The presentation by Daniel Berger, Dirk Hartmann, Justin Hodges, Kai Liu, and Simona Ottaiano, titled "Acceleration of Decision-Making in Development and Operation," highlighted the integration of Artificial Intelligence (AI) and Machine Learning (ML) in engineering simulations to expedite decision-making processes. The talk focused on how AI and ML technologies enhance engineering decisions by accelerating predictions and improving user efficiency. The major use cases discussed included user support, task automation, generative capabilities, acceleration of simulation & optimization, and the sharing & reuse of simulation data. A proof of concept (PoC) was demonstrated through a machine learning-accelerated Computational Fluid Dynamics (CFD) flow simulation in a channel with an obstacle. This PoC emphasized the visualization of turbulent structures and the implementation of high-end CFD simulation for a car. The presentation outlined the advantages of surrogate modeling, which allows for online predictions based on offline data generation/preparation, albeit with limited generalization capability. This was contrasted with traditional simulation solvers that rely on physics equations and offer full generalizability. Key applications discussed included airbag inflation simulation, executable digital twins in operation, surrogate-driven decision making, cabinet configurator at commissioning, and ROM building using game engines. The team from Siemens also discussed the concept and results of surrogate modeling in system-level simulation, particularly in the context of heat exchangers, highlighting significant CPU speed-ups and high fidelity in training and validation. The presentation also covered the concept of a Machine Learning-augmented Navier-Stokes Solver as an early proof of concept, focusing on its ability to provide fast, high-resolution results on low-resolution meshes and its potential for massive speed-ups, especially in 3D simulations. The session concluded with a discussion on using ML to improve user experience, highlighting faster pre-processing, smart search, recommendation systems, and the integration of conversational UI and knowledge graphs. The concept of a ChatGPT-based chatbot for modeling and simulation, aimed at democratizing simulation for customers, was also introduced.