Google is enhancing its virtual apparel try-on tool, accelerating the tech giant’s transition into AI-driven fashion retail. This feature is expected to transform how consumers interact with clothing during online shopping.

Google’s improved virtual try-on assistant is now available across Google Search, Shopping, Images, and AI Mode. The new version allows users to see how garments would look on their own bodies through realistic visualizations, strengthening decision-making before purchase.

This update is part of Google’s “Shopping Graph” strategy, which brings together billions of product listings from global retailers under a single AI-powered ecosystem. As competition among e-commerce platforms intensifies, Google’s investment in immersive shopping tools signals a shift toward a digital fashion discovery experience that rivals physical retail.

Artificial Intelligence Makes Online Fashion More Realistic and Personal

Google’s enhanced virtual try-on experience uses advanced generative AI models that analyze full-length photos. This enables the simulation of how fabrics stretch, fold, and drape across different body types. Unlike previous versions, the new system supports a much larger catalog—tops, bottoms, dresses, outerwear, and footwear—and increases visual accuracy while reducing rendering times. This technology eliminates one of online fashion’s biggest challenges: uncertainty around fit and appearance.

Google Strengthens Its Competitive Position Against AI-Powered Discovery Rivals

The virtual try-on feature also enhances Google’s competitive strength against rivals investing in AI-led discovery systems, such as Amazon, Temu, and TikTok Shop. Analysts predict that when combined with Google’s robust product data infrastructure, virtual try-on technology will become one of the foundational elements of future fashion retail.

UAE President Sheikh Mohamed met with Jeff Bezos, the founder of Amazon, in Abu Dhabi. The UAE leader discussed with Bezos the efforts to advance high-technology partnerships.

The meeting, held at Qasr Al Shati in Abu Dhabi, was attended by Sheikh Tahnoon bin Zayed Al Nahyan, Deputy Ruler of Abu Dhabi and Chairman of the Artificial Intelligence and Advanced Technology Council (AIATC); Khaldoun Khalifa Al Mubarak, Chairman of the Executive Affairs Authority; and several senior officials.

Focus of the Meeting: Artificial Intelligence

The meeting addressed the importance of strengthening cooperation and fostering partnerships in innovation, advanced technology, and artificial intelligence. Discussions particularly focused on applying these tools in vital sectors such as education, healthcare, and the economy.

The stated objective was to advance development, enhance quality of life, and contribute to human progress and prosperity. The two leaders discussed efforts to strengthen partnerships in innovation, artificial intelligence, and other forms of advanced technology. It was emphasized that the purpose of the meeting was to accelerate progress in vital sectors and improve overall quality of life.

Bezos Praises the UAE’s Vision

Bezos praised the UAE’s forward-looking vision and its approach to embracing future technologies in ways that support society and development. Jeff Bezos, the billionaire entrepreneur who founded the technology, streaming, and e-commerce company Amazon in 1994 and remains its executive chairman, commended the UAE’s enterprising approach to adopting future technologies to support societal advancement.

Sheikh Tahnoon Previously Met with Musk and Bezos Together

Meanwhile, Sheikh Tahnoon bin Zayed Al Nahyan, Deputy Ruler of Abu Dhabi and Chairman of the Artificial Intelligence and Advanced Technology Council (AIATC), had previously met with Bezos together with Elon Musk, the founder of Tesla and owner of X. Those meetings focused on “strategies to strengthen collaborative efforts” in artificial intelligence. Sheikh Tahnoon held these discussions as part of Sheikh Mohamed’s official visit to the United States in September.

Jeff Bezos Takes on a New Role in an AI Venture

Four years after stepping down as Amazon’s CEO, Jeff Bezos appointed himself last month as co-CEO of an artificial intelligence startup called “Project Prometheus.” The former Amazon CEO will co-lead Project Prometheus together with technology executive Vik Bajaj.

Jeff Bezos Introduces New Artificial Intelligence Venture Project Prometheus

Amazon founder Jeff Bezos is stepping into the world of artificial intelligence with a vision that differs from the traditional technology approach. His new venture, Project Prometheus, was launched with $6.2 billion in funding. This amount makes Project Prometheus one of the best-funded early-stage artificial intelligence startups in the world.

Project Prometheus marks a departure from Bezos’ previous e-commerce-focused endeavors. The venture deeply focuses on areas such as robotics, advanced manufacturing, and space infrastructure. Leading the initiative as co-CEO alongside physicist Vik Bajaj, Bezos aims to develop AI systems capable of performing physical tasks, which goes far beyond digital applications.

Project Prometheus Targets Physical Artificial Intelligence Solutions

While many AI companies are involved in digital tasks, Project Prometheus has adopted a different approach. The company plans to develop AI agents capable of performing real-world tasks, ranging from robotics to manufacturing processes. This “physical AI” approach responds to the growing belief that general intelligence must be connected to the physical world. Bezos’ interest in space infrastructure also aligns with the long-term goals of Blue Origin, and Project Prometheus could become a key player in off-world exploration and technology.

Bezos Will Take His First Official Operational Role Since 2021

Jeff Bezos’ new venture enters a competitive field, with other tech giants such as OpenAI, DeepMind, and Google making significant investments in AI systems for the physical world. However, Project Prometheus has a notable advantage in terms of funding and leadership. With Bezos’ proven track record in capital-intensive projects and a team selected from top AI research labs, the venture is well-positioned to be a significant player in the emerging field. As competition intensifies, the integration of AI into industrial applications could be the game-changing factor that sets Project Prometheus apart from others.

Jeff Bezos left his role as CEO of Amazon in 2021. This will be the first time he takes on an official operational role in a company since then. The board of directors of ProjectPrometheus also includes Jack Altman, brother of Sam Altman, former president of Y Combinator.

Jeff Bezos Predicts Huge Societal Gains from AI Amid Growing Investment Bubble

A new study by Zebra Technologies Corporation reveals that 87 % of retail decision-makers now believe generative artificial intelligence (Gen AI) and automation solutions will play a major role in loss-prevention efforts. The findings come from Zebra’s 18th Annual Global Shopper Study, published in November 2025. Business Wire+1

Key Findings

• 87 % of retailers identified Gen AI and automation as “significant tools” for loss prevention. Business Wire

• Shopper satisfaction is declining: satisfaction for in-store experiences fell to 79 % and online to 73 % globally. Business Wire

• Associates are also facing challenges: 88 % of store associates reported difficulty obtaining timely assistance or information (up from 82 % last year). Business Wire

• Inventory challenges remain a pain point: 84 % of retailers cited real-time inventory synchronisation as a top priority. And 51 % (vs 57 % last year) of shoppers said they left stores without all items they intended to purchase. Business Wire

Why This Matters

Loss prevention has become a critical area for retailers amid rising costs, supply-chain disruptions and changing shopper behaviours. The fact that nearly nine in ten retail executives expect Gen AI to impact this function signals that AI is shifting from experimentation to operational deployment. The study suggests retailers are no longer looking at AI solely for marketing or personalisation—they now see it as a tool to protect margins and inventory integrity.

Moreover, the study’s findings on declining shopper satisfaction for both in-store and online channels highlight the added pressure on retailers to improve operational reliability, staff support and inventory availability. The connection to Gen AI adoption implies that reducing shrinkage, improving accuracy and automating workflows will be key differentiators in the next phase of retail competition.

Strategic Implications for Retailers

1. Deployment of AI-Driven Loss Prevention Tools
Retailers are likely to invest in systems powered by Gen AI such as computer-vision-based analytics to detect theft, automation of exception workflows, predictive-analytics alerts for shrinkage risks, and advanced data-feeds tying product movement, returns and inventory.

2. Integration of Associate Tools and Frontline Workflows
With 88 % of associates flagging challenges in accessing information, retailers will need to deploy AI assistants and real-time insight platforms—reducing friction in store operations, improving productivity and supporting loss-prevention routines.

3. Enhanced Inventory and Visibility Control
Since inventory gaps remain a major driver of consumer dissatisfaction and margin loss, Gen AI solutions that integrate sensors/RFID, supply-chain data and predictive models will become essential. 84 % of retailers cited real-time inventory synchronisation as a key priority.

4. Optimization of Omnichannel Operations
Loss prevention is not a store-only issue anymore—returns, online fraud, click-and-collect and fulfillment gaps all contribute. Retailers that extend Gen AI tools across channels stand to gain an edge.

Challenges & Considerations

• Data Readiness & Integration: Deploying Gen AI for loss prevention requires clean, high-quality data—inventory movement, transaction logs, video feeds, exception history—and integration across store, online and logistics systems.

• Change Management: The shift to AI-led loss-prevention workflows requires training, change in role definitions, and aligning staff with new tools and metrics.

• Privacy & Ethical Implications: Using AI for surveillance and shrinkage prevention raises questions around consumer consent, privacy, bias in detection systems and regulatory compliance.

• Measuring ROI: While the intent is clear, retailers must develop robust metrics to measure how much shrinkage has been prevented, how recovery has improved and whether AI-tool costs are justified.

What to Watch

• The percentage of major retailers publicly reporting shrinkage reduction tied to Gen AI tools.

• Investment levels by region and size of retailer in “intelligent operations” projects addressing loss prevention.

• Case studies illustrating AI-driven store associates improving productivity, reducing exceptions and improving customer service while also managing loss risks.

• Evolution of AI-loss-prevention regulation and best-practice frameworks, especially in Europe, North America and Asia-Pacific.

Conclusion

The Zebra study highlights that loss prevention is rapidly becoming a strategic priority for retailers—and that Gen AI is seen as a core tool in the effort. With 87 % of decision-makers signalling its importance, the industry appears to be moving beyond hype toward tangible deployment of AI in frontline and operational roles. Retailers that invest in the right data, workflows and change management stand to improve margin protection, customer experience and operational resilience in an increasingly complex retail environment.

New transaction data from Adobe Inc. Analytics reveals that consumer comfort with AI-powered shopping accelerated sharply in October 2025, underscoring how generative-AI sources are driving stronger conversion rates and deeper engagement across online retail. Furniture Today

According to the report authored by Joanne Friedrick, traffic driven by generative AI increased by 1,200 % year-over-year in October. Among visitors arriving via AI-powered paths, conversion exceeded traffic from non-AI sources by approximately 16 %—marking a turning point in how consumers are interacting with digital commerce. Furniture Today

Key Findings from the Data

• In October, consumers arriving via AI-powered channels (such as chatbot assistants, AI recommendation engines or interactive search tools) were 16 % more likely to convert than those coming through traditional online routes such as paid search, affiliates, email, organic search or social media. Furniture Today

• Engagement metrics were stronger too: AI-source visitors showed 13.6 % higher engagement—they viewed more content, spent longer on site and were less likely to bounce immediately. Furniture Today

• E-commerce spend for the month rose to US$88.7 billion, up 8.2 % year-over-year. Mobile devices accounted for a 51.4 % share of spending (~US$45.6 billion). Furniture Today

• The period also saw spikes in specific categories: for example, holiday-decor spending in October rose 130 % compared to average September levels, and major appliance categories like refrigerators/freezers saw a 55 % uplift. Furniture Today

Why This Matters

This data suggests a significant pivot in consumer behavior: as AI-driven shopping interfaces become more mature, shoppers are increasingly comfortable interacting through assistant-style experiences and completing purchases via these new flows. According to Adobe’s director of digital insights, the fact that AI-driven traffic has now exceeded conversion rates of non-AI traffic for two months in a row signals an inflection. Furniture Today

For retailers, this means the importance of AI-enabled entry points—chatbots, smart search, recommendation engines, voice/image-based discovery—can no longer be treated as experimental. They are increasingly central to the path-to-purchase.

Implications for Retailers & Merchants

1. Optimising AI-Assisted Entry Channels
   Retailers need to ensure their AI driven discovery and assistant tools are fully integrated with commerce workflows. Because conversion rates for AI-source traffic are higher, ensuring seamless hand-off from AI-agent to checkout is critical.

2. Raising the Bar on Product Data & Experience
   AI-driven flows rely on structured data (product details, inventory status, pricing, delivery lead-times) more than ever before. Retailers must ensure their back-end systems are configured to serve accurate, machine-readable feeds.

3. Mobile-First & Instant Fulfilment
   With over half of e-commerce spending coming from mobile, and AI users showing faster intent-to-purchase, retailers must focus on mobile experience, quick site-loading, intuitive assistant interfaces and fast fulfilment.

4. Emerging Category Opportunities
   The data highlights strong acceleration in categories like holiday décor, large-appliance home goods and mobile-first consumer purchases. Retailers in these segments should double-down on AI-driven personalization and category-specific offers.

Challenges & Considerations

• Data privacy and transparency: As AI becomes a more significant driver of traffic and conversion, concerns about how consumer data is used, how algorithmic decisions are made and how outcomes are explained will intensify. Retailers must adopt clear governance.

• Integration complexity: Many retailers have legacy systems; integrating AI assistance with checkout, inventory, logistics and fulfillment can be complex and expensive. The payoff is measurable—but timing matters.

• Differentiation risk: If all retailers build similar AI-assistant experiences, differentiation may shift to price, fulfilment or brand. Standing out will require unique product-data experiences or specialized assistant workflows.

• User trust and UX design: Higher conversion by AI-traffic suggests increasing trust—but retailers must design assistants that feel helpful, transparent and reliable, not forced or opaque.

What to Watch

• Monitor successive months to see whether the conversion advantage of AI-driven traffic continues to widen beyond the 16 % margin seen in October.

• Check for retailer case-studies: which brands are reporting fastest conversion uplift, greatest reduction in bounce rates, or highest ROI from AI-powered flows.

• Category-level breakdowns: how do AI-driven shopping behaviours vary across categories (e.g., home furnishings, electronics, apparel)?

• Impact on fulfilment metrics: if AI-users convert faster, can the supply-chain keep pace with expectations in delivery speed, returns handling or service?

• Privacy/regulation tracking: whether regulators begin to scrutinize AI-shopping flows, data use, tracking and attribution frameworks.

Conclusion

As the Adobe data reveals, AI-powered shopping is no longer a fringe experiment—it is now driving measurable gains in conversion and engagement. For online retailers, the message is clear: aligning your commerce architecture with AI-entry points should be a strategic priority. The advantage goes not to those who simply adopt AI, but to those who design it throughout the shopper’s path—from discovery, through personalization, to frictionless checkout and fulfilment.

Customer-engagement platform MoEngage has secured US $100 million in a funding round led by Goldman Sachs Alternatives and A91 Partners, taking its total funding to over US $250 million. The capital will be deployed to scale its suite of AI-powered marketing agents and expand its presence in North America, Southeast Asia (SEA) and Australia/New Zealand (ANZ). Yahoo Finance+3PR Newswire+3Newswire+3

According to the company, North America now contributes the largest share of revenue after doubling its growth in the region over the past year. TechCrunch+1

Background and Funding Details

MoEngage’s latest capital injection comprises primarily growth-equity funds: around 60 % of the US $100 million is primary capital dedicated to platform development, while the remaining 40 % is a secondary share sale, allowing early investors to exit. TechCrunch+1

The startup will use the funds to accelerate its flagship product suite, called Merlin AI, which includes decision-making agents focused on campaign launch, offer personalization and conversion optimisation. One cited case: client Glance reduced its campaign go-live time by 50 % using Merlin AI. Newswire+1

Executive leadership emphasises the move as evolution from legacy “marketing clouds” toward agile, data-driven engagement platforms built for B2C brands serving mobile-first consumers. CEO and co-founder Raviteja Dodda said the firm is “excited to partner with Goldman Sachs and A91 as we continue our global mission.” TechCrunch

Strategic Implications

1. Product & Technology:
By leveraging the new funding, MoEngage aims to enhance its AI-agent stack—focusing not only on creative-copy generation, but on channel-and-timing decisioning based on first-party data. The company maintains that many enterprises are cautious about exposing data directly to large language models, and positions MoEngage as a secure intermediary layer. The Times of India

2. Market Expansion:
With North America now representing the largest revenue region for the firm, the investment enables further hires in go-to-market and customer-success teams across North America, EMEA, SEA and ANZ. The emphasis on SEA and ANZ reflects growth ambition beyond traditional Indian and Asian markets. Newswire

3. Competitive Landscape:
Analysts view this round as validation of the private-market shift toward alternatives in marketing-technology (martech)—with MoEngage growing at roughly 40 % year-on-year and reporting over 1,350 global brands. The Times of India+1 The ability to migrate large customers quickly (e.g., migrating 120 million users for client SoundCloud) is being cited as a competitive differentiator. TechCrunch

Challenges & Risks

• Scaling Global Operations: While MoEngage has momentum, executing global expansion across multiple regions (North America, SEA, ANZ) presents complexities in localisation, hiring, support and regulatory alignment.

• Data Privacy & AI Oversight: As it builds out Merlin AI, handling first-party consumer data and ensuring compliance with privacy standards (GDPR, CCPA) is critical.

• Platform Differentiation: In a crowded martech-space, maintaining product differentiation and value delivery will be key—especially as legacy players (e.g., Adobe, Oracle, Salesforce) adapt their offerings.

• Monetisation and Liquidity: The private-markets thesis suggests substantial runway, but converting growth into sustainable profitability and possibly preparing for IPO will be long-term objectives.

What to Watch Next

• Announcement of new enterprise customers in North America, SEA and ANZ and measurable results (e.g., conversion uplift, campaign-launch speed, retention gains).

• Monitoring of hire announcements: Go-to-market, customer-success, regional leadership in key growth zones.

• Technology road-map updates: enhancements in Merlin AI, release of new modules (e.g., offer decisioning, cross-channel orchestration) and partnerships with data or cloud-providers.

• Competitive response: how other martech firms react or invest to keep pace with AI-agent-led marketing.

• Potential exit strategy: given MoEngage’s growth and funding scale, whether the company sets sights on IPO or major acquisition within 2-3 years.

Conclusion

MoEngage’s US $100 million funding round represents a significant milestone for the company and the broader martech sector. With AI agents at its core and global expansion in sight, MoEngage is positioning itself as a next-generation partner for brands seeking deeper engagement, higher conversion and faster innovation. However, delivering on this vision will require seamless execution across product, region and scale. The outcome will shape how B2C marketers adapt to the evolving demands of data-driven, AI-powered customer experience.