AI readiness is less about how modern the data platform is and more about whether the data can be trusted, traced, and accessed in the right context. Even a well-maintained warehouse can produce unreliable AI answers if records are stale, permissions aren't preserved, or conflicting sources have no clear precedence.
In production AI work at IT Path Solutions, I’d pay particular attention to data lineage and permission-aware retrieval. An AI system needs to know not just what information it found, but where it came from, how current it is, and whether the requesting user is allowed to see it. Those controls become especially important when enterprise data feeds RAG systems or autonomous agents.
A useful readiness test is to trace one AI-generated answer all the way back to its source records and access rules. If the team can't explain why that answer was produced and whether the underlying data was appropriate to use, modernizing the infrastructure alone hasn't solved the problem.
AI readiness is less about how modern the data platform is and more about whether the data can be trusted, traced, and accessed in the right context. Even a well-maintained warehouse can produce unreliable AI answers if records are stale, permissions aren't preserved, or conflicting sources have no clear precedence.
In production AI work at IT Path Solutions, I’d pay particular attention to data lineage and permission-aware retrieval. An AI system needs to know not just what information it found, but where it came from, how current it is, and whether the requesting user is allowed to see it. Those controls become especially important when enterprise data feeds RAG systems or autonomous agents.
A useful readiness test is to trace one AI-generated answer all the way back to its source records and access rules. If the team can't explain why that answer was produced and whether the underlying data was appropriate to use, modernizing the infrastructure alone hasn't solved the problem.