Writing Acceptance Criteria for AI Features: A Product Owner Field Guide

The Problem

As a Product Owner, you're familiar with writing acceptance criteria (ACs) for features using the traditional 'Given/When/Then' format. However, when it comes to AI-driven features, this approach breaks down. Probabilistic models don't always produce binary outcomes, making it challenging to define a clear 'Then' clause. For instance, consider a feature that uses machine learning to predict customer churn. How do you write ACs for a model that's inherently uncertain?

What the Research Says

Discussions on r/agile and LinkedIn posts from senior Product Owners suggest that many teams struggle with writing effective ACs for AI features. Some common misconceptions include thinking that traditional ACs are sufficient or that AI features can't be tested using standard agile methodologies. However, practitioner experience indicates that AI features require a different approach. Recent developments in the field, such as the increasing adoption of large language models, have highlighted the need for more nuanced ACs.

For example, fairness thresholds are essential in ensuring that AI models don't perpetuate existing biases. Edge-case enumeration is critical in identifying potential failures in AI models. Furthermore, audit-trail ACs are necessary for tracking and explaining the decisions made by AI models.

How LeadAI Academy Solves This

LeadAI Academy's DocLab offers a range of scenarios and document types, including acceptance criteria templates specifically designed for AI features. With the guidance of Donna/VECTOR, Product Owners can learn to write effective ACs using five key patterns:

• Kill-switch criteria: defining the conditions under which the AI feature should be disabled or overridden
• Fairness thresholds: establishing the acceptable levels of bias or disparity in the AI model's outputs
• Edge-case enumeration: identifying and testing the extreme scenarios that could cause the AI feature to fail
• Audit-trail ACs: specifying the requirements for logging and explaining the decisions made by the AI model
• Regression-on-update tests: ensuring that updates to the AI model don't introduce new bugs or degrade existing functionality

These patterns can be applied using DocLab's acceptance criteria templates, which provide a structured approach to writing ACs for AI features. For instance, a Product Owner can use the template to define the kill-switch criteria for an AI-driven customer support chatbot, ensuring that the feature is disabled when a certain threshold of user complaints is reached.

TL;DR & Next Steps

• Traditional acceptance criteria are insufficient for AI-driven features
• Five patterns can help Product Owners write effective ACs for AI features: kill-switch criteria, fairness thresholds, edge-case enumeration, audit-trail ACs, and regression-on-update tests
• LeadAI Academy's DocLab offers scenarios and document types to support writing ACs for AI features Run the 60-second Enterprise AI Readiness Assessment at /diagnostic to identify areas for improvement in your team's AI feature development process. Start a DocLab session at /doclab to learn more about writing acceptance criteria for AI features and apply the five patterns to your own projects.