Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Raokatta, Sivakoteswara; Akshaya, A.V.R.; Rajeswari, M. Sree; Sathiyaseelan, Vasanthaseelan Title A name given to the resource Uses of Generative AI for SAP HANA Data Management Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Artificial Intelligence in Detecting Autism;pp.237-246 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003541318-23" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003541318-23</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037439465?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037439465?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raokatta S., Singapore Institute of Technology, Singapore; Akshaya A.V.R., School of Business and Management, CHRIST University, Bengaluru, India; Rajeswari M.S., Department of Computer Science and Engineering, Jeppiaar Institute of Technology, Chennai, India; Sathiyaseelan V., Department of Autotronics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil N?du, 602105, India Description An account of the resource This chapter examines the transformative role of generative artificial intelligence (AI) in enterprise analytics, with emphasis on the Generative Pre-trained Transformer (GPT) family and related attention-based architectures. In contrast to conventional machine-learning pipelines whose performance is constrained by task-specific supervision and rigid feature engineering generative models exploit large-scale self-supervised pre-training, enabling emergent reasoning and effective transfer across heterogeneous downstream tasks. We demonstrate these advantages through a pragmatic integration of GPT-class large language models (LLMs) within an SAP HANA environment. By fine-tuning the LLM on domain-specific SQL corpora and curated schema metadata, the system learns to synthesise syntactically correct, execution-ready SQL statements that align with the underlying business logic. This design obviates costly data-centralisation efforts: users can pose natural-language questions and obtain HANA-compliant queries over distributed enterprise data without deep knowledge of relational algebra or SAP-specific functions. Moreover, explicit injection of domain ontologies during fine-tuning improves semantic grounding and materially increases query-generation accuracy. A sales-reporting case study substantiates these claims, showing that the approach streamlines complex analytic workflows, reduces time-to-insight, and enhances report reliability. Collectively, the findings position generative AI as a catalytic technology for modernising enterprise data management and accelerating data-driven innovation. 2026 Ram Kumar Chenthur Pandian, Shanmuga Raju Sekar, Subrata Chowdhury, Muhammad Rukunuddin Ghalib, and Kassian T.T. Amesho. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104053668-1; 978-103286653-6; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online