Automotive CAD-CAx loops, or more precisely CAD-CAE loops, have a long tradition in automotive development and engineering processes. During the entire development process of a complete vehicle, several of these loops have to be performed in the course of usually three to seven main cycles. These CAD-CAE loops are necessary to meet safety, durability, and optimization requirements in various areas, such as vehicle safety, aerodynamics, layout, and packaging. But this comes with a huge demand of resources and needs much time to be performed. On the other hand, decades of automotive development and design processes have shown that the number of these CAD-CAE optimization loops can hardly be reduced. There is enormous pressure on automotive manufacturers and suppliers to speed up automotive development and engineering processes in order to increase competitiveness. Since the options to reduce the number of CAD-CAE loops are limited, process optimization requires new approaches. In this context, this research article introduces a novel approach of enhanced virtual products (EVPs) supporting efficient and sustainable development and design processes in the automotive industry. In order to optimize the development of automotive products and to accelerate the development and further engineering processes, the focus is placed on the early stages of automotive development. Especially in the early phases of automotive development processes, there is enormous potential to implement efficient and sustainable optimization measures through timely and targeted interventions, thus accelerating the processes and minimizing resources at the same time. For this purpose, a combination of knowledge-based engineering (KBE) / knowledge-based design (KBD) and artificial intelligence (AI) methods is applied to reduce the number of parameters in vehicle conception and design stages. Several thousand of these parameters are necessary to completely define a car. This expands the solution space, i.e., the number of combinations of how the parameters are combined, to an almost infinite number of possibilities. Reducing parameters narrows the solution space to a countable number of solutions. This reduces the number of CAD-CAE optimization loops while increasing product quality even in early stages of development.