Data quality for machine learning, generative AI, and agentic AI

Nobody builds bad models on purpose. They build them on data that looked clean until it wasn’t. By the time the defect surfaces, a pricing model has shipped a $2.3M margin shortfall, a chatbot has delivered confident wrong answers to customers, or an autonomous agent has committed budget on incomplete data. Poor data quality is the most common reason AI projects stall, drift, or fail silently in production. In traditional ML, these failures are at least visible. A dashboard shows the wrong number, an analyst catches it, someone retrains the model. The damage is contained.