Methodology for Leveraging Artificial Intelligence to Formulate Problems of Discovering Novel Growth Models and Enabling Strategic Management of Complex Socio-Economic Systems

M. S. Varenik () and D. M. Zhuravlev ()

Administrative Consulting, 2026, issue 1

Abstract: Amid the convergence of technological, macroevolutionary, and demographic singularities, conventional approaches to analyzing and forecasting economic growth are increasingly losing relevance and predictive power. The surge in information overload underscores the urgent need to shift from reactive to proactive (preventive) governance — a transition that presents economic science with a fundamentally novel challenge: the timely and accurate identification of emerging trends and sustainable competitive advantages within massive, dynamic data streams.The core objective of this study is to formalize a practical framework for updating economic growth models in complex socio-economic systems. To achieve this, the research employs an interdisciplinary synthesis drawing on classical growth theory, institutional economics, strategic management (strategizing), complex systems theory, mathematical modeling, and cutting-edge advances in human — machine collaboration.The outcome is a universal strategic analysis methodology, structured around nine logically integrated stages: (1) Multi-level environmental scanning and trend forecasting; (2) OTSW analysis (Opportunities, Threats, Strengths, Weaknesses) as a strategic sense-making tool; (3) Systemic goal formulation; (4) Lifecycle-oriented process management of value chains; (5) Identification and prioritization of core strategic components; (6) Development of a digital analytical infrastructure leveraging big data; (7) Mathematical modeling of causal relationships and detection of critical inflection points; (8) Multi-agent AI-assisted interpretation and decision support; (9) Generation of a transformation roadmap via digital twin — based behavioral simulation.This methodology enables a decisive shift, from descriptive analysis to actionable strategy, from correlation to causation, and from rigid, static planning to dynamic, adaptive governance. Crucially, the proposed framework is not a mere technical supplement but a paradigm shift in strategic thinking: a collaborative partnership between humans and intelligent systems in co-designing the future. Its value lies in bridging critical divides: theory and practice, datadriven insights and contextual judgment, global-scale challenges and implementable solutions.Potential applications span national, regional, and corporate governance, with particular emphasis on enhancing institutional capacities in education and research.

Date: 2026
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