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AI Integration In ERP Evaluation Across Trends and Architectures
arXiv:2512.11805v1 Announce Type: new
Abstract: The incorporation of Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) is a dramatic transition from static, on-premises systems to systems that can adapt and operate in cloud-native architectures. Cloud ERP solutions like Workday illustrate this evolution by incorporating machine learning, deep learning, and natural language processing into a centralized data-driven ecosystem. As the complexity of AI-driven ERP solutions expands, traditional evaluation frameworks that look at cost, function, and user satisfaction suffer from a lack of consideration for algorithmic transparency, adaptability, or ethics. This review will systematically investigate the latest trends, models of computing architecture, and analytical methods applied in assessing the performance of AI-integrated ERP services, specifically on cloud-based platforms. Based on academic and industry sources, the paper distills current research in line with architectural integration, analytical methodologies, and organizational impact. It identifies critical performance metrics and emphasizes the absence of any standard assessment frameworks or AI-aware systems capable of evaluating automation efficiency, security concerns as well as flexible learning modes. We put forward a theoretical model that brings AI-enabled capabilities -- such as predictive intelligence or adaptive automation -- into alignment with metrics in performance assessment for ERPs. By combining current literature and identifying major gaps in research, this paper attempts to present a complete picture of how innovations in AI are changing ERP evaluation. These research and methodological findings are intended to steer researchers and practitioners towards developing rigorous, data-driven assessment approaches, aligning with the fast-developing world of intelligent self-optimizing enterprise ecosystems
Abstract: The incorporation of Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) is a dramatic transition from static, on-premises systems to systems that can adapt and operate in cloud-native architectures. Cloud ERP solutions like Workday illustrate this evolution by incorporating machine learning, deep learning, and natural language processing into a centralized data-driven ecosystem. As the complexity of AI-driven ERP solutions expands, traditional evaluation frameworks that look at cost, function, and user satisfaction suffer from a lack of consideration for algorithmic transparency, adaptability, or ethics. This review will systematically investigate the latest trends, models of computing architecture, and analytical methods applied in assessing the performance of AI-integrated ERP services, specifically on cloud-based platforms. Based on academic and industry sources, the paper distills current research in line with architectural integration, analytical methodologies, and organizational impact. It identifies critical performance metrics and emphasizes the absence of any standard assessment frameworks or AI-aware systems capable of evaluating automation efficiency, security concerns as well as flexible learning modes. We put forward a theoretical model that brings AI-enabled capabilities -- such as predictive intelligence or adaptive automation -- into alignment with metrics in performance assessment for ERPs. By combining current literature and identifying major gaps in research, this paper attempts to present a complete picture of how innovations in AI are changing ERP evaluation. These research and methodological findings are intended to steer researchers and practitioners towards developing rigorous, data-driven assessment approaches, aligning with the fast-developing world of intelligent self-optimizing enterprise ecosystems
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