What are Specific DoR Considerations for AI/ML Features?

When developing AI/ML capabilities, a standard DoR often falls short. Specific criteria must be added, such as: is the training data identified, accessible, and cleaned? Are data privacy and security requirements documented? Are initial model performance metrics (e.g., accuracy, precision, recall) defined as acceptance criteria? Is the deployment strategy (e.g., MLOps pipeline integration, model monitoring plan) considered? These questions ensure that the unique lifecycle and dependencies of AI models are addressed before development begins.

For product managers working on AI products, this specialized DoR is crucial for managing expectations and identifying risks early. Agile coaches can guide teams in developing and adopting these tailored DoR checklists, fostering a deeper understanding of AI development nuances. Enterprise executives gain assurance that AI investments are being managed with a comprehensive understanding of their distinct challenges, leading to more successful AI product launches and reduced technical debt in AI systems.