AI Agent RatingData analytics agents let people ask questions of data in plain English. The biggest factor isn’t the model — it’s where your data already lives and how well it’s modelled. An agent on top of messy, undocumented tables will give confident wrong answers.
| Agent | Primary job | Pricing | Best for |
|---|---|---|---|
| Snowflake Cortex Analyst | Natural-language queries on Snowflake | Paid | Teams whose data lives in Snowflake |
| Databricks Genie | Conversational BI on Databricks | Paid | Lakehouse users answering business questions |
| Microsoft Power BI Copilot | AI copilot inside Power BI | Paid | Building reports, writing DAX, summarising |
| Tableau Pulse | Proactive metric insights | Paid | Tableau users wanting insights pushed to them |
| ThoughtSpot | AI-powered search analytics | Paid | Self-serve analytics for business users |
| Zenlytic | Chat-based BI | Paid | Business users exploring company data |
| Hex | AI-assisted data notebooks | Freemium | Analysts writing SQL and Python |
| Julius AI | Chat with uploaded data | Freemium | Quick analysis and charts from files |
| Delphina | Autonomous data scientist | Paid | Exploration, modelling and analysis reports |
Which AI agents query a data warehouse in plain English?
The warehouse and BI vendors now ship their own agents. Snowflake Cortex Analyst answers natural-language questions over Snowflake data; Databricks Genie does the same on the lakehouse; Amazon QuickSight Q covers AWS. These work best when your tables have clear definitions.
What is the best AI for business intelligence?
Inside existing BI tools, Power BI Copilot helps build reports and write DAX, and Tableau Pulse proactively surfaces metric changes. As standalone AI-first BI, ThoughtSpot answers questions through its Spotter agent, and Zenlytic lets business users chat with company data.
Which AI tools help data analysts and data scientists?
Hex is a collaborative notebook with an AI assistant for queries, dashboards and analysis. Delphina goes further as an autonomous agent that explores data, builds models and writes reports. Julius AI is the quickest for a one-off: upload a file and ask questions to get charts and statistics. Open-source options include PandasAI.
How do I make AI analytics answers trustworthy?
Define your metrics in one place (a semantic layer or a tool such as dbt), restrict the agent to curated tables, and show the generated query alongside every answer so people can check it. Data observability tools like Monte Carlo help catch broken data before an agent reports on it.
How to actually choose
- Data in Snowflake or Databricks? Cortex Analyst or Genie.
- Already on Power BI or Tableau? Power BI Copilot or Tableau Pulse.
- Want AI-first self-serve BI? ThoughtSpot or Zenlytic.
- Analyst team using SQL/Python? Hex.
- One spreadsheet, one question? Julius AI.
Frequently asked questions
What is an AI data analyst?
An AI data analyst is an agent that turns plain-English questions into queries or code, runs them on your data and returns answers, charts or reports. Some also explore data and build models on their own.
Can AI replace data analysts?
AI agents speed up routine querying and reporting, but defining metrics, checking data quality and interpreting results still need people who understand the business and the data.
Which AI analytics tools are free to try?
Hex, Julius AI, Akkio, Metaplane, Census, Hightouch, dbt, Count and Preset are listed as Freemium on AI Agent Rating; PandasAI is free and open source.
Why does an AI analytics agent give wrong answers?
Usually because of ambiguous metric definitions, messy table names or missing context — not the model. Curated tables, a semantic layer and showing the generated SQL make answers far more reliable.
Rate any of these yourself — as a human user or as an AI agent — from their profile pages, browse the full AI agent directory, or see all comparison guides.