This paper presents the design, implementation, and evaluation behind a Large Language Model (LLM) agent that chats with an industrial production-grade ERP system. The agent is capable of interpreting natural language queries and translating them into executable SQL statements, leveraging open-weight LLMs. A novel dual-agent architecture combining reasoning and critique stages was proposed to improve query generation reliability. Keywords: LLMs, Text to SQL, AI Agents 1. Introduction Enterprise Resource Planning (ERP) systems are complex software platforms that integrate and manage core business processes across departments such as manufacturing, logistics, finance, and human resources. These systems are essential for coordinating operations, ensuring data consistency, and enabling data-driven decision-making in industrial environments.

Enterprise Resource Planning (ERP) systems serve as the digital backbone of modern financial institutions, yet they continue to rely on static, rule-based workflows that limit adaptability, scalability, and intelligence. As business operations grow more complex and data-rich, conventional ERP platforms struggle to integrate structured and unstructured data in real time and to accommodate dynamic, cross-functional workflows. In this paper, we present the first AI-native, agent-based framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents (GBPAs) that bring autonomy, reasoning, and dynamic optimization to enterprise workflows. The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation of complex tasks such as budget planning, financial reporting, and wire transfer processing. Unlike traditional workflow engines, GBPAs interpret user intent, synthesize workflows in real time, and coordinate specialized sub-agents for modular task execution. We validate the framework through case studies in bank wire transfers and employee reimbursements, two representative financial workflows with distinct complexity and data modalities. Results show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance by enabling parallelism, risk control insertion, and semantic reasoning. These findings highlight the potential of GBPAs to bridge the gap between generative AI capabilities and enterprise-grade automation, laying the groundwork for the next generation of intelligent ERP systems.

Enterprise Resource Planning (ERP) consultants play a vital role in customizing systems to meet specific business needs by processing large amounts of data and adapting functionalities. However, the process is resource-intensive, time-consuming, and requires continuous adjustments as business demands evolve. This research introduces a Self-Adaptive ERP Framework that automates customization using enterprise process models and system usage analysis. It leverages Artificial Intelligence (AI) & Natural Language Processing (NLP) for Petri nets to transform business processes into adaptable models, addressing both structural and functional matching. The framework, built using Design Science Research (DSR) and a Systematic Literature Review (SLR), reduces reliance on manual adjustments, improving ERP customization efficiency and accuracy while minimizing the need for consultants.

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.

Software applications, especially Enterprise Resource Planning (ERP) systems, are crucial to the day-to-day operations of many industries. Therefore, it is essential to maintain these systems effectively using tools that can identify, diagnose, and mitigate their incidents. One promising data-driven approach is the Subgroup Discovery (SD) technique, a data mining method that can automatically mine incident datasets and extract discriminant patterns to identify the root causes of issues. However, current SD solutions have limitations in handling complex target concepts with multiple attributes organized hierarchically. To illustrate this scenario, we examine the case of Java out-of-memory incidents among several possible applications. We have a dataset that describes these incidents, including their context and the types of Java objects occupying memory when it reaches saturation, with these types arranged hierarchically. This scenario inspires us to propose a novel Subgroup Discovery approach that can handle complex target concepts with hierarchies. To achieve this, we design a pattern syntax and a quality measure that ensure the identified subgroups are relevant, non-redundant, and resilient to noise. To achieve the desired quality measure, we use the Subjective Interestingness model that incorporates prior knowledge about the data and promotes patterns that are both informative and surprising relative to that knowledge. We apply this framework to investigate out-of-memory errors and demonstrate its usefulness in incident diagnosis. To validate the effectiveness of our approach and the quality of the identified patterns, we present an empirical study. The source code and data used in the evaluation are publicly accessible, ensuring transparency and reproducibility.

ERP (Enterprise Resource Planning) is the brain of your organization's technology apparatus. The brain coordinates the activities of your body. It is responsible for telling the body what it should do. A well-planned Enterprise Resource Planning system is essential for any organization to function. But things will change over time. Digital transformation is an important driving force in today's business world. Businesses that want to make the most of Industry 4.0's technological advances will need them. Enterprise services that are efficient and error-free make it possible to use machine learning and artificial Intelligence technologies in real time and automate operations. This is a significant influence on digital transformation. One of the significant impacts of ML is the potential enhancement of Enterprise resource plan (ERP) applications.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

Self-motivating, enthusiastic with a can-do attitude and the ability to work independently.

Manufacturing & Logistics IT spoke with leading analysts and vendors about current developments within the manufacturing technology space and what future innovations might emerge over the next few years. The world of manufacturing technology is changing, and digital is certainly the watchword. As Rowan Litter, research analyst, enterprise mobility, VDC Research, points out, the primary development within the manufacturing technology space is Digital Transformation. This, he explains, can be as simple as upgrading from outdated/legacy systems or pen-and-paper or enabling an entire smart factory with automation, and machine learning/AI capabilities. "As these technologies become tested and proven, manufacturers are realising that correctly incorporating these innovations will lead to increased operational efficiency and greater production to meet rises in consumer demand," he says. Litter believes a very important piece that needs to be talked about is the enabler of these innovations and technology. "That enabler comes from connectivity and networks; what will allow businesses to adopt and connect more technologies, process data faster and provide the best security from a rise in cybersecurity threats, as well as the overall risks with digitalisation," he says. "Private Wireless Networks have emerged as the enabler for manufacturers who are interested in implementing these new technologies. A challenge for many organisations comes from not knowing what to prioritise and where to start. With labour critical to support operations in many of these environments and organisations challenged with optimising workflows, we find that enabling the mobile worker with digital tools is the optimal jumping off point." In terms of drivers for change, Litter maintains that the impacts of COVID-19 highlighted inefficiencies in the manufacturing sector.