The demand for wrought aluminum alloysWrought aluminum alloys (WAAs) with a high proportion of post-consumed scrapPost-consumed scrap (PCS) is steadily increasing due to their characteristic of sustainabilitySustainability. However, due to the differences in composition between primary aluminumAluminum and PCS, the design and production of these alloys dictate certain changes to the chemical composition and process parameters. In this research we wanted to answer the question of whether, by increasing the permissible concentration limits of selected alloying elements and optimizing the process parameters, we could achieve the target set of WAA properties. We used artificial intelligenceArtificial intelligence (IWM-IBM Watson Metallurgy) to design the alloy composition and the process parameters. We experimented with selected alloys of the 6xxx and 2xxx series in such a way as to compare the predicted and actually achieved mechanical propertiesMechanical properties and formability. We found a very good match (better than 90%), which is a promising starting point for the further development of such alloys. With the help of AI, we have (i) raised the upper limits of the permitted concentrations of some alloying elements and impuritiesImpurities, adapted to the high proportion (more than 80%) of PCS in the melting mixture, and (ii) changed some parameters of the process path (ultrasoundUltrasound-treatment melting, castingCasting and homogenizationHomogenization of bars, and extrusionExtrusion and heat treatmentHeat treatment of bars) in order to achieve the desired set of properties and the targeted productivityProductivity of the production process.