Factorization of AI Application in HRM

Panicker, Aneesya; Sharma, Avnish; Khandelwal, Utkal

doi:10.1007/978-981-19-0976-4_53

Cited by 2 publications

References 8 publications

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Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review

et al. 2023

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The industrial impacts on the environment need to be minimized to reduce climate change, which will benefit human beings. Industry 4.0, the new production paradigm, promises productivity gains for companies that manage to implement it, but it is also dependent on natural resources, impacting the environment. The aim of this study is to identify and analyze possible cleaner production strategies associated with Industry 4.0 to optimize manufacturing systems in Industry 4.0 implementation projects, in addition to reducing the environmental impacts of these companies. Through a literature search, cleaner production strategies associated with Industry 4.0 were identified and classified into ten dimensions (strategy, waste, recycling, life cycle, resources, energy, production, work, performance and environment) contributing to the theory. The possibilities of using Industry 4.0 technologies were analyzed to meet each dimension. The relevance of this study lies in presenting possibilities for using and developing technologies and applications to meet these dimensions of cleaner production and helping those involved in Industry 4.0 projects to implement it more stably, contributing to the theory and practice.

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Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review

et al. 2023

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AI-Enhanced Strategies for Workforce Involvement

Gowda,

Kureethara,

Jaiwant

2024

Advances in Human Resources Management and Organizational Development

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The incorporation of Artificial Intelligence (also known as AI) into workforce management is transforming traditional employee engagement tactics by providing creative ways to increase worker participation. This chapter investigates AI-enhanced worker involvement techniques, specifically how AI technology might be used to boost employee engagement, contentment, and productivity. Organisations may build increasingly flexible and adaptable work environments by implementing AI-driven analytics, customised feedback systems as a whole and intelligent automation. This chapter looks at how AI can provide real-time insights on employee behaviours and preferences, allowing for tailored interventions that promote a more engaged and inspired workforce. Furthermore, the ethical implications and obstacles of implementing AI in engagement among workers are examined, with a focus on openness and fairness. AI has already given some meaning at workforce, as: Automation is a key function of artificial intelligence. It handles tedious and time-consuming activities, freeing free human workers to concentrate on highly innovative and tactical responsibilities. This not only increases productivity but also lowers the likelihood of errors. AI analyses large databases and gives data-driven insights, allowing for better decision-making. This assistance includes anything from projecting trends to optimising resource allocation. Organisations may make more informed decisions by enhancing their human intelligence by AI-driven insights. This chapter highlights the usefulness of AI-enhanced tactics in promoting worker involvement and offers strategies for embedding AI into organisational processes using a variety of secondary resources, including book chapters, academic articles, and empirical data. The findings show that when used correctly, AI may considerably increase workforce engagement, resulting in increased organisational efficiency and employee well-being.

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Factorization of AI Application in HRM

Cited by 2 publications

References 8 publications

Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review

Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review

AI-Enhanced Strategies for Workforce Involvement

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