This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management.

The digital transformation in the United States is at a critical crossroads, influenced by the integration of artificial intelligence (AI), business analytics (BA), and the quest for IT project excellence. In contrast to previous digitization efforts that emphasized efficiency, the current transformation is strategic, systemic, and value-oriented, addressing national priorities including economic competitiveness, cybersecurity, sustainability, and equity. This study contends that AI and BA function as complementary facilitators of this transformation: AI automates processes, improves decision-making, and stimulates creativity, whilst BA supplies the analytical foundation for evidence-based strategies, workforce optimization, and stakeholder alignment. They redefine IT project excellence as a multifaceted concept that encompasses efficiency, adaptability, security, and social responsibility.

Autism Spectrum Disorder (ASD) impacts millions worldwide, posing distinct obstacles in schooling, communication, and access to therapy. Conventional educational and therapeutic approaches, while sometimes helpful, frequently lack the adaptability and customization necessary to meet the varied needs of kids with ASD. This research tackles existing gaps by integrating advanced Artificial Intelligence (AI) techniques and data-driven personalization in digital health systems. This study presents a novel, AI-driven adaptive learning and treatment system specifically developed for kids with autism spectrum disorder (ASD). This research assesses the efficacy of AI-driven personalization in improving learning outcomes, communication skills, and therapeutic accessibility through the utilization of simulated experimental data. Adaptive machine learning algorithms, natural language processing, and reinforcement learning techniques were incorporated into digital platforms, resulting in tailored intervention models that dynamically adjust to the cognitive and communicative profiles of each learner. Results from the simulated experiments demonstrate substantial enhancements in tailored adaptive learning pathways, quantifiable progress in communication skills, and heightened therapeutic accessibility and engagement compared to conventional methods. The performance assessment of AI models reveals strong accuracy, responsiveness, and efficiency in customizing instructional and therapeutic content to meet individual learner requirements. This research enhances previous work by providing empirical insights and practical consequences, demonstrating how AI-driven devices can substantially improve educational experiences and treatment outcomes for kids with ASD. Future directions encompass empirical testing, ongoing enhancement of AI models, and additional investigation into scalable application options within educational and healthcare contexts.

In the era of Industry 4.0, industrial operations are becoming increasingly complex, requiring efficient management of resources, workflows, and real-time data. Traditional Management Information Systems (MIS) are essential for consolidating operational data but often fall short in dynamically adapting to the demands of modern industrial environments. With the advent of cloud computing, MIS platforms can now be enhanced to deliver real-time, scalable, and automated workflow management across distributed industrial environments. This paper explores the integration of cloud-based MIS platforms to automate industrial workflow management, resource allocation, and performance monitoring. The integration of cloud computing offers several advantages, including centralized data storage, seamless access to real-time data, improved collaboration, and enhanced scalability for expanding industrial operations.We propose a cloud-based MIS architecture specifically designed for industrial automation, which integrates IoT sensors, enterprise resource planning (ERP) systems, and machine learning-driven predictive analytics for decision support. The framework facilitates automated task scheduling, resource management, and real-time monitoring, thereby reducing operational inefficiencies and improving system responsiveness. Through simulations based on industrial case studies, we show how cloud-based MIS can achieve significant improvements in resource utilization, cost reduction, and system reliability. Additionally, the paper discusses the challenges of data security, system integration, and the importance of maintaining compliance with industry regulations when transitioning to cloud-based systems.The results demonstrate that cloud-based MIS offers a scalable, flexible, and secure solution for automating workflows in industrial settings. By moving traditional MIS operations to the cloud, industrial enterprises can achieve a higher degree of automation, efficiency, and operational agility, paving the way for more intelligent, data-driven manufacturing processes.

Industrial engineering projects are highly complex, involving multiple stakeholders, large budgets, and safety-sensitive operations. These projects are often exposed to risks such as delays, equipment failures, safety hazards, and cost overruns, which can significantly impact outcomes. Traditional Management Information Systems (MIS) support project managers by centralizing data and generating reports, but they remain largely reactive, documenting risks only after they occur rather than predicting them in advance. This paper proposes an AI-powered MIS framework designed to transform risk management in industrial engineering projects. The framework integrates predictive analytics, anomaly detection, and natural language processing into MIS dashboards to provide proactive risk detection. By analyzing both structured data, such as financial records, project schedules, and sensor outputs, and unstructured data like safety logs and incident reports, the system can identify anomalies, forecast delays, and anticipate budget overruns. Simulation-based evaluations demonstrate that the AI-powered MIS improves risk visibility, enabling project managers to detect emerging issues 25-30% earlier than traditional systems. This early detection enhances decision-making, reduces financial losses, and strengthens workplace safety. The modular design also ensures scalability across industries, from manufacturing and construction to energy and transportation. By embedding AI-driven intelligence into MIS workflows, the proposed framework evolves project management systems from static reporting tools into dynamic, predictive platforms. The results highlight its potential to reduce cost overruns, improve safety outcomes, and enhance overall project resilience in data-intensive environments.