A4 Article in conference proceedings
A Multiple Case Study of Artificial Intelligent System Development in Industry (2020)


Nguyen-Duc, Anh; Sundbø, Ingrid; Nascimento, Elizamary; Conte, Tayana; Ahmed, Iftekhar; Abrahamsson, Pekka (2020). A Multiple Case Study of Artificial Intelligent System Development in Industry. In EASE '20 : Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering. New York: ACM, 1-10. DOI: 10.1145/3383219.3383220


JYU authors or editors


Publication details

All authors or editors: Nguyen-Duc, Anh; Sundbø, Ingrid; Nascimento, Elizamary; Conte, Tayana; Ahmed, Iftekhar; Abrahamsson, Pekka

Parent publication: EASE '20 : Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering

Place and date of conference: Trondheim, Norway, 15.-17.4.2020

ISBN: 978-1-4503-7731-7

Publication year: 2020

Pages range: 1-10

Publisher: ACM

Place of Publication: New York

Publication country: United States

Publication language: English

DOI: http://doi.org/10.1145/3383219.3383220

Open Access: Publication channel is not openly available


Abstract

There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.


Keywords: artificial intelligence; software development; software engineering; software business

Free keywords: artificial intelligent system; software development; software engineering; business opportunity; Pivot; AI business pattern; SEMAT


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

Preliminary JUFO rating: 1


Last updated on 2020-09-07 at 23:13