In the early wave of AI adoption, AI systems depended heavily on human labor behind the scenes. Data labelers and content moderators were tasked with cleaning up data, identifying objects, and flagging anomalies or graphic content. After all, you just needed volume, right? The more data you had, the better. That’s no longer the case. Data quality has become mission-critical. It’s not just raw input to feed into models, but potentially a liability when handled poorly. 

Only 12% of companies say their data is ready for AI. And it shows, because teams are spending more time cleaning inputs than building anything meaningful. Relying solely on low-cost data labelers or junior analysts just doesn’t cut it anymore. The volume and complexity have outpaced what manual processes can handle. What used to be manageable datasets are now petabytes of unstructured information that require domain expertise, not just extra hands.

Would you board a plane if you found out the fuel had been watered down to save costs? That’s how most AI programs take off, with bad inputs. And the cost, in turn, is that AI models often fail to scale or sustain performance because they’re held together by the data equivalent of duct tape. As reported by an MIT study, 95% of enterprise AI solutions fail, and it isn’t about model quality. It’s about tools that don’t learn, integrate poorly, or match workflows, all problems that trace back to how data is prepared and managed.

This guide is about fixing that. We’ll walk through five core areas of data readiness: use cases for strategy, asset inventory, infrastructure overhaul, hiring a data team, and heralding a data-driven culture. But before we do that, let’s zoom in on what data readiness means.