The automotive subframe, also referred to as a cradle, is a critical chassis structure that supports the engine/electric motor, transmission system, and suspension components. The design of a subframe requires specialized expertise and a thorough evaluation of performance, vehicle integration, mass, and manufacturability. Suspension attachments on the subframe are integral, linking the subframe to the wheels via suspension links, thus demanding high performance standards. The complexity of subframe design constraints presents considerable challenges in developing optimal concepts within compressed timelines. With the automotive industry shifting towards electric vehicles, development cycles have shortened significantly, necessitating the exploration of innovative methods to accelerate the design process. Consequently, AI-driven design tools have gained traction. This study introduces a novel AI model capable of swiftly redesigning subframe concepts based on user-defined raw concepts. By leveraging design data from previous subframe projects, this model enhances the manufacturability and performance of user input designs by integrating validated features from past concepts. The implementation of this AI model results in significant reductions in design development time, thereby improving efficiency. Additionally, this paper provides a detailed analysis of the time and cost savings achieved through the adoption of this AI model throughout the design development phase.