In the context of increasing market volatility and the digital transformation of business processes, the problem of the time lag between the implementation of strategic initiatives and the acquisition of an evidence-based assessment of their effectiveness has become particularly relevant. This article is devoted to the development and theoretical substantiation of a conceptual scheme for applying the surrogate index as a systematic scientific and practical approach for making operational corporate decisions. The authors identify a fundamental contradiction between the long-term nature of modern strategies and the retrospective nature of classical KPI systems, which often leads to the emergence of the "surrogate paradox". The proposed approach is based on the methodology of causal inference and the use of directed acyclic graphs to visualize the channels of managerial effect transmission. The primary result of the study is the formalization of a two-level management architecture, "Surrogate – Strategy, " divided into two interconnected loops. The Learning Loop is based on the analysis of historical data to construct a surrogate function h (S), while the Execution Loop allows for the predictive evaluation of effects in current experiments without waiting for long-term outcomes. To improve forecasting accuracy, the use of the Latent Surrogate Representation method based on variational autoencoders is proposed, which allows for accounting for hidden determinants of success and neutralizing the influence of "noisy" data. It is proven that the application of the developed approach contributes in the management feedback cycle while maintaining an accuracy of up to 95%. The paper presents a classifier of applied tasks and highlights key domains of corporate management (HR, marketing, finance, ESG) where the implementation of the surrogate index ensures a transition to evidence-based mathematical strategy modeling.
Shapurov et al. (2026) studied this question.