Why AI Fails Without Intent Completeness

AI systems rely on pattern recognition and prediction rather than true understanding, causing failures when user prompts lack clarity, context, or specificity—a gap the author terms 'intent completeness.' Incomplete intent leads to irrelevant outputs, wasted iteration, and systemic inefficiency as AI scales across workflows. The author argues the focus should shift from model capability to ensuring complete, structured intent before AI execution.