A Conceptual Framework for Market Risk Assessment with AI-Driven CRM and BI in Digital Ecosystems

Digital ecosystems are reshaping how organizations collect, analyze and use data to support decision-making in volatile and uncertain markets. In this context, organizations are using a wide variety of digital tools to observe customer behavior, identify changes in the market and to predict potential risks. However, Customer Relationship Management (CRM), Business Intelligence (BI) and Artificial Intelligence (AI) are often implemented and used as separate solutions, although their combined use may offer a more coherent basis for market risk assessment. This paper examines how suitable AI-driven Customer Relationship Management and Business Intelligence tools are in the context of market risk assessment in digital ecosystems. The study is qualitative and conceptual. It is based on a structured analysis of the existing literature on digital ecosystems, customer relationship management, business intelligence and artificial intelligence. Based on this analysis, the paper proposes three conceptual research propositions and proposes an integrated conceptual framework that shows the interaction of these tools in order to support early risk detection, analytical insight and predictive decision-making. The results of the study are conceptual and highlight the complementary roles of the three tools. Customer Relationship Management helps in the collection and organization of customer-related data, Business Intelligence allows analytical processing and visualization of the information, while Artificial Intelligence improves these processes through prediction and pattern recognition. The proposed framework shows how the integration of these tools within a digital ecosystem can improve information connectivity, reduce uncertainty and support proactive market risk assessment. The paper contributes to the literature on digital ecosystems by providing an integrative view on the role of digital analytics tools in market risk assessment. From a practical and managerial perspective, the proposed framework may help organizations and decision-makers structure their digital analytics capabilities to identify early warnings signals, anticipate market developments and respond more effectively to uncertainty.