Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 11 (2025)
Articles

Harnessing Generative AI and Intelligent Systems for Enhanced Retail and Industrial Operations: A Comprehensive Analysis

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  • Dilnoza Zubayd qizi Ismoilova
Dilnoza Zubayd qizi Ismoilova
Assistant of the Department of Medical and Biological Chemistry, Bukhara State Medical Institute, Uzbekistan

Published 2025-11-30

Keywords

  • Generative AI,
  • Intelligent Systems,
  • Retail Analytics,
  • Machine Learning Pipelines

How to Cite

Dilnoza Zubayd qizi Ismoilova. (2025). Harnessing Generative AI and Intelligent Systems for Enhanced Retail and Industrial Operations: A Comprehensive Analysis. Stanford Database Library of American Journal of Applied Science and Technology, 5(11), 273–276. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/65

Copyright (c) 2025 Dilnoza Zubayd qizi Ismoilova

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The rapid integration of generative artificial intelligence (AI) and advanced computational systems into both retail and industrial sectors has transformed operational frameworks, customer engagement strategies, and data-driven decision-making processes. This study explores the multifaceted applications of generative AI, machine learning (ML), and intelligent automation in optimizing workflows, enhancing consumer experiences, and mitigating algorithmic bias and data privacy concerns. By synthesizing contemporary literature on AI-driven retail environments, automated software pipelines, and privacy-preserving machine learning techniques, the research identifies significant opportunities and challenges associated with AI adoption. The study provides a theoretical elaboration of ambient intelligence in smart retailing, AI-enabled marketing strategies, and the deployment of generative AI in industrial process optimization. Furthermore, critical analysis is conducted on the ethical, methodological, and operational dimensions of AI systems, including bias detection, fairness in algorithmic scoring, and CI/CD pipelines for large language models (LLMs). The findings suggest that while generative AI and intelligent systems offer substantial efficiency gains and innovation potential, they require comprehensive governance frameworks, robust privacy safeguards, and adaptive operational strategies to realize sustainable benefits. The study concludes by proposing a conceptual roadmap for integrating AI-driven solutions across retail and industrial domains while ensuring ethical compliance, operational resilience, and enhanced customer satisfaction.

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References

  1. Akpınar, M.T. Generative Artificial Intelligence Applications Specific to the Air Transport Industry. In Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century II; Kaygusuz, K., Ed.; Özgür Publications: İstanbul, Turkey, 2023.
  2. Ajiga, D.; Okeleke, P.A.; Folorunsho, S.O.; Ezeigweneme, C. The role of software automation in improving industrial operations and efficiency. Int. J. Eng. Res. Update 2024, 7, 22–35.
  3. AI Agents Directory. AgentOps Review & Alternatives, 2025. https://aiagentsdirectory.com/agent/agentops
  4. Chandra, R. OPTIMIZING LLM PERFORMANCE THROUGH CI/CD PIPELINES IN CLOUD-BASED ENVIRONMENTS. International Journal of Applied Mathematics, 38(2), 183-204, 2025.
  5. Davidavičius, S.; Markus, O.; Davidavičienė, V. Identification of opportunities to improve customers’ experience. Journal of e-commerce logistics and informatics, 2020, 42-57.
  6. Ebert, C.; Louridas, P. Generative AI for software practitioners. IEEE Softw. 2023, 40, 30–38.
  7. Google Cloud. MLOps: Continuous delivery and automation pipelines in machine learning Cloud Architecture Center, 2024. https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
  8. Hasan, A.; Davidovic, J.; Brown, S. The audit algorithm. Society & Big Data, 2021, 8:2053951720983865.
  9. Hitaj, B.; Ateniese, G.; Perez-Cruz, F. Deep models under the GAN: Information leakage from collaborative deep learning. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 603–618, 2017.
  10. Konstantopoulos, C.; Kasapakis, V.; Giannakopoulou, K.; Zaroliagis, C.; Pantziou, G.; Kypriadis, D. Enhancing shopping experiences in smart retailing. Humanized Computing and Ambient Intelligence, 2021, 1-19.
  11. NVIDIA. Deloitte Builds Drug Discovery Pipelines With Generative AI in a Few Clicks, 2025. https://resources.nvidia.com/en-us/dgx-cloud/generative-ai-in-drug-discovery
  12. Pandey, S.K.; Chintalapati, S. Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 2022, 64:38-68.
  13. Rigaki, M.; Garcia, S. A survey of privacy attacks in machine learning. ACM Comput. Surv. 2023, 56, 1–34.
  14. Schertzlel, E.; Reynolds, C.; Ohri, L.; Kusumoto, L.; Cook, A.V. The quiet revolution: Augmented shopping insights. Deloitte, 2020, 1-16.
  15. Schwartz, R.; Schwartz, R.; Vassilev, A.; Greene, K.; Perine, L.; Burt, A.; Hall, P. Towards a Standard for Identifying and Managing Bias in Artificial Intelligence; US Department of Commerce, National Institute of Standards and Technology: Gaithersburg, MD, USA, 2022.
  16. Song, C.; Raghunathan, A. Information leakage in embedding models. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 377–390, 2020.
  17. Wang, L.; Wu, L.; Chen, P. Fairness in AI data management. Journal of Big Data and Society, 2023, 8:2053951720983865.