Use Cases for Artificial Intelligence in the Product Experimentation Lifecycle
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Authors
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.
Original language | English |
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Title of host publication | Product-Focused Software Process Improvement : 25th International Conference, PROFES 2024 Tartu, Estonia, December 2–4, 2024; Proceedings |
Editors | Dietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar |
Number of pages | 16 |
Place of Publication | Cham |
Publisher | Springer Nature Switzerland AG |
Publication date | 2025 |
Pages | 289-304 |
ISBN (print) | 978-3-031-78385-2 |
ISBN (electronic) | 978-3-031-78386-9 |
DOIs | |
Publication status | Published - 2025 |
Event | 25th International Conference on Product-Focused Software Process Improvement - PROFES 2024 - Tartu, Estonia Duration: 02.12.2024 → 04.12.2024 Conference number: 25 https://conf.researchr.org/home/profes-2024 |
- Business informatics