ABSTRACT
The rapid advancement of artificial intelligence (AI) technologies has transformed traditional media strategy paradigms, enabling unprecedented levels of personalization, optimization, and automation in brand positioning efforts. This paper systematically reviews the emerging landscape of AI-augmented media strategies and proposes a comprehensive conceptual framework designed to guide competitive brand positioning in increasingly complex digital ecosystems. By synthesizing over a hundred academic and industry sources, the study elucidates how AI tools ranging from predictive analytics and natural language processing to generative content and programmatic advertising are reshaping media planning, content creation, and consumer engagement. The framework integrates strategic, technological, and organizational dimensions, emphasizing adaptability, real-time responsiveness, and ethical considerations. This research contributes to both scholarly discourse and practical applications by charting the evolving role of AI in brand media strategies and providing actionable insights for marketers aiming to sustain competitive advantage in 2025 and beyond.
References
- [1] “Social Business By Design: Transformative Social Media Strategies for the … – Dion Hinchcliffe, Peter Kim – Google Books.” Accessed: May 30, 2025. [Online]. Available: https://books.google.co.za/books?hl=en&lr=&id=pk9LefSEyCAC&oi=fnd&pg=PR7&dq=The+last+decade+has+witnessed+a+seismic+shift+in+how+brands+conceptualize,+design,+and+execute+media+strategies&ots=-SHp7vFaQ3&sig=FX2XFSVN82yyG0qMioVeCJz1Wtc&redir_esc=y#v=onepage&q&f=false
- [2] Messaoudi, Z. Guessoum, and L. Ben Romdhane, “Opinion mining in online social media: a survey,” Soc Netw Anal Min, vol. 12, no. 1, pp. 1–18, Dec. 2022, doi: 10.1007/S13278-021-00855-8/TABLES/3.
- [3] Uchendu, O. D. Akintayo, and A. O. Dagunduro, “Strengthening Workforce Stability by Mediating Labor Disputes Successfully,” International Journal of Engineering Research and Development, vol. 20, no. 11, pp. 998–1010, 2024.
- [4] Aguirre, A. L. Roggeveen, D. Grewal, and M. Wetzels, “The personalization-privacy paradox: implications for new media,” Journal of Consumer Marketing, vol. 33, no. 2, pp. 98–110, Mar. 2016, doi: 10.1108/JCM-06-2015-1458.
- [5] J. Isibor, C. P. M. Ewim, A. I. Ibeh, E. M. Adaga, N. J. Sam-Bulya, and G. O. Achumie, “A generalizable social media utilization framework for entrepreneurs: Enhancing digital branding, customer engagement, and growth,” International Journal of Multidisciplinary Research and Growth Evaluation, 2021.
- [6] Odeshina, O. Reis, F. Okpeke, V. Attipoe, and O. Orieno, “Developing Compliance-Oriented Social Media Risk Management Models to Combat Identity Fraud and Cyber Threats,” International Journal of Multidisciplinary Research and Growth Evaluation, vol. 4, pp. 1055–1073, 2023, [Online]. Available: https://www.researchgate.net/publication/390723496
- [7] Liu, Z. Sui, C. Kang, and Y. Gao, “Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data,” PLoS One, vol. 9, no. 1, 2014.
- [8] Stark and J. Hoey, “The ethics of emotion in artificial intelligence systems,” FAccT 2021 – Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 782–793,Mar.2021,doi: 10.1145/3442188.3445939;JOURNAL:JOURNAL:ACMCONFERENCES;
- [9] Usmanova, A. Aziz, D. Rakhmonov, and W. Osamy, “Utilities of Artificial Intelligence in Poverty Prediction: A Review,” Sustainability 2022, Vol. 14, Page 14238, vol. 14, no. 21, p. 14238, Oct. 2022, doi: 10.3390/SU142114238.
- Assunção, B. Patrão, M. Castelo-Branco, and P. Menezes, “An Overview of Emotion in Artificial Intelligence,” IEEE Transactions on Artificial Intelligence, vol. 3, no. 6, pp. 867–886, Dec. 2022, doi: 10.1109/TAI.2022.3159614.
- Ajiga and D. I, “Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust,” (2021). Strategic framework for leveraging artificial intelligence to improve financial reporting accuracy and restore public trust. International Journal of Multidisciplinary Research and Growth Evaluation, vol. 2021), 2021.
- Osamika, A. D., K.-A. B. S., M. M. C., A. Y. Ikhalea, and N, “Artificial intelligence-based systems for cancer diagnosis: Trends and future prospects,” , Kelvin-Agwu, M. C., Mustapha, A. Y., & Ikhalea, N. (2022). Artificial intelligence-based systems for cancer diagnosis: Trends and future prospects. IRE Journals, vol. 2022), 2022.
- Odeshina, O. Reis, F. Okpeke, V. Attipoe, and O. Orieno, “Artificial Intelligence Integration in Regulatory Compliance: A Strategic Model for Cybersecurity Enhancement,
- Samuel Fanijo, Uyok Hanson, Taiwo Akindahunsi, Idris Abijo, and Tinuade Bolutife Dawotola, “Artificial intelligence-powered analysis of medical images for early detection of neurodegenerative diseases,” World Journal of Advanced Research and Reviews, vol. 19, no. 2, pp. 1578–1587, Aug. 2023, doi: 10.30574/wjarr.2023.19.2.1432.
- Re-Thinking et al., “Re-Thinking Data Strategy and Integration for Artificial Intelligence: Concepts, Opportunities, and Challenges,” Applied Sciences 2023, Vol. 13, Page 7082, vol. 13, no. 12, p. 7082, Jun. 2023, doi: 10.3390/APP13127082.
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