Four things make the biggest difference:
- Well-modeled tables. The less transformation the agent has to do before answering a question, the more reliable the output.
- Clearly defined metrics. If "churn" isn't precisely defined in the data, the agent will measure it wrong.
- High-quality data. Accurate and complete, with known gaps documented so the agent can account for them.
- Auto-generated context. Metadata and definitions that stay up to date as schemas change, because manual documentation can't keep pace.
According to Gartner, 60% of AI projects will fail this year due to lack of an AI-ready data foundation. The cost of skipping this work isn't theoretical.
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