To decide long-term strategic policies, there is a need in large organisations to predict how these policies may play out in varied hypothetical futures. AI techniques can greatly help with this by accounting for diverse interconnected characteristics of an organisation in forecasting, but those using AI software need to be able to trust the outputs to apply them in their decision-making. _Transparency_, the ability to see an explanation of how an AI's output was arrived at, is vital to this trust, as without knowing the reasoning an AI performed was justified and complete, it is unwise to base a critical decision upon its conclusions. Artificial intelligence can be classified into data-driven and model-based approaches, where in data-driven techniques such as machine learning the AI's reasoning comes from a model derived automatically from processing data while in model-based AI that model is created from human expert knowledge and concepts. These classes of technique are applicable to different problems, and transparency is one inherent advantage of model-based AI. Within model-based AI, there are two kinds of transparency: _step transparency_, being able to explain each step in reasoning towards a prediction, and _emergence transparency_, being able to explain the predicted outcomes given a vast number of interdependent steps. While existing systems offer step transparency through traceability, visualisation, etc., emergence transparency is a largely unaddressed problem. For Aerogility, the lack of emergence transparency in commercial AI technology limits the business problems to which it can apply, both in aerospace, where most of our current clients operate, and in new sectors. In this project, we will conduct a feasibility study on research approaches which may, in complement, provide emergence transparency and construct a consortium to provide the full set of skills required to implement and evaluate the solution.