AI in BI: From Lane Assist to Full Self-Driving Analytics

Speaker(s):
Maxime Beauchemin
Maxime Beauchemin
Jun-12 14:00-14:30 UTC (2025-06-12T14:00:00.000Z-2025-06-12T14:30:00.000Z your timezone)
Add to Calendar 06/12/2025 2:00 PM 06/12/2025 2:30 PM UTC OSACon: AI in BI: From Lane Assist to Full Self-Driving Analytics Presented by Maxime Beauchemin.

The promise of AI in business intelligence is enticing—ask a question in natural language and receive an accurate chart or metric, no SQL required. While this works in controlled environments, real-world analytics demand precision where 90% accuracy isn’t sufficient. Drawing parallels to self-driving cars, we’re currently at a “lane assist” stage in BI, where AI can suggest and automate parts of the workflow but still requires human oversight.

In this talk, I’ll delve into the lessons learned from building AI features like text-to-SQL on top of Apache Superset at Preset. We’ll explore where AI excels in BI, where it falls short, and what it takes to build trust in AI-assisted analytics. Key topics include the importance of context-rich interfaces, seamless human-AI handoffs, and robust feedback loops. We’ll also discuss the challenges of real-world data environments and the necessity of transparency in AI outputs.
By Maxime Beauchemin

The promise of AI in business intelligence is enticing—ask a question in natural language and receive an accurate chart or metric, no SQL required. While this works in controlled environments, real-world analytics demand precision where 90% accuracy isn’t sufficient. Drawing parallels to self-driving cars, we’re currently at a “lane assist” stage in BI, where AI can suggest and automate parts of the workflow but still requires human oversight.

In this talk, I’ll delve into the lessons learned from building AI features like text-to-SQL on top of Apache Superset at Preset. We’ll explore where AI excels in BI, where it falls short, and what it takes to build trust in AI-assisted analytics. Key topics include the importance of context-rich interfaces, seamless human-AI handoffs, and robust feedback loops. We’ll also discuss the challenges of real-world data environments and the necessity of transparency in AI outputs.