The steel industry faces major challenges in the upcoming years. On the one hand, a digital transformation is absolutely imperative to remain competitive and, on the other hand, processes must be optimized and altered to reduce CO2 emissions. Scrap plays a significant role in the transformation to green steel and the Electric Arc Furnace (EAF) route. EAF steelmaking is critical to the European steel industry, and utilizing the scrap for EAF is key to achieving high efficiency and CO2 emission goals while retaining high-quality steel production. The use of modern digital technologies and Artificial Intelligence (AI) is inevitable to make optimal use of scrap. AI has shown significant advances in a wide palette of fields, and can help optimize the scrap process through e.g. automatized classification, confusion checks, heightened process control etc. Moreover, a digitalization of the scrap process can help reduce overall costs, increase processing speed and be a foundation for the application of more advanced AI models for process optimization. With AIScraPE, we have created opportunities to automate and optimize the process of scrap procurement, management and use. First, we use AI to classify and detect the different types of scrap to automate the mix-up check. The work in this direction has proved that AIScraPE in industrial setting is viable. The prediction power of newly developed AI systems are dependent on the quality of datasets used for training. We use technologies to significantly reduce the labeling effort of the images and finally use these results to improve the use of scrap. Moreover AIScraPE shows the possibilities for the use of AI technologies in the transformation process towards green steel and offers options for sustainable scrap usage.