The presentation titled "Acceleration of Decision Making in Development and Operations" focused on the integration and impact of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the efficiency of engineering processes. The key theme revolved around how AI and ML technologies can significantly speed up decision-making in the development and operational phases of engineering projects. The presenters, Daniel Berger, Dirk Hartmann, Justin Hodges, Kai Liu, and Simona Ottaiano, emphasized the importance of AI and ML in boosting engineering decisions by accelerating predictions and improving user efficiency. They highlighted several major use cases where AI and ML can be particularly effective, including user support, task automation, and generative capabilities. A significant portion of the presentation was dedicated to demonstrating how these technologies can facilitate the acceleration of simulation and optimization processes, as well as enable the sharing and reuse of simulation data. A proof of concept (PoC) was showcased, featuring an ML-accelerated Computational Fluid Dynamics (CFD) simulation in a channel with an obstacle. This example illustrated how AI and ML could provide faster insights into complex data arising from detailed numerical simulations during virtual product development. The presentation also covered the development and application of surrogate models in system-level simulations, such as in airbag inflation and operational digital twins. These models are designed to offer online predictions based on offline data generation, with a focus on achieving high accuracy and speed. Furthermore, the team discussed the ML-augmented Navier Stokes Solver, highlighting its performance and efficiency improvements, particularly in 3D simulations. This solver is an example of how ML can enhance traditional simulation methods, leading to faster and more accurate results. The session concluded with a discussion on the use of AI for improving the user experience in engineering simulations, encompassing aspects like multi-modal classification, faster pre-processing, smart search, recommendation systems, conversational user interfaces, and knowledge graphs.