A Review of Netflix’s Adoption of Artificial Intelligence and Impact on Strategic Business Decision Making
Authors
Department of Information Technology, University of Agriculture and Environmental Sciences, Umuagwo, Imo State (Nigeria)
Department of Information Technology, University of Agriculture and Environmental Sciences, Umuagwo, Imo State (Nigeria)
Department of Information Technology, University of Agriculture and Environmental Sciences, Umuagwo, Imo State (Nigeria)
Department of Computer Science, University of Agriculture and Environmental Sciences, Umuagwo, Imo State (Nigeria)
Article Information
DOI: 10.51584/IJRIAS.2026.11040005
Subject Category: Computer Science
Volume/Issue: 11/4 | Page No: 82-94
Publication Timeline
Submitted: 2026-03-31
Accepted: 2026-04-05
Published: 2026-04-24
Abstract
Many business organizations are quickly leveraging on powerful state-of-the-art Artificial Intelligence (AI) technologies and algorithms to learn from growing amounts of customer data in order to arrive at better business decisions that has the potential to improve services to customers, and by extension, their business operations. This paper x-rays how AI technologies are being utilized to enhance business decision-making processes and strategies. The global streaming giant Netflix was purposively selected for this analysis due to its global recognition as a pioneer in AI-driven personalization, content optimization, and data-driven strategic decision-making. The company provides publicly documented evidence of its AI research and deployment, making it a suitable and information-rich case for examining the relationship between AI technologies and business strategy. This review paper identified some specific AI technologies strategically adopted by Netflix which are providing enhancements in some business areas such as personalized user experiences, recommendation systems, optimized content delivery, streaming quality and optimization, etc. Some of these AI technologies include machine learning, computer vision, natural language processing, and predictive analytics. This paper equally emphasizes the growing ethical concerns in the utilization of AI technologies, especially in modern business operations and decision-making strategies. Some of the challenges reviewed with respect to Netflix AI-based operations include consideration for data privacy, algorithmic bias, and possible job displacement. The submission of this article demonstrate that the integration of AI has significantly improved Netflix’s business decision making, positioning it as a leader in the use of AI for business optimization.
Keywords
Netflix, Artificial Intelligence, Machine learning
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References
1. Kaggwa, S., et al. (2024). AI in decision making: Transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), 423–444. https://doi.org/10.51244/ijrsi.2023.1012032 [Google Scholar] [Crossref]
2. Sailesh, D. H. (2022). The use of artificial intelligence programmes to help speed up decision-making [Master’s thesis, Selinus University of Sciences and Literature]. [Google Scholar] [Crossref]
3. Ahmad, Z., Azam, U., & Ahmed, S. (2024). AI: A tool revolutionizing decision making. International Journal of Scientific Research and Engineering Development, 7(May). Retrieved from https://www.ijsred.com [Google Scholar] [Crossref]
4. Ibrahim, U. A., & Nwobilor, C. J. (2020). Artificial intelligence as a tool for decision making: A perspective from the Central Bank of Nigeria. International Journal of Managerial Studies and Research, 8(1), 76–85. https://doi.org/10.20431/2349-0349.0801008 [Google Scholar] [Crossref]
5. Tornatzky, L.G. and Fleischer, M. (1990) The Processes of Technological Innovation. Lexington Books, Lexington. [Google Scholar] [Crossref]
6. Elragai, A & Elgengy, N. (2024). A data-driven decision-making readiness assessment model: The case of a Swedish food manufacturer. Decision Analytics Journal 10, 100405 [Google Scholar] [Crossref]
7. Madhani, P. M. (2010), “The Resource - Based View (RBV): Issues and Perspectives.”, PACE, A Journal of Research of Prestige Institute of Management, Vol. 1, No. 1, pp. 43-55, January 2010. [Google Scholar] [Crossref]
8. Khandelwal, K., et al. (2023). A study to know—Use of AI for personalized recommendation, streaming optimization, and original content production at Netflix. International Journal of Scientific Research and Engineering Trends, 9(6), 1738–1743. https://doi.org/10.61137/ijsret.vol.9.issue6.119 [Google Scholar] [Crossref]
9. Li, R., & Duan, S. (2024). Business model analysis of Netflix. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (pp. 285–292). https://doi.org/10.54254/2754-1169/92/20231106 [Google Scholar] [Crossref]
10. Gonçalves, A. R., et al. (2024). Artificial intelligence vs. autonomous decision-making in streaming platforms: A mixed-method approach. International Journal of Information Management, 76, 102748. https://doi.org/10.1016/j.ijinfomgt.2023.102748 [Google Scholar] [Crossref]
11. Charitha, C. P., & Hemaraju, B. (2023). Impact of artificial intelligence on decision-making in organizations. International Journal of Multidisciplinary Research, 5(4), 1–10. https://doi.org/10.1109/IEMENTech60402.2023.10423502 [Google Scholar] [Crossref]
12. Kitsios, F., & Kamariotou, M. (2021). Artificial intelligence and business strategy towards digital transformation: A research agenda. Sustainability, 13(4), 2025. https://doi.org/10.3390/su13042025 [Google Scholar] [Crossref]
13. Zuo, B. (2024). The impact of artificial intelligence on business operations. Global Journal of Management and Business Research, 24(1), 1–8. [Google Scholar] [Crossref]
14. Wehle, H. (2017). Machine learning, deep learning, and AI: What’s the difference? In ML – AI – Cognitive (pp. 1–6). [Google Scholar] [Crossref]
15. Erdoğan, Z. (2023). Netflix’s machine learning, personalization, culture interaction, and its evolution in COVID-19. Intermedia International e-Journal, 10(18), 1–14. https://doi.org/10.56133/intermedia.1066604 [Google Scholar] [Crossref]
16. Rastogi, D., Parihar, T. S., & Kumar, H. (2023). A parametric analysis of AVA to optimise Netflix performance. International Journal of Information Technology. Advance online publication. https://doi.org/10.1007/s41870-023-01281-z [Google Scholar] [Crossref]
17. Van Es, K. (2022). Netflix & big data: The strategic ambivalence of an entertainment company. Television & New Media, 1–17. https://doi.org/10.1177/15274764221125745 [Google Scholar] [Crossref]
18. Moumgiakmas, S. S., Samatas, G. G., & Papakostas, G. A. (2021). Computer vision for fire detection on UAVs—from software to hardware. Future Internet, 13(8), 200. https://doi.org/10.3390/fi13080200 [Google Scholar] [Crossref]
19. Scispace. (2024). How does Netflix use business intelligence for marketing? https://www.scispace.com [Google Scholar] [Crossref]
20. Zielnicki, K., Aridor, G., Bibaut, A., Tran, A., Chou, W., & Kallus, N. (2025). The value of personalized recommendations: Evidence from Netflix. arXiv. https://arxiv.org/abs/2511.07280 [Google Scholar] [Crossref]
21. Marketingino (2025). The Netflix recommendation algorithm: How personalization drives 80% of viewer engagement. https://marketingino.com/the-netflix-recommendation-algorithm-how-personalization-drives-80-of-viewer-engagement/?utm_source=chatgpt.com [Google Scholar] [Crossref]
22. Rapheal, C. (2016). How machine learning fuels your Netflix Addiction, RTInsights, Available at: https://www.rtinsights.com/netflix-recommendations-machine-learning-algorithms/?utm_source=chatgpt.com Accessed on: 6/4/26 [Google Scholar] [Crossref]
23. Ng, K. T. (2024). Role of corporate culture, leadership and strategy in corporate failures: A case study analysis approach [Doctoral dissertation, Selinus University]. [Google Scholar] [Crossref]
24. Schweidel, D. A., & Foutz, N. Z. (2014). Forecasting the adoption of a service using trial-based diffusion models. Marketing Science, 33(5), 620–637. https://doi.org/10.1287/mksc.2014.0865 [Google Scholar] [Crossref]
25. Sundar, D. (2023). Machine Learning Frameworks for Media Consumption Intelligence across OTT and Television Ecosystems. International Journal of Artificial Intelligence, Data Science and Machine Learning, 4(2), 124–134. [Google Scholar] [Crossref]
26. Smith, J., & Ribeiro, M. T. (2022). Machine learning for media recommendation in digital streaming platforms: Evidence from Amazon Prime Video. Journal of Digital Media Analytics, 8(2), 115–133. [Google Scholar] [Crossref]
27. Gomez-Uribe, C. A., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), Article 13. https://doi.org/10.1145/2843948 [Google Scholar] [Crossref]
28. Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems. https://doi.org/10.1145/2959100.2959190 [Google Scholar] [Crossref]
29. Bangera, S., Nagaonkar, V., Tiwari, A., & Ansari, S. (2024). Spotify Recommendation System. International Research Journal of Modernization in Engineering Technology and Science, 06(02), 1695–1701. https://doi.org/https://www.doi.org/10.56726/IRJMETS49566 [Google Scholar] [Crossref]
30. Vall, A., Rachuri, K. K., & Zangerle, E. (2022). Characterizing Spotify’s Discover Weekly: A large-scale analysis of recommender system impact on listening behavior. Journal of Music Information Retrieval, 5(1), 22–35. [Google Scholar] [Crossref]
31. Min, C. (2023, January 18). Netflix Japan is drawing ire for using A.I. to generate the background art of its new anime short: The Dog and the Boy. Artnet News. https://news.artnet.com/art-world/netflix-japan-ai-anime-dog-and-boy-2251247 [Google Scholar] [Crossref]
32. OECD. (2019). OECD principles on artificial intelligence. Organisation for Economic Co-operation and Development. https://oecd.ai/en/ai-principles [Google Scholar] [Crossref]
34. Referenced Links: [Google Scholar] [Crossref]
35. Netflix Machine Learning Research. (n.d.). Netflix Research. https://research.netflix.com/research-area/machine-learning [Google Scholar] [Crossref]
36. Netflix Computer Vision and Graphics Research. (n.d.). Netflix Research. https://research.netflix.com/research-area/computer-vision-and-graphics [Google Scholar] [Crossref]
37. Netflix NLP and Conversations Research. (n.d.). Netflix Research. https://research.netflix.com/research-area/nlp-and-conversations [Google Scholar] [Crossref]
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