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Choosing the right path for AI integration in engineering companies: A strategic guide
Eindhoven Univ Technol, Math & Comp Sci, Eindhoven, Netherlands..
Chalmers Univ Technol, Comp Sci & Engn, Gothenburg, Sweden..ORCID iD: 0000-0003-2854-722X
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-7700-1816
2024 (English)In: Journal of Systems and Software, ISSN 0164-1212, E-ISSN 1873-1228, Vol. 210, article id 111945Article in journal (Refereed) Published
Abstract [en]

The Engineering, Procurement and Construction (EPC) businesses operating within the energy sector are recognizing the increasing importance of Artificial Intelligence (AI). Many EPC companies and their clients have realized the benefits of applying AI to their businesses in order to reduce manual work, drive productivity, and streamline future operations of engineered installations in a highly competitive industry. The current AI market offers various solutions and services to support this industry, but organizations must understand how to acquire AI technology in the most beneficial way based on their business strategy and available resources. This paper presents a framework for EPC companies in their transformation towards AI. Our work is based on examples of project execution of AI-based products development at one of the biggest EPC contractors worldwide and on insights from EPC vendor companies already integrating AI into their engineering solutions. The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution. The framework identifies how various factors influence the choice of approach toward AI project development within large international engineering corporations. By presenting a practical guide for optimal approach selection, this paper contributes to the research in AI project management and organizational strategies for integrating AI technology into businesses. The framework might also help engineering companies choose the optimum AI approach to create business value.

Place, publisher, year, edition, pages
Elsevier, 2024. Vol. 210, article id 111945
Keywords [en]
Machine learning, Deep learning, Artificial intelligence, Developing and deploying AI project, Engineering procurement and construction, industry
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:mau:diva-66157DOI: 10.1016/j.jss.2023.111945ISI: 001152187200001Scopus ID: 2-s2.0-85182456889OAI: oai:DiVA.org:mau-66157DiVA, id: diva2:1841061
Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-02-27Bibliographically approved

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Olsson, Helena Holmström

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