From Data Debtto AI Readiness

When preparing to adopt AI, most businesses look at the latest models and capabilities. However, Shiv, data scientist at Razor, argues that the first place to start is your own data health.

You need to understand how much data is manually handled, isolated in offline spreadsheets, or held together by ad hoc fixes. Unstructured, messy data is the single biggest barrier to meaningful AI integration. If critical processes rely on 'magic spreadsheets', the foundation isn't stable enough for automation.

AI models learn directly from the data they receive. If the existing datasets are noisy, inconsistent, or constantly rewritten through manual processes, the model will learn this noise instead of actual patterns.

As you scale from an SME to an enterprise, those early ad hoc fixes become extremely brittle. Introducing AI into this shaky mix simply converts your existing data debt into AI debt. Your AI initiatives will crawl, becoming slower, significantly more expensive, and returning gibberish rather than reliable insights.

When the messy data is finally cleaned, you'll feel the change. A sure sign of AI readiness is when conversations stop asking 'Are these numbers right?' and instead pivot towards 'What do we do about these numbers?'.

Furthermore, many dashboards fail because they simply throw metrics at users. Unless a dashboard is designed with a very specific decision-making process in mind, it becomes an added source of stress. A disciplined data environment enforces clear ownership and definitions, turning metrics into tangible operational or commercial improvements. Only then is an environment truly ready for proper AI implementation.