EnterpriseAI: A Transformer-Based Framework for Cost Optimization and Process Enhancement in Enterprise Systems

Coordination among multiple interdependent processes and stakeholders and the allocation of optimal resources make enterprise systems management a challenging process. Even for experienced professionals, it is not uncommon to cause inefficiencies and escalate operational costs. This paper introduces EnterpriseAI, a novel transformer-based framework designed to automate enterprise system management. This transformer model has been designed and customized to reduce manual effort, minimize errors, and enhance resource allocation. Moreover, it assists in decision making by incorporating all interdependent and independent variables associated with a matter. All of these together lead to significant cost savings across organizational workflows. A unique dataset has been derived in this study from real-world enterprise scenarios. Using the transfer learning approach, the EnterpriseAI transformer has been trained to analyze complex operational dependencies and deliver context-aware solutions related to enterprise systems. The experimental results demonstrate EnterpriseAI’s effectiveness, achieving an accuracy of 92.1%, a precision of 92.5%, and a recall of 91.8%, with a perplexity score of 14. These results represent the ability of the EnterpriseAI to accurately respond to queries. The scalability and resource utilization tests reflect the astonishing factors that significantly reduce resource consumption while adapting to demand. Most importantly, it reduces the operational cost while enhancing the operational flow of business.