Machine learning (ML) and artificial intelligence (AI) are redefining constitutive modeling by enabling efficient surrogates for complex material behaviors. In this work, we present a fully integrated ML-based framework embedded within MSC.Marc via Digimat, validated on industrial-scale use cases such as open-hole coupon tests and crash box crushing. This integration ensures seamless deployment in commercial CAE workflows, bridging the gap between research and practical engineering applications. Our approach leverages synthetic data generated through computational micromechanics simulations to train recurrent neural networks (RNNs) for predicting stress–strain responses under diverse loading paths and fiber volume fractions. We explored several architectures, including MLP and advanced RNN models such as GRU and LSTM. Key innovations include adaptive learning strategy, architecture optimization, two-step training strategy and data augmentation which all lead to a robust surrogate model. We have further investigated transfer learning strategy leveraging fast Mean-field simulations to train a pretrained model which is then finetuned on a small set of high-fidelity full-field data. The trained models exhibit strong generalization beyond the training range. They deliver robust performance in implicit FE simulations without convergence issues for a wide range of constitutive models and microstructure types. By replacing costly meso-scale resolutions with AI surrogates, this methodology offers a realistic route to achieving FE² or FE-FFT multiscale simulations at affordable computational costs - a long-standing challenge in computational mechanics. Furthermore, ongoing developments address time-step sensitivity for explicit analyses through advanced recurrent units such as SC-MRU, paving the way for accurate, history-dependent modeling in dynamic scenarios. These results demonstrate that ML-driven constitutive modeling is not only feasible but ready for industrial adoption, unlocking unprecedented efficiency for multiscale simulations.