Product development teams often struggle to add value-en- hancing features without increasing maintenance costs at the same time. A data-driven approach, especially through controlled online experiments (A/B tests), is crucial. A/B testing compares a control variant (exist- ing product) with a treatment variant (modified product) in real-world settings, allowing companies to make informed decisions based on user behavior data. This paper explores how AI can streamline the experimen- tation lifecycle by enhancing efficiency and reducing manual workload. Based on a qualitative-empirical study, we identified AI use cases in each step of the lifecycle, which could facilitate the experimentation activities. Focusing on AI’s role in hypothesis formulation, experiment design, and data analysis, the paper advances the understanding of how to automate and optimize experimentation in product development. The presented framework guides practitioners in identifying potential use cases of AI in the product experimentation lifecycle.