TextQL vs Legacy BI: Is This the End of Traditional Dashboards?

“Your data is fine. Your AI isn’t good enough.” That is the bold statement behind TextQL, and it immediately caught my attention here at Gartner. I sat down with Ethan Ding, Co-Founder, CEO & Head of Product, TextQL, to unpack what he means by that and why they are challenging many assumptions around BI and analytics.

Most enterprises have spent years building ETL pipelines, cleaning data, and preparing dashboards. The belief has been that AI will only work once data is perfectly structured.

Ethan disagrees.

He believes the real limitation has been the AI systems themselves.

We talked about:

-- What enterprises are misunderstanding today about AI and data quality

-- Why traditional BI tools like Tableau or Power BI were built for a different era

-- How TextQL enables AI analytics even when data is messy or not fully ETL’d

-- Why they believe seat-based pricing for dashboards is broken

-- How their approach focuses on trust and verification so enterprises can validate AI-generated answers

One idea stood out during the conversation.

Executives do not just want answers.

They want conviction that the answer is correct.

That is where their “Query to Conviction” concept comes in. AI does not just generate an answer. It shows the reasoning, the data path, and the verification behind it.

For CIOs walking the Gartner floor, Ethan had a simple suggestion. Do not ask vendors how good their AI looks. Ask them how their AI proves it is right.

#data #ai #textql #gartnerda #theravitshow