The Hidden Cost of Semantic Chaos Why Your AI Investment Is Underperforming—And What To Do About It

The paper’s causal model is:

inconsistent definitions → contradictory machine inputs → unreliable synthesis → operational error and loss of trust

Its four symptoms are:

• hallucination: plausible completion across definitional gaps;
• context leakage: internal associations entering inappropriate outputs;
• decision drift: cumulative automated error caused by misaligned definitions;
• trust collapse: users abandon or manually duplicate the system.

The proposed remediation has three parts:

1. Semantic Audit - inventory actual usage; - identify conflicts; - classify risk; - prioritize remediation.

2. Terminological Governance - assign ownership; - manage changes; - connect terminology to data governance; - sustain definitions over time.

3. AI-Ready Infrastructure - expose definitions to machine systems; - preserve context through metadata; - test semantic consistency; - standardize relevant prompts and workflows.

The paper recommends focusing on the small set of terms with the highest operational impact rather than attempting complete enterprise ontology construction.