Challenges in developing and deploying AI in the engineering, procurement and construction industry
Paper in proceeding, 2022

AI in the Engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-depth studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Deep Learning

innovation

Machine Learning

engineering

procurement and construction (EPC) industry

AI in the EPC industry

Artificial Intelligence

Author

Rimman Dzhusupova

McDermott

Jan Bosch

Chalmers, Computer Science and Engineering (Chalmers), Interaction Design and Software Engineering

Helena Holmström Olsson

Malmö university

Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022

1070-1075
9781665488105 (ISBN)

46th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2022
Virtual, Online, USA,

Subject Categories

Social Sciences Interdisciplinary

Other Engineering and Technologies not elsewhere specified

Software Engineering

DOI

10.1109/COMPSAC54236.2022.00167

More information

Latest update

1/3/2024 9