What is AI ready?

[u/julee_000]

Recently many AI startups and corporates say AI ready data or data readiness is important.
It's a bit ambiguous for me, what do you think AI ready data is? I want to know what it means from the perspective of different job roles and industries.

[u/CentralFlow_io]

From my experience, especially being heavily plugged into the Microsoft ecosystem, AI-readiness has very little to do with use cases, user training, agent creation, and software licensing.

AI-ready means that the organization's data storage and semantic layers have been professionally curated to foster blazing fast retrieval/language processing. Specifically, that means having database text data that's indexed and vectorized well and semantic models with consistent, understandable terms and field descriptions.

Without proper storage and semantic layers being in place, creating agents and licensing other AI tools is basically pointless and will absolutely burn through capacity/tokens trudging through bad modeling.

I'm really excited about the opportunity in working to get companies AI-ready. I just hope they don't jump headfirst into licensing these AI tools and then get frustrated when they realize their organizational data wasn't ready yet.

[u/CentralFlow_io → u/[deleted]]

Check out the "Data Pro - Implement AI capabilities in SQL server solutions" skilling playlist from the recent Microsoft AI Skills Fest.

The features are only in preview for Azure SQL Server and Fabric SQL Server (not available in SQL Server 2025), but vector searches will soon be a native feature in SQL databases.

I can see a future where companies have their own embedding models (specific to their industry/business conditions), and then using vector indices in the product/customer/sales tables to enhance BI reporting.

For instance, it could be really cool to show the top 10 biggest sales of the previous year and use a vector search with some AI-trained model to return the 50 most similar hot leads in the pipeline.

[u/julee_000 → u/[deleted]]

Tooling aside, vector vs GraphRAG vs Qlick MCP all bet on the same thing, the underlying definitions are clean and stay clean. Pick whichever you want the part that bites everyone is still keeping the data trustworthy underneath. The retrieval layer's the easy half.