Shared leadership and ai-enabled contingency planning in Ukrainian firms during the 2022–2025 war

The full-scale invasion of Ukraine by the Russian Federation, which began in February 2022, caused acute and long-term disruptions in the activities of Ukrainian companies, forcing them to quickly rebuild their organizational structures, decision-making mechanisms, and planning practices. This article offers a conceptual and analytical explanation of how wartime conditions accelerated the transition from centralised management measures to cross-functional crisis teams, shared leadership mechanisms and emerging learning operating models. This study is based on secondary data analysis and illustrative case studies, rather than original empirical research. It combines business survey data, institutional and corporate reports, and academic literature. On this basis, the article outlines an organizational trajectory from centralization to distributed cross-functional decision-making and beyond to what this study defines as a learning operating model. The article also highlights that AI-enabled practices (including situational dashboards, scenario modeling, demand forecasting, and knowledge management) supported by generative AI can accelerate collective situational awareness in wartime. However, role overload and coordination burdens can also increase in the absence of clarity of purpose. Thus, shared leadership in the study is viewed as potentially “a dual-effect organizational mechanism”. The conclusions outline the implications for Ukrainian business practices and compare the identified trajectories of organizational response of Ukrainian companies with future constraints associated with the European model of AI governance. This paper addresses a gap in the literature by establishing a link among shared leadership, contingency planning, and AI-based situational analysis in the specific context of business resilience in wartime. And its practical contribution is to identify organizational conditions under which AI tools can support distributed decision making without increasing coordination overhead.