India, July 25 -- It's tempting to blame the model when an enterprise artificial intelligence (AI) system gives a bad answer. Chanakya Bellam, whole-time director at AION-tech Solutions, makes a different case: most generative AI (GenAI) projects don't fail because of what the model can or can't do. They fail because of what the model was fed, how its output gets watched, how its code gets shipped, how much freedom its agents are given, and how much unseen debt accumulates behind all of it while nobody's looking.

In enterprise environments, answer quality tends to come down less to the model itself and more to whether it can reach accurate, relevant, and timely information in the first place. One of the most common architectural mistakes...