Generative Artificial Intelligence for Cross-Domain Knowledge Synthesis and Intelligent Automation

Generative Artificial intelligence (GAI) has become a cohesive paradigm in the very wide field of application including intelligent automation, decision support systems, and extensive knowledge management, where the incorporation of heterogeneous knowledge about domains is of critical importance. Synthesizing, reasoning and automating across a variety of different data is a basic challenge in these areas. This paper introduces a generative AI knowledge synthesis model and generative automation framework of synthetic cross-domain knowledge grounded on the learning of shared latent representation. The suggested methodology utilizes the heterogeneous inputs domain specific encoders to map to a single latent space with attention-based fusion, generative decoding, and reasoning-based policy learning to adapt the automation. Semantic alignment, synthesis accuracy and decision effectiveness are achieved by a joint optimization goal. Through experimental assessment, it has been established that the suggested has been found to provide better performance results against current methods with a better Knowledge Synthesis Accuracy (0.91), Cross-Domain Alignment Score (0.88) and Automation Decision Accuracy (0.93) parameter over state-of-the-art multimodal systems. These findings support the usefulness of the generative modeling concept in facilitating strong, scalable, and knowledge-based intelligent automation throughout domains.