The lack of systematic organizational development (OD) methodologies and their limited application led to low productivity. Combining “systemic development” methods could help managers improve their management and leadership skills. Unfortunately, two created systematic development practices have spread in North America (C. Argyris and D. Schön's Harvard Business School practice “Action Science”) and developed quite different operational performance instruments in Scandinavia. Therefore, there is a need for research on effective OD methods that can unify the measures, considering the developed management context. This research aims to develop a set of digital indicators for the evaluation of OD at enterprises using artificial neural networks. Quantitative estimation of indicators is planned to be carried out using Artificial Neural Network (ANN) system tools, which will more effectively replace previously widely used decision support systems based on comparative analysis. The competence of leadership and management of human resources in organizations contributes to the resilience capability that helps rapidly adapt to continuously changing business environments and maintain the competition in markets. The research will identify clusters of indicators and the most significant groups of indicators, based on which OD evaluation model will be created. The set of indicators will be digitalized for usage in artificial neural networks. This research presents the determination of clusters of digital quantitative parameters in known OD methodologies to evaluate OD and increase the productivity of organizations. Digital evaluation requires modern AI methods. An innovative intersection for classifying sets of indicators of OD methodologies is presented. Research results will provide leaders with digital indicators to evaluate OD and contribute to building leadership capacity in any country.