Recent advancements in artificial intelligence have significantly expanded both the capabilities of AI tools and potential modes of human-AI interaction, giving rise to augmented intelligence systems that enhance human cognitive abilities through AI-based tools while maintaining human oversight of critical functions including final decision-making, goal-setting, coordination, and control. This paper examines the application of augmented intelligence to decision support systems. Beginning with an analysis of fundamental decision-making processes, current trends in relevant AI domains are identified and reviewed. An integrated approach is proposed that combines conversational, generative, and evaluative AI techniques. The proposed approach is characterized by the synergistic integration of data- and model-based techniques, along with the novel application of modern large language models as the foundation for human-AI interaction in decision-making processes. To demonstrate practical implementation, the approach is evaluated through a meeting scheduling case study, illustrating its effectiveness in real-world scenarios.