Unverified50% confidenceOpinionExact time
The root cause of AI delivery failures lies in three missing pieces of information: failure to clearly state what is needed, failure to make rules persistent, and failure to define what counts as done.
1
Sources
50%
Confidence
Long-term
Relevance
9/29/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedOne common reason for AI project failures is an ambiguous business model that cannot answer who will pay continuously, with high compute costs further compressing the survival margin.78% similarUnverifiedWhat blocks AI deployment is not model capability, but four things AI cannot independently resolve: vague requirements, dirty data, fragmented systems, and unaccountable delivery.77% similarVerified大多数 AI 编程智能体无法有效完成复杂任务的主要原因是上下文缺失,而非模型能力不足77% similarUnverifiedAI摘要在安全场景失效的深层原因是优化目标与信息论重要性之间的错位,混淆了信息频率与信息重要性76% similarUnverifiedBeginners commonly encounter three typical problems when describing requirements: vague descriptions, lack of structure, and neglect of edge cases and technical constraints—these are the main reasons AI output deviates from expectations.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/958445API
curl https://kongchang.com/api/v1/knowledge/claims/958445MCP
get_claim(id=958445)