An AI interface does not fix weak data foundations. It exposes them faster. The safest first move is to prepare the semantic layer, security model and business glossary behind the questions people want to ask.
Define the questions AI should answer
Start with business questions, not model choice. Which margin, inventory, pipeline, utilization or project-cost questions should people ask? Which answers would be dangerous if interpreted loosely?
Use curated measures instead of raw tables
Agents and NLQ experiences are more reliable when they call governed measures, dimensions and relationships. Raw source tables often contain technical fields and operational exceptions that require interpretation.
Permissions must follow the business model
AI must respect the same data access rules as reporting. Row-level security, audience definitions and sensitive fields should be designed before the assistant is widely promoted.
Govern prompts, answers and escalation
AI analytics needs monitoring: what people ask, where answers are weak, which terms cause ambiguity and when the right response is to link to a trusted report instead of improvising.
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