For maximizing the benefit of today’s ISR (Intelligence, Surveillance and Reconnaissance) systems, an improved collection planning is essential. In our paper we present an approach to apply artificial intelligence and machine learning in support of collection planning tasks. One subtask in collection planning requires matchmaking between ISR-resources (further referred as assets, combining sensors and their corresponding carriers) and collection requirements, taking additional operational constraints (for example mission risk) into account. This subtask requires high competence in assessment of asset capabilities in relation to collection requirements taking actual and future operational constraints into account and is mostly conducted in a time sensitive environment. We derive a general model of our matchmaking problem. This model serves, in combination with existing requirements derived from the operational domain, as input for the analysis of artificial intelligence and machine learning methods to work out their fundamental suitability and adaptability for our model. This subset will be further analyzed for its pros and cons, if only few operational data is available and the evolving knowledge of the use of resources during mission operation has to be taken into account.