Summary
This Argent white paper identifies and discusses the seven deadly sins of AI that organizations may be committing, which undermine quality, privacy, and reproducibility in AI processes. The document outlines how these sins are not related to complex algorithms but rather to practical process control issues that lead to data loss and policy violations. Each sin is described in detail, starting with the need to avoid exposing private data to public systems, followed by the risks of using interactive tools for production tasks. The paper emphasizes the importance of maintaining a central SQL database for work and results, as well as the necessity of modular query design to enhance transparency and security. It also highlights the critical need for logging and automation in AI workflows. The worst sin identified is the failure to implement a disciplined automation and orchestration layer, which is essential for operational survival in organizations relying on AI for strategic decisions.
