Transparency means you can see and understand how an AI system arrived at its outputs. For enterprise AI, this matters at multiple levels: developers need to understand model behavior to debug and improve it, compliance teams need to explain decisions to regulators, and end users need enough visibility to trust the system appropriately.
Opaque AI systems are harder to maintain, harder to govern, and harder to defend when something goes wrong. Transparency is not just an ethical consideration — it is a practical requirement for building AI systems that can be maintained, audited, trusted, and improved over time.
