Effective real-time data presentation is essential in small-group interactive contexts, where discussions evolve dynamically and presenters must adapt visualizations to shifting audience interests. However, most existing interactive visualization systems rely on fixed mappings between user actions and visualization commands, limiting their ability to support richer operations such as changing visualization types, adjusting data transformations, or incorporating additional datasets on the fly during live presentations. This work-in-progress paper presents VisAider, an AI-assisted interactive data presentation prototype that continuously analyzes the live presentation context, including the available dataset, active visualization, ongoing conversation, and audience profile, to generate ranked suggestions for relevant visualization aids. Grounded in a formative study with experienced data analysts, we identified key challenges in adapting visual content in real time and distilled design considerations to guide system development. A prototype implementation demonstrates the feasibility of this approach in simulated scenarios, and preliminary testing highlights challenges in inferring appropriate data transformations, resolving ambiguous visualization tasks, and achieving low-latency responsiveness. Ongoing work focuses on addressing these limitations, integrating the system into presentation environments, and preparing a summative user study to evaluate usability and communicative impact.

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