AI Data Readiness Checklist: Prepare Your Business Data Before You Build
Most AI projects do not fail because the model is weak. They fail because the model receives messy, stale, incomplete, or unsafe data. A chatbot connected to outdated policies gives bad answers. A workflow agent without clear permissions exposes the wrong records. A recommendation engine trained on duplicate events optimizes for noise.
Before you add AI to a mobile app, admin panel, internal tool, or customer portal, make the data layer ready for AI. This checklist gives founders, CTOs, and product teams a practical way to audit data quality, structure, access, freshness, and feedback loops before the first production prompt.
