used when shared values are being tested, for example, during a merger or acquisition of an organization.

70:20:10 Model

This model can be applied when creating AI training opportunities for employees. According to the 70:20:10 Model, workplace learning accounts for 70% of experiential learning, 20% of social learning, and 10% of formal learning (Developing world-class employees, 2018). As noted by scientists, organizations need to implement AI, as this increases their competitiveness in the market. Simultaneously, conventional training may not be enough to implement AI and robotics, as humans are not yet quite ready to perceive robots as ‘co-workers.’ Therefore, combining the trust-building steps with the 70:20:10 Model will yield the best results.

Hofstede’s Cultural Dimensions Theory

This theory makes it possible to adapt AI implementation in different countries, drawing on the dimensions inherent in various cultures. Hofstede’s Cultural Dimensions Theory assumes high and low power distance, collectivism vs. individualism, uncertainty avoidance index, femininity vs. masculinity, short-term vs. long-term orientation, and restraint vs. indulgence indicators. For example, Chinese organizations embrace hierarchy, collectivism, are comfortable with uncertainty, have middle level of power vs. nurture importance, futuristic and long-term orientation, and normative repression regarding satisfaction of needs (What is Hofstede’s Cultural Dimensions Theory, n.d.). On the opposite, US organizations are more egalitarian, individualist, comfortable with uncertainty, have a middle level of power vs. nurture importance, traditional and short-term orientation, and more prone to indulge needs’ satisfaction.

Big Five Trait Theory and Emotional Intelligence Theory

Big Five Trait Theory can be applied in selecting personnel able to cope with more complex service tasks in organizations where AI is used. According to the theory, people can be characterized according to the prevalence of five qualities – extraversion, agreeableness, openness, conscientiousness, and neuroticism (Cherry, 2020). The most successful service workers will likely have high levels of extraversion, agreeableness, openness, conscientiousness, and low levels of neuroticism. However, this theory’s application will be more relevant to services that require superficial emotional involvement. The selection of personnel for traditionally social or complex emotional services will require a different approach.

Moreover, AI and robotics are not yet ready to show emotional intelligence, the quality inherent in good leaders. Emotional intelligence involves the ability to control one’s own and others’ emotions by recognizing, understanding, and choosing what an individual thinks or feels (Emotional Intelligence Theory, n.d.). Therefore, emotional intelligence is based on self-awareness, self-management, social awareness, and social skills. However, over time, robots can learn to copy the behavior patterns of emotionally intelligent leaders.

Critically review and provide clear recommendations based on your analysis.

Recommendations

AI implementation in organizations often starts with the HR department. In particular, AI simplifies HR managers’ work thanks to chatbots, speech pattern examinations, and interview scheduling. The HR department is responsible for creating a favorable atmosphere in the team and ensuring ambidexterity when working in parallel with humans and robots. Implementing this task can be helped by applying business models to develop an effective management strategy. McKinsey 7s Model, 70:20:10 Model, Hofstede’s Cultural Dimensions Theory, Big Five Trait Theory, and Emotional Intelligence Theory may be the most adequate for the task.

The McKinsey 7s Model defines the role of technology in an organizational structure. Technologies perform decision-making tasks being an element of the company’s ‘system’ and enrich employees’ job methods as a component of the company’s ‘skills.’ Next, the 70:20:10 Model provides an opportunity to deliver employee training with adequate attention to experiential, social and formal learning. Hofstede’s Cultural Dimensions Theory is useful when implementing AI and robotics in different branch offices and companies worldwide. Big Five Trait Theory can help hire employees in the service industries where the most significant reductions will occur after introducing AI and robotics. Finally, Emotional Intelligence Theory enables companies to develop better software for robots by integrating the behaviors of effective leaders in robots’ operations.

Conclusion

Thus, if organizations can successfully integrate AI and human input, it will benefit the wider society. In particular, employees will be freed up for more exciting work that only a human can handle, leaving the machines to perform monotonous and tedious tasks. At the same time, clients will receive services of higher quality and in a shorter time frame. The main drawback may be job cuts for low-skilled ‘white collars,’ leading to a widespread increase in the need for education, and possibly greater diversity in human professions.

The implications of introducing technology into workflows will depend primarily on how carefully companies integrate AI and robotics. In particular, much will depend on the cooperation of AI and robotics with human employees. If HR departments of companies correctly present innovations and ensure building trust in the AI, this cooperation will occur at a high level. As a result, employees and companies will receive the maximum benefit and competitive advantage from technology adoption. Companies’ progress and their successful work with customers will lead to significant economic growth, which will allow states to solve the problem of unemployment and raise the level of education of those who need it.

References

Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257.

Cherry, K. (2020). The Big Five personality traits.Web.

Developing world-class employees with the 70:20:10 Model. (2018). Web.

Emotional Intelligence Theory: Highlighting and developing leadership skills(n.d.). Web.

Erdélyi, O. J., & Goldsmith, J. (2018). Regulating artificial intelligence: Proposal for a global solution. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 95-101).

Galloway, C., & Swiatek, L. (2018). Public relations and artificial intelligence: It’s not (just) about robots. Public relations review, 44(5), 734-740.

Lazanyi, K. (2018). Readiness for artificial intelligence. In 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 235-238). IEEE.

Li, J. J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172-181.

Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47-53.

Wirtz, J. (2019). Organizational ambidexterity: Cost-effective service excellence, service robots, and artificial intelligence. Organizational Dynamics, 10, 1-8.

What is Hofstede’s Cultural Dimensions Theory? (n.d.) Web.

What is the McKinsey 7S Model?(n.d.) Web.

Yawalkar, M. (2019). A study of artificial intelligence and its role in human resource management. International Journal of Research and Analytical Reviews, 20-24.

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