Artificial intelligence as an effective tool for transforming modern socially responsible systems

Artificial intelligence (AI), and above all, large language models (LLM), are considered as an infrastructure for the transformation of modern socially responsible systems under triple pressure: the climate agenda, the underachievement of the Sustainable Development Goals (only 17% of indicators are on track), and accelerated digitalization. The importance of the global world's transition from an “information economy” to a “prediction and trust economy” is highlighted, where algorithms do not replace humans but enhance their cognitive, creative, and moral potential, significantly impacting the sustainability of socio-economic systems. The specific and distinctive features of AI have been identified, ensuring multi-level collection, normalization, and verification of ESG data, scenario analysis of “double materiality”, and increasing stakeholder trust in the context of regulatory consolidation. The article explores conceptual approaches to the content of AI, its impact on the corporate governance system, and the development of AI as an architectural foundation for responsible corporate governance focused on long-term value, ESG compliance, stakeholder engagement, and transparency. The article concludes that in the context of current changes in global governance systems, the maturity of AI is rapidly proressing, and AI technologies are proposed for managing key performance indicators (KPIs). It is noted that AI technologies are a powerful catalyst for the formation of new growth models – not linear, but sustainable, not only effective, but also ethically sound, not detachedly automated, but deeply integrated into the human and social context. The author's interpretation of the methodological approaches to the formation of a sovereign Russian AI platform with data localization and integration with national registries is presented. AI is not a “superstructure” but a supporting structure for responsible development, reducing transaction costs and accelerating the decision-making cycle in corporate and government practices.