Despite rising turf maintenance challenges, related to e.g. climate change, fungicide and pesticide restrictions and cost inflation, turf maintenance is to a large extent a manual and non-optimised process. Turf care optimisation is complex and multivariate, i.e. it depends on multiple and co-varying parameters, including maintenance, soil properties and environmental parameters. Hence, optimisation of turf maintenance using domain knowledge and physical experiments alone is unfeasible. A digital and data-driven approach, powered by AI will be evaluated for identification of sustainability and playability optimal turf maintenance prescription in the endless space of possible maintenance configuration combinations. AI modelling will be applied for simulation of future turf properties, including playability and stress metrics, as a function of current turf state, a maintenance scheme and environmental data. Since continuous data collection is resource-consuming, the project aims to identify the data type/s and data volume required to make accurate predictions and to assess whether AI-generated turf maintenance prescription is viable.