When creating real-world machine learning applications, system developers have to deal with the challenges of a dynamic environment where conditions change frequently, data has uncertainty, and new unanticipated situations are encountered. This requires a flexible approach in deciding how to use, adapt and create an AI model. Ensemble learning, where multiple models are trained, and use concurrently provides one way to address some of the issues. However, ensembles as used within the AI literature have primarily focused on creating a better model in a static environment. If we couple ensemble models with the concept of policy based control, we can create a system that is able to deal better with real-world scenarios. If we further augment the system so that it can generate its own policies, we can make progress towards the goal of a broad AI which can dynamically adapt itself. In this paper, we present an architecture for policy based ensemble, and show how it can lead to an approach towards broad AI.