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As we are approaching the high-luminosity era of the LHC, the computational requirements of the ATLAS experiment are expected to increase significantly in the coming years. Notably, simulation of Monte Carlo (MC) events is immensely computationally demanding, and their limited availability is one of the major sources of systematic uncertainties in many physics analyses. The main bottleneck in detector simulation is the detailed simulation of electromagnetic and hadronic showers in the ATLAS calorimeter system using Geant4. To increase MC statistics and to leverage the available CPU resources for LHC Run 3, the ATLAS Collaboration has recently put into production a refined and significantly improved version of its state-of-the-art fast simulation tool AtlFast3. AtlFast3 uses classical parametric and machine learning-based approaches such as Generative Adversarial Networks (GANs) for fast simulation of LHC events in the ATLAS detector. This work presents the newly improved version of AtlFast3 that is currently in production for simulation of Run 3 samples. In addition, ideas and plans for the future of fast simulation in ATLAS are also discussed.