Technically, AI models can be trained without annotation, but the results are far superior when humans guide the process by rewarding good outputs and penalizing bad ones. Without annotation, these models tend to misidentify patterns which can lead to producing poor-quality results that pollute the dataset and increase errors over time. This not only diminishes accuracy but also wastes valuable resources like computing power, time, and money, making the entire process inefficient.

Unannotated models also struggle with scalability since they are more prone to errors when encountering new inputs beyond their limited training. What’s more, is they pose compliance and data security risks, since many regulations require transparency in handling sensitive data. Poor annotation makes audits difficult and increases the risk of failing to safeguard personally identifiable information (PII) which could expose companies to legal and regulatory consequences.