How Generative AI is Transforming Business Intelligence: Trends and Practices

This article is excerpted from Baidu Technology Salon 115, titled “Baidu Data Middle‑Platform Technical Salon – Exploring the Data Middle‑Platform in the AI Era.” It analyzes the business value and technical practice of combining Business Intelligence (BI) with generative models from three perspectives.

Key Content

BI technology development and new opportunities brought by large models

Design concepts and platform introduction of ChatBI

Technical insights behind ChatBI

Implementation results

1. Technical Perspective

From a technical viewpoint, any emerging technology must become widely accessible, allowing more people to use it at lower cost and thereby generate greater value. The same principle applies to BI. The article reviews the evolution of BI through three stages.

First stage – Report‑oriented BI

With the rise of big‑data technologies such as HDFS and MapReduce, early BI products required custom development based on analyst requests, leading to long cycles and high marginal costs, which limited broad adoption.

Second stage – Self‑service BI

Advances in hardware, MPP architectures, vectorization, and in‑memory processing improved query efficiency by more than tenfold. Dynamic queries on wide data sets became feasible, reducing dependence on data engineers and enabling users to perform self‑service, visualized queries directly on BI platforms.

Third stage – Intelligent BI

Leveraging the powerful understanding and reasoning capabilities of large models, intelligent BI hides underlying complexities. Users no longer need to choose platforms, locate data sources, or write query dialects; they can simply converse in natural language to obtain data extraction and insight analysis, dramatically lowering the barrier to analysis.

ChatBI Design and Platform

ChatBI integrates generative AI with Baidu’s data middle‑platform to deliver an intelligent, conversational BI experience. Its architecture emphasizes seamless model integration, real‑time data access, and a user‑centric dialogue interface that transforms raw data into actionable insights.

Implementation Experience

During Baidu’s rollout of ChatBI, challenges such as latency, model‑data alignment, and scalability were encountered. The team addressed these by optimizing data pipelines, fine‑tuning large models for domain‑specific queries, and employing distributed serving strategies to ensure responsive, reliable performance.