Dinmohammadi, Fateme (2023) Adopting Artificial Intelligence in Industry 4.0: Understanding the Drivers, Barriers and Technology Trends. In: 2023 28th International Conference on Automation and Computing (ICAC), 30 Aug - 01 Sep 2023, Birmingham, UK.

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Abstract

Artificial intelligence (AI) is recognized as an area of strategic importance and a key driver of sustainable development in the era of Industry 4.0. AI is a field of research that deals with the simulation of human intelligence processes using machines and computer systems. Many industries within the manufacturing, energy, construction, aerospace, transport, and healthcare sectors are adopting AI technologies and advanced data analytics tools with the goal to improve their business performance and user experience. Despite some success stories, numerous surveys report that many industries have been slow or failed to adopt AI beyond the proof-of-concept phase to the enterprise scale. This study aims to explore the drivers and barriers to the adoption of AI, and then assess the readiness of industries in implementing AI and big data technologies within their organizations. To gather industry's views on AI and its impact on business models and customer relationships, a close-ended questionnaire is designed and distributed to the respondents. A strategic analysis using the SWOT method is performed to identify the strengths, weaknesses, opportunities, and threats that may face industries during the AI implementation process. The major challenges identified include the lack of access to IT infrastructure and skilled AI talent; lack of good quality data; weak business cases; and complications around policies, regulations, and ethics. To examine the maturity of industries in adopting AI technologies, we used the NASA Technology Readiness Level (TRL) metric, that is based on a scale from 1 to 9 with 1 being the least mature and 9 being the most mature technology. A large majority of respondents have indicated that their TRL in terms of AI infrastructure is between 3 and 4, whereas the TRL in terms of software platforms lies between 3 and 6. Finally, some recommendations are made to help industries overcome challenges in moving to a higher TRL scale.

Item Type:	Conference or Workshop Item (Paper)
ISBN:	9798350335859
Identifier:	10.1109/ICAC57885.2023.10275230
Identifier:	10.1109/ICAC57885.2023.10275230
Subjects:	Computing > Intelligent systems
Depositing User:	Marc Forster
Date Deposited:	09 Dec 2024 14:52
Last Modified:	09 Dec 2024 14:52
URI:	https://repository.uwl.ac.uk/id/eprint/12984

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