Intelligent Steering Triplet: Towards Proactive Management Through AI

Dorra Dridi, Younes Boujelbene

Organizational performance management traditionally relies on the Objective–Indicator–Action Lever triplet, which structures the translation of strategic goals into operational decisions. However, in increasingly complex and uncertain environments, this classical framework exhibits significant limitations, including low adaptability, reactive decision-making, and limited ability to anticipate disruptions. To address these challenges, this paper proposes an intelligent management triplet that integrates Artificial Intelligence (AI) into the traditional framework. The proposed approach enhances the classical loop by enabling the prediction of performance indicators, the optimization of action levers, and the dynamic adjustment of objectives. As a result, the system evolves into a self-learning and adaptive framework capable of real-time decision support. The contribution of this research lies in transforming a descriptive and reactive management model into a predictive, proactive, and datadriven system. An illustrative application in hospital inventory management is provided to demonstrate the operational relevance of the proposed approach in complex environments characterized by demand variability. The results highlight the potential of the intelligent triplet to improve responsiveness, decision quality, and overall performance, while supporting flexible and efficient management.