Reshaping Strategic Corporate Communication Practices in the Digital Era: The Role of AI-Driven

The rapid advancement of digital technologies has transformed how organizations manage communication, with artificial intelligence (AI) emerging as a powerful enabler of change. Applications such as chatbots, natural language processing, sentiment analysis, and predictive analytics are increasingly integrated into organizational practices. However, while much of the existing research emphasizes AI’s role in marketing communication and customer engagement, its wider strategic impact on corporate communication remains underexplored. This paper addresses this gap by conceptualizing AI not merely as a technological tool, but as a structural driver that redefines how organizations design and execute communication strategies. Guided by established frameworks such as Excellence Theory and Situational Crisis Communication Theory, this study critically reviews emerging literature to highlight three key areas where AI is reshaping communication: (1) intelligent internal communication systems that personalize employee engagement and foster organizational culture; (2) predictive crisis monitoring and rapid response mechanisms that strengthen resilience and reputation management; and (3) stakeholder relationship management that enhances transparency and trust through data-driven insights. The proposed conceptual framework positions AI as a transformative force in achieving communication effectiveness, while also recognizing potential risks such as ethical dilemmas, reduced authenticity, and overdependence on automation. By synthesizing perspectives from communication theory and technological innovation, this paper contributes to the ongoing discourse on the future of corporate communication. It underscores the importance of adopting AI responsibly, ensuring that technological progress is balanced with ethical considerations to safeguard organizational legitimacy and strengthen stakeholder trust in the digital era.

Artificial Intelligence, Strategic Communication, Corporate Communication

1. Camilleri, M. A., Troise, C., Strazzullo, S., & Bresciani, S. (2023). Creating shared value through open innovation approaches: Opportunities and challenges for corporate sustainability. Business Strategy and the Environment, 32(7), 4485-4502. [Google Scholar] [Crossref]

2. Chatterjee, S., Rana, N. P., Khorana, S., Mikalef, P., & Sharma, A. (2023). Assessing organizational users’ intentions and behavior to AI integrated CRM systems: A meta-UTAUT approach. Information Systems Frontiers, 25(4), 1299-1313. [Google Scholar] [Crossref]

3. Coombs, W. T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate reputation review, 10(3), 163-176. [Google Scholar] [Crossref]

4. Cornelissen, J., Höllerer, M. A., & Seidl, D. (2021). What theory is and can be: Forms of theorizing in organizational scholarship. Organization Theory, 2(3), 26317877211020328. [Google Scholar] [Crossref]

5. Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2021). How to design AI for social good: Seven essential factors. In Ethics, governance, and policies in artificial intelligence (pp. 125-151). Cham: Springer International Publishing. [Google Scholar] [Crossref]

6. Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of management annals, 14(2), 627-660. [Google Scholar] [Crossref]

7. Grunig, J. E., Grunig, L. A., & Dozier, D. M. (2006). The excellence theory. Public relations theory II, 21-62. [Google Scholar] [Crossref]

8. Grunig, J., & Hunt, T. (1984). Managing public relations. [Google Scholar] [Crossref]

9. Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A human–machine communication research agenda. New media & society, 22(1), 70-86. [Google Scholar] [Crossref]

10. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49(1), 30-50. [Google Scholar] [Crossref]

11. Ito-Jaeger, S., Lane, G., Dowthwaite, L., Webb, H., Patel, M., Rawsthorne, M., ... & Perez Vallejos, E. (2023). TrustScapes: a visualisation tool to capture stakeholders’ concerns and recommendations about data protection, algorithmic bias, and online safety. International Journal of Qualitative Methods, 22, 16094069231186965. [Google Scholar] [Crossref]

12. Jayasekara, D., Prissé, B., Deng, R., & Ho, J. Q. (2025). Exploring Trust in Artificial Intelligence (AI) Systems: Insights from a Repeated Trust Game. Available at SSRN 5229860. [Google Scholar] [Crossref]

13. Kinney, M., Anastasiadou, M., Naranjo-Zolotov, M., & Santos, V. (2024). Expectation management in AI: A framework for understanding stakeholder trust and acceptance of artificial intelligence systems. Heliyon, 10(7). [Google Scholar] [Crossref]

14. Van der Meer, T. G., & Jin, Y. (2022). Crisis misinformation and corrective strategies in social-mediated crisis communication. In social media and crisis communication (pp. 130-141). Routledge. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study