Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 37 references
TL;DR
It is concluded that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
Abstract
AI-powered predictive systems for decision support are revolutionizing the way that smart enterprises and industrial organizations are analysing data, predicting future conditions, and making operational and strategic decisions. The systems include machine learning, deep learning, predictive analytics, prescriptive analytics, real-time monitoring, and intelligent recommendation systems to enhance decision-making accuracy, efficiency, and responsiveness. They are used in business forecasting, customer and financial analytics, supply-chain and inventory management, predictive maintenance, production optimization, quality control, energy management, workplace safety and asset monitoring. The addition of new technologies like the Internet of Things, Industrial Internet of Things, digital twins, cloud and edge computing, robotics, blockchain and next generation networks further improve system connectivity, scalability and real-time performance. The successful implementation of these steps needs a structured framework for problem identification, data collection, preprocessing, feature engineering, model selection, training, validation, system integration, deployment, and continual monitoring. Despite these progressions, data quality, interoperability, scalability, algorithmic bias, explainability, privacy, cybersecurity, organizational readiness, and regulatory compliance are all important challenges that still need to be addressed. There is still a need for human oversight, especially when dealing with safety-critical and high-impact decisions. It includes the technological foundations, system architecture, implementation processes, enterprise and industrial applications, performance evaluation, governance requirements, and future directions of AI-supported predictive decision support systems. It concludes that the systems that are trustworthy, secure, transparent, sustainable and intelligent are enterprise and industrial operations.
The rapid advancement of Industry 4.0 has transformed conventional manufacturing into intelligent smart factories by integrating Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, and artificial intelligence (AI). As manufacturing environments become increasingly complex, traditional human-driven decision-making is insufficient for real-time production optimization. Autonomous Decision Support Systems (ADSS) address this challenge by combining AI, machine learning, digital twins, edge computing, and predictive analytics to enable intelligent, data-driven decision-making with minimal human intervention. This paper presents a scalable ADSS framework that integrates IIoT, edge-cloud computing, and digital twin technology for real-time monitoring, predictive maintenance, dynamic scheduling, and autonomous production optimization. The proposed architecture includes data acquisition, preprocessing, feature engineering, predictive analytics, decision optimization, autonomous execution, and continuous learning. Reinforcement learning and explainable AI improve decision accuracy, adaptability, and transparency, while federated learning enhances data privacy and reduces communication latency. Experimental results demonstrate significant improvements in production efficiency, equipment utilization, predictive maintenance, energy efficiency, quality control, and manufacturing responsiveness compared to conventional decision support systems. The proposed framework provides a scalable foundation for Industry 5.0, enabling sustainable, resilient, and intelligent manufacturing through seamless collaboration between human expertise and autonomous AI systems.
Jose Fernandez, Marta Silva· International Journal of Int...· 0 citations
Findings indicate that AI-enabled digital twins significantly improve equipment reliability, reduce unexpected failures, enhance resource utilization, and enable proactive manufacturing strategies, however, challenges related to interoperability, cybersecurity, computational complexity, data quality, and governance remain critical barriers to widespread industrial adoption.
H. Mahmood· European International Journ...· 0 citations
An integrated AI-IoT framework for smart manufacturing that continuously acquires machine data, performs real-time analytics, predicts equipment failures, optimizes production scheduling, and supports data-driven decision-making is proposed.
Anand Singh, B. Mishra, Amjid Nadeem et al.· Journal of Intelligent Decis...· 0 citations
It is concluded that AI-driven business intelligence frameworks represent a transformative approach to enterprise management by enabling organizations to anticipate future challenges, optimize strategic decisions, improve resource utilization, and create resilient business ecosystems capable of adapting effectively to rapidly evolving economic and technological environments.
Shi-Hu Gan· International journal of com...· 0 citations
Enterprise commerce and financial ecosystems are being transformed by Artificial Intelligence (AI), Cloud Computing, Big Data, Blockchain, the Internet of Things (IoT), and intelligent automation. However, conventional enterprise information systems primarily support operational automation and often lack real-time predictive intelligence, adaptive decision-making, and integrated visibility across business processes. These limitations lead to data silos, inaccurate forecasting, operational inefficiencies, financial risks, and reduced organizational agility. This paper proposes a Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation that integrates intelligent data acquisition, digital twin modeling, hybrid AI analytics, financial intelligence, enterprise optimization, and continuous learning into a unified computational framework. The architecture combines machine learning, deep learning, reinforcement learning, knowledge graphs, explainable AI, and optimization techniques with dynamic digital twins that continuously synchronize with data from ERP, CRM, IoT devices, cloud platforms, and financial systems. The proposed framework enables predictive analytics, business process simulation, anomaly detection, financial forecasting, automated risk assessment, and explainable decision support. Organizations can evaluate multiple operational scenarios within a virtual environment before implementation, thereby reducing uncertainty, improving resilience, and enhancing strategic planning. Mathematical models are also introduced to evaluate enterprise operational efficiency, digital twin synchronization, predictive intelligence, and financial transformation performance. The proposed architecture is expected to improve forecasting accuracy, optimize supply chain operations, strengthen fraud detection, reduce operational costs, enhance customer experience, support regulatory compliance, and promote sustainable enterprise growth. By integrating Hybrid AI with Digital Twin technology, the framework provides a scalable and intelligent foundation for autonomous commerce, resilient financial management, and data-driven enterprise transformation across diverse industrial sectors.
Alan Bundy, Karen Spärck Jones· International Journal of Dat...· 0 citations
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