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AI IMPACT ON JOB AUTOMATION
Abstract
Artificial intelligence (AI) has quickly become a transformational force that is reshaping several sectors and changing how work is done. Automating jobs is a crucial component of AI's effect. As artificial intelligence (AI) technology develops, it has the potential to automate operations that are now done by people, creating both possibilities and difficulties for the employment market. AI's influence on job automation has many different facets. However, automation powered by AI has the potential to improve many industries' productivity, efficiency, and accuracy. AI systems may be used to do repetitive and boring activities, freeing up human employees to concentrate on more important, creative, and strategic duties. Increased work satisfaction and creativity may result from this. Automation fueled by artificial intelligence has already had a substantial impact on sectors including manufacturing, shipping, and customer service. However, there are also worries regarding the displacement of human labor as a result of job automation. Certain predictable and regular jobs may be carried out more effectively by robots as AI technology develops. This may lead to changes in work patterns as well as job losses. Manual labor-intensive jobs and routine data processing tasks are especially susceptible to automation. Discussions regarding the future of work and the need of retraining and upskilling the workforce to stay relevant in an AI-driven economy have been triggered by concerns about widespread unemployment. While AI may automate certain employment tasks, it also increases the need for human labor and opens up new possibilities. Intelligent experts that can design, develop, and maintain these technologies are needed for the integration of AI systems. Data scientists, machine learning engineers, and AI experts are in high demand right now. New businesses and employment categories will develop as AI technology advances, highlighting the need of lifelong learning and adaptation in the workforce. Additionally, AI-driven automation may improve the quality and security of employment. Robots may be used to do hazardous and physically taxing activities, lowering workplace injury risk and creating safer working conditions for people. AI may help employees make decisions by giving them insightful information and enhancing their talents. Increased productivity and job satisfaction may result from collaborative work settings where humans and AI systems play to one other's strengths. The difficulties must be addressed by legislators, educators, and corporations in order to minimize the possible negative effects of AI-driven job automation. Investments in education and programs for lifelong learning may provide people the tools they need to adapt to a changing labor market. Governments may aid employees impacted by automation by supporting reskilling programmes and offering social safety nets. To guarantee ethical AI deployment and reduce prejudice and discrimination, laws and regulations must also be in place. In summary, the influence of AI on job automation is profound and intricate. While technology presents chances for improved effectiveness, productivity, and creativity, it also presents problems in terms of job displacement. It's critical to strike a balance between the advantages of automation and the necessity to assist and retrain the workforce. Societies can use AI to their advantage by fostering collaboration between people and AI systems, investing in education and skill development, and enacting thoughtful policies. This will result in a future where people and machines coexist peacefully and a more prosperous and inclusive economy.
... To address this, retraining and skill development programs are essential. Well-designed training can help workers adapt to technological changes and maintain their relevance in the job market, emphasizing its importance (Santhosh et al., 2023). ...
... The literature shows that the negative impact of technology and AI on demographics still receives less attention than their positive impacts on the economy. Automation and AI are likely to replace manual and repetitive work, as identified by (Santhosh et al., 2023) and (Felten et al., 2019) but research focusing on their impact on social inequality and labor migration is still limited. ...
... To reduce the negative impact of AI on jobs, it is important to invest more in the training and development of new skills. A well-designed training program can help workers adapt to technological changes and maintain relevance in the job market (Santhosh et al., 2023). However, AI can also reduce workers' motivation to learn and develop new skills, particularly for those who feel that their jobs are threatened by technology. ...
Indonesia's demographic potential in the digital era offers significant opportunities but also presents challenges as the nation strives toward the vision of Golden Indonesia 2045. This research aims to explore the impact of technological advancements, particularly artificial intelligence (AI), on Indonesia’s demographic landscape, including social inequality, urbanization, unemployment, and skilled labor migration. The research employs a descriptive-analytical method, leveraging secondary data from sources such as the Central Statistics Agency and the Indonesian Internet Service Providers Association. The findings reveal significant digital and socio-economic disparities between urban and rural areas, with lower internet penetration and ICT skills in rural regions exacerbating inequality. Automation and AI further contribute to job market polarization, threatening low-skilled jobs while creating high-skilled employment opportunities. Urbanization amplifies issues such as congestion, air pollution, and housing shortages, while the migration of skilled workers abroad hinders domestic development. The research highlights the necessity of inclusive policies to bridge digital divides, investments in digital infrastructure and education, and tailored smart city and smart village initiatives to support equitable development. These strategies are critical for leveraging technology to ensure sustainable and inclusive growth, aligning with Indonesia’s demographic goals in the digital era.
... While robots and automation are changing industries with productivity and efficiency improvements, they're also leaving jobs. Automating mundane and hazardous tasks creates productivity increases in manufacturing, logistics, and service sectors (McManus, 2023;Santhosh et al., 2023). Although robotics will take away some jobs, it will create new ones for individuals who are prepared for it (Santhosh et al., 2023;Adhikari, 2024). ...
... Automating mundane and hazardous tasks creates productivity increases in manufacturing, logistics, and service sectors (McManus, 2023;Santhosh et al., 2023). Although robotics will take away some jobs, it will create new ones for individuals who are prepared for it (Santhosh et al., 2023;Adhikari, 2024). These technologies are especially visible in manual work and the data processing industry, where repetitive work is getting automated by robots (Santhosh et al., 2023;Raj et al., 2024). ...
... Although robotics will take away some jobs, it will create new ones for individuals who are prepared for it (Santhosh et al., 2023;Adhikari, 2024). These technologies are especially visible in manual work and the data processing industry, where repetitive work is getting automated by robots (Santhosh et al., 2023;Raj et al., 2024). ...
In this review paper, we consider transdisciplinary approaches to robotics and how they can be applied to promote social innovation to solve pressing problems such as climate change, workforce displacement, and resilience in times of disruption. It demands the establishment of cooperative models to bridge the technological gap between social realities and fairer, sustainable futures. Businesses such as SkyGrow, which makes automated tree-planting robots for targeted reforestation, show the promise of such technologies. The paper highlights the need for assessments to understand community needs and engage the community and industries involved to find possible use cases for robotics. It encourages participatory designs rooted in end-user insights, which will make the technologies being created reflect local demand. After all, it takes a collective for effectively navigating the complexities of today’s problems in robotics and social innovation.
... After feeding in the data point (1,0), ChatGPT delivered the polynomial model y = 6-18x+12x 2 . Next, the authors fed in a fourth data point (2,12) and received y = 6-15x+18x 2 -x 3 as ChatGPT's response. This, apparently, is inconsistent. ...
Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), in particular, have quite recently vehemently stirred the understanding of Artificial Intelligence (AI). The expectations of revolutionary AI applications and business as well as societal impact are very high and, in contrast, reports about disastrous case studies and hallucinating GPTs are fearsome. This investigation narrows the focus to human-AI collaboration for processes of research and discovery including higher education. Based on a qualitative analysis of decisive deficiencies of Generative AI (GAI), there is developed an original approach that allows for preservation of the GAI’s full power and bears the potential to mitigate detected weaknesses and to increase the AI’s reliability. Essentially, the technology consists in symbolic wrapping of sub-symbolic AI. The result of wrapping GAI is a hybrid AI system. Scientific discovery with theory formation is a key area relevant to the progress of science, technology, and societal applications. Humans are challenging modern AI to support this process taking advantage of the Generative AI’s strength as a conversationalist. But scientific discovery and theory formation is intricate, as Albert Einstein put it in a letter to Karl Popper as early as in 1935, because theory cannot be fabricated out of the results of observation, it can only be invented. Theory is not squeezed out of data. The emergence of theory takes the form of sequences of conjectures being subject to critical analysis possibly including refutations. This requires reasoning, a pain point of Generative AI, as GAI dispenses with a sound calculus. Wrapping mitigates the deficiency. A symbolic wrapper validates GAI responses w.r.t. the prompts put in. It asks back, if necessary, to arrive at improved AI responses.
... The automation proficiency of Artificial Intelligence has the potential to render certain tasks obsolete, consequently leading to job displacement (Santhosh et al., 2023). Simultaneously, employees may need to undergo a transformative process in their skill sets, presenting challenges in terms of adaptation. ...
This study makes a significant contribution to comprehending the intricate interconnections among Management Information Systems, Artificial Intelligence, digitalization, and innovation. For this objective, it evaluates the impacts of Management Information Systems on Artificial Intelligence, digitalization, and innovation individually. Moreover, this distinguishes itself from studies in the literature by providing a comprehensive outlook that considers both favorable and unfavorable aspects. Integrating Artificial Intelligence with Management Information Systems unlocks potent advantages like enhanced data analysis, automation, and personalized customer experiences. But, navigating potential pitfalls like privacy concerns and employment shifts is crucial. Similarly, digitalization fuels process automation and innovation, yet presents challenges like security risks and employee resistance. Managing these trade-offs strategically is key. Finally, Management Information Systems fosters innovation through data analysis and digital transformation, but must be balanced against potential drawbacks like job displacement and cost increases. The fact that the relationship between these variables has not been studied in all its aspects in this way before makes the results of the study unique.
This chapter explores the transition from Construction 4.0 to Construction 5.0, highlighting the importance of integrating human-centric and ethical considerations into advanced technologies such as artificial intelligence (AI) and robotics. While Industry 4.0 has driven automation, efficiency and digital transformation in the construction sector, the chapter argues that its focus on productivity often overlooks significant ethical challenges such as job displacement, data protection and algorithmic bias. In contrast, Industry 5.0 advocates a balanced collaboration between humans and intelligent machines, prioritising sustainability, inclusivity, and ethical innovation. By embedding frameworks such as Responsible AI and adhering to standards such as the European AI Act, the construction industry can mitigate risks and foster trust among stakeholders. The chapter highlights the need for robust governance, bias mitigation, and the development of ethical AI metrics to ensure fairness, transparency, and accountability throughout the AI lifecycle. Finally, the chapter calls for a cultural shift in the industry, advocating for comprehensive workforce training and the promotion of ethical awareness. By aligning AI applications with societal values, Construction 5.0 aims to enhance human well-being while driving sustainable and equitable growth in the construction sector.
Specialized, Refined, Unique, and Innovative” (SRUI) enterprises are a crucial part of the national innovation system and play a significant role in enhancing national core competitiveness by leveraging strengths and addressing weaknesses. Given the limited quantitative analysis on the productivity of SRUI enterprises in the academic field, this paper constructs an enterprise‐level artificial intelligence index based on text analysis and machine learning. Using panel data from 2018 to 2022, we investigate the impact of artificial intelligence on the productivity of SRUI small‐ and medium‐sized enterprises (SMEs). The research findings indicate that (1) artificial intelligence significantly enhances the productivity of SRUI SMEs, and for each one standard deviation increase in artificial intelligence, the productivity level of SRUI enterprises will increase by 6.82%. (2) Mechanism tests reveal that artificial intelligence improves productivity by optimizing labor structure, improving labor resource allocation, stimulating endogenous motivation and innovation vitality within enterprises, and enhancing management level and investment efficiency. (3) Heterogeneity analysis indicates that at the regional level, artificial intelligence significantly boosts the productivity of enterprises with well‐developed network infrastructure and robust intellectual property protection systems. At the industry level, AI has a more pronounced effect on the productivity of technology‐intensive and competitive industries. Additionally, we also find that the impact of AI on productivity is more significant in enterprises with younger executive teams and executives with digital and intelligent education backgrounds. This study expands the research scope of productivity and provides valuable insights for the government to optimize digital economy policies and for enterprises to formulate digital innovation strategies.
Aim/Purpose
This paper aims to investigate the recent developments in research and practice on the transformation of professional skills by artificial intelligence (AI) and to identify solutions to the challenges that arise.
Background
The implementation of AI in various organisational sectors has the potential to automate tasks that are currently performed by humans or to reduce cognitive workload. While this can lead to increased productivity and efficiency, these rapid changes have significant implications for organisations and workers, as AI can also be perceived as leading to job losses. Successfully adapting to this transformation will lead companies and institutions to new working and organisational models, which requires implementing measures and strategies to upskill or reskill workers. Organisations, therefore, face considerable challenges such as guiding employees towards the change process, dealing with the cost of training, and ensuring fairness and inclusion posed by age, gender, and cultural diversity.
Methodology
A narrative review has been conducted to analyse research and practice on the impact of AI on human skills in organisations.
Contribution
This work contributes to the body of knowledge by examining recent trends in research and practice on how AI will transform professional skills and workplaces, highlighting the crucial role played by transversal skills and identifying strategies that can support organisations and guide workers toward the upskilling and reskilling challenges.
Findings
This work found that introducing AI in organisations combines many organisational strategies simultaneously. First, it is critical to map the transversal skills needed by workers to mitigate the current skills gap within the workplace. Secondly, organisations can help workers identify the skills required for AI adoption, improve current skills, and develop new skills. In addition, the findings show that companies need to implement processes to support workers by providing ad hoc training and development opportunities to ensure that workers’ attitudes and mental models towards AI are open and ready for the changing labour market and its related challenges.
Recommendation for Researchers
AI is a complex and multifaceted field that encompasses a wide range of disciplines, including computer science, mathematics, engineering, and behavioural and social sciences. Researchers should take a transdisciplinary approach to enable the integration of knowledge and perspectives from different fields that are essential to understanding the full range of implications and applications of AI.
Future Research
Further research is needed to understand the impact of AI on human skills and the role of soft skills in the adoption of AI in organisations. Future studies should also consider the challenges presented by Industry 5.0, which is likely to involve the integration of new technologies and automation on an even greater scale.
In this essay, I begin by identifying the reasons that automation has not wiped out a majority of jobs over the decades and centuries. Automation does indeed substitute for labor—as it is typically intended to do. However, automation also complements labor, raises output in ways that leads to higher demand for labor, and interacts with adjustments in labor supply. Journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor. Changes in technology do alter the types of jobs available and what those jobs pay. In the last few decades, one noticeable change has been a "polarization" of the labor market, in which wage gains went disproportionately to those at the top and at the bottom of the income and skill distribution, not to those in the middle; however, I also argue, this polarization is unlikely to continue very far into future. The final section of this paper reflects on how recent and future advances in artificial intelligence and robotics should shape our thinking about the likely trajectory of occupational change and employment growth. I argue that the interplay between machine and human comparative advantage allows computers to substitute for workers in performing routine, codifiable tasks while amplifying the comparative advantage of workers in supplying problem-solving skills, adaptability, and creativity.
We examine the implications of tax and subsidy policies for employment in the ?three worlds of welfare?, Anglo-Saxon, Continental European and Scandinavian. We argue that home production is key to a proper evaluation of the employment outcomes. Anglo-Saxon low-support policies encourage more overall market employment. Continental transfer policies encourage more home production in services with close substitutes at home. Scandinavian policies give incentives to move home production in social services to the market but discourage other service activity. We find support for our claims in sectoral employment data for five representative countries, United States, Britain, France, Italy and Sweden.
Empirical work has been limited in its ability to directly study whether skill requirements in the workplace have been rising and whether these changes have been related to technological change. This article answers these questions using a unique data set from West Germany that enabled me to look at how skill requirements have changed within occupations. I show that occupations require more complex skills today than in 1979 and that the changes in skill requirements have been most pronounced in rapidly computerizing occupations. Changes in occupational content account for about 36% of the recent educational upgrading in employment.
We present a framework for understanding the effects of automation and other types of technological changes on labor demand, and use it to interpret changes in US employment over the recent past. At the center of our framework is the allocation of tasks to capital and labor—the task content of production. Automation, which enables capital to replace labor in tasks it was previously engaged in, shifts the task content of production against labor because of a displacement effect. As a result, automation always reduces the labor share in value added and may reduce labor demand even as it raises productivity. The effects of automation are counterbalanced by the creation of new tasks in which labor has a comparative advantage. The introduction of new tasks changes the task content of production in favor of labor because of a reinstatement effect, and always raises the labor share and labor demand. We show how the role of changes in the task content of production—due to automation and new tasks—can be inferred from industry-level data. Our empirical decomposition suggests that the slower growth of employment over the last three decades is accounted for by an acceleration in the displacement effect, especially in manufacturing, a weaker reinstatement effect, and slower growth of productivity than in previous decades.
Are smarter machines our children’s friends? Or can they bring about a transfer from our relatively unskilled children to ourselves that leaves our children and, indeed, all our descendants – worse off? This, indeed, is the dire message of the model presented here in which smart machines substitute directly for young unskilled labor, but complement older skilled labor. The depression in the wages of the young then limits their ability to save and invest in their own skill acquisition and physical capital. This, in turn, means the next generation of young, initially unskilled workers, encounter an economy with less human and physical capital, which further drives down their wages. This process stabilizes through time, but potentially entails each newborn generation being worse off than its predecessor. We illustrate the potential for smart machines to engender long-term misery in a highly stylized two-period model. We also show that appropriate generational policy can be used to transform win-lose into win-win for all generations.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
This paper shows the employment structure of 16 European countries has been polarizing in recent years with the employment shares of managers, professionals and low-paid personal services workers increasing at the expense of the employment shares of middling manufacturing and routine office workers. To explain this job polarization, the paper develops and estimates a simple model to capture the effects of technology, globalization, institutions and product demand effects on the demand for different occupations. The results suggest that the routinization hypothesis of Autor, Levy and Murnane (2003) is the single most important factor behind the observed shifts in employment structure. We find some evidence for offshoring to explain job polarization although its impact is much smaller. We also find that shifts in product demand are acting to attenuate the polarizing impact of routinization and that differences or changes in wage-setting institutions play little role in explaining job polarization in Europe.
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