The Role of AI in Stakeholder Management: Predicting Engagement and Sentiment for Improved Communication Strategies
Published 27-12-2023
Keywords
- Artificial Intelligence,
- stakeholder management,
- engagement prediction,
- sentiment analysis,
- communication strategies
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Stakeholder management is crucial in project management, significantly impacting project success and stakeholder satisfaction. With the increasing complexity of projects and diverse stakeholder groups, traditional methods of stakeholder engagement and communication are becoming insufficient. This paper explores how Artificial Intelligence (AI) can enhance stakeholder management by analyzing engagement patterns and sentiment, enabling project managers to develop more effective communication strategies. AI technologies such as natural language processing (NLP), machine learning, and data analytics provide valuable insights into stakeholder behavior and preferences throughout the project lifecycle. By predicting engagement levels and sentiment, project managers can tailor their communication approaches, thereby improving stakeholder satisfaction and project outcomes. This paper discusses the current landscape of AI in stakeholder management, examines case studies demonstrating successful applications, and identifies challenges and future directions for integrating AI into stakeholder communication strategies.
Downloads
References
- 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.
- Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- C. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
- D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, 2016.
- Y. Bengio, “Learning deep architectures for AI,” Foundations and Trends in Machine Learning, vol. 2, no. 1, pp. 1–127, 2009.