Google Cloud surveyed 1,402 IT leaders worldwide and found that 83% must upgrade their infrastructure to run agentic AI in production. Here's what the data means for your stack.

Many organizations want to roll out AI solutions: chatbots, workflow optimization, predictive analytics. But they crash into the same brutal truth: their data is not ready for AI. Bad or fragmented data leads to unreliable outputs, biased models, and failed projects.

This guide explains what it actually takes to prepare your data so AI systems can work reliably on real business information.