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 Shingate, Kranti; Grover, Veena; Khanna, Shivi; Gupta, Gaurav; Chintale, Pradeep; Nerella, HarshaVardhan; Vanipriya, C.H.; Gochhait, Saikat Title A name given to the resource The Role of Artificial Intelligence in Simulating, Automating, and Analyzing Business Operations Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1196 LNNS;pp.51-61 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-9526-0_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-9526-0_6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009871288?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009871288?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 Shingate K., Symbiosis Institute of Digital and Telecom Management, constituent of Symbiosis International Deemed University, Pune, India; Grover V., NIET, Greater Noida, India; Khanna S., Christ University, Bangalore, India; Gupta G., Shoolini University, Himachal Pradesh, Solan, India; Chintale P., Borough, United States; Nerella H., Cleveland, United States; Vanipriya C.H., Sir M Visvesvaraya Institute of Technology, Bangalore, India; Gochhait S., Symbiosis Institute of Digital and Telecom Management, constituent of Symbiosis International Deemed University, Pune, India Description An account of the resource Business processes have been transformed with the advent of artificial intelligence. However, to efficiently utilize the technology and to close the gap, we reviewed the literature to find these solutions in this work. We ensured that styles worked because they allowed for extensions and replication. In these studies, we correlated patterns that assisted with task automation and helped analysts create, expand, or re-engineer business processes with the confidence to make judgments. The authors used various AI methods, including swarm intelligence, Bayesian networks, and K-means. Our analysis gives data on the approaches and issues being dealt with and indicates potential future directions. Processes for predictive business future planning and activity prediction are examples of monitoring jobs that are becoming less significant as new technologies allow for the intelligent automation of company processes. Deep learning models are used in recent work on this subject to encapsulate historical event information without further processing. The data context, which includes the dependence of conditions and particular traits, might also have an impact on the anticipated data, even though it was not taken into account in earlier research. We present a novel encoding strategy for state data, encompassing non-existent, multi-character private, and regular event states. We present the transformer and LSTM deep learning models, two new deep learning models, and two popular deep learning models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence,; Automation analysis; Business operations; Learning models; Simulation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981979525-3; 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