TEJAS Journal of Technologies and Humanitarian Science

AI-Driven Predictive Analytics with the Help of IoT for Organizational Change Management

Esha Srivastava
Scholar, National P.G College, Lucknow, India

Author

Shraddha Yadav
Scholar, National P.G College, Lucknow, India

Author

Mahesh Kumar Tiwari
Assistant Professor , Computer Science Department, National P.G College, Lucknow, India

Author

📜 License:

This work is licensed under a Creative Commons Attribution 4.0 International License .

• Share — Copy and Redistribute the material
• Adapt — Remix, Transform, and build upon the material
• The licensor cannot revoke these freedoms as long as you follow the license terms.

Abstract:

Artificial Intelligence (AI) and Internet of Things (IoT) are increasingly finding their way to the current workplaces thereby transforming Organization Change Management (OCM). The present paper discusses the opportunities of transforming the way organizations should handle change through the integration between AI-driven predictive capabilities and data measured in real-time through the IoT devices. The conventional change models are usually built on basis of stagnant planning and instinctive decisions with the AI and IoT empowering dynamic decisions and in light of data. IoT Sensors are able to record real-time behavioral, physiological, and environmental data that is processed by an AI system to analyze the data to find potential signs of resistance, disengagement, or stress. These insights can be used to intervene in a timely and personalized manner that helps in facilitating the transitional process and aiding in employee wellbeing. Based on the case studies and analyses, this paper demonstrates the opportunities and the ethical implications of such technologies in change management practice. It also ends with propositions on how to create more versatile, individualized, and morality-based change plans in future.

References

[1]. Zong, Zhijuan, and Yu Guan. "AI-driven intelligent data analytics and predictive analysis in Industry 4.0: Transforming knowledge, innovation, and efficiency." Journal of the Knowledge Economy 16.1 (2025): 864903.
[2]. Elkahlout, Mohammed, et al. "AI-Driven organizational change: transforming structures and processes in the modern workplace." (2024).
[3]. Aakula, Ajay, Vipin Saini, and Taneem Ahmad. "The Impact of AI on Organizational Change in Digital Transformation." Internet of Things and Edge Computing Journal 4.1 (2024): 75-115.
[4]. Ramya, J., et al. "AI and Machine Learning in Predictive Analytics: Revolutionizing Business Strategies through Big Data Insights." Library of Progress-Library Science, Information Technology & Computer 44.3 (2024).
[5]. Prakash, Divya. "Data-driven management: The impact of big data analytics on organizational performance." International Journal for Global Academic & Scientific Research 3.2 (2024): 12-23.
[6]. Nama, Prathyusha, Suprit Pattanayak, and Harika Sree Meka. "AI-driven innovations in cloud computing: Transforming scalability, resource management, and predictive analytics in distributed systems." International research journal of modernization in engineering technology and science 5.12 (2023): 4165.
[7]. Adimulam, Thejaswi, Manoj Bhoyar, and Purushotham Reddy. "AI-Driven Predictive Maintenance in IoTEnabled Industrial Systems." Iconic Research And Engineering Journals 2.11 (2019): 398-410.
[8]. Selvarajan, Guru. "Leveraging AI-enhanced analytics for industry-specific optimization: A strategic approach to transforming data-driven decision-making." International Journal of Enhanced Research In Science Technology & Engineering 10 (2021): 78-84.
[9]. Badmus, Oluwaseun, et al. "AI-driven business analytics and decision making." World Journal of Advanced Research and Reviews 24.1 (2024): 616-633.
[10]. Achumie, Godwin Ozoemenam, et al. "AI-driven predictive analytics model for strategic business development and market growth in competitive industries." J Bus Innov Technol Res (2022).
[11]. Selvarajan, Guru Prasad. "Harnessing AI-Driven Data Mining for Predictive Insights: A Framework for Enhancing Decision-Making in Dynamic Data Environments." International Journal of Creative Research Thoughts 9.2 (2021): 5476-5486.