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 Conference Paper Faculty Publications- Conference Papers 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 Ram Kumar, K. Title A name given to the resource Transforming Finance into an AI-Driven, Data-Centric, and Analytics-Focused Function: Implications for Organizational Impact 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 Lecture Notes in Networks and Systems;Volume;1538 LNNS;pp.401-416 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-9245-3_31" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-9245-3_31</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105023307053?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105023307053?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 Ram Kumar K., Christ University, Bengaluru, India Description An account of the resource The financial function within organizations is evolving from a traditional cost-center role to a strategic partner powered by artificial intelligence (AI), data analytics, and advanced technologies. This transformation is reshaping how financial professionals analyze data, predict trends, and support decision-making. By leveraging AI, finance teams can automate repetitive tasks, enhance data accuracy, and deliver actionable insights in real-time. Integrating predictive and prescriptive analytics further empowers organizations to forecast financial outcomes, optimize resource allocation, and mitigate risks with precision. This shift to an AI-driven, data-centric finance model fosters agility, innovation, and enhanced collaboration across departments. It aligns financial strategies with organizational goals, improving operational efficiency and driving sustainable growth. However, the transition demands investments in digital infrastructure, a redefinition of roles, and an upskilling of personnel to handle complex analytics tools. The implications for organizational impact include enhanced competitiveness, informed decision-making, and a stronger ability to navigate an increasingly volatile economic landscape. This chapter explores these transformations, their challenges, and the opportunities they unlock for organizations aiming to thrive in a data-dominated era. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence in finance; Data-centric decision-making; Digital finance; Financial forecasting; Financial transformation; Organizational agility; Predictive analytics; Prescriptive analytics; Risk management Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981969244-6; Language A language of the resource English Type The nature or genre of the resource Conference paper 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