In the face of emergent external factors (e.g., supply chain disruptions or public health crises like COVID-19), businesses must adapt their business model quickly in order to ensure service continuity. However, providing recommendations regarding changes should be made to the business model is a challenging problem. First, it requires details of interactions between different components of the business (e.g., service offerings, inventory, staffing, demand) to understand what possible courses of action will have the most business impact. Second, automated models may provide recommendations on changes required in the business operations. However, with lack of human insight, it will be hard to verify the feasibility of these recommendations. Third, a generic model may not be able to provide good recommendations for diverse set of business models. Fourth, the model may not have enough features or training data to provide good recommendations.In this paper, we propose a novel approach to provide actionable items that can be recommended to business users given their business features and recommendations given to businesses in similar domain. Here we first use clustering to find the business domain and similar feature set of the domain. Then, we build a machine-learning model with explainable insights to provide recommendations on different business actions that can be taken to ensure business operations in the face of emergent external factors. Next we augment our approach with human-in-the-loop to improve its performance. Finally, we federate the machine-learning model in a similar domain to add more explainable and trusted insights and recommendations by other businesses. We describe our method, illustrate its utility with results from our implementation, and discuss areas for future work.