Use of AI for the creation of new efficiency metrics and their impact on managerial decision-making

Abstract

Abstract: This article examines the problem of the metric gap that arises in modern organizations due to the use of outdated linear KPIs, which fail to reflect the speed and complexity of the digital economy. The relevance of the study is substantiated by the results of a global survey, which show that 60% of executives consider their current metrics inapplicable to contemporary realities, yet only one-third of companies implement AI solutions for their renewal. This study aims to examine ways for creating new, clever, effective measures from artificial intelligence formulas and to judge their effect on the management's decision-making. The newness of the research rests in the offered sorting of smart signs into explaining, predicting, and advising kinds as well as in the making of a five-loop rule for joining AI-based measures into business while keeping openness and responsibility. The methodological foundation relies on the synthesis of quantitative data from a global survey, results of McKinsey industry studies, a systematic review of theoretical approaches, and content analysis of practical cases from Wayfair, Siemens, and ESG initiatives. The main findings demonstrate that algorithmic reconceptualization of metrics through data mining, process mining, and digital twins transforms indicators from lagging measurements into active predictive mechanisms, resulting on average in a threefold increase in EBIT and a reduction of the reaction window by at least one quarter. Simultaneously, the greatly heightened complexity of the system demands very tight explainability procedures (SHAP/LIME), cross-functional governance, and a manual rollback risk red button. The suggested five-loop framework includes a cross-functional KPI committee, an AI-Governance maturity matrix, double certification of metrics, pilot-to-rollout via digital twins, and integration of ESG loops. The article will be useful for executives, analysts, and developers of corporate AI solutions engaged in optimizing indicator systems and improving the quality of managerial decisions.