The rapid growth of AI-generated content (AIGC) has dramatically reshaped digital marketing and online consumer behavior, offering marketers and businesses worldwide both unique opportunities and new challenges. As organizations increasingly utilize AIGC to engage audiences, it becomes crucial to accurately forecast content diffusion paths and evaluate market impact. This is a complex task due to the diverse nature of data sources, the non-linear ways in which content spreads, and the continuously changing dynamics of consumer interactions. Addressing these challenges, a recent study presents an innovative AI-driven Decision Support System (DSS) aimed at enabling better-informed marketing decisions by effectively predicting the dissemination and influence of AIGC. The system assimilates various data streams from multiple platforms—such as social media activity, marketing spend records, consumer engagement logs, and real-time sentiment trends—to thoroughly capture the diverse factors influencing content performance within the digital environment. At the core of this approach is a sophisticated hybrid framework that merges Graph Neural Networks (GNN) with Temporal Transformer architectures. This dual-channel model learns both the structural diffusion of content across interconnected user networks and the temporal progression of influence over time. By integrating these perspectives, the system accurately models the complex, time-sensitive propagation patterns typical of AIGC across different digital platforms. Moreover, the system includes causal inference modules that disentangle the complex effects of marketing actions on key performance indicators such as return on investment (ROI) and market visibility.

These modules allow marketers to distinguish the direct and indirect impacts of advertising efforts, improving prediction interpretability and yielding actionable insights. Extensive experimentation using large-scale, real-world datasets sourced from major online platforms—including Twitter, TikTok, and YouTube advertising campaigns—demonstrates that this system outperforms existing baseline models across six evaluation metrics. This marked improvement highlights the benefits of integrating heterogeneous data and applying advanced AI techniques to understand and anticipate AIGC diffusion. By providing real-time, interpretable insights into how AI-generated content spreads and shapes market trends, the proposed Decision Support System empowers marketing professionals to make data-driven decisions with increased confidence and accuracy. This development not only optimizes marketing resource allocation but also facilitates anticipation of consumer reactions and strategic adjustments in dynamic market conditions. The advent of such an advanced AI-driven tool represents a significant milestone in adapting to the evolving digital marketing landscape, where content creation and dissemination are becoming increasingly automated and intricate. As AI technologies advance further, systems like this Decision Support System are poised to play a vital role in aligning technological prowess with human strategic oversight, fostering innovation in consumer engagement and market growth. In conclusion, this research offers a comprehensive solution to the critical challenge of forecasting and optimizing the diffusion of AI-generated content and its market effects. By combining multi-source data integration, state-of-the-art AI models, and causal analysis, the Decision Support System emerges as a valuable resource for modern marketers seeking to navigate and leverage the fast-changing digital content ecosystem.