Macromolecular assemblies underpin essential cellular processes, yet their structural characterization remains challenging. Integrative modeling provides an approach for determining structures of macromolecular assemblies, combining diverse experimental data with physical principles, the statistics of previous structures, and prior models. There is a growing interest in leveraging the implicit structural knowledge learned by artificial intelligence-based structure-prediction methods such as AlphaFold (AF), for integrative modeling. Here, we discuss recent methods that combine AF with experimental data for integrative modeling in four ways: validating AF-based ensembles with experimental data; combining structural priors from AF with experimental data; fine-tuning AF with experimental data; and incorporating experimental data at inference time. We also outline key challenges in integrative structure determination using AF.