Use Cases for Artificial Intelligence in the Product Experimentation Lifecycle

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-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 languageEnglish
Title of host publicationProduct-Focused Software Process Improvement : 25th International Conference, PROFES 2024 Tartu, Estonia, December 2–4, 2024; Proceedings
EditorsDietmar Pfahl, Javier Gonzalez Huerta, Jil Klünder, Hina Anwar
Number of pages16
Place of PublicationCham
PublisherSpringer Nature Switzerland AG
Publication date2025
Pages289-304
ISBN (print)978-3-031-78385-2
ISBN (electronic)978-3-031-78386-9
DOIs
Publication statusPublished - 2025
Event25th International Conference on Product-Focused Software Process
Improvement - PROFES 2024
- Tartu, Estonia
Duration: 02.12.202404.12.2024
Conference number: 25
https://conf.researchr.org/home/profes-2024

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

    Research areas

  • Business informatics - Artificial Intelligence, Continuous Experimentation, Experimentation Automation

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