From Process Digital Twins to Portfolio and Network Intelligence: Enterprise-Level Knowledge Infrastructure Driving Decision Intelligence

Artificial intelligence has widely optimised material screening, reaction tuning and unit-level digital twins in chemical and energy industries, facilitating clean processes. Yet existing tools fail to support high-level strategic decisions on product portfolios and cross-site production networks. Fragmented cross-departmental data and static planning models delay agile responses to volatile feedstock, demand and logistics conditions. Drawing on knowledge-graph process twin frameworks, this commentary argues industrial artificial intelligence should evolve from isolated process optimisation to enterprise-wide portfolio-network intelligence. Machine-readable unified knowledge bases integrating products, assets, supply chains and carbon constraints underpin scalable business digital twins, enabling consistent, traceable cross-functional optimisation. Such infrastructure empowers artificial intelligence to evaluate technical feasibility alongside profitability, asset utilisation and carbon performance under uncertainty, guiding optimal product selection and site allocation. This paper concludes that structured corporate knowledge infrastructure, rather than standalone algorithms, constitutes the long-term foundation for artificial intelligence-driven holistic business decision-making across chemical value chains.