Enhancing decision-making with data-driven insights in critical situations: impact and implications of AI-powered predictive solutions

Identifying early signals of crisis or insolvency is essential, for firms, to enable timely interventions, preserve financial health, and sustain competitive advantage. Traditional financial models, while foundational in this domain, often fall short in complex environments and there is a widening gap between the theoretical advancements in predictive modeling and their practical application in real-world decision-making, particularly for small and medium-sized enterprises, that may lack the resources to implement sophisticated risk management tools. Considering the Italian business landscape, this research examines the integration of advanced AI-driven techniques, showcasing their ability to handle vast and diverse data sources while improving predictive accuracy. By employing an inductive, multi-layered approach to model implementation and refinement, the study demonstrates how AI-based models can identify early indicators of distress up to five years before insolvency, thus providing a robust framework for preventive strategies. A key added value is the focus on enhancing explainability and understandability within AI models, delivering critical insights for both academia and managers; by improving transparency, these models not only strengthen predictive capacity but also enable stakeholders to better understand and interpret the drivers of financial and economic distress. Furthermore, the study highlights the challenges of adopting new technologies in organizational contexts, addressing issues such as data ethics, managerial literacy, over-reliance risks and the alignment of AI-based decision tools with regulatory standards. The findings contribute both to academic discourse and practical applications by advocating for a cultural shift toward data-driven decision-making in critical phases of business life; by balancing the precision of Machine Learning with the interpretability required for managerial planning, it is underscored that, while AI just complements human expertise, it is, today, instrumental in surviving in complex economic landscapes with greater foresight and resilience.