A technical analysis on why AI projects fail due to poor data architecture, the reality of dirty data, and the engineering prerequisites for RAG systems.
A comprehensive examination of why most software project problems stem not from code or technology, but from decision-making structures, unclear ownership, and organizational ambiguity.
Widely accepted but rarely questioned assumptions in software projects that often lead to costly decision-making mistakes.
