Agentic AI fails in enterprises not on model selection, but on fragmented data, lack of semantic clarity, and insufficient traceability when operating autonomous systems.
Schema drift and outdated baselines in data pipelines lead to higher false-positive rates and detection gaps—a problem that cannot be solved through model tuning alone.
AI projects fail in the production phase not due to technology, but due to unprepared data conditions, unclear processes, and underestimating the effort required to transition from pilot to production environments.
The use of AI for mass production of content causes AI systems to increasingly learn from other AI-generated content, allowing errors and biases to accumulate rather than be corrected.
