How to Cite:
Muthu Selvam, "Product-Centric AI and Operational Technology Integration for Intelligent Enterprise Decision-Making" International Journal of Computational Science, Technology and Management, Vol. 1, No. 1, pp. 1-6, 2026.

Abstract:
The convergence of artificial intelligence (AI), product-centric digital architectures, and operational technology (OT) is transforming the way enterprises generate, interpret, and execute business decisions. Traditional enterprise decision-making has generally separated product management, information technology (IT), industrial operations, and business intelligence, resulting in fragmented data, delayed feedback, and limited visibility across the product lifecycle. Product-centric AI provides an alternative paradigm in which products, their associated data, operational states, customer interactions, and lifecycle events become continuous sources of intelligence. When integrated with OT environments such as industrial control systems, sensors, programmable logic controllers, robotics, manufacturing execution systems, and edge platforms, AI can establish a closed decision loop connecting physical operations with enterprise-level strategic objectives. This research proposes a conceptual framework for Product-Centric AI and OT Integration (PCAOTI) that combines product intelligence, industrial telemetry, edge analytics, enterprise AI, digital twins, and human-supervised decision orchestration. The methodology adopts a design-oriented conceptual research approach involving architecture development, capability mapping, comparative analysis, and evaluation against decision latency, operational efficiency, predictive accuracy, scalability, resilience, and governance dimensions. The proposed framework demonstrates how OT data can be transformed into actionable product and business intelligence while maintaining operational safety and cybersecurity boundaries. The analysis indicates that integrating product-centric AI with OT can improve real-time decision responsiveness, predictive maintenance, production optimization, quality management, and lifecycle intelligence. However, interoperability, legacy-system integration, data governance, cybersecurity, explainability, and human oversight remain critical research challenges. The study contributes a unified architecture for connecting product intelligence with physical operations and establishes a foundation for intelligent enterprise decision-making across industrial and digitally enabled organizations.

Keywords: Product-Centric AI, Operational Technology, Intelligent Decision-Making, Industrial AI, Digital Twins, Edge Computing, Enterprise Intelligence, Predictive Analytics, IT/OT Convergence, Industry 4.0.

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