Cheap AI, Wrong Targets: The Cost of Skipping Upstream Work

This article exposes a critical flaw in how AI-powered agents are deployed: companies prioritize cheap agent creation but skip essential upstream work like data validation, targeting rules, and output filtering. When these safeguards are missing, even well-written messages end up targeting decade-old records or unrelated prospects, creating waste and enabling spam operations. The pattern reveals a fundamental truth: AI failures often stem not from writing capability, but from organizational negligence in configuration and guardrails.