This paper shows how sensitivity analysis can be used as part of model verification and validation Sensitivity analysis provides insights on where future data validation processes should focus and which inputs may be considered for model reduction. We compared two approaches, one using a systematic variation of parameter values, another using an optimised algorithm to make more efficient the search of their space. Analysis was conducted on an agent-based model that explores the emergence of innovation within business networks, where successful innovation is considered an increase in knowledge and financial resources within the network. The two sensitivity analysis approaches differed both on their time efficiency and on the type of information provided. While the systematic individual sensitivity analysis assisted us in identifying inputs with substantial impact upon the results and suggest solutions for model simplification, the optimised search provided insights on the network resources likely to achieve higher levels of innovation. Genetic algorithms found parameter values that produced different results in the agent-based model. © 2014 Australian and New Zealand Marketing Academy.