Make Your Data AI-Ready: The Missing Layer Between Your Database and AI

For the last couple of years, we’ve been told that AI will let everyone talk to their data. Just ask a question in plain English. The AI writes the SQL. The database returns the answer. Simple. Except real company databases aren’t simple. They contain hundreds or thousands of tables. Business terminology that doesn’t exist in the database schema. Metrics that have very specific definitions. Relationships that aren’t obvious from column names. Permissions that shouldn’t be ignored. And suddenly: Text-to-SQL isn’t really the problem. The problem is that AI doesn’t automatically understand your data. That’s why I think we’re entering a new phase of data infrastructure: AI-ready data. What does “AI-ready” actually mean? An AI-ready database isn’t necessarily a database with perfect column names or beautifully documented tables. It’s a database where an AI system has enough business and structural context to reason about the data correctly. For example, imagine a company has: customers orders order_items products payments refunds A human analyst may immediately understand: customers ↓ orders ↓ order_items ↓ products But an LLM doesn’t inherently know that this is the correct analytical path. It sees columns. It sees names. It sees relationships. But it doesn’t automatically know which relationships matter for a particular question. Now consider the word: Revenue Does that mean: SUM(orders.total) or: SUM(order_items.net_revenue) or: SUM(orders.total) - SUM(refunds.amount) Those queries may all execute perfectly. Only one may represent the company’s definition of revenue. That’s the real problem. Your database has a language of its own Every business develops its own vocabulary. One company might say: Customer Another might say: Buyer Another might define a customer as: Anyone who has completed at least one purchase. Another might define an active customer as: A customer who purchased within the last 90 days. The physical database might contain none of those definitions. It might simply contain: customer_id order_id order_status order_date This is where AI needs help. It needs a semantic layer. The semantic layer becomes AI context Traditionally, semantic layers were primarily designed for analytics and BI. They helped define: metrics dimensions relationships business terminology calculations Now there’s another consumer for that information: AI agents. Instead of asking an LLM to rediscover the meaning of your database every time someone asks a question, you give it the context explicitly. For example: Revenue → Net revenue after refunds Customer → Buyer with at least one completed order Order date → order_purchase_timestamp And: customers ↓ orders ↓ order_items ↓ products Now AI isn’t guessing. It’s operating with context. This changes Text-to-SQL Without semantic context: User ↓ "What were our best-selling products?" ↓ LLM ↓ Guess what "best-selling" means ↓ Guess which tables matter ↓ Guess how they join ↓ SQL With semantic context: User ↓ "What were our best-selling products?" ↓ Intent ↓ Business definitions ↓ Relevant entities ↓ Known relationships ↓ SQL The second system has a much easier job. The AI isn’t being asked to understand the entire company from scratch. It’s being given the information required to reason about the question. AI readiness is also about governance There’s another piece that gets overlooked. Giving an AI access to your database isn’t simply a SQL-generation problem. You also need to answer: What is this AI allowed to see? Maybe an analyst can query: customers orders products but shouldn’t see: customer_email customer_phone payment_details AI needs to respect those rules too. So an AI-ready data environment needs more than descriptions. It needs: Context + relationships + metrics + permissions. This is why I built Data Convo I’ve been working on Data Convo around this idea. Instead of treating the database as a giant list of tables and asking an LLM to figure everything out, Data Convo uses a semantic layer where teams can define: business terminology aliases metrics relationships permissions analytical context Then AI can use that information when answering questions. The goal isn’t simply: Generate SQL. It’s: Understand the question → understand the business context → understand the data relationships → generate the right analysis. The next interface to your database isn’t SQL For decades, the interface to data was SQL. Then BI tools made that interface visual. Now AI is making it conversational. But conversation alone isn’t enough. The AI needs to understand what the conversation means in the context of the business. That’s the layer we’re starting to build now. And I think that layer will become increasingly important as companies put AI agents directly on top of their data. The question isn’t: Can AI write SQL? It clearly can. The better question is: Does AI understand what the SQL is supposed to mean? That’s what makes data AI-ready. Want to see what AI-ready data looks like? Try Data Convo →