Most companies don't have a data problem. They have a data readiness problem. They already have plenty of data. What they don't have is data an AI task can actually use. I sat down with Ho Bae, CEO of CUBIG on The Ravit Show, to talk about what it takes to make enterprise data ready for AI. Here are my takeaways:
1. Having data and having AI-ready data are 2 different things. The right information may already be in your systems, but it often needs to be prepared, changed, or validated before AI can use it.
2. "AI-ready" depends on the task. Data isn't ready in general. It's ready for a specific job.
3. Real enterprise data is messy in ways sample data isn't. Circuit IDs, project codes, and node names only make sense because of how they connect inside the business.
4. Removing too much data can break the AI. If you strip out the relationships, the model can't tell that 2 records belong to the same asset or event.
5. The answer should come back inside the workflow. LLM Capsule sits inside the tools teams already use and returns results with the real business values, ready to act on.
6. You can keep your current model. The model stays your choice. The data layer around it is what changes.
7. Start with 1 blocked AI task, not a product checklist. Know what you want to run, what's stopping it, and what success looks like.
Ho also walked through a live demo, showing how 2 network alerts get traced back to the same device without the model ever seeing the original device name.
If your AI works on sample data but struggles on real data, this one is worth your time.
CUBIG will also be at Oracle AI World 2026 in Las Vegas, October 25 to 28.
#data #ai #enterprisedata #dataengineering #genai #llm #cubig #theravitshow
