INTELLECTUALIZATION AND AI-ORIENTED TRANSFORMATION OF ACCOUNTING AND ANALYTICAL DATA IN SOCIO-ECONOMIC SYSTEMS OF ENTERPRISES
DOI:
https://doi.org/10.31359/2312.394X.2026.39.1.113Keywords:
accounting, analytical data, artificial intelligence, intellectualization of management, information support, socio-economic system of enterprise, digital transformationAbstract
The article investigates the theoretical and methodological foundations of intellectualization and AI-oriented transformation of accounting and analytical data within socio-economic systems of enterprises. It substantiates the necessity of transitioning from traditional accounting and information management models toward AI-oriented data architectures capable of ensuring the seamless integration, adaptive processing, interpretation, and predictive use of enterprise data. The study emphasizes the strategic role of accounting and analytical data as a cognitive foundation for intelligent systems, highlighting how advanced analytical algorithms, machine learning algorithms, and automated pattern recognition mechanisms enhance the decision-making capabilities of management while simultaneously increasing the transparency, reliability, and predictive value of internal information flows.
The research conceptualizes AI-oriented transformation as a multidimensional process that restructures conventional stages of data handling – including collection, processing, aggregation, and analytical reporting – into iterative, self-optimizing cycles that facilitate real-time plan-fact analysis, proactive risk assessment, and anticipatory management of socio-economic processes. Particular attention is given to the synergistic interaction between expert human knowledge and machine learning models, which allows for the development of integrated dashboards and predictive indicators that monitor enterprise performance, forecast demand fluctuations, evaluate financial sustainability, and assess reputational and ESG-related risks.
In addition, the article explores the architecture of AI-enabled internal audit modules as critical components of contemporary control systems in enterprises, illustrating how continuous auditing mechanisms, anomaly detection algorithms, and automated compliance checks strengthen internal governance, mitigate operational and financial risks, and enhance strategic oversight. The study also demonstrates the transformative potential of AI in extending the analytical scope of enterprise accounting, enabling the processing of both structured and unstructured data, and supporting iterative refinement of analytical procedures through predictive modeling and scenario analysis.
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