TwinBI: An Agentic Digital Twin for Efficient Augmented Interactions with Business Intelligence Dashboards
arXiv:2606.13731v1 Announce Type: new Abstract: Business intelligence (BI) increasingly combines dashboard interaction with LLM-based assistance, but these two modes often fall out of sync during multi-step analysis. As users switch between direct dashboard manipulation and natural-language queries, it becomes difficult to preserve a consistent analytical state across filters, hierarchies, metrics, and chart context. We present TwinBI, an agentic digital-twin framework that couples an LLM-based agent system with an executable BI dashboard state. TwinBI unifies conversational interaction, dashb
The rapid advancement of large language models (LLMs) and the increasing demand for more intuitive data analytics tools are converging to create solutions that augment traditional business intelligence.
This development indicates a significant step towards more autonomous and integrated AI systems that can interpret and act upon complex data, fundamentally changing how businesses interact with their intelligence platforms.
The paradigm shifts from human-driven, manual dashboard interaction to an AI-assisted, conversational approach where agents maintain analytical state and context across multimodal inputs.
- · AI software developers
- · Business intelligence platforms
- · Data analysts
- · Enterprises adopting AI-driven analytics
- · Traditional BI tool vendors slow to adapt
- · Manual data navigation training providers
Increased efficiency and accuracy in business intelligence analysis through seamless AI assistance.
Reduced need for specialized data analysts, as AI agents handle complex querying and state management.
The acceleration of fully autonomous 'AI agent as a service' offerings capable of performing entire analytical workflows unassisted.
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Read at arXiv cs.AI