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BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence

Researchers introduce BI-Agent and BI-Bench to automate end-to-end business intelligence tasks, finding even advanced LLMs struggle with traditional BI workflows.

By Chuxuan Hu, Yeye He, Penny Zhou, Wee Hyong Tok, Daniel Kang, Surajit Chaudhuri·Sep 21·arxiv.org·1 min read

Intelligence analysis by Qwen 2.5 (3B)

BI-Agent and BI-Bench: Towards Automating End-to-End Business Intelligence
Image: arxiv.org

Researchers develop BI-Agent and BI-Bench to automate business intelligence tasks, highlighting the limitations of current large language models in handling traditional BI workflows.

Why it matters

This work addresses the challenges of automating business intelligence tasks, which are crucial for enterprise decision-making, and could lead to more efficient and accurate data analysis.

Imagine you have a big spreadsheet with lots of numbers. Instead of you figuring out which numbers go together and what to do with them, a smart tool (BI-Agent) does it all for you and gives you the right answers to your questions about the numbers.

Analysis

{"#BI-Agent Design":"BI-Agent is designed to decompose BI workflows into subtasks such as search, join, and transform, and orchestrates specialized data management methods across BI stages. It achieves significant accuracy gains with vanilla LLMs and further post-training using SFT and RL.","#BI-Bench":"BI-Bench is the first benchmark to systematically study LLMs' ability on end-to-end BI. It consists of a large collection of real-world BI projects and pairs of (questions, ground-truth answers) from real user dashboards.","#Limitations of LLMs":"Even frontier LLMs perform poorly on BI-Bench, with less than 50% accuracy. This highlights the need for tool-augmented reasoning and domain-specific post-training in complex BI workflows."}

Key points

  • BI-Agent and BI-Bench are introduced to automate end-to-end business intelligence tasks.
  • Even advanced LLMs struggle with traditional BI workflows, highlighting their limitations.
  • BI-Agent achieves significant accuracy gains with vanilla LLMs and further post-training.
The Upside

With further development, BI-Agent could help businesses make better decisions by automating the complex data analysis tasks that are currently done manually.

The Downside

However, the current limitations of LLMs mean that BI-Agent might not be perfect yet, and there could be some mistakes or missing information in the answers it provides.

Originally reported at

arxiv.org

Discernion covers the story. Read the full piece at the source.

Tagsai-agentsbusinessmachine-learningcomputation-and-languagedatabases

Author

Chuxuan Hu, Yeye He, Penny Zhou, Wee Hyong Tok, Daniel Kang, Surajit Chaudhuri

Intelligence analysis by

Qwen 2.5 (3B)

Published

Sep 21, 2026

Source

arxiv.org

Share

Topics

ai-agentsbusinessmachine-learningcomputation-and-languagedatabases

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