Skip to content
Open access

Artificial Intelligence-Enabled Predictive Decision Support Systems for Smart Enterprise and Industrial Applications

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.

Read PDF

Similar papers

Open access 2025

Autonomous Decision Support Systems for Intelligent Factory Operations

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, and provides a scalable foundation for Industry 5.0.

Jose Fernandez, Marta Silva · 0 citations
Review Open access Jul 2026

Artificial Intelligence-Enabled Digital Twins for Smart Manufacturing and Predictive Maintenance

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 rem...

H. Mahmood · 0 citations
Open access Aug 2026

Smart Manufacturing: Integrating IoT and AI for Learner Operations

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 · 0 citations
Open access Aug 2026

Artificial Intelligence–Driven Business Intelligence Frameworks for Predictive Enterprise Decision-Making

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...

Shi-Hu Gan · 0 citations
Open access 2025

Smart Manufacturing Analytics Using Industrial Internet of Things

A multi-tier smart manufacturing framework combining IIoT, machine learning, cloud-edge computing, and optimization algorithms enables real-time asset monitoring, anomaly detection, predictive maintenance, resource allocation, and production optimization.

Alan Turing, Donald Davies · 0 citations
Open access 2021

Hybrid AI and Digital Twin Architecture for Enterprise Commerce and Financial Transformation

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 is proposed.

Alan Bundy, Karen Spärck Jones · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.