Why engineering leaders must fix data readiness before scaling AI
Scaling AI successfully depends on more than powerful models. Without strong data readiness for AI, engineering teams struggle with inconsistent data, hidden schema issues, and unreliable pipelines. Before organizations invest further in AI development, leaders must ensure their data is accessible, structured, and trustworthy enough to support it.
