Choosing the Right Path for AI Integration in Engineering Companies: A
Strategic Guide
- URL: http://arxiv.org/abs/2402.00011v1
- Date: Mon, 25 Dec 2023 11:58:37 GMT
- Title: Choosing the Right Path for AI Integration in Engineering Companies: A
Strategic Guide
- Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmstrom Olsson
- Abstract summary: The paper covers the entire life cycle of building AI solutions, from initial business understanding to deployment and further evolution.
The framework might also help engineering companies choose the optimum AI approach to create business value.
- Score: 4.327763441385369
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
Related papers
- Machina Economicus: A New Paradigm for Prosumers in the Energy Internet of Smart Cities [19.506961581014107]
Energy Internet (EI) is emerging as new share economy platform for flexible local energy supplies in smart cities.
EI aims to unlock peer-to-peer energy trading and sharing among prosumers.
This study will focus on how the introduction of AI will reshape prosumer behaviors on the EI.
arXiv Detail & Related papers (2024-02-28T02:53:17Z) - Green Edge AI: A Contemporary Survey [49.47249665895926]
We present a contemporary survey on green edge AI.
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of deep learning (DL)
We explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - LeanAI: A method for AEC practitioners to effectively plan AI
implementations [1.213096549055645]
Despite the enthusiasm regarding the use of AI, 85% of current big data projects fail.
One of the main reasons for AI project failures in the AEC industry is the disconnect between those who plan or decide to use AI and those who implement it.
This work introduces the LeanAI method, which delineates what AI should solve, what it can solve, and what it will solve.
arXiv Detail & Related papers (2023-06-29T09:18:11Z) - Towards a Capability Assessment Model for the Comprehension and Adoption
of AI in Organisations [0.0]
This article presents a 5-level AI Capability Assessment Model (AI-CAM) and a related AI Capabilities Matrix (AI-CM)
The AI-CAM covers the core capability dimensions (business, data, technology, organisation, AI skills, risks, and ethical considerations) required at the five capability maturity levels to achieve optimal use of AI in organisations.
arXiv Detail & Related papers (2023-05-25T10:43:54Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - Do We Need Explainable AI in Companies? Investigation of Challenges,
Expectations, and Chances from Employees' Perspective [0.8057006406834467]
Using AI poses new requirements for companies and their employees, including transparency and comprehensibility of AI systems.
The field of Explainable AI (XAI) aims to address these issues.
This project report paper provides insights into employees' needs and attitudes towards (X)AI.
arXiv Detail & Related papers (2022-10-07T13:11:28Z) - Building AI Innovation Labs together with Companies [5.316377874936118]
In the future, most companies will be confronted with the topic of Artificial Intelligence (AI) and will have to decide on their strategy.
One of the biggest challenges lies in coming up with innovative solution ideas with a clear business value.
This requires business competencies on the one hand and technical competencies in AI and data analytics on the other.
arXiv Detail & Related papers (2022-03-16T08:45:52Z) - Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering.
In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
arXiv Detail & Related papers (2022-03-06T10:12:56Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Validate and Enable Machine Learning in Industrial AI [47.20869253934116]
Industrial AI promises more efficient future industrial control systems.
The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models.
arXiv Detail & Related papers (2020-10-30T20:33:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.