As artificial intelligence (AI) technology enters the era of machine learning, technological and industrial innovation has been significantly stimulated. The rapid development of machine learning has overcome the limitations of traditional rule-driven systems, enabling AI’s evolvement into a more flexible and intelligent stage. Among these developments, AI-generated content (AIGC) marks a new phase in the efficient transformation of text, image, audio, and video modalities. AIGC reduces the barriers to creative production and simplifies learning processes, thus empowering users’ quick adoption of tools for efficient content generation with ease. In the design industry, AIGC has not only subtly transformed the designers’ creative medium but also promoted the iteration of the logic and pathways of creative generation. However, while AIGC provides designers with an efficient tool, its role in the design process remains uncertain. Excessive human intervention and uncertainty regarding AIGC-generated results often lead to misunderstandings of its role in design practice. In response, this paper examines the role of ALGC in the design process, particularly its impact on the reconstruction of creative pathways, with a focus on the driving role of data in creative generation. Data plays a crucial role throughout the entire design lifecycle, from requirement gathering and creative iteration to implementation and optimization. Through case studies and heuristic strategies, this paper further explores the potential integration of AIGC with design systems. It suggests the potential for the gradual expansion of data-driven expression into the physical world in the future.