Retail Reinvented with Generative AI: A Paradigm Shift
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P111Keywords:
Generative AI, Machine Learning, Cloud Computing, Data Science, Distributed Training, Resource Optimization, Cloud-Based AIAbstract
Gen-AI is revolutionizing the retail sector, catalyzing unprecedented advancements in operational efficiency, customer engagement, and strategic decision-making. This paper delves into the transformative impact of Generative AI across the retail value chain, from inventory management and supply chain optimization to personalized customer experiences and dynamic pricing models. Capitalizing on AI-powered algorithms and machine learning methodologies, retailers are not simply adjusting to the swiftly transforming market forces but are proactively molding the retail landscape's future. The incorporation of AI technologies, encompassing predictive analytics, natural language processing, and computer vision, empowers retailers to gain immediate understandings and highly individualized approaches, consequently improving customer contentment and allegiance. These technologies streamline precise demand prediction, automated stock management, and the optimization of supply chains, decreasing expenditures and minimizing wastage. Moreover, AI-driven recommendation systems and conversational agents are overhauling customer engagement by providing customized purchasing experiences, cultivating stronger bonds between consumers and brands. This paper also investigates the strategic consequences of AI assimilation within retail, emphasizing its significance in fostering innovation and competitive advantage. Retailers that effectively utilize AI capabilities are better positioned to foresee consumer preferences, react to market trends, and establish distinctive value offerings. Furthermore, the moral considerations surrounding AI implementation, including data security and algorithmic prejudice, are critically evaluated to guarantee accountable and enduring AI integration. This study underscores the crucial function of AI in advancing the retail sector toward a future distinguished by amplified effectiveness, flexibility, and customer focus. By embracing AI, retailers are not merely maneuvering through the intricacies of the digital era but are also establishing novel benchmarks for operational superiority and customer interaction. The outcomes of this research furnish valuable perspectives for retail professionals, policymakers, and academics, providing a thorough comprehension of how AI is revolutionizing the retail environment and its implications for the industry's trajectory
References
[1] N. Thakur, A. Singh, A.L. Sangal, Cloud services selection: A systematic review and future research directions, Computer Science Review 46 (2022) 100514. https://doi.org/10.1016/j.cosrev.2022.100514.
[2] Belgacem, S. Mahmoudi, M. Kihl, Intelligent multi-agent reinforcement learning model for resources allocation in cloud computing, Journal of King Saud University - Computer and Information Sciences 34 (2022) 2391–2404. https://doi.org/10.1016/j.jksuci.2022.03.016.
[3] Z. Zhou, L. Zhao, Cloud computing model for big data processing and performance optimization of multimedia communication, Computer Communications 160 (2020) 326–332. https://doi.org/10.1016/j.comcom.2020.06.015.
[4] J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. Ranzato, A. Senior, Large Scale Distributed Deep Networks, Advances in Neural Information Processing Systems 25 (2012) 1–9.
[5] M. Bahrami, M. Singhal, The Role of Cloud Computing Architecture in Big Data, Studies in Big Data 8 (2015) 275–295. https://doi.org/10.1007/978-3-319-08254-7_13.
[6] S.A. El-Seoud, H.F. El-Sofany, M. Abdelfattah, R. Mohamed, Big data and cloud computing: Trends and challenges, International Journal of Interactive Mobile Technologies 11 (2017) 34–52. https://doi.org/10.3991/ijim.v11i2.6561.
[7] S.A. Bhat, N.F. Huang, Big Data and AI Revolution in Precision Agriculture: Survey and Challenges, IEEE Access 9 (2021) 110209–110222. https://doi.org/10.1109/ACCESS.2021.3102227.
[8] H.K. Mistry, C. Mavani, A. Goswami, R. Patel, The Impact Of Cloud Computing And Ai On Industry Dynamics And Competition, Educational Administration: Theory and Practice 30 (2024).
[9] C. Quinn, Future Trends and Emerging Technologies in AI-Driven Healthcare, Artificial Intelligence in Medicine (2024) 295–314. https://doi.org/10.1142/9789811284113_0018.
[10] N. Ahmed, A. Abraham, Modeling Cloud Computing Risk Assessment Using Machine Learning, Advances in Intelligent Systems and Computing 334 (2015) 315–325. https://doi.org/10.1007/978-3-319-13572-4.
[11] I.A. Ansari, M. Pant, Quality assured and optimized image watermarking using artificial bee colony, International Journal of Systems Assurance Engineering and Management 9 (2018) 274–286. https://doi.org/10.1007/s13198-016-0568-2. 6171–6180. ttps://doi.org/10.1109/ACCESS.2016.2613278.
[12] N. Thakur, A.K. Sharma, Data Integrity Techniques in Cloud Computing: An Analysis, International Journal of Advanced Research in Computer Science and Software Engineering 7 (2017) 121. https://doi.org/10.23956/ijarcsse.v7i8.36.
[13] N. Thakur, A. Singh, A.L. Sangal, Comparison of Multi-Criteria Decision-Making Techniques for Cloud Services Selection, Lecture Notes in Electrical Engineering 855 (2022) 669–682. https://doi.org/10.1007/978-981-16-8892-8_51.
[14] N. Thakur, A.K. Sharma, DATA INTEGRITY CHECK IN CLOUD COMPUTING : A, International Journal of Computer Engineering and Applications XI (2017).
[15] V.K. Damera, A. Nagesh, M. Nagaratna, Trust evaluation models for cloud computing, International Journal of Scientific and Technology Research 9 (2020) 1964–1971.
[16] Ravi Kumar, Neha Thakur, Ahmad Saeed, Chandra Jaiswal, Enhancing Data Analytics Using AI-Driven Approaches in Cloud Computing Environments, Software Engineering, Vol. 11 No. 2, 2024, pp. 11-18. doi: 10.5923/j.se.20241102.01.
[17] Ravi Kumar, Dinesh Kumar, Ahmad Saeed, Image Captioning Using Deep Learning Models, Computer Science and Engineering, Vol. 14 No. 6, 2024, pp. 162-168. doi: 10.5923/j.computer.20241406.06.
[18] Ravi Kumar, Dinesh Kumar, Ahmad Saeed, Chandra Jaiswal, Artificial Intelligence, Campaign Effectiveness Model, Computer Science and Engineering, Vol. 14 No. 6, 2024, pp. 169-174. doi: 10.5923/j.computer.20241406.07
