The integration of Artificial Intelligence (AI) systems in medical diagnostics for advancing personalized patient care, along with offering accuracy and early detection. This AI technology utilizes machine learning and deep learning algorithms for processing wide datasets accurately and quickly for providing healthcare professionals with clear and valuable inputs in diagnostics, such as disease detection, analysis, and treatment plans for patients.

The market for AI in diagnostics is growing at a rapid rate, driven by the AI technology applications across diverse medical fields in managing diseases by early interventions and decreasing diagnostics-related errors, and improving detection of disease outcomes. As these AI tools utilize datasets to provide objective evaluation and decrease variability and diagnostic mistakes, compared to traditional methods, which depend on subjective interpretation and are not consistent, and can result in wrong diagnoses due to human errors. This is which is growing its adoption among the precision healthcare industry across specialties, inclusive of pathology, radiology, dermatology, and cardiology, among others, for precise medicine to improve patient outcomes.

Additionally, the growing investment and new innovations by companies in the advancement of AI systems that could increase precise healthcare applications are also increasing the market requirement in the years to come. The market is estimated to hold a strong CAGR of 34.46%, reaching $10.140 billion by 2030 from USD 2.310 billion in 2025.

The following are the benefits of AI in Diagnostics, increasing their demand across different applications:

• Advanced AI solutions improve the analysis of medical images, assisting radiologists in recognizing abnormalities.
• AI processes a large amount of data from electronic health records (EHRs), offering interpretations of diagnoses that are complex to attain manually.
•  AI systems offer evidence-based advice to healthcare professionals for assisting them in making informed decisions.
• The predictive model of AI works in data-backed forecasts of disease detection and progression, and assists in preventive care.

The following trends across diverse medical fields will shape the demand for AI in Diagnostics over the next five years.

Major Application of AI in Diagnostics Acceleration its Demand in the Healthcare Sector:

• Pathology and Histopathology

The pathology branch works in the examination of tissue samples under a microscope for identifying the specific cellular and structural abnormalities that are indicators of changes that are characteristic of diseases, including cancer. Meanwhile, histopathology is a critical diagnostic tool for diverse health-related issues, including cancerous tissue identification and characterization, which assist medical professionals in demonstrating the type and stage of cancer.

The rise in integration of innovative solutions like advanced AI algorithms and computer-aided diagnostic techniques is transforming computational pathology and histopathology with AI-enabled diagnostics for advancing precision medicine for cancer.

The AI in diagnostics is expected to grow in this field due to the rise in cancer cases, and these systems promote the precision identification of cancer subtypes and provide personalized treatment plans for patients. According to data from the American Cancer Society (ACS) reported that new cancer cases in the United States were 2,001,140, while projected deaths due to cancer were accounted for 611,720 in 2024.

Meanwhile, the World Health Organization (WHO) stated in February 2024 data that there were 20 million new cancer cases globally in 2022, with 9.7 million cancer deaths, with three major cancer types being lung, breast, and colorectal cancers. Lung cancer was 2.5 million new cases globally, while breast cancer and colorectal cancer new cases were reported for 2.3 million and 1.9 million, respectively, in 2022.

The organization also highlighted that there is a significant increase in cancer cases, with a 77 percent rise by 2050 from 2022 cases. They projected that new cancer cases will exceed 35 million in 2050. This leads to an increase in the adoption of AI in diagnostics for facilitating early detection of these diseases by efficiently and accurately analyzing cellular patterns of cellular samples. Furthermore, these AI streamlines the workflow for medical professionals while also delivering data-backed and faster outcomes and decreasing manual tasks that work in promoting the efficiency of the diagnostics laboratories.

• Cardiology and Cardiovascular Diagnostics

The AI is increasingly rising in adoption by the cardiology field for effective prediction of cardiovascular risks in patients and detection of abnormalities in echocardiograms and electrocardiograms. The deep learning AI models are better than traditional methods as they analyze ECG data to identify the risk factors for heart diseases and are more sensitive.

Additionally, with the rise in cardiovascular disease cases, there will be an increase in AI in diagnostics for providing preventive measures, which can enhance patient outcomes. According to the University of Utah’s report of heart disease statistics for 2024, coronary artery disease causes 40 percent of heart-related deaths every year in the United States.

Moreover, as per the U.S. Centers for Disease Control and Prevention data of October 2024, a person dies every 33 seconds in the United States due to cardiovascular disease, and it is a leading cause of death in the country. Additionally, about 702,880 individuals died from heart disease, which is equivalent to 1 in every 5 deaths in 2022 in the USA.

Further, each year, approximately 805,000 individuals suffer from a heart attack in the United States, while coronary artery disease causes 371,506 deaths in 2022.   This is expected to promote the AI in the diagnostics market for supporting early detection and decreasing the severe cardiovascular complications in patients, as reducing risk assessment. According to an article published in the American Heart Association in 2024, stated that AI worked on predicting cardiac arrest more than 50 minutes before its onset in about 91 percent of patients in the pediatric ICU, in comparison with prediction by clinicians was 6 percent, this represents a positive predictive analysis in supporting effective and quick analysis and detection in preventing the risk of cardiovascular diseases.

• Medical Imaging and Radiology

The AI technologies are increasingly leveraged in medical imaging and radiology for the analysis of medical images such as CT scans, X-rays, and MRIs. The AI in the diagnostics market is expected to grow as it streamlines workflow in this medical field by providing automated repetitive work like lesion detection and segmentation of images, which allows radiologists to focus on more complex medical cases. This also supports informed decision making for the radiologist, and the AI-enabled cloud solutions in diagnostics also offer remote diagnostics in underserved regions.

In addition, the companies are increasingly enhancing the performance of AI in the diagnosis of medical images and the reduction of cost and time, which is beneficial for patient treatment. For instace, in February 2025, Royal Philips announced the launch of its SmartSpeed Precise, which is integrated with Dual-AI driven engines, to enhance MRI to advance the outcome of patients.  This technological innovation integrates advanced AI into its MRI system to offer faster scanning and enhanced image quality. Moreover, the company’s AI-enabled MRI workplace software provides decreased scan time, enhanced patient results with more accurate diagnoses. These developments offer advancement of generative AI and learning technologies for enhancing diagnostic precision with reduced healthcare cost, which in turn will potentially grow the AI in the diagnostic market in the future.

The Following Trends are advancing the deployment of AI technologies in Diagnostics:

• Integration with Healthcare Systems: These AI tools offer increased integration with systems like EHRs and LIMs, which work in making a healthcare ecosystem that provides increased data analysis and work efficiency in precision healthcare.
• Increase the Focus on Underserved and Remote Regions: The remote, rural, and underserved locations have limited medical infrastructure, which promotes the AI adoption for connecting to these regions and also offering access to enhanced diagnostics in these regions.
• Enhancement in Multimodal Data Analysis: The AI technologies feature of enabling diverse types of data, such as genomics, imaging, and other health determinants, which drives the growth of AI in the diagnostics market, which will promote accurate and personalized diagnosis.
• Rise in Commercialization and Investment: The growing utilization of AI technologies in diagnostics is attracting investments by major market players such as Microsoft and Google for the development of advanced diagnostic tools. In June 2025, Microsoft AI teams shared the MAI-DxO, which is a Microsoft AI diagnostics orchestrator that enables the provision of diagnoses for more than 85 percent of NEJM cases, which is more than 4 times then analysis from an experienced physician. This will decrease healthcare costs by providing cost-effective tests.

Competitive Landscape by Industry Leaders

The AI in Diagnostics market is undergoing a huge transformation and will witness robust growth and innovation. Listed below are a few market players offering new product launches and technological innovation:

• In June 2025, NeuralCure AI announced the launch of its first diagnostic and digital twin OS for preventive healthcare infrastructure. It offers a self-navigating system with the utilization of AI technologies that bypasses the traditional method and is estimated to account for USD590 billion by 2029.
• In April 2025, Royal Philips, which is a leader in health technology, announced the launch of the upgrade of its AI-enabled Elevate Platform on the EPIQ Elite ultrasound imaging platform at UltraFest 2025 held in India. This innovative solution focuses on enhancing efficient diagnostics and streamlining workflows, along with enabling timely decision-making for clinicians for a large range of patients.

AI Semiconductor Race

The edge AI semiconductor market is being transformed in 2024–25 through its rapid growth from a disruptive niche to a mainstream technology. The growth as a result of the increasing deployment of AI inference directly on devices such as industrial sensors, smart cameras, autonomous vehicles, and smartphones has unlocked demands further downstream. The rationale of being able to process data at the edge is fairly simple: reduce latency, increase privacy, and normalise the strain on data centres. Development is happening at a furious pace; Intel detailed its new Core Ultra processors designed for edge AI in January at CES 2025 (Lindsay, 2025), IBM released Power11 chips for inference workloads ai in business usages, memory-chip companies like Micron are tackling the “memory wall” that limits the AI-architecture (AI on-device) capabilitie, and governments are not remaining passive. South Korea’s National Artificial Intelligence Committee pledged 9.4 trillion won by 2027 to support the domestic AI-semiconductor ecosystem; China’s US 8.2 billion National AI Industry Investment Fund seeks to accelerate chip innovation; and India’s 2024–25 budget doubled its semiconductor and display allocation to ₹6,903 crore, approved three new fabs in February, and extended R&D programs for chip startups. These breakthroughs in technology, government funding, and the larger move to decentralise AI workloads are moving the edge AI semiconductor market altogether from an interesting niche to a salient component of the AI computing practice.

Here are the top names powering the edge AI semiconductor boom:

1. Nvidia

NVIDIA disclosed quarterly earnings for Q1 FY2026 of $44.1 billion in revenue, growing 69 % year-over-year, from $26.1 billion in the same quarter last year, and dominating the AI semiconductor sector. Despite restrictions by the U.S. that prevented it from selling H20 chips to China, effectively costing it about $4 billion this quarter, it performed better than expected, plus provided a prediction of $45 billion in Q2 revenue.

On the edge engine front, NVIDIA is not letting up. It revealed at GTC 2025 its GB10 Grace‑Blackwell SiP coming to AI workstations, providing 1 PFLOPS FP4 performance in a small package, perfect for edge inferencing starting July 22, 2025. Also, while CES 2025 introduced developments that included the RTX 50-series chips and NIM microservices aimed at providing the capability for foundation models to run locally on RTX AI PCs.

2. Intel Corporation

Intel had revenue of $53.1 billion in revenue in 2024, with both the Network & Edge Group and the Client Computing Group moving toward edge AI semis. The company embraced edge AI, launching both its modular Open Edge Platform and Edge AI Suites bundles of software and hardware oriented toward retail, industrial, and smart cities, announced at MWC 2024. In Q1 2025, Intel launched the Tiber Edge Platform, with the Geti toolkit for computer‑vision model training at the edge. Also, from a report by Reuters, new CEO Lip‑Bu Tan is advancing a homegrown strategy to outpace Nvidia by focusing on edge AI devices and systems instead of purchasing many startups.

3. Google (Alphabet Inc.)

Alphabet is advancing the edge AI semiconductor space with its custom AI chips. In 2024, Google introduced Trillium, its sixth-generation TPU, which is optimised for on-device inference to improve energy efficiency, and with 4.7× more compute compared to the last TPU v5e and 67% improvement in efficiency. During Google I/O 2024, Google introduced Gemini Nano, tailored for mobile and edge devices, along with Trillium TPUs in preview via Google Cloud services. At the most recent Google Cloud Next 2025, Alphabet introduced Ironwood, its current seventh-generation TPU with an impressive delivery of up to 3,600× performance and 29× better energy efficiency versus its original TPU. When combined with intelligent models built for edge, Google is firmly established in the edge AI semiconductor space with plans to strengthen its position as a dominant provider of edge inference.

4. AMD (Advanced Micro Devices)

AMD is making a strong foray into the edge AI semiconductor segment with its flexible, power-efficient adaptive SoCs and embedded platforms. First quarter of 2025, revenue was $7.4 billion, gross margin was 50%, operating income was $806 million, net income was $709 million, with a growing embedded and edge AI revenue represented in its admission.

On February 6, 2024, AMD unveiled its Embedded+ architecture, which combines the first Ryzen Embedded CPUs, and Versal adaptive SoCs on the same board, bringing together the compute-rich power and capabilities of adaptive SoCs to develop new ways to simplify sensor fusion and establish a low-latency AI inference platform for industrial, medical, and automotive projects.

A few months later, on April 9, 2024, AMD introduced the Versal AI Edge Series Gen 2 second-generation adaptive SoCs with next-generation AI Engines sporting up to 3× TOPS-per-watt, and with integrated Arm CPUs for true end-to-end edge AI acceleration.

5. Qualcomm Technologies, Inc.

Qualcomm is strongly established in the edge AI semiconductor market, providing best-in-class AI acceleration across mobile, IoT, automotive, and enterprise devices. In 2025, Qualcomm made known their Edge AI Box, a plug-and-play combination of AI inference accelerators and 5G connectivity for smart cities, surveillance, and smart factory use cases. At Embedded World 2025, Qualcomm launched developer kits featuring Edge Impulse and RB3 Gen2, providing access to over 170,000 developers to proof and prototype AI models on microcontrollers and edge processors. In March 2025, Qualcomm announced a partnership with Palantir that combined their real-time data analytics with Qualcomm’s edge AI platforms for industrial and manufacturing use cases. These media releases, from Qualcomm’s newsroom, share a clear embrace of empowering AI at the edge of the network, making Qualcomm a leader in edge AI semiconductor derivatives.

6. Arm Holdings

Arm is still a foundational vendor in the edge AI semiconductor market, providing processor IP, AI accelerators, and development tools. Arm launched its first Armv9 edge AI platform in February 2025, the Cortex‑A320 CPU plus Ethos‑U85 NPU, which was capable of running on-device AI models with one billion parameters, targeting IoT and smart city use cases. In October 2024, Arm announced ExecuTorch, a PyTorch framework on its compute platform, which would allow efficient deployment of quantised Llama 3.2 AI models onto mobile and edge devices. Arm’s year-in-review report A-to-Z 2024, released in November 2024, further emphasised Arm’s advances in edge AI, including new Ethos accelerators, as well as the KleidiAI performance library for developers. All these announcements, from Arm’s newsrooms, reaffirmed a clear embrace of empowering AI at the edge of the network.

7. Graphcore

Graphcore is certainly disrupting the edge AI semiconductor market by simplifying the deployment of AI workloads in proximity to the AI data’s point of origin. The UK-based company was founded in 2016 and designs state-of-the-art Intelligence Processing Units (IPUs), along with a software stack called Poplar that provides APIs for AI workloads. In November 2024, Graphcore initiated its first recruitment drive since the acquisition by SoftBank in July 2024, with 75 new positions in silicon, systems and software, in increased capacity to develop next-generation AI compute platforms, which was reported on “Graphcore’s Blog”. SoftBank’s acquisition reflects confidence in Graphcore’s USA-developed IPU technology, which is seeing increased adoption in edge computing to help deploy large AI models out of traditional data centres. Although Graphcore does not manufacture its chips, and partners with foundries to design integrated chips for deployment in clients’ edge systems, which helps consolidate Graphcore’s points of engagement in the AI semiconductor market.

8. MediaTek

MediaTek is a powerhouse in the edge AI semiconductor market, providing AI-optimised SOCs for smartphones, IoT, automotive, and more. At MWC February in 2025, MediaTek launched Hybrid Computing device-cloud and RAN capabilities for low-latency Gen-AI at the edge. At Computex May in 2025, MediaTek’s CEO announced the company’s first 2 nm chip and collaborative efforts with NVIDIA to produce the GB10 Grace‑Blackwell Superchip, which includes merging MediaTek’s ASIC knowledge with the AI fabric of NVIDIA. And these are not just demos: MediaTek also announced in March 2025 the Genio 720 and 520 IoT platforms, which support generative AI workloads within smart environments. These are official releases demonstrating MediaTek’s vertically integrated approach.

9. Synopsys

Synopsys is an important behind-the-scenes player in the edge AI semiconductor market for its EDA tools and IP. Synopsys noted on June 19, 2025, in announcing a deep collaboration with Samsung Foundry, to successfully tape‑out HBM3-based customer designs with advanced sub‑2 nm technology nodes, incorporating its AI‑driven flows and 3DIC Compiler to accelerate development and improve power, performance, and area. Synopsys also achieved first-pass silicon success in developing its IP stack with TSMC’s 2 nm N2 process in late April 2025, establishing low‑power AI chips for high-efficiency edge mobile devices. Synopsys also collaborated with SiMa.ai in late 2024 to improve its SoCs for automotive edge AI and showcased its work at CES 2025.

These developments, advanced process support, high-efficiency IP, and ecosystem alignments—position Synopsys as an enabler in the edge AI semiconductor market, despite not building its silicon.

10. Huawei Technologies Co., Ltd.

Huawei is still a powerful player in the edge AI semiconductor market as it continues to deliver in-house AI rockstar silicon and state-of-the-art edge inference systems. In April 2025, Huawei started mass shipping its Ascend 910C, a dual-chiplet SoC (likely in response to Nvidia’s H100) with ~60% of inference performance, built on SMIC’s industry-leading 7 nm N+2 process. That same month, Huawei launched CloudMatrix 384, a supernode with 384 Ascend 910C NPUs connected through ultra‑high-bandwidth fabric, developed to provide high-powered edge and data centre AI. Beyond these flagship chips, Huawei’s Ascend 310—a 16 TOPS AI inference SoC has been deployed in real-world healthcare

Conclusion

The edge AI semiconductor market is no longer emerging; it is exploding. From global leaders in chip design like NVIDIA, Intel, and Qualcomm to smaller, specialised innovators like Graphcore and Arm, these companies are not only developing innovative chip designs, but they are also re-inventing where and how AI happens. As AI moves further beyond the cloud and into devices, factories, vehicles, and cities, the demand will (and should only) increase for silicon that is faster, smaller and smarter at the edge. We are already seeing not just real investment and government support, but also partnerships. It is clear that edge AI is the new normal, and these are organisations building the silicon that will make it a reality.

As per Knowledge Sourcing Intelligence (KSI) latest report, the AI in Fashion Market is expected to grow at a CAGR of 42.72% from USD 2.5 billion in 2025 to USD 14.8 billion by 2030.

AI in Fashion Market Highlights:

• Personalized Shopping: AI enhances customer experiences with tailored recommendations and virtual try-ons.
• Supply Chain Optimization: AI streamlines operations, reducing costs and improving efficiency.
• Trend Forecasting: AI analyzes data to predict fashion trends and innovate designs.
• North American Leadership: Strong AI investments and tech giants drive market growth.

The effective use of Artificial Intelligence (AI) is revolutionizing fashion brands, which is being driven by the consumer demand for personalization, sustainability, and efficiency. With the US fashion market at the forefront, the adoption of AI technologies is accelerating, which is further being fueled by a tech-savvy consumer base, a robust technological infrastructure, followed by significant investments from both public and private sectors.

The incorporation of AI is one of the significant factors influencing the growth of several segments of the fashion business. AI technology boosts client satisfaction and loyalty via personalized shopping experiences using data analysis. AR & AI Virtual Try-On and Fitting Room solutions enhance the customer shopping experience, reducing return times and ensuring a high level of certainty. The industry will continue to benefit from supply chain optimization, dynamic pricing, fraud detection, trend research, and new product innovation, focusing on sustainability. These initiatives will help address rapidly changing customer expectations by improving secure transactions and operational efficiency. The fashion market is a fluid environment that also improves standards, although the introduction of AI has a broader impact on driving this industry.

The Booming US AI in Fashion Market: A Snapshot of Growth

According to the USCC data of April 2023, it is anticipated that more people will be shopping online and being influenced in their buying decisions by social media. By 2022, the fast fashion business was expected to account for $106.4 billion in revenue.

Using a centralized and fast-moving supply chain and leveraging the search histories of consumers on its app, Shein is taking major market share in the United States, competing with rivals such as Zara and H&M, according to the same source. Other fashion-related Chinese businesses are taking an interest in this AI business model.

Governmental investments in the AI-based technology sector witnessed major growth within the past few years, especially in countries like the USA, UK, India, Canada, and Japan. The UK Government introduced AI Tech Missions Funds, under which the government allotted about GBP 110 million, whereas about GBP 900 million was allotted for AI Research Resources.

Some of the major market players operating are Microsoft Corporation, Amazon Web Services Inc., IBM Corporation, Intelistyle, Stylumia Intelligence Technology Pvt. Ltd., LALALAND, True Fit Corporation, Stitch Fix, Inc., ZMO.AI, Zalando SE, Neural Fashion AI, and Resleeve, among others.

The Increasing Popularity of AI in the Fashion Industry

The adoption of AI is expected in fashion, with more picky customers seeking unique style preferences and experiences. AI-led recommendations, influencer inspiration, and customizable products are driving a growing proportion of this shift. Rather than thinking about a one-size-fits-all, current times focus more on the individual. As such, consumer demands lean towards personalization, and brands respond by adopting innovative technologies. It demonstrates a significant shift in how people connect with the industry they engage with, and also deeply offers their input for advancements through data insights.

In addition, Mango, one of Europe’s top fashion companies, is pushing the boundaries for its digital transformation with the introduction of Lisa. Lisa is an in-house conversational generative AI platform that was introduced in October 2023. In under nine months, the fashion company developed its conversational generative AI platform that uses a spectrum of models Lisa works with, some proprietary and others open-source. This new platform is built on the backbone of Mango’s tailored interface, similar to ChatGPT, and is intended for use by its partners and workforce.

Increasing Global Ownership of Smartphones

 The major factor propelling artificial intelligence (AI) in the fashion market’s growth is the increasing ownership of smartphones worldwide. With the increasing ownership of smartphones, consumer accessibility to the fashion sector is increasing rapidly. The global smartphone ownership witnessed a major growth, with about 4.3 billion individuals, or about 54% of the total global population owning smartphones, as stated by the GSMA.

In the fashion sector, various global retailers introduced AI-based try-on features that enable the consumer to try clothes and accessories virtually, increasing the demand for AI in the fashion industry. Smartphones also enhance the customer experience and feature data-driven insight into the latest trends.

Virtual Try-Ons and Immersive Experiences: Redefining Retail

The combination of AI with virtual reality (VR) and augmented reality (AR) is revolutionizing the retailing experience in the US AI in Fashion market. Computer vision and machine learning-based virtual try-on solutions enable customers to see how clothing or makeup would appear prior to actual purchase.

In June 2023, Google launched an AI-driven virtual try-on for women’s tops, allowing customers to view how clothing falls and fits on various models.

The US AI in the Fashion industry is also witnessing an increase in AI-based photorealistic visuals for marketing and design. These technologies allow US companies to develop immersive, interactive experiences that endear them to consumers, further affirming the United States’ position at the forefront of embracing innovative retail technologies.

Trend Forecasting and Design Innovation: Staying Ahead of the Curve

Integrating AI technologies within fashion design, where deep design plays a major role, is changing how fashion is conceived, produced, and marketed. Some of the changes are taking their cue from technological developments, others from changing consumer preferences, and others from investments that stakeholders are making into the business.

Moreover, as AI was introduced in the industry, governments worldwide established regulations and policies for its ethical usage, protection of data, and consumer protection.

• The General Data Protection Regulation by the European Union holds any processing of data very seriously, defining clear protocols for them on how exactly they collect and use consumer information through AI.
• The United States Federal Trade Commission issued guidelines on the use of AI in business operations, urging transparency and fairness. Although government statistics on AI adoption in fashion are sparse, the augmented regulatory focus denotes its increasing importance in the industry.

Fashion brands and tech companies with their AI Drive Technologies

 Shein uses artificial intelligence to dynamically make changes to its supply chain and consumer demand. Up to 600,000 listings for a global market are sold. Using this AI reduced unnecessary inventory and costs associated with predictions of style and preferences.

• In March 2024, Oracle introduced new artificial intelligence (AI) capabilities within Oracle Fusion Cloud Customer Experience (CX) to help marketers, sellers, and service agents accelerate deal cycles.
• In September 2023, with the growing interest in AI-powered consumer technologies, Humane Inc. unveiled the complete form factor of its first product, the Humane AI Pin. Before its official release on November 9, the Ai Pin was showcased at Paris Design Week in 2024 as part of a partnership with the design label Coperni. It is a screenless, stand-alone device and software platform made just for AI called the Humane AI Pin. Incorporating a considerable range of sensors, the wearable, made up of intelligent clothing, is designed to integrate with users’ daily routines, thereby allowing for more natural and intuitive computer interactions
• In April 2023, the first beta of a fashion assistant powered by ChatGPT, made available on the app and website of Europe’s leading online clothing and leisure goods retailer, Zalando.
• In June 2022, Pinterest announced that it had finalized the acquisition of THE YES, an AI-powered fashion purchasing platform that lets users browse a customized feed based on their active input regarding brand, style, and size.

Future Outlook: A Dynamic and Transformative Landscape

Artificial Intelligence (AI) in the fashion industry has played a very significant role in the ongoing trends, along with the increasing transformative era, which is redefining the operations of the fashion brands. From optimizing the supply chains to personalizing the shopping experiences for customers, AI’s role in the sector has set up unprecedented precision and efficiency.  As the US remains a global leader in fashion innovation, AI’s role in this sector is expanding rapidly, driven by technological advancements and evolving consumer expectations.

Moreover, with the ongoing increase in online shopping and the collaboration of fashion brands with AI chatbots, users are experiencing efficiency in their shopping experience. In this regard, as per the US Census Bureau, e-commerce now accounts for a considerable percentage of the US apparel sales, which is further expected to increase and grow with the growth of retailers who are ready to invest in AI.  One common example is Amazon Fashion’s AI-powered visual search tool, launched in 2024, which allows customers to upload images to find similar products, contributing to its dominance in online fashion retail.