AI-Powered Enterprise Systems: Transforming Organizational Workflows Using Generative AI
Keywords:
Generative AI; Enterprise Systems; Workflow Automation; Salesforce Agentforce; Microsoft Copilot; AI Agents; Retrieval-Augmented Generation; Digital TransformationAbstract
Generative Artificial Intelligence is rapidly transforming enterprise systems by creating clever ecosystems that enable natural conversational interaction‚ end-to-end workflow automation‚ and context-aware‚ enterprise-scale decision-making․ This is a step beyond customary enterprise systems that digitized business processes and data silos at every layer of an organization․ We explore the impact of Generative AI on enterprise workflows through the lens of Salesforce Agentforce and Microsoft Dynamics 365 Copilot‚ the most advanced examples of enterprise Generative AI․ We discuss the evolution from rule-based workflow automation systems to AI-native workflows‚ Generative workflow automation‚ agentic system architectures‚ RAG‚ and other governance considerations for AI-empowered enterprises looking to responsibly scale their operations․ Organizations may gain efficiencies in enterprise operations‚ customer experience and workforce productivity by implementing Generative AI as a part of their enterprise ecosystem‚ as long as security‚ bias and accountability are appropriately managed․ As AI technology matures‚ convergence of cloud infrastructure‚ workflow orchestration and generative AI capabilities may produce a highly adaptable enterprise ecosystem able to respond dynamically to changing opportunities and customer demands․
Downloads
References
T. Brown et al., "Language Models are Few-Shot Learners," Advances in Neural Information Processing Systems, vol. 33, pp. 1877–1901, 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
E. Brynjolfsson, D. Li, and L. Raymond, "Generative AI at Work," The Quarterly Journal of Economics, vol. 140, no. 2, pp. 889–942, 2025. DOI: 10.1093/qje/qjae044
W. M. P. van der Aalst, M. Bichler, and A. Heinzl, "Robotic Process Automation," Business & Information Systems Engineering, vol. 60, no. 4, pp. 269–272, 2018. DOI: 10.1007/s12599-018-0542-4
A. Vaswani et al., "Attention Is All You Need," Advances in Neural Information Processing Systems, vol. 30, 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
A. Dorri, S. S. Kanhere, and R. Jurdak, "Multi-Agent Systems: A Survey," IEEE Access, vol. 6, pp. 28573–28593, 2018. DOI: 10.1109/ACCESS.2018.2831228
N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, "A Survey on Bias and Fairness in Machine Learning," ACM Computing Surveys, vol. 54, no. 6, pp. 1–35, 2021. DOI: 10.1145/3457607
Z. Ji et al., "Survey of Hallucination in Natural Language Generation," ACM Computing Surveys, vol. 55, no. 12, pp. 1–38, 2023. DOI: 10.1145/3571730
P. Lewis et al., "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks," Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
N. Reimers and I. Gurevych, "Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks," Proceedings of EMNLP-IJCNLP 2019, pp. 3982–3992, 2019. [Online]. Available: https://aclanthology.org/D19-1410/
J. Wei et al., "Emergent Abilities of Large Language Models," Transactions on Machine Learning Research, 2022. [Online]. Available: https://openreview.net/forum?id=yzkSU5zdwD
M. Raza et al., "Industrial Applications of Large Language Models," Scientific Reports, vol. 15, article no. 13755, 2025. DOI: 10.1038/s41598-025-98483-1
M. C. Buiten, A. de Streel, and M. Peitz, "The Law and Economics of AI Liability," Computer Law & Security Review, vol. 48, article no. 105794, 2023. DOI: 10.1016/j.clsr.2023.105794
N. Haefner, V. Parida, O. Gassmann, and J. Wincent, "Implementing and Scaling Artificial Intelligence: A Review, Framework, and Research Agenda," Technological Forecasting and Social Change, vol. 197, 2023. DOI: 10.1016/j.techfore.2023.122301
J. Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," Proceedings of NAACL-HLT 2019, pp. 4171–4186, 2019. [Online]. Available: https://aclanthology.org/N19-1423/
C. Zhuo and J. Chen, "Can Digital Transformation Overcome the Enterprise Innovation Dilemma: Effect, Mechanism and Effective Boundary," Technological Forecasting and Social Change, vol. 190, article no. 122378, 2023. DOI: 10.1016/j.techfore.2023.122378
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
