Artificial Intelligence and Sustainability in Fashion: A Triple Bottom Line Approach

Emily Rosa Shahaj ()
Additional contact information
Emily Rosa Shahaj: University for the Creative Arts, School of Fashion and Textile

Chapter Chapter 23 in Sustainable Digital Marketing for Fashion and Luxury Brands, 2025, pp 555-580 from Springer

Abstract: Abstract As artificial intelligence (AI) undergoes an arms race of investment and development, the fashion industry has readily begun to explore potential use cases across their operations. However, AI’s impact on sustainability remains under-discussed, and its potential to increase revenue is often treated as an afterthought. As European Union legislation increasingly seeks to regulate the environmental impact of both the fashion industry and AI, fashion brands must consider AI and sustainability together in order to continue selling to European consumers. Applying a three-factor definition of corporate sustainability as long-term positive impact on (1) planet, (2) people, and (3) profits, this chapter outlines AI sustainability considerations for all three factors within the luxury fashion sector. Specifically, this chapter evaluates AI tools’ impact on both the environment and various jobs within the fashion industry, as well as potential avenues for luxury fashion to leverage such tools for alleviating current financial burdens implicit within the medium of online retail. Beyond the hype and headlines, this chapter identifies where sustainability is and is not being discussed in regard to AI, as well as the inherent opportunities, risks, and limitations involved with its uptake across a fashion business’s operations.

Keywords: Artificial intelligence; Fashion innovation; Fashion sustainability; Luxury strategy; Triple bottom line (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-82467-8_23

Ordering information: This item can be ordered from
http://www.springer.com/9783031824678

DOI: 10.1007/978-3-031-82467-8_23

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().