Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Artificial Intelligence in Change Management: Automating Impact Assessment and Stakeholder Communication

Jane Smith
Professor of Management, University of Technology, New York, USA
Cover

Published 05-12-2023

Keywords

  • Artificial Intelligence,
  • Change Management,
  • Impact Assessment,
  • Automation,
  • Organizational Change,
  • Technology Adoption
  • ...More
    Less

How to Cite

[1]
Jane Smith, “Artificial Intelligence in Change Management: Automating Impact Assessment and Stakeholder Communication”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 420–425, Dec. 2023, Accessed: Nov. 14, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/165

Abstract

This research paper examines the integration of Artificial Intelligence (AI) into change management, focusing on its potential to automate impact assessments and stakeholder communication. In an era where rapid technological advancements necessitate agile change management strategies, AI presents opportunities to enhance the effectiveness of these processes. By automating impact assessments, organizations can analyze potential changes' implications on various business dimensions, including financial, operational, and human factors. Additionally, AI facilitates seamless stakeholder communication by ensuring timely and accurate information dissemination. The findings indicate that incorporating AI not only accelerates the change implementation process but also improves the accuracy and reliability of assessments. The paper highlights real-world applications, challenges, and future directions for AI in change management, suggesting that organizations adopting these technologies will be better equipped to navigate complex change scenarios.

Downloads

Download data is not yet available.

References

  1. Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
  2. Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
  4. Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
  5. Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
  6. Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
  7. Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
  8. Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
  9. Katari, Pranadeep, et al. "Remote Project Management: Best Practices for Distributed Teams in the Post-Pandemic Era." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 145-167.
  10. Y. Zhang and Q. Yang, "A survey on multi-task learning," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 12, pp. 5586-5609, Dec. 2022.
  11. Y. Wang, Q. Chen, and W. Zhu, "Zero-shot learning: A comprehensive review," IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2172-2188, Jul. 2019.
  12. D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2015.
  13. M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015.
  14. J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of deep bidirectional transformers for language understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2019, pp. 4171-4186.
  15. A. Vaswani et al., "Attention is all you need," in Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 5998-6008.