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Harnessing Artificial Intelligence: Risk Assessment and Sustainable Business Model Pathways for the Industrial Sector

Ramaprasad, Ananya Nag LU (2025) In IIIEE Master Thesis IMEM01 20251
The International Institute for Industrial Environmental Economics
Abstract
The global industrial sector is a highly polluting and greenhouse gas-emitting sector. As industries increasingly explore Artificial Intelligence (AI) to enhance operational efficiency and drive innovation, questions emerge regarding its alignment with sustainability objectives. While AI is often positioned as a key enabler of digital transformation, its sustainable integration into industrial operations remains fragmented and fraught with risk. This thesis investigates how AI deployment in the industrial sector—specifically within secondary industries such as machinery and manufacturing—poses sustainability risks, while also presenting opportunities for sustainable business model innovation. Drawing on an extensive literature review and... (More)
The global industrial sector is a highly polluting and greenhouse gas-emitting sector. As industries increasingly explore Artificial Intelligence (AI) to enhance operational efficiency and drive innovation, questions emerge regarding its alignment with sustainability objectives. While AI is often positioned as a key enabler of digital transformation, its sustainable integration into industrial operations remains fragmented and fraught with risk. This thesis investigates how AI deployment in the industrial sector—specifically within secondary industries such as machinery and manufacturing—poses sustainability risks, while also presenting opportunities for sustainable business model innovation. Drawing on an extensive literature review and semi-structured interviews with industry stakeholders, the study identifies six key sustainability risk areas linked to AI integration: energy consumption and carbon emissions, hardware and e-waste, inequalities and workforce displacement, privacy and cybersecurity, bias and programming and regulatory developments. The research highlights a gap in comprehensive risk frameworks tailored to industrial applications of AI, as well as a lack of alignment between existing AI-enabled business models and sustainability principles. The study proposes pathways for responsible AI adoption through business model innovation that mitigates these risks while leveraging AI’s potential for sustainable value creation. These include green AI and AI-as-a-Service, circular AI hardware management, inclusive AI upskilling platforms, ethical AI certification services and privacy-first AI solutions. By embedding AI into operations, products, and services in a manner that is both strategic and ethically sound, industrial firms can better balance digital transformation with long-term sustainability goals. Ultimately, this thesis contributes to the emerging discourse on AI governance by offering practical recommendations for risk-informed AI integration and catalyzing the development of sustainability-driven frameworks for the industrial sector. (Less)
Please use this url to cite or link to this publication:
author
Ramaprasad, Ananya Nag LU
supervisor
organization
course
IMEM01 20251
year
type
H2 - Master's Degree (Two Years)
subject
keywords
artificial intelligence, industrial sector, sustainability risks, sustainable business models, risk assessment
publication/series
IIIEE Master Thesis
report number
2025:15
ISSN
1401-9191
language
English
id
9209977
date added to LUP
2025-08-19 09:40:48
date last changed
2025-08-19 09:40:48
@misc{9209977,
  abstract     = {{The global industrial sector is a highly polluting and greenhouse gas-emitting sector. As industries increasingly explore Artificial Intelligence (AI) to enhance operational efficiency and drive innovation, questions emerge regarding its alignment with sustainability objectives. While AI is often positioned as a key enabler of digital transformation, its sustainable integration into industrial operations remains fragmented and fraught with risk. This thesis investigates how AI deployment in the industrial sector—specifically within secondary industries such as machinery and manufacturing—poses sustainability risks, while also presenting opportunities for sustainable business model innovation. Drawing on an extensive literature review and semi-structured interviews with industry stakeholders, the study identifies six key sustainability risk areas linked to AI integration: energy consumption and carbon emissions, hardware and e-waste, inequalities and workforce displacement, privacy and cybersecurity, bias and programming and regulatory developments. The research highlights a gap in comprehensive risk frameworks tailored to industrial applications of AI, as well as a lack of alignment between existing AI-enabled business models and sustainability principles. The study proposes pathways for responsible AI adoption through business model innovation that mitigates these risks while leveraging AI’s potential for sustainable value creation. These include green AI and AI-as-a-Service, circular AI hardware management, inclusive AI upskilling platforms, ethical AI certification services and privacy-first AI solutions. By embedding AI into operations, products, and services in a manner that is both strategic and ethically sound, industrial firms can better balance digital transformation with long-term sustainability goals. Ultimately, this thesis contributes to the emerging discourse on AI governance by offering practical recommendations for risk-informed AI integration and catalyzing the development of sustainability-driven frameworks for the industrial sector.}},
  author       = {{Ramaprasad, Ananya Nag}},
  issn         = {{1401-9191}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{IIIEE Master Thesis}},
  title        = {{Harnessing Artificial Intelligence: Risk Assessment and Sustainable Business Model Pathways for the Industrial Sector}},
  year         = {{2025}},
}