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Purpose: The aim of the study was to investigate the application of artificial intelligence and digital technologies in fashion design and innovation. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: AI and digital technologies revolutionize fashion by aiding design through data analysis, enabling virtual prototyping, and providing personalized recommendations. They optimize supply chains, enhance sustainability, and offer virtual try-on experiences. Customization options expand, driven by predictive analytics for trend forecasting, while AI-driven customer engagement tools enhance support. These innovations foster efficiency, sustainability, customization, and customer engagement in fashion. Unique Contribution to Theory, Practice and Policy: Social identity theory, diffusion of innovation theory & social learning theory may be used to anchor future studies on the application of artificial intelligence and digital technologies in fashion design and innovation. Implement AI-driven customization tools that allow customers to design their own clothing, leading to increased customer satisfaction and brand loyalty. Encourage the adoption of technologies that promote inclusivity and diversity in fashion by providing incentives for companies to invest in personalized design solutions.
Amara Adekunle (Sun,) studied this question.