Chapter 22: Product Discovery and Trend Analysis |
Summary |
Product discovery and trend analysis represent two sides of the same challenge in modern retail and e-commerce: understanding what consumers want before they fully know it themselves, and getting the right products in front of them at the right moment. Artificial intelligence has transformed both disciplines from reactive to predictive. Trend analysis, once the domain of fashion forecasters who worked months ahead based on intuition and sporadic cultural observation, now operates in near-real time through systems that ingest social media feeds, search data, and purchase signals to identify emerging patterns before they reach mainstream awareness. Product discovery, meanwhile, has evolved from keyword-based search to conversational, intent-driven experiences that anticipate shopper needs based on context, behavior, and natural language. |
This chapter examines how leading retailers and brands across multiple sectors are deploying AI for trend analysis and product discovery. The cases span fashion, general merchandise, consumer goods, and food. Each illustrates a different dimension of the same fundamental shift: moving from human-paced observation to machine-accelerated anticipation, and from search-driven retrieval to conversation-driven curation. The chapter concludes with a detailed summary of key patterns, results, and strategic implications. |
|
22.1 Trend Analysis: From Intuition to Real-Time Signal Processing |
22.1.1 Target's Trend Brain: Generative AI for Apparel Development |
Target, the American general merchandise retailer, faced a specific competitive threat: fast-fashion platforms like Shein and Temu were compressing trend cycles from months to weeks, while traditional retailers remained locked in seasonal calendars that left them perpetually behind. The company's response, unveiled in spring 2025, was Trend Brain, a generative AI platform designed to help designers anticipate where trends are heading rather than react to where they have already been. |
Trend Brain draws on a diverse set of inputs: social media feeds, runway photography from fashion shows, and real-time purchasing data from Target's own channels. The system does not simply aggregate what is popular; it looks for early signals of momentum, identifying styles, colors, and materials that are gaining traction before they achieve broad visibility. This predictive orientation is the critical distinction. Traditional trend forecasting relied on reports compiled weeks or months in advance. Trend Brain operates continuously, updating its assessments as new data arrives. |
The operational impact has been measured in weeks saved from product lead times. Target's head of apparel, Gena Fox, described how the tool was applied to the swim category ahead of the critical summer season. Trend Brain read silhouettes and print patterns, enabling the company to get designs online quickly and into the hands of customers. Perhaps more importantly, the system flagged a polka dot design as an early winner before spring had even begun, allowing Target to buy more inventory in that style while pulling back from weaker performers. This kind of agile inventory adjustment, previously impossible at scale, represents a direct financial benefit: capital flows toward demand signals rather than lagging indicators. |
Target has been careful to frame Trend Brain as an enhancement to human creativity rather than a replacement for it. The company's chief information and product officer, Prat Vemana, emphasized that 'it's the designers who are the ones making the decision. AI is there to help'. This philosophy is reflected in the workflow. When Target's teams developed a Western-themed collection, they did not simply follow algorithmic outputs; they visited rodeos and mountain towns to immerse themselves in the cultural context of the trend. The AI surfaced the opportunity and accelerated the analysis; the humans understood and executed it. |
Target's broader strategy has involved deploying AI across multiple merchandising functions. The company has rolled out a demand forecasting engine across categories, with predictions improving with each iteration. It has also applied agentic AI to seller applications for its third-party marketplace, automating the analysis of vendor applications to surface relevant information for human analysts. By August 2025, Target had deployed more than 10,000 new AI licenses across the company and brought in OpenAI to train staff on the technology. The scale of this investment reflects a recognition that AI adoption is as much an organizational challenge as a technical one. |
The results have been significant enough that Target has begun extending its agile production model. The company reported that 85 percent of Gen Z and millennial consumers are likely to purchase trending products within a day of seeing them on social media. Meeting that expectation requires production timelines measured in weeks, not months. Target's 'speed tracks' initiative has enabled rapid design across multiple owned brands, with products like an oversized hoodie from Wild Fable moving from concept to shelf in a fraction of the usual time. AI-driven trend analysis is the intelligence layer that makes this speed possible without sacrificing accuracy. |
|
22.1.2 ZURU: AI Trend Intelligence for Consumer Goods |
While Target applied AI to trend analysis within an established retail framework, ZURU, a New Zealand-founded consumer goods company, built an AI system from the ground up to address a fundamental challenge in the toy and consumer products industry: the speed at which trends emerge and fade on social media. Traditional trend identification involved social teams manually watching feeds, then taking one to two weeks to produce content. By the time that content was live, the trend had often already passed. |
ZURU's response was to build an AI-based trend intelligence system on Amazon Web Services that processes approximately 20,000 videos per day across TikTok, YouTube, and Meta. The system analyzes not just what is popular but why: it identifies recurring hooks, patterns, and emotional signals in posts, extracting the structural elements that make content engaging. This intelligence feeds directly into both product development and content creation, allowing the company to act while demand is still growing rather than after it has peaked. |
The results have been striking. ZURU reduced its product development cycle from 12 to 18 months down to five months. One product developed through this system, inspired by rising interest in stickers on social media, is on track to generate USD 20 million in first-year revenue. The company's Fuggler plush toy brand provides another instructive example. The AI system identified French-language TikTok creators discussing the brand organically, without paid promotion. ZURU redirected content spending toward those creators, and within 14 days Fuggler recorded an 84 percent increase in reach and 1.6 million additional views. |
ZURU's chief marketing officer, Brittany Oliver, described the shift in operational posture: 'We used to have social teams watching social media feeds to identify trends... By the time the content was live, the trend had already faded away. To be globally competitive, we needed to move faster, so we created an AI system that watches content at massive scale, identifies what's working and why, and feeds that intelligence directly into our content creation pipeline'. |
The company's approach also embraces rapid experimentation and early rejection. ZURU reported that 50 percent of products and 90 percent of content are retired before reaching scale. Oliver framed this as a point of pride rather than failure: 'If our success rates were higher, it would mean we weren't experimenting enough'. This is a mature understanding of AI-assisted innovation: the technology's value lies in expanding the funnel of possibilities and accelerating the testing process, not in guaranteeing success. |
|
22.1.3 CJ Foods: Food AI 360 for Trend-Driven Product Innovation |
CJ Foods, the South Korean food company behind brands like bibigo and Hetbahn, has developed Food AI 360, an internal AI platform that integrates real-time trend analysis, concept development, consumer-response simulation, and commercialization support into a single system. The platform was first introduced to CJ's Korea headquarters in 2025 and expanded to the United States in mid-2026. |
What distinguishes Food AI 360 is its focus on proactive trend prediction rather than reactive trend following. Rather than merely identifying trends that have already emerged, the platform analyzes shifts in consumer interest and their direction in real time to anticipate where demand is heading. The system includes a feature called Concept Studio, which translates predicted trends into specific product ideas, visualizing proposed product directions, key features, and packaging formats. It also uses AI-based product evaluation to simulate consumer responses, helping teams assess potential strengths and weaknesses before committing to development. |
CJ Foods has already launched products developed using Food AI 360. One example is Matcha Hetbahn, a product that emerged from the platform's analysis of the sustained matcha trend and growing consumer demand for healthy, convenient options. Another is bibigo Salmon Steak, which surpassed 1.4 million units in sales within six months of its Korean launch. The platform identified a shift in consumer demand within the protein category toward white meat and seafood proteins, as well as growing interest in salmon, and these insights were incorporated at the product planning stage. |
CJ Foods plans to expand Food AI 360 to other key regions, including Japan and Europe. The company views the platform as a way to turn consumer needs into differentiated products more quickly, accelerating the growth of Korean food in global markets. |
|
22.1.4 FamilyMart: AI-Designed Snacks in Convenience Retail |
Japanese convenience store chain FamilyMart took a novel approach to AI-assisted product development, tasking an AI system with analyzing point-of-sale data and trend information to propose a new snack product. The result was 'Oimono Canel¨¦,' a sweet potato-flavored pastry launched in September 2026. |
The AI analyzed POS data to identify common characteristics of popular products, emerging trends, and features of underperforming items. It determined that products convenient to eat with one hand and those with contrasting textures were popular. Based on this analysis, it proposed a pastry with a crunchy caramel exterior and a moist sweet potato interior. The system also recommended a price point: it found that consumer trial probability dropped significantly above 300 yen, leading to a suggested price of 285 yen. |
FamilyMart plans to release ten AI-proposed products by the end of 2026. Importantly, while the AI handles ideation and proposal, final decisions on product development and launch remain with human managers. The company estimated that AI utilization has reduced workload related to idea generation by approximately 20 percent. This represents a measured, incremental approach to AI integration: not replacing human judgment but eliminating the time-consuming preliminary work that precedes it. |
|
22.1.5 NTT DATA: AI Agents for Consumer Goods Planning |
NTT DATA, the global technology services provider, launched an AI agent service for early-stage product planning in consumer goods in July 2026. The service targets food, beverage, and consumer goods companies during the initial phases of product development, supporting idea generation, internal alignment, and initial reviews across branding, compliance, and marketing. |
The system produces structured product concept proposals covering feature design, naming, value propositions, sales forecasts, and visual concept imagery, all formatted for direct business review and decision-making. It can be configured around a client's brand guidelines, target segments, and product strategy, ensuring that outputs reflect the manufacturer's specific positioning rather than generic suggestions. |
NTT DATA's service reflects a growing recognition that the early stages of product development, where concepts are generated and assessed, are often the most time-consuming and least structured parts of the innovation process. By bringing AI into this phase, the company aims to shorten the period before concepts reach formal internal evaluation. The service draws on work with global consumer goods manufacturers in Europe and Japan and includes integrated sales forecasting for early assessment of market potential. |
The platform is built on NTT DATA's own AI systems, using retrieval-augmented generation and multi-agent architectures. This design allows different agents to handle distinct tasks, such as generating concepts, assessing branding fit, or evaluating compliance considerations. NTT DATA intends to expand the service into later phases of product development, including formulation, packaging, and production feasibility. |
|
22.2 Product Discovery: From Search to Conversation |
22.2.1 Zalando: AI Assistant for Fashion Discovery |
Zalando, Europe's largest online fashion and lifestyle platform, serving more than 50 million customers across 25 countries, has developed an AI assistant that represents one of the most sophisticated deployments of conversational AI in retail. The assistant, built in collaboration with OpenAI using GPT-4o mini, provides personalized recommendations and facilitates product discovery through natural language interaction. |
The results from the upgraded assistant have been substantial. Compared to the previous version, the assistant achieved a 23 percent increase in product clicks and more than 40 percent increase in products added to wish lists. These metrics indicate that users are not merely engaging with the assistant conversationally; they are finding products they want and taking action on them. |
What distinguishes Zalando's approach is the depth of personalization. The assistant considers location, weather, occasion, and purchase history when making recommendations. A user asking for outfit suggestions for a specific event receives recommendations informed not only by their stated preferences but by contextual factors they may not have articulated. The assistant can recognize where customers are within the Zalando platform and provide tailored conversation starters accordingly. It is connected to customer accounts, allowing it to understand individual preferences and shopping history. |
The technical evolution of the assistant illustrates the importance of evaluation frameworks in deploying AI at scale. Zalando's initial assistant, launched in four markets in 2023, used GPT-3.5. While functional, it struggled with complex instructions. When users requested seasonal outfits or clothing for specific occasions, results were often generic. The collaboration with OpenAI identified two priorities: improving the evaluation process and upgrading the underlying model. |
Zalando adopted a granular evaluation approach, testing individual system components such as routing and response generation in isolation. The team also improved the quality and precision of few-shot prompts, showing the model examples of high-quality and low-quality responses to better calibrate its outputs. With the evaluation framework in place, Zalando migrated to GPT-4o mini, a more cost-effective model better suited to multilingual tasks and instruction-following. |
The migration was completed rapidly, with half of the assistant's users on the new model within two weeks. The assistant now operates across more than 20 European languages, with localized content and recommendations adapted to each market. The model reduced latency and operational costs, ensuring scalability as user numbers grew. |
Zalando's broader personalization strategy extends beyond the assistant. The company has launched in-house generative AI tools for creating campaign visuals and digital models, cutting campaign lead times from six weeks to under one week. Algorithmic fashion discovery powers personalized homepages and feeds driven by mood, intent, and historical behavior. Predictive sizing and fit tools help shoppers choose with greater confidence, reducing return rates, particularly in footwear and womenswear. Return data actively shapes future recommendations, tightening the feedback loop between what customers keep and what they send back. |
|
22.2.2 Meesho: Discovery-Led Commerce for Emerging Markets |
Meesho, the Indian e-commerce platform serving 264 million annual transacting users, has built its product discovery strategy around a fundamental insight: the next hundred million Indians coming online will not search, they will discover. As Debdoot Mukherjee, Meesho's chief data scientist, put it: 'They will not type, they will speak, browse and expect technology to meet them where they are'. |
Meesho's PRISM (Personalised Ranking & Intent Signal Module) recommendation engine now powers more than 75 percent of orders on the platform, representing a decisive shift away from traditional keyword-based search. The system is powered by a network of more than 100 AI ranking models trained on over 400 trillion input signals, executing more than 6 trillion inferences every day within milliseconds. During peak traffic periods, the system handles up to 100 million inferences per second. |
The architecture is designed for the specific characteristics of the Indian market. PRISM supports more than 10 multilingual experiences across Hindi, Bengali, Marathi, Tamil, Telugu, Kannada, Malayalam, Gujarati, Punjabi, and Odia. It includes an LLM-powered discovery engine called Trendpulse, which interprets emerging demand patterns across regions, cities, and local consumer clusters, enabling Meesho to surface products aligned with evolving shopping behaviors across India's diverse consumer cohorts. |
Meesho developed BharatMLStack, its in-house machine learning infrastructure platform, to support these high-throughput AI workloads at significantly lower inference costs than conventional cloud infrastructure. This investment in proprietary infrastructure reflects a recognition that at Meesho's scale, the economics of AI deployment are as important as the algorithms themselves. |
The company's voice shopping agent, Vaani, launched in mid-2026 and crossed 1.5 million users within its first month, delivering a 22 percent conversion lift. This performance suggests that voice and conversational interfaces are not merely novelties but effective channels for product discovery among users who may be more comfortable speaking than typing, or who prefer the guided experience that conversation provides over the cognitive load of searching. |
|
22.2.3 Reformations: AI Recommendations on Product Detail Pages |
Reformation, the sustainable fashion brand known for its data-driven merchandising model, sought to improve product discovery on its e-commerce site through AI-powered recommendations. The company partnered with Zoovu, a composable AI recommendations engine, to personalize its high-traffic product detail pages. |
The implementation was notably fast. Zoovu deployed an AI model trained to maximize revenue, with front-end event tracking running for approximately one week to gather the minimum data required before go-live. The total time to launch was two weeks, and the A/B test reached statistical significance in three weeks. |
The results demonstrated a meaningful improvement over the incumbent recommendation system. Zoovu delivered a 4 percent lift in revenue per user compared to Salesforce Commerce Cloud Einstein recommendations. While 4 percent may appear modest in isolation, in the context of a high-traffic e-commerce site, it represents a significant incremental revenue gain. The case illustrates that product discovery optimization is not solely about dramatic overhauls; targeted improvements to specific high-impact pages can deliver measurable returns. |
The speed of implementation is also instructive. Reformation achieved this result not through a lengthy integration project but through a composable AI approach that could be deployed alongside existing systems. This model, where AI capabilities are modular and can be inserted into existing experiences, lowers the barrier to experimentation and allows brands to test and iterate without committing to wholesale platform changes. |
|
22.2.4 Kohl's, Michaels, DICK'S Sporting Goods, and OTTO: Conversational AI as Digital Sales Associate |
A cluster of retailers across different sectors has converged on a common approach: using conversational AI to bring the advisory expertise of in-store staff into the digital shopping experience. Each has adapted the model to its particular category and customer needs. |
Kohl's, the American department store chain, launched Gift Finder, a chatbot built on Google Cloud's Gemini Enterprise for Customer Experience. The tool addresses one of retail's persistent pain points: gift shopping. Rather than requiring customers to navigate category filters, Gift Finder leads them through a conversation about the recipient's interests, hobbies, and style before making concrete suggestions. Customers can upload a photo to find similar items or spark new ideas, browse products directly within the chat, compare details, and add items to their cart. Kohl's views the tool as a learning system, with every conversation feeding back into the ongoing development of the shopping experience. |
Michaels, the craft and decor retailer, developed Ask Mike, an AI assistant designed to translate vague creative inspiration into concrete, purchasable ideas. Customers describe their vision in their own words, and the AI agent delivers curated, personal suggestions rather than a list of keyword-matched results. Early adoption has been strong: Ask Mike has logged nearly 75,000 conversations, with more than 60 percent of interactions centered on product discovery. The company's president and chief customer officer, Heather Bennett, described the tool as a 'personal creative partner' rather than a simple search box. |
DICK'S Sporting Goods built Coach by DICK'S, an AI assistant that brings the kind of advice store associates give into the company's mobile app. The assistant accompanies sports enthusiasts through every stage of their athletic journey, from taking up a new sport to preparing for competition, offering tailored product recommendations and training tips. The rollout began in June 2026, with additional features planned. |
OTTO, the German retail platform, developed an AI assistant that conducts natural, spoken advisory dialogues, drawing on detailed knowledge of approximately 19 million items to provide fitting recommendations. CEO Boris Ewenstein framed AI as a path to better customer service through hyperpersonalization, or as he put it, 'expert advice from assistants that never run out of patience'. |
These four cases share a common recognition: the value of conversational AI in retail is not in replacing search with chat but in translating vague human intent into concrete purchasing decisions. The retailers that succeed will be those that scale genuine advisory expertise through AI, whether that expertise lies in gift selection, creative inspiration, athletic performance, or product knowledge. |
|
22.2.5 The Bear House and Glance: Agentic Commerce in Action |
The Bear House, an Indian direct-to-consumer fashion brand, faced three common challenges: improving conversions, increasing time on site, and reducing returns. Before deploying an AI shopping agent from Glance (a consumer technology platform from InMobi Group), customers typically spent 30 to 50 seconds on the website, selected a single product, and checked out. |
After deploying the AI agent, the changes were dramatic. Conversions increased fivefold. Time spent on site more than tripled. Customers began purchasing complete looks rather than individual products. Average order value rose from 2,000 to 3,500 rupees. Using Glance's virtual try-on feature, The Bear House reduced returns by 20 percent. |
The shift that produced these results was from product discovery to intent-led shopping. Instead of searching for a black shirt after comparing dozens of options, customers tell the AI agent where they are going and what they want to wear. The agent uses signals including preferences, budget, context, and appearance to recommend products, create complete looks, and enable virtual try-ons. |
Piyush Shah, cofounder of InMobi and Glance, described the broader implication: 'Consumers no longer need to navigate every stage of the purchase journey'. As AI removes layers of search, comparison, and evaluation, brands that understand the reason behind a purchase may gain an advantage. The intent customers express while shopping can inform future product decisions, creating a feedback loop between discovery and development. |
|
22.2.6 Alibaba and the 'Conversation-to-Order' Paradigm |
In China, the integration of AI assistants with e-commerce platforms has advanced rapidly. In May 2026, Alibaba's Qwen assistant was fully integrated with Taobao, enabling users to move from conversation to order placement within a single interface. The assistant draws on Taobao's 4 billion product catalog and more than 20 years of accumulated shopping scenario data to understand consumption intent from natural language, recommend products accurately, and complete the selection, comparison, and purchase process. |
The integration addresses three specific pain points in consumer decision-making. When users have complex filtering criteria, the AI can complete multi-condition cross-filtering in a single conversation. When users have only vague memories or fragmented information, the AI can infer and match based on those fragments. For scenario-based needs, the AI can directly output combination product recommendations. |
This 'conversation-as-commerce' model represents a significant departure from the search-and-browse paradigm that has dominated e-commerce for two decades. Other Chinese platforms have pursued similar strategies: ByteDance integrated its Doubao assistant with Douyin e-commerce, Meituan launched an AI search assistant within its app, and JD.com announced plans to leverage AI across its pharmacy and home improvement businesses. |
The 2026 '618' shopping festival was described by industry observers as the first 'AI-native' major promotion, with AI permeating consumer interfaces, merchant operations, and logistics. On the consumer side, AI shopping assistants guided product selection, while virtual try-on features reduced the uncertainty that drives apparel returns. On the merchant side, AI tools handled content creation, customer service, trafficͶ·Å, and inventory management, reducing costs and improving efficiency. |
|
22.2.7 Daydream: Agentic AI for Fashion Discovery |
Daydream, launched in mid-2025 as the world's first chat-based shopping agent built exclusively for fashion, assembled a catalog of approximately 2 million products from more than 10,000 brands. The platform operates on a no-ads affiliate model that its partners describe as 'relevance over rent.' The case study developed by London Business School frames the strategic question that Daydream and similar platforms face: should the company remain a destination consumers visit directly, or become the fashion intelligence layer inside the horizontal agents that may soon mediate shopping |
This question captures a tension that runs through the entire field of AI-powered product discovery. On one side are vertical specialists like Daydream, which develop deep expertise in a category and control the user experience. On the other are horizontal platforms like , which have the user relationships and distribution but rely on partners for inventory and expertise. OpenAI has put checkout inside , Walmart has announced a shopping partnership, Google is positioning itself as a matchmaker of agentic commerce, and Amazon has taken legal action against an agent that shopped its shelves without authorization. |
The resolution of this tension will shape how consumers discover products in the coming years. Daydream's case study describes fashion discovery as 'an information problem rather than a search problem'. This framing is important. Search assumes the user knows what they want and can articulate it. Information problems, by contrast, involve uncertainty, incomplete preferences, and the need for guidance. The AI systems that succeed in product discovery will be those that help users navigate that uncertainty, not simply those that match keywords to inventory. |
|
22.3 Cross-Industry Patterns |
Several patterns emerge from these cases that apply across sectors and geographies. |
Trend analysis has moved from periodic reporting to continuous sensing. Traditional trend forecasting produced reports at intervals, often months apart. AI-powered systems like Target Trend Brain, ZURU's social media intelligence system, and CJ Foods' Food AI 360 operate continuously, ingesting new data and updating their assessments in near-real time. This shift compresses the time between trend emergence and organizational response from months to weeks or days. |
Product discovery is becoming conversational and intent-driven. The search box, for decades the primary interface for e-commerce, is being supplemented and in some cases displaced by conversational interfaces that interpret natural language and infer intent from context. Zalando's assistant, Kohl's Gift Finder, Michaels' Ask Mike, and Alibaba's Qwen integration all represent variations on this theme. The common thread is a recognition that many shoppers cannot fully articulate what they want, and that conversation is a more effective way to surface latent preferences than keyword search. |
Personalization is expanding from recommendations to the entire experience. Zalando personalizes not only product recommendations but content, campaign timing, and even inventory placement. The company's campaign lead times have dropped from six weeks to under one, enabling hyper-localized campaigns tailored to regional style sensibilities. Return data feeds back into recommendations, closing the loop between what customers keep and what they send back. This infrastructure-level personalization goes far beyond the recommendation widgets that characterized earlier e-commerce personalization. |
Speed is the competitive imperative. Whether measured in product development timelines, campaign cycles, or time-to-purchase, AI is being deployed to compress every stage of the retail value chain. Target cut production timelines by up to 80 percent for trend-focused products. ZURU reduced product development from 12 to 18 months to five months. Zalando cut campaign lead times from six weeks to under one. Reformations achieved a 4 percent revenue lift from AI recommendations deployed in two weeks. The companies that win are those that can move fastest without sacrificing quality or coherence. |
Human judgment remains central. Despite the sophistication of these systems, every case emphasizes the continued importance of human decision-making. Target's designers visit rodeos to understand Western trends; the AI surfaces the opportunity but does not dictate the design. FamilyMart's AI proposes products, but humans make the final call on development and launch. ZURU's leadership frames their low success rates as evidence of a healthy experimentation culture, not a failure of AI. The most effective deployments treat AI as an accelerator of human creativity and judgment, not a replacement for either. |
|
22.4 Detailed Summary |
Target Trend Brain represents one of the most comprehensive deployments of generative AI for trend analysis in general merchandise retail. The platform ingests social media feeds, runway photography, and real-time purchasing data to predict emerging trends, enabling Target to compress product development timelines by up to 80 percent for trend-focused items. The system flagged polka dots as an early winner before spring, allowing inventory adjustment ahead of demand. Target has deployed more than 10,000 AI licenses and brought in OpenAI for staff training, reflecting a commitment to organizational adoption alongside technical deployment. |
Zalando's AI assistant achieved a 40 percent increase in high-value interactions (likes and add-to-cart actions) during testing, alongside a 23 percent increase in product clicks. The assistant considers location, weather, occasion, and purchase history when making recommendations, and now operates across more than 20 European languages. Zalando's broader personalization infrastructure has cut campaign lead times from six weeks to under one week and reduced return rates through predictive sizing and fit tools. |
ZURU built an AI trend intelligence system on AWS that processes 20,000 videos daily across TikTok, YouTube, and Meta, cutting product development cycles from 12 to 18 months down to five months. One trend-driven product is on track for USD 20 million in first-year revenue. The company reports that 50 percent of products and 90 percent of content are retired before reaching scale, reflecting a deliberate strategy of rapid experimentation and early rejection. |
Meesho's PRISM recommendation engine now powers more than 75 percent of orders on the platform, handling 6 trillion daily inferences and 100 million inferences per second at peak. The system supports 10 Indian languages and includes Trendpulse, an LLM-powered discovery engine that tracks regional demand patterns. Meesho's voice shopping agent, Vaani, crossed 1.5 million users within its first month with a 22 percent conversion lift. |
CJ Foods' Food AI 360 integrates trend analysis, concept development, consumer-response simulation, and commercialization support. Products developed using the platform include Matcha Hetbahn and bibigo Salmon Steak, the latter surpassing 1.4 million units in six months. The platform identifies shifts in consumer demand and translates them into concrete product concepts before competitors have identified the trend. |
Reformation, working with Zoovu, deployed AI-powered recommendations on product detail pages in two weeks, achieving a 4 percent lift in revenue per user compared to the incumbent Salesforce Einstein recommendations. The case demonstrates that targeted improvements to specific high-impact pages can deliver measurable returns quickly. |
The Bear House, using Glance's AI shopping agent, increased conversions fivefold, tripled time on site, and raised average order value from 2,000 to 3,500 rupees. Virtual try-on reduced returns by 20 percent. The case illustrates the shift from product discovery to intent-led shopping, where customers express what they want to achieve rather than what they want to buy. |
|
FamilyMart launched an AI-designed snack, Oimono Canel¨¦, after the system analyzed POS data and proposed a product with specific texture, price, and format characteristics. The company plans to release ten AI-proposed products in 2026 while maintaining human decision-making authority over development and launch. |
NTT DATA launched an AI agent service for consumer goods planning that produces structured product concept proposals covering feature design, naming, value propositions, sales forecasts, and visual imagery. The service is designed to shorten the early stages of product development and is built on multi-agent architectures with retrieval-augmented generation. |
Kohl's, Michaels, DICK'S Sporting Goods, and OTTO have each deployed conversational AI assistants that bring in-store advisory expertise into digital channels. Kohl's Gift Finder guides gift selection through conversation; Michaels' Ask Mike translates creative inspiration into purchasable ideas; DICK'S Coach provides athletic guidance and product recommendations; OTTO's assistant draws on knowledge of approximately 19 million items to provide spoken advisory dialogues. |
Alibaba's integration of Qwen with Taobao enables conversation-to-order commerce, drawing on 4 billion products and 20 years of shopping scenario data. The 2026 '618' festival was described as the first AI-native major promotion, with AI permeating consumer interfaces, merchant operations, and logistics. |
Daydream assembled a catalog of approximately 2 million products from more than 10,000 brands, operating on a no-ads affiliate model. The platform frames fashion discovery as an information problem rather than a search problem, positioning itself as a potential intelligence layer for the emerging agentic commerce ecosystem. |
These cases, taken together, describe a field in rapid transition. Trend analysis has become continuous and predictive rather than periodic and reactive. Product discovery has become conversational and intent-driven rather than keyword-based and transactional. The companies that succeed will be those that integrate these capabilities into their operating models, treat AI as an accelerator of human judgment rather than a replacement for it, and maintain the speed of response that modern consumers expect. |
cs@easiersoft.com