Generative AI for Retail and Healthcare: Redefining User Interaction with Data Systems

Abstract

Generative Artificial Intelligence (AI) is transforming the user interaction with data systems in the main aspects of the economy, such as retail and healthcare. The present paper will discuss how the recent advances in the sphere of generative models, as well as the sound data engineering behavior, can change the user experiences so as to provide them with highly personalised, interactive and smart systems. Generative AI is applied in retail to create content that is more personalized, predictive demand, and refined recommendation system; in healthcare, it helps in synthetic data generation, patient-centred documentation, assisting diagnosis, and health literacy tools. The systems ensure reliability, compliance and performance by incorporating key data engineering requirements, which include data ingestion, preprocessing, pipeline design, quality assurance, metadata management, versioning, and lineage. We examine the architectural designs, workflow and model streamlining policies allowing such integrations. Moreover, we also address use-cases that explain how generative AI can simplify the burden of administration, decision-support, and user confidence, and also outline key issues: data privacy, model explainability, bias, ethical protection and regulation. At the end of the paper, future directions are also offered such as federated learning, multimodal data fusion, and real-time adaptive pipelines to make sure that generative AI is safely and efficiently deployed.