Use Cases for Artificial Intelligence in the Product Experimentation Lifecycle
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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Product-Focused Software Process Improvement: 25th International Conference, PROFES 2024 Tartu, Estonia, December 2–4, 2024; Proceedings. ed. / Dietmar Pfahl; Javier Gonzalez Huerta; Jil Klünder; Hina Anwar. Cham: Springer Nature Switzerland AG, 2025. p. 289-304 (Lecture Notes in Computer Science; Vol. 15452).
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Improvement - PROFES 2024, Tartu, Estonia, 02.12.24. https://doi.org/10.1007/978-3-031-78386-9_19
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Bibtex
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RIS
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
KW - Artificial Intelligence
KW - Continuous Experimentation
KW - Experimentation Automation
UR - http://www.scopus.com/inward/record.url?scp=85211896270&partnerID=8YFLogxK
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 -