The AI Definition of Done: Human in the Loop Is Not a Quality Standard (age-of-product.com)

A new framework called the "AI Definition of Done" aims to establish clear quality standards for AI-assisted outputs, addressing a significant gap in accountability and verification. Unlike traditional product increments, which benefit from an established Definition of Done, AI-generated materials—such as status reports and stakeholder communications—often lack quality checks, leaving teams vulnerable to errors and miscommunication. The AI Definition of Done introduces a concise, team-agreed standard for each output type, outlining verification processes, data hygiene protocols, and provenance labels, ensuring that AI-driven content is reliable and defensible. This initiative is significant for the AI/ML community as it promotes robustness in AI deployments, transitioning from a reliance on subjective assessments to a structured approach that can be audited and taught. By borrowing principles from Agile practices, the framework delineates criteria specific to various task classes and formalizes responsibilities for validating outputs. With the increasing scrutiny of AI technologies, particularly in light of regulations like the EU AI Act, having a documented standard not only enhances operational trust but also aids compliance, making AI outputs more accountable and acceptable in professional settings.