Theoretical foundations of the closing impact of artificial intelligence on regional industrial systems

Abstract

The article examines the closing effect of artificial intelligence at the meso-level of the economy, represented by regional industrial systems. The relevance of the study stems from the fact that the impact of AI on industrial development is usually analysed either at the level of individual enterprises or through its macroeconomic consequences, whereas the mechanisms through which enterprise-level technological changes are transformed into systemic effects remain insufficiently explored. The purpose of the article is to identify the specific features of the formation of the closing effect of AI technologies at industrial enterprises and to determine the mechanisms of its extension to regional industrial systems.

The closing effect is interpreted as the achievement of the limit of production-process efficiency available within an established system of production relations without changing its basic configuration. Unlike a disruptive effect, which transforms the structure and principles of a system, a closing effect strengthens the ability of the existing system to reproduce itself by exhausting the available potential for improvement. At the enterprise level, this effect may arise through the improvement of existing products and processes, the more effective use of accumulated competencies, the automation and augmentation of current tasks, and the development of new links between the existing functional elements of an enterprise.

Three AI technologies with closing potential are identified. Predictive analytics of equipment condition makes it possible to anticipate failures, optimise maintenance schedules and increase the stability of production rhythms. Intelligent production planning aligns production tasks with available resources, equipment capacity and changing operating conditions. Intelligent optimisation of energy consumption coordinates production needs with actual energy-use patterns and reduces losses associated with inefficient or peak consumption. In each case, the technology closes a specific enterprise-level niche for improvement while preserving the existing organisation of production.

The study demonstrates that the closing effect does not automatically extend from individual enterprises to regional industrial systems. Without integration, enterprise-level AI applications generate only conventional positive economic effects at higher levels, such as improved production efficiency or aggregate productivity. A meso-level closing effect emerges only when local technological solutions are integrated into a broader network of production relations. Predictive analytics can coordinate maintenance cycles and compensatory actions among technologically connected enterprises. Intelligent production planning can synchronise supply, production and processing schedules. Energy optimisation can coordinate the aggregate energy-consumption profile of an industrial system and reduce peak loads on shared infrastructure.

The speed and scale of this integration depend on the ownership structure and the coordination mechanisms within the industrial system. A unified management centre can establish common technological standards and make the results of integration more predictable, thereby increasing investment attractiveness. Where enterprises belong to independent owners, horizontal integration requires institutional guarantees that partners will fulfil their obligations. The key conclusion is that the closing effect spreads from the bottom up, from the enterprise to the meso- and subsequently macro-level, and therefore requires systemic incentives for cooperation among economic actors in integrating AI solutions into broader production networks.