Business Data for AI: Essential Prep Steps to Avoid Risk
Most AI projects stall between a convincing demo and something the business can depend on, and the reason is almost never the model. It is the material underneath it: duplicated customer records, nine versions of the same price list, scanned PDFs no parser can read, and permissions that were never designed to be queried by a machine. This guide sets out a seven-step preparation programme for the business data estate — inventory, quality profiling, structure and formats, metadata, classification and security, the delivery layer, and the measurement that keeps it honest. It closes with realistic costs, timelines, what each AI use case actually demands, and the mistakes that quietly stall these programmes.
