Impact of Artificial Intelligence on Business Management Essay

Introduction

Artificial Intelligence (AI) is an integral part of business management in various industries. Human resource departments have adopted AI for recruiting, job assigning, paying, among other processes. The marketing departments have adopted AI in managing social media activities and networks (Graesch, Hensel-Börner, and Henseler, 2021). Furthermore, with the emergence of unprecedented pandemics such as the novel coronavirus, business management activities have been automated through AI (Alam et al., 2021, p.3797). AI remains a crucial tool in managing business activities through automation of the activities. Integration of AI among companies has various impacts on the firm’s operations and the employees. This study will seek to answer the following research questions:

1. What is the impact of AI integration among businesses on business management structure?
2. What is the impact of AI integration among businesses on the employees’ motivation and activities?
3. What is the impact of AI integration among businesses on product output quality?
4. How can businesses effectively integrate AI in management activities during the emergence of unprecedented pandemics?

Research Aim

Although AI integration is common among businesses, its impact on product output and employee motivation has not been widely reported. The primary aim of this research is to gain an in-depth understanding of the impact of AI integration among various firms’ departments on the employees’ motivation and output and the product output (Priyono, Moin, and Putri, 2020, p.104). AI has made work easier and more efficient by automating activities such as salary dissemination and managing social media activities (Wihlborg and Gustafsson, 2021, p.246). With the invention of AI, many managers have been motivated to carry out managerial activities which only require analysis of automated outputs (Kazak et al., 2019). Consequently, AI has eased companies’ management activities, leading to quality output. The managers involved in quality inspection use automated machines with high proficiency in quality control (Louw and Droomer, 2019). Therefore, AI integration is beneficial to companies since it allows for quality output.

Many companies have been affected by the emergence of the Covid-19 pandemic, among other unprecedented disasters. While companies adopt AI applications, many employees have lost their jobs (Cubric, 2020). Furthermore, the companies integrating AI have found themselves at financial dead ends (Puntoni et al., 2021, p.142). Therefore, this research also aims to understand how businesses can effectively integrate AI during the emergence of unprecedented pandemics. This research will discuss the various steps companies can take during AI for the mutual benefit and their employees.

Research Objectives

AI has insurmountable advantages to various businesses that have integrated the technology. Integrating AI improved output and eased employees’ working activities, especially business managers and leaders (Priyono, Moin, and Putri, 2020, p.104). However, AI integration can also lead to job loss among employees (Tschang and Almirall, 2021). Many businesses integrating AI pay little attention to the impact of AI on their managerial activities within the various departments (Ernst, Merola, and Samaan, 2019). AI leads to improved product quality management and the final product output (Gutierrez-Gutierrez, Barrales-Molina, and Kaynak, 2018). Furthermore, AI allows companies to efficiently conduct marketing activities (Kumar et al., 2019). To get an in-depth understanding of the impact of AI integration on business management, this research will be guided by the following objectives:

1. To establish the positive impact of AI integration on product quality output
2. To establish the impact of AI integration on the employees’ motivation
3. To identify the various advantages of AI integration among the various firms’ department
4. To establish an effective technique that various businesses can adopt to effectively implement AI integration during the emergence of unprecedented pandemics such as Covid-19
5. To identify the negative impact of AI integration on employees

Reasoned Justification for the Study

This study provides an in-depth analysis of the importance of AI integration among business activities. AI is beneficial in companies’ communication and management systems since it enhances expeditious communication and management processes (Li et al., 2017, p.177). The study will use data collected from the selected samples to determine the impact of AI on business operations and management. The study results are essential among the existing businesses that have not yet integrated AI into their activities (Kumar et al., 2019). Furthermore, the research is beneficial among various companies utilizing AI. The study will allow the departments to identify effective AI integration processes. Through the study, the various departments adopt AI by ensuring that their activities mutually benefit the employees and the company (Lee et al., 2018, p.21). Therefore, this research is significant among the companies and the employees of the companies.

Many businesses have shifted their activities to online due to the novel coronavirus pandemic. Many companies with a well-laid technological infrastructure easily shift their activities online (Ghoshal, 2020, p.250). However, some companies, especially small-medium enterprises, found it difficult to shift their activities online (Gashi, Sopa, and Havolli, 2021). The shifted activities include adopting e-commerce and virtual conferences for companies’ meetings (Costa and Castro, 2021, pp.3051-3057). This study dissects the impact of AI integration during unprecedented pandemics. Furthermore, the research aims to design an effective AI integration technique that companies can adopt during unprecedented pandemics. The study will help companies prepare for future pandemics and effectively integrate technology.

With the increasing AI integration among companies, the employees’ activities are greatly affected. The business managers’ activities have been eased through activities’ automation (Tang et al., 2020). Through AI, enterprise resource planning (ERP) systems have been automated to minimize manual labor among companies (Gashi, Sopa, and Havolli, 2021). Consequently, the managers and other employees have found their work easy to execute. The simple and easy work execution has motivated employees to increase their output quality Tschang and Almirall, 2021). This study will investigate and give further suggestions on how AI can improve employees’ output. Therefore, the research topic and scope are relevant in business management.

Study Limitations

This study will involve data collection from the selected samples. The sample represents a smaller population of the large business community utilizing AI. The limited number of respondents could lead to the recording of partial data, which is not backed up by the majority. The partial data could affect the study’s outcome and conclusions. Furthermore, this study involves a wide scope that needs much time researching and data analysis. Time limitations greatly affect the significance of this study. With a very limited budget, study expenses such as travel expenses will not be covered. The availability of enough resources promotes research effectiveness and quality output. Few scholars have contributed to the research topic limiting the resources available for further research.

Relevant Themes and Disciplines

Various business management theories and thematic areas will be discussed in this study. The relevant theories to this study include the systems, the administrative management principles, human relations, and bureaucratic management theories. AI involves improving the available communication and working systems to make work easier (Yarlagadda, 2018). The system theory holds that any business is composed of various systems or departments working together in realization the company’s goals and objectives (Ünal, Urbinati, and Chiaroni, 2019). Every business operating system is subject to system entropy and synergy. System entropy involves the systems that are susceptible to running down with the emergence of the Covid-19 pandemic that allows physical interactions among the employees failed due to governmental social distance policies. Therefore, many companies integrated AI to avoid physical interactions among the employees (Ünal et al., 2019). System synergy involves the effective working together of the various departments with companies for effective work output.

Business management involves various activities that should be in tandem with administrative management principles. The principles include organization, coordination, planning, forecasting, and controlling business activities (Prasad, 2020, p.92). Forecasting and planning can be tedious, and AI helps avoid such detriments. The AI tools help business managers collect and analyze the data (Nocker and Sena, 2019, p.273). The analyzed data is then used to predict the future outcomes of various business activities. The outcomes analyzed include employee performance and product sales (Nocker and Sena, 2019, p.273). AI has made the management’s organization, coordination, and control of business activities easier. Furthermore, AI is crucial in determining the companies’ human relations.

The employees’ attitude towards the business operations and management is crucial for effective work. The human relations theory posits that social factors highly motivate employees in a working environment (Çetin and Aşkun, 2018). AI makes the employees’ works easier, motivating them to work effectively. For instance, AI has eased the accounting and marketing activists among the employees working in the finance and marketing departments. The employees working in the departments with automated services develop a positive attitude towards work and spread the same to other employees (Çetin and Aşkun, 2018). Therefore, AI plays a crucial role in motivating and positively influencing employees. Employees with a positive attitude effectively provide substantial product output, increasing a company’s output.

Max Weber’s bureaucratic theory involves the importance of structuring businesses in hierarchical manners with a clear set of rules (Valeri, 2021, p.90). Businesses are structured into different departments each executing various distinct roles. The managers are involved in dividing labor among the existing employees for effective output. AI helps the human resource department predict an employee’s capabilities and the kind of labour they can proficiently execute. Furthermore, AI helps the various departments to accurately keep records for future references (Cubric, 2020). AI has enabled the hiring process to be based on qualifications, which agrees with Weber’s bureaucratic theory. The human relations, administrative management, systems management, and bureaucratic theories are relevant for this study.

Initial Theoretical Research

De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K.U. and von Wangenheim, F., 2020. Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, pp.91-105.

AI presents insurmountable opportunities to various businesses despite its financial strain on companies. The article helps understand the various opportunities available to businesses intending to automate their activities. The article will help develop hypotheses and study motivating factors for AI integration.

Gillath, O., Ai, T., Branicky, M.S., Keshmiri, S., Davison, R.B. and Spaulding, R., 2021. Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, p.106607.

The paper discusses the importance of AI in building human relations at workplaces. The authors identify the association between attachment styles and trust in AI. The attachment styles identified in the paper include the way people feel, think, and behave when among people (Giliath et al., 2020, p.106607). The article is relevant to the study since it helps understand the role of AI in human relations as a management theory.

Han, R., Lam, H.K., Zhan, Y., Wang, Y., Dwivedi, YK and Tan, K.H., 2021. Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions. Industrial Management & Data Systems.

Marketing is one of the activities transformed by Artificial Intelligence. This paper discusses how businesses can integrate AI in the wake of pandemics and new technology. The article focuses on businesses’ approaches in business-to-business marketing (B2B). The study investigates the impact of AI in B2B marketing and how companies can improve their marketing activities. The article is relevant to the study since it helps understand the systems theory and gives recommendations to companies that have not adopted AI.

Sipior, J.C., 2020. Considerations for development and use of AI in response to COVID-19. International Journal of Information Management, 55, p.102170.

The author discusses the rationale for integrating AI among companies in response to the Covid-19 pandemic. The article discusses the importance of aligning business strategies with information technology in virtual conferences due to Covid-19. The article is crucial for this study since it helps design a strategy business can adopt during AI integration in response to unprecedented pandemics.

Tschang, F.T. and Almirall, E., 2021. Artificial intelligence as augmenting automation: Implications for employment. Academy of Management Perspectives, 35(4), pp.642-659.

The article discusses the implications of AI on employment among companies. The authors examine the impact of automation on companies and how AI integration brings work balance. According to the authors, AI increases unemployment but creates new job opportunities (Tschang and Almirall, 2021, p.642). The article helps in understanding the AI impact on employment and work organization.

Velikorossov, V.V., Maksimov, M.I., Orekhov, S.A., Huseynov, S.E.O., Filin, S.A. and Tserenchimed, S., 2020. Artificial Intelligence as an Innovative Tool of the Support System for Making Management Decisions. DEStech Transactions on Social Science, Education and Human Science.

This research article discusses the application of AI in making decisions among companies. The authors discuss how various organizations could improve their bureaucracy through AI integration. According to the article, AI integration has eased management processes and procedures (Velikorossov et al., 2020).The article is relevant to the study since it helps understand AI and business administration principles.

Types of Data Employed

This study will involve quantitative data collected from companies in China. The companies involved must have integrated AI within their activities. The companies involved will be three processing factories, three service industries including banks, and three manufacturing factories. The data will be collected from various companies that have integrated AI. The study will also involve at least six SMEs that have utilized automated activities. The data collected from the large corporations include the estimated number of clients among the corporations before and after the emergence of Covid-19.

Furthermore, the study will involve investigating the number of employees who have been affected by the integration of AI among companies. The data collected from the SME include the estimated increase or decrease number of consumers due to AI integration. The data collected will also include data related to the impact of Covid-19 among SMEs and large corporations. The financial data, profits and losses, of the large corporations will also be collected to determine the performance of the corporations.

Data Collection Method

Given the research’s wide scope, this study will involve the use of online questionnaires distributed among SMEs and large corporations. The study will use Google forms for data collection. The forms will be designed and distributed among the managers in the selected large corporations and entrepreneurs. The advantages of online questionnaires include flexibility, affordability, ease of analysis, and improved accuracy (SmartSurvey, 2019). Furthermore, the study will involve content analysis of the large corporations’ financial reports available to the public for the past four years. The employment rate data will also be collected from the companies’ reports available to the public.

Preliminary Hypotheses

This study’s primary hypothesis is three-fold based on the past researches conducted by various scholars. Firstly, AI integration helps companies improve their product quality since the machines tend to be more accurate than human beings (Davenport and Ronanki, 2018, p.113). Complex programs have been set up to automate companies’ systems, such as payroll systems. Manufacturing companies using specific raw materials proportions employ AI by use of robots to increase the accuracy of the mixture. Secondly, AI integration relieves the employees from complex tasks that would otherwise demotivate their performance (Kretschmer and Khashabi, 2020). AI has taken over the risky and complex tasks such as complex financial calculations that are head knocking for human beings.

Consequently, the work has been made easier, increasing employee effectiveness. With the use of AI, many employees do not worry about their quality output but focus on improving their effectiveness at work. Managing routine business activities has been eased since the managers and supervisors involved the AI in tracking employee performance and generating employee reports. The reports generated are essential in managerial decision-making among the companies. Therefore, technology integration is a motivating factor among employees.

Lastly, the emergence of unprecedented pandemics such as Covid-19 has led to financial strain among the companies and small-medium enterprises that have had to digitize their systems (Bai, Quayson, and Sarkis, 2021). During the coronavirus pandemic, many businesses were directed to comply with governmental measures such as keeping social distance and enhancing sanitization within the organization. The companies were forced to end physical activities and encourage virtual conduction of business activities. The digitization of the activities required the companies to acquire expensive communication and management systems. Managing the digitized system is very expensive since it requires expert system developers and managers and complex AI systems that automate communications within the organizations. Therefore, many unprepared companies had financial difficulty in shifting their activities. Consequently, most companies recorded losses, and others were altogether shut down. AI integration helps companies improve product quality, motivates the employees, and presents financial strain among the companies.

Data Analysis

This study will involve quantitative data collected from the selected large corporation and SMEs. The data include the employment rate before and after AI integration and the profitability of the businesses before and after AI integration as reported by the companies through their annual financial reports. Furthermore, the study will involve the companies’ sales data and the number of clients since the onset of the Coronavirus. Data analysis will take place in data validation, data editing, and data coding phases. The collected data will be screened during the data validation to ensure that it meets the pre-set standards. Data that seems biased will strike out, and any incomplete questionnaires will be eliminated. The second phase, data editing, will involve the removal of errors from the collected data. The errors include transcription and transposition errors made during data collection. The phase will help improve the accuracy of the data collected.

Data coding will involve assigning values to survey responses and data collected. During this stage, charts will be used to present the data collected. Using linear bar graphs will help identify the trend in profitability and employability among the companies. The use of charts will also help identify the most effective technique in adopting AI among companies. The data collected will also be represented using various statistical methods such as tabulation, statistical averages, among other methods.

Directions for Further Research

The impact of AI integration among companies on the various business activities is a wide area requiring intensive research. This study is limited to sample, time, and economic resources. However, with enough time and economic resources, further research can be carried out on the topic. Furthermore, further research can involve studying specific types of companies, for instance, banks. Studying the impact of AI integration among specific industries helps understand how the industries can take specific steps in mitigating the negatives of AI. The research could also be conducted on the impact of AI on specific departments among the companies. Since this study will only involve companies in China, a further study should be conducted on companies outside China to understand the impact of AI at the global level. Furthermore, this study was limited to complex financial data analysis tools that could help predict future financial advantages of AI integration. Further research might involve the use of complex financial data analysis tools. Therefore, further research on the topic might involve other countries, sufficient time and financial resources, and the use of the complex financial data analysis tools.

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