TY - CHAP

T1 - Use Cases for Artificial Intelligence in the Product Experimentation Lifecycle

AU - Stotz, Nils

AU - Drews, Paul

N1 - Conference code: 25

PY - 2025

Y1 - 2025

N2 - Product development teams often struggle to add value- enhancing 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 (existing 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 experimentation 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.

AB - Product development teams often struggle to add value- enhancing 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 (existing 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 experimentation 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.

KW - Business informatics

U2 - 10.1007/978-3-031-78386-9_19

DO - 10.1007/978-3-031-78386-9_19

M3 - Article in conference proceedings

SN - 978-3-031-78385-2

T3 - Lecture Notes in Computer Science

SP - 289

EP - 304

BT - Product-Focused Software Process Improvement

A2 - Pfahl, Dietmar

A2 - Gonzalez Huerta, Javier

A2 - Klünder, Jil

A2 - Anwar, Hina

PB - Springer Nature Switzerland AG

CY - Cham

T2 - 25th International Conference on Product-Focused Software Process<br/>Improvement - PROFES 2024

Y2 - 2 December 2024 through 4 December 2024

ER -