1 Introduction

Sustainability, as defined in Ref. [1], entails meeting present needs without endangering the requirements of future generations. It involves safeguarding the natural environment and social well-being while ensuring that economic growth and development do not pose threats. Sustainability is a critical concern in the industry [2]. Today, it is imperative to integrate social and environmental considerations into business decision-making and operations [3]. In specific industries, including agriculture, mining, renewable energy, and manufacturing, sustainability has emerged as a central objective aimed at mitigating adverse impacts on the environment and society [4].

Sustainability comprises three key dimensions: economic, social, and environmental, as noted by Kristensen [5]. The economic dimension ensures that production meets present needs without compromising future capacity [5, 6]. The social dimension focuses on parameters for social equity, access to essential services, security, and citizen participation in governance [7]. The environmental dimension emphasizes responsible resource management and waste control to prevent overconsumption and environmental degradation [5, 8]. These dimensions are interconnected and integral to sustainable development.

The United Nations argues that strategies to generate economic growth must go hand in hand with strategies to promote prosperity and protect the planet [9]. These include a range of social needs such as education, health, and job opportunities, while at the same time ensuring that climate change is halted and the environment is protected.

The fashion industry, notorious for its sustainability shortcomings [10], consumes vast resources in clothing and accessory production, particularly straining water resources. It ranks among the largest water consumers, often with inadequate treatment post-use [2]. Countries like Bangladesh face issues of heavy metal and microplastic pollution in water sources [11, 12], leading to health problems among nearby residents who consume this water [13].

Furthermore, the fashion industry is marred by significant labor exploitation, often subjecting workers to grueling shifts exceeding eight hours a day. Additionally, the transportation of clothing and accessories further exacerbates greenhouse gas emissions. Moreover, the fast fashion model has accelerated the production and disposal of clothing, resulting in a surge in waste generation and unsustainable resource consumption [10].

As a countermeasure to all these problems, the fashion industry is increasingly trying to find solutions and tools that will enable it to achieve sustainability goals. As a result, the fashion industry is increasingly turning to AI to help improve sustainability [14]. AI is a branch of computing that develops systems for simulating the cognitive capabilities of humans, especially in problem-solving tasks [15]. AI is a trending area, and its use has spread to multiple areas, such as medicine, science, and industry.

Through AI, the fashion industry can optimize various processes in the apparel production process [16, 17]. Moreover, AI algorithms enable companies to use their resources better, leading to cost reduction, increased efficiency and effectiveness, and increased production speed [17]. In addition, this benefits the environment and society as AI makes it possible to process and use natural and human resources better [18].

This study provides a focused examination of how AI can enhance sustainability within the fashion industry. We concentrate on evaluating specific areas where AI can be applied in fashion, as well as assessing the performance of different AI techniques in bolstering sustainability. Our primary aim is to discern both the merits and drawbacks associated with these AI approaches, offering valuable insights for experts and stakeholders in these fields.

2 Methodology

This systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statements [19], the most commonly used reporting guidelines for systematic reviews [20]. The following is a description of the stages used to carry out this work.

2.1 Research questions

This article aims to explore how AI is used in the fashion industry to improve sustainability and answer the following questions:

Q1:

In which ways can artificial intelligence improve sustainability in the fashion industry?

Q2:

What are the advantages of using artificial intelligence as a tool to achieve sustainability?

Q3:

What are the limitations of using artificial intelligence as a tool to achieve sustainability?

2.2 Eligibility criteria

2.2.1 Inclusion criteria

Regarding the inclusion criteria (IC), an article was included as long as it met all of the following criteria:

IC1:

Empirical research, not books, manuals, or tutorials.

IC2:

Research that explicitly uses AI techniques as a potential tool to drive sustainability in the fashion industry.

IC3:

Research published between 2010 and 2022.

IC4:

Research published in peer-reviewed journals only.

2.2.2 Exclusion criteria

Regarding exclusion criteria (EC), an article was excluded if it failed to meet any of the following criteria:

EC1:

Research that does not involve approaches based on AI.

EC2:

Research not published in a peer-reviewed journal.

EC3:

Research not written in English.

EC4:

Document not available.

2.3 Information sources

We decided to use multiple databases and search engines to expand the number of relevant articles considered. Details of these are given in Table 1.

Table 1 Online sources used in this work
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Other sources, such as trial registers or other grey literature sources, were not used.

2.4 Search strategy

We performed a search string based on the previously mentioned inclusion criteria to search for articles in the databases. This was: ( ( ( "sustainability" AND "fashion industry" ) OR "sustainable fashion" ) AND ( "artificial intelligence" OR "machine learning" OR "deep learning" OR "expert systems" OR "knowledge-based systems" ) ). Likewise, we apply a series of filters, where possible, in each of the databases to obtain only relevant articles for our review. Details of this can be seen in Table 2. The databases were searched on December 30, 2022.

Table 2 Filters and specifications for searching online sources
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2.5 Selection process

From the preselected articles, titles and abstracts were imported into CovidenceFootnote 1 systematic review software for screening. First, using Covidence tools, duplicate articles were identified and removed. Then, the articles were manually reviewed by two researches (LR and FR) to remove any remaining duplicates. Next, all the researchers independently screened the titles and abstracts of the articles. In case of disagreement, the consensus was reached to determine articles to screen in the next stage by discussion.

The next step was to remove articles that could not be accessed. That is, articles whose full-text was unavailable were removed and not included for full-text screening. This was done by one researcher (LR).

Then, the next step consisted of retrieving the articles and screening them by full-text reading. In this step, the inclusion and exclusion criteria were considered to determine which articles passed to the next stage and which did not. This was done by two researchers (LR and FR) and verified by the other (AGP and EC).

Finally, the articles that met the inclusion criteria and evidenced a relevant contribution to the objectives of this study were included in the review.

2.6 Data collection process

A data extraction sheet was developed for this stage. The first version of the extraction sheet was made by two researchers (LR and FR). This was first tested with ten randomly selected articles. Subsequently, the other researchers (AGP and EC) verified and validated that the extraction sheet worked correctly and allowed all relevant information to be obtained. They also made corrections to the extraction sheet when necessary.

2.7 Information extraction

To address our research questions, the articles selected for review were thoroughly examined to extract the following main information:

  • Potential application domain or area.

  • AI class used.

  • Aim of the work.

  • Main technique(s) used.

  • Relevant findings.

  • Publication year.

In a complementary manner, the publishing company and the journal in which each article was published were also extracted. The information extraction was carried out and cross-checked by all the researchers in this work.

3 Results

We identified 616 articles from the selected online sources. Articles were screened and selected, as shown in Fig. 1, resulting in 37 studies meeting our inclusion criteria and ultimately being included in the review.

Fig. 1
figure 1

PRISMA flow diagram used in this work

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Figure 2 shows the distribution of relevant articles retrieved per year. From this graph, it can be seen that from 2019 onwards, there has been an increase in sustainability, the fashion industry, and AI research. Most of the research is focused on the year 2020. The year 2022 presents a low number of articles since most of the articles we could not access are from this year. Concerning the publishers, it is evident that Elsevier is the academic publishing company that contributes most to this work. It provides more than 40% of the articles retrieved, as shown in Table 3. In second place is Springer, followed by the other publishing companies.

Fig. 2
figure 2

Overall distribution of retrieved articles over time

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Table 3 Distribution of articles based on publisher
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Given that we studied different domains in this work, a wide variety of journals host this type of research. Table 4 below highlights the top four journals among those that contributed significantly to this study.

From this table, the Journal of Cleaner Production stands out as the journal that contributes the most to this study, providing 10.81% of the articles reviewed. To integrate the textile industry with technology, the International Journal of Clothing Science and Technology is the journal that makes the second most significant contribution, with 8.11%. This could be due to the significant development of technology and its irruption in different industries in recent years. Then, the journals Multimedia Tools and Applications and Textile Research Journal contribute 5.41% of the articles. Finally, all the other journals have a contribution of 2.7% each.

Table 4 Four leading journals contributing to this review
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Subsequently, we categorized the articles based on their potential applications in promoting sustainability within the fashion industry. The allocation of each article to a specific domain was achieved through extensive discussions and consensus among all the researchers involved in this study. The findings reveal that a majority of the reviewed articles focus on optimizing the supply chain, comprising 40.54% of the total articles. Following closely, 27.03% of the articles are dedicated to sustainable clothing design and sales. Reducing waste represents 18.91% of the articles, while data analysis contributes 13.52%. These insights are summarized in Table 5.

Table 5 Distribution of articles based on domain of application
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Upon closer examination of the technologies used in the retrieved articles, artificial neural networks (ANNs) are the most frequently employed. Figure 3 illustrates the prevalent use of ANNs in various forms, including traditional multi-layer feed-forward neural networks, convolutional neural networks (CNNs) for image analysis, and generative adversarial networks (GANs) for image generation. Additionally, fuzzy logic and classical machine learning (ML) algorithms like k-means, random forests, and support vector machines are commonly featured. Other technologies, such as blockchain and the Internet of Things (IoT), are also utilized.

Fig. 3
figure 3

Distribution of articles by AI used over time. It was selected the most relevant technology used in each retrieved article

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We also classified the articles based on the sector to which their contributions are oriented, including government, business, or customer orientation. Similarly, we categorized each article according to the sustainability dimension that best represents its contribution. In cases where an article could belong to more than one dimension or target sector, we selected the most representative one through consensus among all the researchers involved in this study.

The results regarding the sustainability dimension are summarized in Table 6. These findings reveal that the majority of the articles are situated within the economic dimension, focusing primarily on measures aimed at satisfying the present and future needs of customers. Specifically, 41% of the reviewed articles fall within this dimension. Following closely is the environmental dimension, encompassing 35% of the articles. This sector is notable for its contributions related to waste management, control, and reduction, with several works dedicated to enhancing recycling practices. Lastly, the social dimension comprises 24% of the articles, driven by contributions aimed at improving accessibility and services

Table 6 Articles according to the sustainability dimension assigned to each
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Regarding the target sector, the outcomes of this classification are detailed in Table 7. The data indicates that the majority of the articles are business-oriented (84%), emphasizing sustainability measures implemented within company operations. These encompass actions concerning the supply chain, manufacturing processes, and design, all aimed at enhancing sustainability. In the second position is the customer sector (16%), focusing on initiatives aimed at improving the customer’s shopping experience while encouraging the consumption of sustainable products. Conversely, the government sector exhibits minimal relevance in this context (0%).

Table 7 Articles according to the target sector assigned to each
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The summary of selected articles in terms of their main characteristics is presented in Table 8. This data extraction table serves as a comprehensive guide to understanding the landscape of the research included in this review. It details key aspects such as the year of publication, methodologies employed, objectives, and key findings. This consolidation of information is designed to offer readers an easily navigable overview, thereby facilitating a deeper comprehension of the review’s scope, methods, and results.

Table 8 Summary of reviewed articles
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4 Discussion

From the reviewed articles, it was possible to highlight four main application areas in which AI can help to improve sustainability in the fashion industry. Each of these is described below.

4.1 Supply chain optimization

The supply chain encompasses the entire process, from the creation of a product or service to its delivery to the end consumer. It can be visualized as a network [59] comprising human and material components, all striving to minimize costs and maximize efficiency without compromising the final product or service’s quality. However, real-world supply chains encounter numerous challenges [57], including shortages, delayed deliveries, and difficulties in adapting to changing market demands [57, 60].

Sustainability has become a central theme in supply chains, especially within the fashion industry. However, many supply chains in this sector still need to become more sustainable [61]. AI offers a myriad of benefits to supply chains that would be otherwise unattainable [57]. It provides a range of tools applicable throughout the supply chain, from procurement and raw material processing to manufacturing, distribution, and final product delivery, all while promoting sustainability [62].

Numerous works have already incorporated AI to optimize supply chains. For instance, in Refs. [31, 30], ML and ANNs, respectively, are proposed for classifying clothing categories. Both approaches achieved high accuracy rates (> 80%), correctly categorizing clothing and subcategories. Models like these enable fashion companies to automate classification tasks, ensuring organized and efficient product category management.

In the manufacturing domain [36], suggests using ML, specifically, support vector machine to detect common fabric defects, such as neps, broken ends, broken picks, and oil stains from images, obtaining high accuracy (> 98%). Similarly, in Ref. [33], CNNs are employed for color difference detection, a common defect in warp-knitted fabrics. The authors utilized the YOLO neural architecture and achieved real-time accuracy. These examples illustrate AI’s positive impact on sustainable apparel manufacturing, enhancing efficiency and resource optimization by identifying manufacturing faults and preventing defective garments from reaching consumers, ultimately reducing waste.

Continuing with manufacturing [52], proposes a ML-based system that combines dimensionality reduction techniques and k-means based on 3D scans to define adaptive morphotype mannequins. This innovative approach eliminates the need for predefined tables, ensuring garments better fit customers’ shapes, thus optimizing manufacturing resources.

AI can also be utilized to select the best components for garments. For example, in Ref. [27], an expert system is developed to identify and select the best type of cotton fiber for product creation, leveraging documented knowledge sources and customer experience. The system optimizes clothing manufacturing, reducing long-term costs and waste generation. Promising results were obtained through real case testing.

Likewise, in Ref. [25], ANNs are employed to discover the properties influencing burst strength and air permeability in single jersey knitted fabrics, as well as to predict these properties. In [32], ANNs are used to predict the relationship between drape parameters and fabric mechanical properties. These AI applications provide quantitative insights into material characteristics and their influencing parameters, facilitating resource optimization and high-quality garment and textile manufacturing.

In the first case, it was found that the burst strength of single knit fabrics is affected by fiber strength, fiber elongation, and fiber mean length. Similarly, air permeability is affected by fiber mean length, yarn twists per inch, yarn count and number of wales and courses. In the second case, bending, shear and aerial density were found to affect the drape parameters the most. In both cases, AI helped to understand quantitatively and in more detail the materials’ characteristics and the parameters that influence them.

In a complementary vein [22], employs ANNs to determine the optimal inventory level for finished products, considering setup costs, holding costs, material costs, and product demand. This optimization prevents overproduction of merchandise that may go unsold, ultimately reducing waste and resource expenditure.

In terms of resource and product distribution, AI also proves beneficial. For instance [58, 43], utilize ANNs to address routing problems, optimizing distribution vehicle routes to minimize distances, reduce gas emissions, and ensure on-time product delivery based on real-time geographical context information.

Furthermore, in Ref. [23], a decision support model leverages fuzzy logic to predict on-time delivery chances in a complex supply chain environment, mitigating negative consequences of delivery variations, demand forecasting inaccuracies, materials shortages, and distribution lead time uncertainties.

Addressing supplier selection in sustainable supply chain management [47], presents an expert system for circular supply chains that manufacture, dispose of, and recycle, reducing costs and waste. The system combines multi-criteria decision-making, ML, and fuzzy logic to select the most suitable suppliers, as demonstrated in a real-world case study.

Finally, [46] introduces an expert system based on fuzzy logic to evaluate supply chain sustainability comprehensively. This system analyzes various aspects, including environmental, economic, policy, governance, participation, social issues, transparency, and leadership support, yielding a sustainability score that assists fashion companies in evaluating their operations and making decisions to achieve sustainability objectives.

Common limitations in these articles include the high data and processing demands of some AI approaches, such as those based on ANNs. Additionally, the reluctance to adopt this technology due to a lack of knowledge can hinder its widespread use. Nonetheless, these works collectively demonstrate AI’s substantial potential to optimize various aspects of a fashion company.

4.2 Design and sale of sustainable clothing

Sustainable fashion is often perceived as less exciting, of lower quality, or not aligned with the latest fashion trends [63]. Despite this misconception, the sustainable fashion industry and its products often struggle to gain market relevance [64]. However, AI emerges as a potentially valuable tool for sustainable fashion companies, aiding in both the design and promotion of clothing that aligns with current fashion trends [65]. These technologies can significantly contribute to driving the purchase and use of sustainable garments [66].

For instance, in Ref. [28], an application of Artificial Neural Networks (ANNs) is proposed to create a semi-autonomous intelligent system supporting designers during the creative process. This system leverages user preferences, fashion trends, seasonal data, and company constraints to make predictions and design suggestions.

Similarly, in Ref. [37], an expert system is employed to assess consumer perceptions of eco-style. The objective is to analyze consumers and gain deeper insights into eco-fashion and consumer perceptions, thereby ensuring the success of eco-fashion and sustainable product development.

Other works focus on analyzing the latest fashion trends to inform clothing design. In Ref. [50], machine learning methods are used to analyze trends from events like the New York Fashion Week, predicting new design patterns based on this data. Likewise, in Ref. [26], a fuzzy logic-based system is proposed to analyze fashion trends related to color and suggest new color combinations for manufacturers to consider. Both approaches have been evaluated and demonstrated their utility in supporting designers’ decision-making processes.

Design suggestion systems are also explored in Refs. [48, 53], but with a twist. These works introduce Generative Adversarial Networks (GANs) to generate new clothing designs based on fashion trends and user purchasing data. What sets these systems apart is their ability to generate recommendations and graphical design suggestions. This innovation streamlines the design and manufacturing process, offering designers textual feedback and visual representations, significantly enhancing efficiency.

While these developments are instrumental in making sustainable fashion more appealing and aligned with current fashion trends, the ultimate goal of gaining market relevance hinges on increasing the attractiveness and accessibility of sustainable clothing for consumers. This can be achieved through technologies that promote sustainable clothing and improve the search and purchasing experience.

For instance, in Ref. [51], CNNs are employed to provide recommendations that consider not only customer preferences but also their social network. A similar approach is proposed in [55], which generates visual recommendations along with explanations for the recommendations. In Ref. [56], GANs are used to recommend complementary fashion items, assisting customers in completing their outfits by suggesting items that complement their selections.

Furthermore, in Ref. [54], a system is introduced to offer size recommendations, utilizing size tables and ANNs to create an intelligent sizing system. This system was tested in an Iranian store, resulting in time savings and increased customer satisfaction by assisting customers in selecting the right clothing size.

Continuing to enhance the shopping experience, a virtual try-on interface based on GANs is presented in Ref. [49]. This interface allows users to virtually try on clothes from the comfort of their homes, facilitating online shopping and providing a realistic visualization of how the clothes would appear when worn.

In conclusion, AI has the potential to reshape sustainable fashion by aligning it with current trends, enhancing the design process, and improving the shopping experience. This technology bridges the gap between sustainability and market relevance, making eco-friendly fashion more appealing and accessible to consumers.

4.3 Reducing waste

Effective waste management is critical for the fashion industry’s sustainability, especially considering its historical negative environmental impact [67]. Over recent years, the proliferation of fast fashion and a throwaway culture has led to a significant surge in textile production and consumption [68]. Unfortunately, the majority of textiles and clothing ultimately find their way into landfills, with only a small fraction being recycled, making textile waste a pressing global concern [69].

AI offers innovative solutions to address these waste management challenges. In Ref. [42], an intelligent knowledge-based system is applied to sustainable waste management. This comprehensive approach analyzes various facets of waste collection, transportation, and processing. Moreover, it considers critical dimensions of sustainable development, including well-being, health, clean water, and climate change.

Within the realm of supply chains [38], introduces an expert system aimed at waste management. This system focuses on the return of products at the end of their life cycle to various supply chain components for reuse and value recovery. Notably, it incorporates blockchain technology, enabling the transparent processing and recording of data across the entire product lifecycle, thus fostering a circular economy.

AI’s predictive capabilities are harnessed in waste prediction models like those seen in Refs. [44, 45], both utilizing ANNs. By leveraging historical data, these systems provide more accurate waste generation predictions. Consequently, these insights empower the development of strategies to curtail waste, boost recycling rates, and promote sustainability.

AI-driven waste classification is a rapidly advancing field. For instance [40], presents a methodology reliant on CNNs for classifying different fiber materials, even when confronted with limited data. Similarly Ref. [41], demonstrates the effectiveness of CNNs in classifying various materials, including glass, paper, plastic, and organic matter, using images generated from smartphones.

Taking waste sorting to the next level [39], combines CNNs with robotic technology. Equipped with sensors and mechanical grippers, a robot continuously monitors waste flow and autonomously performs sorting tasks. The practical deployment of this system in a major Spanish waste sorting plant underscores its industry relevance and potential for efficient waste management.

In summary, AI presents a powerful toolkit for addressing the fashion industry’s waste management challenges. These innovative applications not only offer solutions for sustainable waste disposal but also align with broader sustainability goals. By leveraging AI for waste prediction, classification, and intelligent systems, fashion companies can not only reduce their environmental impact but also enhance their operational efficiency. These advancements underscore the transformative role of AI in promoting sustainability within the fashion industry, emphasizing the path towards a more eco-friendly and responsible future.

4.4 Data analysis

Data analysis plays a pivotal role in the fashion industry, offering valuable insights and predictive capabilities that can be harnessed to drive sustainability efforts [57, 70, 71]. Customer segmentation, as demonstrated in studies like [21, 24], allows fashion companies to gain a deeper understanding of their customer base. By tailoring strategies to specific market segments, these companies can effectively promote and sell sustainable clothing to a diverse range of consumers.

Moreover, predictive analytics, exemplified in research such as Refs. [29, 34], empowers fashion businesses to make informed decisions and anticipate market trends. Sales predictions and style forecasting enable these companies to optimize inventory management, streamline supply chains, and proactively address future challenges, all of which are essential for sustainable fashion practices.

In the context of sustainable fashion, studies like [35] shed light on consumer behavior and attitudes towards sustainability. By utilizing ML techniques to analyze consumer responses, these studies reveal valuable insights into consumer knowledge and preferences regarding sustainable clothing. The findings underscore the importance of educating consumers about sustainable fashion practices and strategies to encourage their adoption.

In sum, data analysis serves as a powerful tool for fashion companies striving to enhance sustainability. From customer segmentation to predictive analytics, these data-driven approaches empower the fashion industry to make informed decisions and tailor strategies that promote sustainable clothing consumption and production. This underscores the potential of data analysis in driving positive change within the fashion industry.

4.5 Limitations of the study

One notable limitation of this study is its primary reliance on existing literature and readily available sources. This approach may inadvertently omit certain non-academic, ongoing, or unpublished works that could potentially contribute valuable insights to the field of AI in sustainable fashion. Nevertheless, it is worth noting that a comprehensive array of relevant online sources was diligently incorporated, making a concerted effort to ensure that the findings presented in this paper offer a representative and meaningful overview.

Furthermore, this study primarily operates at a high-level examination of the contributions of AI within the sustainable fashion industry. This means that it provides a broad overview and general insights into how AI is being applied to promote sustainability in the fashion sector. However, it may not encompass the intricate details and specific nuances that some readers, particularly those looking for in-depth technical or sector-specific information, may seek.

5 Conclusions

This study aimed to assess the application of Artificial Intelligence (AI) in the fashion industry to promote sustainability. To achieve this goal, we conducted a systematic review of 37 articles sourced from relevant online publications. These articles were subsequently reviewed and analyzed.

The analysis of the selected articles unequivocally demonstrates that AI plays a pivotal role in the fashion industry’s transition towards sustainable development. The findings underscore a multitude of contributions that can be harnessed across various facets of the fashion industry, encompassing supply chain optimization, sustainable clothing design and sales, waste management and control, and data analysis.

Our analysis reveals the pervasive presence of ANNs as the primary technological cornerstone in the integration of AI within the fashion industry. ANNs, with their versatile capabilities, occupy a central role across a diverse array of applications. These encompass but are not limited to waste classification, where ANNs excel in their ability to accurately categorize materials. Furthermore, they play a pivotal role in garment defect detection, swiftly identifying and rectifying manufacturing flaws. In addition, ANNs significantly contribute to the augmentation of sustainable clothing design and production, ensuring that the industry’s environmental footprint is minimized.

Moreover, our investigation uncovered a balanced distribution among sustainability dimensions in the reviewed articles. The majority of contributions are situated within the economic dimension, prioritizing the fulfillment of current and future customer needs. Subsequently, the environmental dimension encompasses a significant portion of articles, primarily focusing on responsible waste management and the promotion of recycling. Finally, the social dimension concentrates on creating accessible environments and providing services that cater to diverse market needs.

The articles predominantly targeted the business and customer sectors. In the business sector, contributions primarily centered on improving garment manufacturing, design, and handling processes. Conversely, the customer sector emphasized equipping users with tools to encourage the purchase of sustainable products while ensuring a delightful and attractive shopping experience. Notably, there were no significant contributions directed towards the government sector.

However, it’s crucial to acknowledge the limitations encountered on this journey towards AI-driven sustainability in fashion. One notable challenge lies in the substantial volume of data required to train neural networks effectively. Acquiring and managing extensive datasets, especially in a domain as dynamic as fashion, can prove daunting. The fashion industry also faces the cost considerations associated with the implementation of AI technologies. These investments encompass not only the acquisition of cutting-edge hardware and software but also the training and upskilling of personnel to harness the full potential of AI systems.

6 Future work

As of 2023, the landscape of AI has undergone a remarkable surge in popularity, marking a global trend like never before. This surge has permeated various industries, including fashion, where AI’s transformative potential is becoming increasingly evident. Therefore, for future research endeavors, it is highly advisable to conduct a dedicated review with a specific focus on the developments and impacts witnessed in this pivotal year.

An exploration centered around the year 2023 can shed light on how this unprecedented surge in AI’s popularity has resonated within the fashion industry. This examination can provide valuable insights into how the fashion sector has harnessed the momentum of AI, whether it be through innovative applications, novel solutions, or heightened integration. Such a study can also help identify emerging trends, challenges, and opportunities unique to this period, offering a comprehensive view of the industry’s trajectory.

Moreover, a complementary study centered around 2023 would not only serve as a testament to the dynamism of AI but also provide an invaluable perspective for researchers, businesses, and stakeholders seeking to navigate the evolving landscape of AI-driven sustainability in fashion. It can illuminate how the fashion industry has adapted and innovated in response to the surge in AI adoption, potentially uncovering novel strategies and best practices for sustainable growth and development.