AI-Driven Workload Optimization: Enhancing Employee Well-Being and Productivity to Promote Sustainable Economic Growth (SDG 8) in Malaysia

AI-Driven Workload Optimization: Enhancing Employee Well-Being and Productivity to Promote Sustainable Economic Growth (SDG 8) in Malaysia

Farhana Akter¹*, Md Rofi Uddin Shakil¹, Azni Abdul Rashid¹, Kanij Fatema¹, Yeasmin Akter¹, S M Asiful Islam Saky², Noor Jannah Afi¹, Kamal Ab Hamid³

¹School of Business and Social Sciences, Albukhary International University, Alor Setar, Malaysia

²School of Computing and Informatics, Albukhary International University, Alor Setar, Malaysia

³International Islamic University Sultan Abdul Halim Mu’adzam Shah Kuala Ketil, Kedah, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2024.ICAME2406

Received: 06 December 2024; Accepted: 18 December 2024; Published: 25 December 2024

ABSTRACT

This study investigates the use of AI-driven workload optimization as a strategic strategy for improving employee well-being and productivity in Malaysia, consequently promoting long-term economic growth consistent with SDG 8. As Malaysian firms prioritize creating employment and economic growth, and the study highlights the significance of artificial intelligence in transforming the dynamics of the workplace. In addition, AI technology may help organizations increase resource allocation, reduce operating costs, and improve employee job satisfaction. The study uses a qualitative research methodology, which includes semi-structured interviews with managers from six different firms that have implemented AI technologies. This approach provides an in-depth analysis of the impact of AI on employee experiences and organizational performance. Furthermore, the study shows the significance of focusing on strengths development approaches that promote a supportive work environment, eventually contributing to professional as well as personal goals. Furthermore, successful implementation of artificial intelligence requires active employee participation, which helps these technologies enrich rather than reduce the abilities of humans. The thematic analysis indicates an obvious connection between effective AI use and improved employee health, job happiness, and work-life balance, all of which contribute to greater productivity and workplace morale. However, the study acknowledges limitations due to its concentrated attention on a small number of businesses, emphasizing the need for more research across many different sectors. Finally, this study recommends a balanced approach that focuses on both technological innovation and employee well-being, which is essential to Malaysia’s economic growth and sustainability in the face of rapid technological advancement and increasing employee requirements.

Keywords: AI-driven workload optimization, Employee well-being and productivity, Sustainable Economic growth, SDG 8, and Work-life balance.

INTRODUCTION

The Sustainable Development Target 8 (SDG 8) focuses on decent work and economic growth, and Malaysian businesses are paying closer attention to this target. From 2016 to 2020, 53% of Malaysian public-listed corporations addressed SDG 8 (Hamad et al., 2023). A suggested framework attempts to improve youth employability management in Malaysia, in line with SDG 8 and national economic goals (Kyei, 2024). Furthermore, Malaysia is actively participating in the United Nations’ 2030 Agenda for Sustainable Development, launching projects such as the National SDG Council and a National SDG Roadmap. In 2021, the Malaysia-SDG Trust Fund will receive RM20 million from the government to increase stakeholder participation in SDG initiatives. However, global SDG implementation remains a difficulty, with only 40% of the world’s largest firms including SDG activities in their sustainability reports. Business leadership is crucial in influencing SDG involvement, and Malaysia relies largely on business participants to meet its SDG targets (Buniamin et al., 2022).

Furthermore, integrated reporting quality has been shown to improve SDG declaration among Malaysian enterprises. Malaysia is a leader in Southeast Asia in pursuing the UN’s Sustainable Development Goals, with strong government involvement in reaching these goals. In 2021, Malaysia’s SDG Index score was 70.9%, placing it 65th overall and fourth in East and South Asia. The country excelled in SDG Goals 1 (No Poverty) and 8 (Decent Work and Economic Growth), but Goal 15 (Life on Land) received less importance. Furthermore, Malaysia’s 2019 SDG report shows progress, with 128 available metrics covering 52% of the global SDG indicators. (Hamad et al., 2023). Aside from that, the SDG8 has four pillars. The first component of ‘rights at work’ includes workers’ fundamental rights, such as freedom of association, non-discrimination in the workplace, and the absence of forced and young labor in abusive conditions (Somavia, 1999). The second pillar of fostering employment is to promote full employment with enough opportunity and remuneration for all types of labor that contribute to society, such as self-employment, informal paid work, and unpaid family work (Frey, 2015; Somavia, 1999). The third pillar,’ social protection’, represents adequate social security in situations that prevent a person from working and earning a consistent income, such as aging, disease, disability, and unemployment (Frey, 2017). It also promotes safe and healthy working environments. The fourth pillar,’ social dialogue’, includes consultation and negotiation between workers and their employers at all levels, from the firm to society as a whole (Frey, 2017; Somavia, 1999). As a result, the International Labour Organisation believes SDG 8 to be critical for sustainable development because it drives several other SDGs (Kobayashi et al., 2019).

According to recent surveys, Malaysian organizations are still in the early stages of using artificial intelligence (AI). While AI adoption in Malaysia has grown by 32% since 2017 (Kamaruddin et al., 2021). Also, many publicly traded corporations have yet to incorporate AI into their governance systems. AI is most commonly utilized in human resource management for recruitment, talent management, and learning and development (Omar et al., 2017). Furthermore, artificial intelligence (AI) is shaking up company foundations and transforming the way people operate around the world (Murray, 2015). It has an impact on occupations and tasks, with the potential to increase organizational efficiency. Machine learning and chatbots are two examples of AI applications that are currently in use in the corporate world.

For example, e-commerce systems such as Amazon utilize machine learning to analyze client purchase data and recommend products based on individual interests, increasing shopping efficiency and sales. Chatbots on customer service websites, such as those used by banks and telecom businesses, handle common queries and transactions, freeing up human agents to focus on more complicated customer demands while enhancing overall service efficiency (Holzinger et al., 2018). Furthermore, the integration of AI into Malaysia’s workforce creates both potential and problems for long-term economic growth and employee well-being. While AI adoption can boost efficiency and production in industries such as tourism, agriculture, and manufacturing (Mius, 2024), it also raises worries about job displacement and skill gaps. For example, the rise of automation technologies, such as AI-powered manufacturing robots, has raised worries about job displacement and skill mismatches, since people may find their current abilities obsolete and suffer difficulties transferring to new tasks (Jamal, 2024). Studies emphasize the importance of complete workforce readiness initiatives, such as reskilling and upskilling programs (Jamal, 2024).

However, AI adoption may have a negative impact on psychological contracts between organizations and people, thereby undermining good working conditions (Braganza et al., 2021). To maximize the benefits of AI while minimizing hazards, researchers emphasize inclusive growth, ethical issues, and equal access to AI education and training (Jamal, 2024; Mius, 2024). Apart from this, policymakers and businesses must prioritize workforce development, social equality, and sustainable growth to become a leaders in Malaysia, especially in the AI-driven global economy. Furthermore, AI adoption can boost productivity and decision-making across a wide range of businesses (Ng, 2024). Despite this, AI systems can carry out complex procedures, reducing the risk of human errors with far-reaching consequences (Aldoseri et al., 2023).

Research Problem

Artificial intelligence (AI) has enormous potential for optimizing workloads, yet many organizations fail to set up AI solutions that promote employee well-being while improving productivity (Cooper, 2024). This study aims to evaluate successful AI-driven strategies for balancing workload management and employee health in Malaysia, with the goal of developing a resilient workforce and promoting long-term economic growth. This study will provide organizations with actionable insights into developing healthier places to work by connecting AI applications with SDG 8 goals.

On the other hand, Malaysia’s rapid adoption of artificial intelligence (AI) is creating disparities in access to employment opportunities and digital skills training, particularly among rural communities and marginalized groups, due to barriers such as limited internet connectivity and socioeconomic inequalities, necessitating targeted policy interventions to promote inclusivity and equitable access (Jukin, 2024)

Research Objectives

1. To investigate employee well-being and sustainable economic growth.
2. To identify productivity influences sustainable economic growth.
3. To identify workload optimization influences sustainable economic growth.

Research Questions

1. Does employee well-being influence sustainable economic growth?
2. Does productivity influence sustainable economic growth?
3. Does Workload Optimization Influence Sustainable Economic Growth?

Significance of the Study

The research on the spread of Artificial Intelligence (AI) among Malaysian publicly traded companies has multiple repercussions. This report provides a thorough evaluation of the current state of AI adoption among these companies, highlighting the level of understanding and willingness to incorporate AI technologies. Understanding the AI adoption stage is essential for increasing operational efficiency and decision-making processes (Omar 2017). The application of artificial intelligence (AI) in Malaysian businesses has had both positive and negative consequences for a long time for development and employment. While the use of AI in human resource management and accounting processes has led to increased productivity, efficiency, and improved customer service, it has also raised concerns about maintaining decent working conditions (Sithambaram & Tajudeen, 2023). Furthermore, AI implementation may have a negative impact on psychological contracts between organizations and employees, distracting from the nature of decent work as stated in SDG 8 (Braganza et al., 2021). Companies that adopt Industry 4.0 technologies, particularly those in the software and services industries, outperform in terms of meeting the Sustainable Development Goals (SDGs) (Shaharudin et al., 2023).

LITERATURE REVIEW

AI Technologies in Workload Optimization

Technologies based on artificial intelligence (AI) are transforming task management in a number of industries. AI-powered workforce management solutions boost productivity as well as efficiency by optimizing employee recruitment, scheduling, and onboarding tactics (sharmila et al., 2024). AI approaches including neural networks, learning through reinforcement, and statistical analysis allow intelligent workload planning, automated capacity provisioning, and environment-friendly management for cloud-based computing systems (Kanungo, 2024). Artificial intelligence, multi-criteria decision-making processes, and knowledge management have all been used in cognitive workflow platforms to improve workflow procedures (Odgers et al., 1999). Moreover, innovative approaches including unpredictable resource supplies, machine learning-based scheduling, and effective task migration are used to solve issues like resource heterogeneity and changing workload features for AI-intensive jobs in cloud computing environments (Fawad, 2023).

However, Al has the power to close transactions, alter the roles of participants in the revenue chain, and create new business possibilities. For instance, AI technology can build new interfaces and eliminate conventional semi-finished goods from the supply chain. Greater precision is made possible by the application of AI technologies, which also enhance business process consistency, accuracy, and efficiency (Rozman et al., 2023). AI may also increase employee engagement at work and free up time for learning and skill development by automating repetitive tasks. A company’s capacity for digital transformation is primarily defined by its executives’ cultivation of an AI-enabled organizational culture and their implementation of an explicit digital plan (Rozman et al., 2023). In order to adapt to several unforeseen changes in the business environment, digital solution implementation and the increased application of AI have become essential (Kambur & Akar, 2022). According to a Microsoft survey (Gergov, 2021), businesses prioritize digital AI. The secret to successfully integrating AI into the organization is developing an entirely new culture that embraces AI, having experimental thinking, and having AI leadership support. Companies now find it essential to adopt digital solutions and apply AI more quickly in order to adapt to the frequent and unforeseen changes in the commercial market (Presbitero & Teng-Calleja, 2022). In today’s dynamic and ever-demanding corporate landscape, digital transformation in business is the key to business success and competitiveness (Wijayati et al., 2022). Because of its features and the quickly advancing technological advances, digital business transformation necessitates a new organizational structure and method of operation inside the company, which calls for an adjustment to organizational culture (Abubakar, 2019).

On the other hand, AI is seen as a danger to their jobs, workers frequently view it with mistrust, but they nevertheless like some parts of its use (Zirar et al., 2023). Continuous usage is impacted by the development of emotional, cognitive, and organizational trust in AI chatbots (Gkinko & Elbanna, 2022). In order to prevent unanticipated consequences and improve job quality, successful AI integration necessitates worker involvement and authority as subject matter experts (Bell, 2023). To cohabit with AI, workers require a blend of technical, human beings, and conceptual abilities; continual reskilling and upskilling are essential (Zirar et al., 2023). The use of AI in the workplace today may limit employees’ capacity to use human abilities like creativity and judgment (Bell, 2023).

AI dynamically improves resource allocation in cloud computing, increasing productivity and decreasing costs (Rao, 2023). Adaptive resource planning with evolutionary algorithms may greatly cut energy usage for AI workloads with changeable real-time activities while meeting deadlines (Annie Nam et al., 2023). Improved efficiency and productivity in the retail, healthcare, and finance industries are the result of AI-powered workforce management solutions that automate repetitive operations, revolutionize HR functions, and support talent management strategies (Rao et al., 2024). Artificial intelligence (AI) solutions, in particular machine learning and processing of natural languages, have the potential to improve quality and safety, and boost operational efficiency (Létourneau-Guillon et al., 2020).

AI is changing how workload optimization is done in all industries. Regarding patient scheduling, various applications of AI in healthcare would lower provider workload, heighten patient satisfaction, and boost productivity (Knight et al., 2023). Banking, healthcare, and retail industries are making strong integrations in using AI to manage and enhance staff for higher output and efficiency (Sharmila Rao, 2024). The Alibaba Smart Warehouse epitomizes the coordination of data, algorithms, and even robots with human skills in e-commerce to ensure the fullest utilization of space, greater productivity by manpower, and a reduction in mistakes (Zhang & Chui, 2021). AI-based rig scheduling platforms have outperformed conventional practices of the oil and gas industry by cutting 99% in planning time, increasing asset utilization by 5%, and decreasing fuel and carbon consumption by 11-24% (Thatcher et al., 2022).

AI’s Effect on Employee Well-Being

The concept of employee well-being is complex and includes several important elements. Subjective well-being (work satisfaction and emotion), eudaimonic wellness (engagement and meaning), and social well-being are the three main categories that are often recognized (Fisher, 2014). A model with three dimensions that include psychological, occupational, and life well-being has been proposed by several researchers (Zheng et al., 2015). Hedonic and eudaimonic elements of well-being are capable of being further subdivided, including the emotional and cognitive dimensions of hedonic well-being (Tov, 2017). According to recent research, energy, social, and physical factors should also be included when defining workplace well-being in addition to hedonic and eudaimonic characteristics (Rook et al., 2020). Understanding the correlations, causes, and cultural variances of well-being is impacted by how it is conceptualized (Tov, 2017). Three essential elements comprise employee well-being: (1) psychological well-being; (2) workplace well-being; and (3) subjective well-being (Page & Vella-Brodrick, 2008).

In addition, workload management has a major influence on employees’ productivity and well-being at work, according to recent studies. Health, job satisfaction, and general quality of life are all components of well-being at work, which may have an impact on productivity both individually and as an organization (Schulte & Vainio, 2010). Research has demonstrated that mental health support programs, such as stress management seminars and employee assistance programs, can improve employee well-being by lowering stress levels and increasing job satisfaction (Bajaj, 2023). Nevertheless, putting well-being initiatives into practice is difficult given the present corporate environment, which emphasizes accomplishing more with less (Kowalski & Loretto, 2017). Workplace stress may be caused by a variety of factors, including excessive workloads, a lack of leadership support, and job instability. These factors can have a detrimental impact on an employee’s physical and mental health as well as increasing absenteeism and low performance at work (Hasin et al., 2023).

According to recent research, workload management has a substantial positive influence on productivity and well-being in a variety of professional contexts, Organizational changes have resulted in an increase in the burden for healthcare managers, which may cause psychological health problems (Boucher et al., 2024). Judges’ well-being is impacted by both positive and negative factors, with time limits and a high workload serving as major stresses (Rossouw & Rothmann, 2020). Through patient-level, GP-level, practice-level, and systems-level strategies such as increasing delegation and creative use of allied healthcare professionals GPs in the UK are proactively reducing workload (Fisher et al., 2017). The happiness and efficiency of intensive care unit nurses in Saudi Arabia are impacted by a range of workload variables, underscoring the need for staff wellness initiatives (Chetty et al., 2021).

AI’s Effect on Employee Productivity

Artificial intelligence innovations and technology have opened up new avenues for people to grow their abilities and talents, which in turn improves performance and fosters job happiness. (Gayathri & Bella, 2024). As well as they won’t have to perform monotonous duties, employees will be happier at work and have more time for important activities. Besides that, the deliberate use of automation boosts productivity and frees up staff members to concentrate on high-value, innovative initiatives as artificial intelligence (AI) specialists take on groundbreaking initiatives and challenging problem-solving (Gayathri & Bella, 2024).

Workload management will be aided by AI-powered tools that prioritize jobs and make optimization recommendations. In addition to reducing burnout and emotional stress, this can increase work satisfaction. One of artificial intelligence’s most important contributions as it continues to transform the workplace is relieving workers of time-consuming and repetitive chores. The emergence of AI technology has brought about a new age in which automating tasks is not just a viable option but also a critical strategic requirement. (Gayathri & Bella, 2024).

Research has shown that artificial intelligence (AI) can enhance human talents, spur innovation, and improve decision-making. It can also have a positive impact on productivity and job satisfaction (Kapur, 2022). However, implementing artificial intelligence can lead to job relocation, lower labor demand, and worker deskilling (Farhan, 2023). Frontline workers’ experience reveals that AI’s impact is influenced by executive and management decisions, with some elements of AI being appreciated by workers (Bell, 2023). Current AI implementations typically limit workers’ capacity to exercise human talents including judgment, compassion, and inventiveness (Bell, 2023). To optimize advantages and avoid harm, it is essential to involve humans as matter-matter experts in AI research and application (Bell, 2023).

The Impact of AI on SDG 8: Balancing Productivity and Employment

The SDGs are intended as a worldwide response to the issues brought on by impending environmental disasters and poverty as a component of the UN’s “2030 Agenda” (UN 2015). The ambitious new plan to “end poverty without inflicting significant costs on Earth’s life-support systems” was the goal of the 17 Sustainable Development Goals (SDGs) and associated 169 goals (Gaffney, 2014). Besides that, SDG 8 strives to achieve full employment, decent work for all, and economic growth. The execution of this faces a challenge due to the presence of rival approaches; for example, human rights groups promote full employment and decent work as a right, while the corporate outlook, under the International Organization of Employers, espouses market-oriented growth (Frey, 2017). Critics of SDG 8 claim that it does not tackle the root drivers of environmental issues, which are over-consumption and economic growth in the Global North, and that this ignorance is exemplary of its neoliberal bias (Kreinin & Aigner, 2021). A model entitled “Sustainable Work and Economic Degrowth” provides alternative sub-objectives and indicators that assess the extent to which societies rely on economic growth. Yet notwithstanding these challenges, SDG 8 has come to be regarded as integral to sustainable development and forms the basis on which various other SDGs are hinged (Kobayashi et al., 2019)

Undoubtedly, AI in labor management plays a significant role in changing the meaning of SDG 8. While AI may create efficiency and boost production, it involves risks to jobs and the welfare of employees (Braganza et al., 2020). AI adoption might adversely affect employee trust, work engagement, and psychological contracts. This could be the basis on which there is a resultant “Alienation” type of psychological contract (Braganza et al., 2020). There are suggestions for using the SDGs as an analytical framework in assessing the impact of AI on sustainability, both with positive and negative impacts at three levels: micro, meso, and macro levels (Saetra, 2021). This would further allow deeper analysis through AI in technological systems that affect the attainment of key social, economic, and environmental objectives (Si, 2022). Such frameworks might also drive more responsible use and increased access to AI by supporting enterprise better measurement and disclosure of the impacts of AI on ESG aspects (Saetra, 2021).

Among those UN SDGs in which AI will make a great impact, it is SDG 8: decent employment and economic growth. In that respect, through real-time automation and analysis of data, AI could potentially enhance productivity and efficiency in diversified sectors such as accounting to accomplish SDG 8, as shown by Peng et al. (2023). AI adoption, on the other hand, may contribute to further deterioration of psychological contracts job engagement, and employee trust, and contribute to increased inequality (Braganza et al., 2020). Laws and regulations will, therefore, need to be enacted to ensure AI adoption is sustainable and ethical. While AI has the potential to contribute toward achieving 128 SDG targets, it can thwart the achievement of 58 targets (Vinuesa et al., 2019). AI can contribute towards SDG 11 on sustainable cities and communities in urban areas through more efficient waste management, easing up traffic flow, and improving energy consumption (Leal Filho et al., 2024).

Current research investigations examine how AI and similar technologies affect decent labor and economic progress. By allowing speedier connection and promoting innovation, the 5G network has the potential to greatly contribute to SDG8, especially in industries like production, wellness, and transportation (Beltozar-Clemente et al., 2023). Adoption of AI, however, may affect labor in different ways. It may have a detrimental effect on interpersonal relationships, job satisfaction, and confidence among workers, even while it can encourage productive employment (Braganza et al., 2020). According to (Bell, 2023), the use of AI frequently limits employees’ capacity to use human abilities including creativity and judgment. Workers must be involved in the development and use of AI as experts in the field in order to optimize benefits and limit downsides (Bell, 2023).

METHODOLOGY

This research adopted a qualitative research method to explore the relationship between AI in workload management on the quality of life and productivity; considering sustainable economic development in the Malaysian context. In the study, six companies employing AI were chosen to examine the impact of AI on the workplace and its role in achieving targets set forth as part of Sustainable Development Goal 8 (SDG 8).

Figure 1: Research Framework

The figure stated above illustrates the fundamental components essential to conducting this qualitative research. It outlines five key areas which are interconnected elements collectively providing a structured approach to qualitative inquiry which ensures methodological rigor, ethical adherence, and comprehensive data analysis to achieve credible and reliable research outcomes.

Research Design

The study design was a case study since this is appropriate when the research issue involves a detailed investigation of a specific phenomenon in its real-life context. This design enabled the important focus to be centered on the experience of the use of AI tools for both, the employee and the organization, and it permitted a nuanced examination of secondary influences bearing on the employees’ welfare and performance.

Interview Process

In this study, data was collected through a technique known as semi-structured interviews with managers who work at six different companies. Depending on the participant’s availability, the interviews were carried out in three formats- face-to-face, audio-online, and written. Interviews ranged from 25 to 30 minutes in average length. To gain written consent from the participants’ face-to-face interviews were carried out using an interview guide and both interviews and notes were taken.

The interview questions addressed several key areas:

1. The impact of AI tools on daily work routines.
2. Strategies for addressing challenges in AI-based workload management.
3. Balancing AI tools with human decision-making in managing workloads.
4. Methods for assessing the effectiveness of AI tools in workload management.
5. Suggestions for improving AI tools in workload management.
6. Predictions on the future of AI in workload management and its potential impacts.
7. Observed changes in employee well-being since adopting AI.
8. The contribution of AI to productivity improvements.
9. Positive outcomes resulting from AI-based workload management.
10. AI’s role in supporting economic growth and job creation.

Sampling Method

A purposive method of sampling was used in the study to obtain participants. Six successful AI-based workload management companies were selected to be representative of the mainline industries; technology, manufacturing, and services. Only the managers who were directly implicated in the process of AI adoption and management were chosen as the respondents as they should be able to give well-grounded answers concerning the connection between AI implementation on the one hand and employees’ well-being and productivity on the other.

Data Analysis

For data analysis, we used Nvivo 15 software. These data were analyzed through thematic analysis. Consequently, data collected in the interviews were qualitatively analyzed using structured coding and the help of the NVivo 15 software, as this method helps find patterns and themes. The analysis focused on:

1. AI-driven workload optimization practices.
2. The impact of AI on employee well-being, including stress, job satisfaction, and work-life balance.
3. The relationship between employee productivity improvements and sustainable economic growth.

This was also followed by an assessment of the number of respondents who held comparable perceptions about particular topics and presented as percentages based on the overall sample size.

Ethical Considerations

This study was conducted ethically basing its research on ethical values. The participants were told the aim of the study and assured of the anonymity of their choices and responses. Participants signed consent forms before the interviews, with informed consent. To ensure the confidentiality of respondents, all the data collected in this study were anonymized.

Limitations

This work has some limitations; and one of them is, that the study is confined to only six companies, thus, the results may not capacitate the richness of AI implementation in companies or, the unique approaches to the implementation by different companies in various industries in Malaysia. Also, the study used largely qualitative data though valuable in capturing qualitative effects may not capture the quantitative effects of AI properly. The studies in the future could combine the paradigms of both qualitative and quantitative research, in order to give a better idea of AI’s impact on the interactions in the workplace.

Company Selection Justification

We chose TAKO, KOLLECT, PINNACLE, PROTON, AIU (HRM) department, and Wavelet companies. These companies were chosen for the study because of they represented key sectors such as manufacturing, services, and technology. Purposive sampling was applied in the study, which focused on businesses that had effectively adopted AI-based workload management. More specifically, managers who are actively involved in the adoption and management of AI and we selected them as participants because they could offer solid perspectives on the relationship between AI implementation and its impacts on worker productivity as well as employee well-being.

FINDINGS

The study’s findings regarding the effects of AI-driven task optimization on employee productivity and well-being in the six companies.

AI’s Impact on Employee Well-Being

Here, we notice that all of the analyzed companies have experienced positive changes in employees’ well-being with the help of AI tools in determining job satisfaction and diminishing stress levels.

Table 1: AI’s Impact on Employee Well-Being Across Companies

Result:

The training of AI has relieved the burden on the minds of the employees since recurrent responsibilities have been assigned to the AI. Therefore, based on the analysis of the responses of 100% of the companies observed, the report shows a positive impact on the overall well-being of the employees.

Productivity and AI-Driven Workload

Here, we see that AI has greatly enhanced productivity across all organizations and companies where it has been implemented, saving time through automating different tasks and minimizing mistakes.

Table 2: AI-Driven Productivity and Workload Optimization Across Companies

Result:

Every organization has experienced a consistent increase in workload optimization by incorporating artificial intelligence. 100% (6 out of 6) of the respondents talked about getting improved productivity because of the workload optimization caused by artificial intelligence in the way it has sped up procedures or through the automation of activities.

Sustainability and Economic Growth (SDG 8)

Here, AI appears to support the growth of economic development by enhancing effectiveness, productivity, and creativity thereby improving personal and organizational outputs.

Table 3: AI’s Contribution to Sustainability and Economic Growth (SDG 8)

Result:

The use of AI is another way through which positive economic development and innovation are encouraged by enhancing organizational productivity. All the selected companies (6/6) acknowledged that AI contributes to ensuring sustainable economies for development in line with the SDG 8 goals.

Figure 2: Impact of AI Across Three Objectives

The above figure which is a bar chart illustrates the percentage changes across three key objectives for six companies. It highlights how AI implementation has positively influenced these areas, with Proton demonstrating the highest improvements in all categories, particularly in productivity and sustainability.

DISCUSSION

The incorporation of AI-driven workload optimization represents a tremendous opportunity to improve employee well-being and productivity, particularly in light of Malaysia’s commitment to reaching Sustainable Development Goal 8 (SDG 8). As Muhtar (2024) points out, the potential for change in AI technologies requires a full understanding of the consequences for the workforce and the whole economic landscape. The study’s findings back up this assertion, demonstrating that AI technology can effectively reduce employee effort and stress by automating repetitive tasks. This automation allows human beings to shift their focus to more creative and critical responsibilities, hence enhancing job satisfaction and engagement. The literature supports the theory that an AI-powered organizational culture, together with excellent leadership and training can significantly influence employees’ opinions on workload and engagement. According to Rožman et al. (2023) found that implementing an AI-enabled culture improves employee engagement and reduces hassles, ultimately leading to corporate success. This is especially crucial in Malaysia, where AI adoption has the potential to improve workplace dynamics and long-term economic growth.

Furthermore, the importance of AI in increasing employee creativity, particularly among those in higher-level jobs, should not be underestimated. Moore (2019) notes that AI assistance in areas such as customer support and lead generation can lead to increased creativity and innovation, resulting in higher sales and job satisfaction. This study emphasizes the significance of employing AI not merely to increase efficiency, but also to empower individuals to engage in higher-value activities that contribute to organizational success. However, using AI in the workplace brings several challenges. According to According to Moore (2019), increased monitoring and oversight can cause employee stress and anxiety. To mitigate these risks, organizations should focus on developing supportive workplace environments that create trust and collaboration. Furthermore, it is essential to provide workers with sufficient training and resources so that they can adapt to AI-augmented operations. Besides that this enables organizations to maximize the benefits of AI while minimizing the hazards associated with its implementation (Rožman et al., 2023).

In Malaysia, the relationship between long-term economic growth and AI-driven productivity gains has been especially significant. According to Geetha et al. (2024), collaboration between business and technology stakeholders is necessary for analyzing AI outcomes and making models to increase their effectiveness. Human monitoring is necessary throughout the AI lifecycle, from data preparation to model testing and retraining, to ensure that AI systems generate accurate results while reducing inefficiencies and risks. Proper AI integration can result in significant advances in productivity and efficiency, ultimately driving economic growth.

The outcomes of this study, which have been supported by the literature, emphasize the numerous benefits of AI-driven workload optimization in terms of employee well-being and productivity. While AI has enormous potential to change the workplace, organizations must address the accompanying issues by creating enabling cultures and prioritizing training for employees. By doing so, they may realize the full potential of AI technologies for sustaining Malaysia’s long-term economic growth, aligning with SDG 8 objectives and developing a resilient workforce for the future.

CONCLUSION

Organizations should integrate AI-driven workload optimization with an emphasis on employee happiness to increase output and ensure employees feel respected. Moreover, employee input may enhance benefits and reduce disadvantages, so it is essential to include them in the creation of AI technologies. Employees must receive ongoing instruction and support to successfully implement AI technologies. To ensure these innovations achieve their intended objectives, ongoing assessments of AI’s influence on worker productivity and well-being should be performed. AI projects that align with the Sustainable Development Goal (SDG 8) can help promote economic growth and create decent jobs.

To conclude, the adoption of AI has enhanced employee attitudes and job satisfaction at companies such as TAKO, KOLLECT, PINNACLE, PROTON, WAVELET, and AIU(HRM) department. Apart from this, increasing operational efficiency and promoting sustainable economic growth require an integrated approach that takes into consideration both like employee demands and technological innovation. Besides that, a greater number of industries should be studied in future research to acquire a deeper understanding of the impact of AI in the workplace.

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APPENDIX

Figure 3: Research key objectives analysis using Nvivo 15 software

Figure 4: Data analysis using Nvivo 15 software