Defining Success in Probabilistic Products: Key Performance Indicators and Lifecycle Management for Generative AI Applications in Enterprise

Abstract

Generative Artificial Intelligence introduces probabilistic behavior into enterprise software systems, fundamentally challenging the deterministic assumptions underlying traditional product management and Software-as-a-Service success metrics. Conventional indicators such as Daily Active Users, churn rate, and uptime fail to capture the economic, operational, and risk dimensions inherent in stochastic model outputs. This paper proposes a novel product management framework for probabilistic AI systems, grounded in enterprise deployments of generative applications launched from zero to production scale. A new class of AI-native Key Performance Indicators is introduced, including Response Accuracy, Hallucination Rate, Token Efficiency, and Human Intervention Rate, alongside a Probabilistic Product Lifecycle model integrating continuous evaluation and human-in-the-loop governance. Through comparative analysis and applied case evidence, the study establishes a new standard for measuring success in enterprise generative AI products, reframing uncertainty from a liability into a measurable and manageable product dimension.