Finite element analysis has often relied on superelements, or substructures, to make the analysis of large, complex models computationally tractable. Alternately, superelements have been used as a way to finalize part representations prior to being included in larger assembly models. However, this approach can lead to a reduction in design flexibility and make it difficult to meet systems-level requirements. One of the barriers to effective system or assembly analysis is the communication overhead involved when incorporating models from part owners, which may be specialists in a particular part design or analysis methodology. The assembly analyst then has limited ability to make independent decisions and must include the part owners in any modifications. In this work, we propose the Smart Superelement (SSE) workflow. The SSE workflow is enabled by machine learning techniques to overcomes these barriers by allowing specialists to package their investigations and knowledge into a usable format for downstream analysts. The specialist can embed predetermined parameter variations into the superelement, which the assembly analyst can adjust at solution time without requiring specialist expertise. This workflow captures the institutional knowledge of the specialist and prior what-if studies, allowing for greater flexibility in meeting systems-level requirements. Specifically, an interface leveraging the Functional Mock-Up Interface standard has been developed to allow Functional Mock-Up Units (FMUs) to be consumed as superelements in finite element analysis with complementary tools developed for packing machine learning models and reduced order models into FMUs. The use of machine learning techniques in this workflow can enhance collaboration between specialists and assembly analysts by making it easier to share models and conduct assembly level what-if studies. This results in a more efficient and effective process for analyzing complex systems. In this work, we will demonstrate an end-to-end workflow example that illustrates the benefits of using this approach.