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Generative AI is revolutionizing the way organizations operate, offering transformative capabilities that span automated content creation, strategic decision-making, and customer engagement through AI-driven chatbots. This paper conducts a comprehensive literature review to explore the applications, challenges, and strategic requirements for adopti...
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A OpenIA é uma empresa que vem se destacando no desenvolvimento da Inteligência Artificial para chatbots na atualidade, criando seu primeiro modelo chamado de GPT, que vem evoluindo a cada dia, trazendo novas versões como o GPT-3.5 e GPT-4. Essa evolução passou de uma Inteligência Artificial que processava somente texto para uma Inteligência Artifi...
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... Blockchain, for example, facilitates enhanced traceability and transparency in supply chains, helping to ensure that products are sourced sustainably and recycled efficiently [15]. Furthermore, the use of generative AI has proven to be pivotal in optimizing material use and product designs, making it easier for brands to adopt circular practices and reduce waste [16]. The "Cradle to Cradle" framework is a significant contributor to the theoretical basis of the circular economy. ...
... Blockchain technology, in particular, has proven to be an essential tool for improving supply chain sustainability by ensuring transparency and accountability in sourcing and recycling efforts [15]. Similarly, the integration of generative AI in fashion design and production has the potential to significantly reduce waste by optimizing material use and production timelines [16]. For instance, leading fashion brands are exploring the implementation of recycling programs, utilizing sustainable materials and promoting product longevity through repair and reconditioning programs [6,28]. ...
The present article analyses the theme of circularity in the fashion industry, with particular attention to the role of technology in favoring the adoption of circular economic models. The article explores the role of technology in supporting the circular economy in the fashion industry, focusing on the implementation of circular business models by three companies: SHEIN, Ralph Lauren, and Cotopaxi. The aim is to investigate the challenges and opportunities associated with the adoption of circular economy practices in an industry known for its environmental impact. The research highlights how technology, particularly software such as Materia MX, can facilitate streamlined supply chains, reduced waste, and optimized resource efficiency, thus, overcoming obstacles such as high investment costs and supply chain complexities. The case studies demonstrate how each company has integrated circular practices, such as recycling, reusing materials, and improving transparency through technologies such as AI, blockchain, and IoT, to promote sustainability. Thus, the study emphasizes the importance of technological innovation in enabling a more sustainable and circular future for the fashion industry while addressing challenges related to consumer awareness, regulatory pressures, and infrastructure. The results suggest that technology is a key factor in the fashion industry’s transition to a circular economy, offering a competitive advantage and facilitating the achievement of environmental objectives.
... Additionally, the lack of high-quality, diverse, and labeled datasets for training AI models in agriculture poses a notable limitation, as many farms operate in unique environmental conditions that are not well-represented in existing datasets [146]. Economic considerations further complicate the adoption of generative AI, as high upfront investment costs for hardware, software, and AI expertise can be prohibitive [147]. Many farmers in developing regions may lack the financial resources to integrate AI-driven solutions into their operations [148]. ...
Artificial intelligence (AI) is transforming industries, generating both enthusiasm and concern. While AI-driven innovations enhance productivity, healthcare, and education, significant ethical issues persist, including misinformation, algorithmic bias, and job displacement. This study examines public perceptions of AI by analyzing large-scale social media discourse and integrating sentiment analysis with expert insights via the Delphi method to assess global perspectives. Findings reveal notable differences across socio-economic contexts. In high-income countries, discussions emphasize AI ethics, governance, and automation risks, whereas in low-income regions, economic challenges and accessibility barriers dominate concerns. Public trust in AI is significantly influenced by governance frameworks, transparency in algorithmic decision-making, and regulatory oversight. Recent advancements in AI governance highlight the increasing role of explainable AI (XAI) and algorithmic fairness, alongside regulatory developments tailored to societal needs. Algorithmic nudging has emerged as a tool for guiding user behavior while maintaining autonomy, and research on user sensemaking in fairness and transparency underscores the importance of interpretability tools in fostering trust and acceptance of AI-driven decisions. These insights emphasize the need for adaptive, context-specific policies that ensure ethical AI deployment while mitigating risks. By bridging public sentiment analysis with governance research, this study provides a comprehensive understanding of AI’s societal impact. Findings offer practical implications for policymakers, industry leaders, and researchers, contributing to the development of inclusive, transparent, and accountable AI governance frameworks that align with public expectations.
