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  • L’Oréal brings Maybelline virtual try-on to ChatGPT

    L’Oréal has announced a collaboration with OpenAI that will bring Maybelline New York’s virtual makeup try-on feature into ChatGPT.

    The announcement was made at VivaTech 2026. The partnership covers consumer-facing shopping tools, product discovery, advertising pilots, research, and internal content production. The collaboration also covers L’Oréal’s internal use of AI in research, formulation, content production, and employee tools.

    OpenAI said in 2026 that ChatGPT had more than 900 million weekly active users and more than 50 million subscribers.

    Maybelline try-on comes to ChatGPT

    Maybelline’s Makeup Virtual Try-On will be available directly within ChatGPT. The feature will use L’Oréal’s ModiFace technology, which allows users to test makeup looks digitally through a conversational interface.

    ModiFace is L’Oréal’s augmented reality and AI beauty technology business. L’Oréal acquired the Canadian company in 2018 to expand its digital beauty services across areas such as virtual makeup try-on, hair colour try-on, and augmented reality shopping.

    L’Oréal’s 2025 Annual Report said its Beauty Tech services had more than 120 million uses across 66 countries and 31 brands by the end of 2025.

    Product discovery and advertising

    L’Oréal will also work with OpenAI to improve how its products are surfaced in ChatGPT in the United States. The company said the work will cover brands including Lancôme and Kérastase.

    L’Oréal said the ChatGPT work also includes product discovery. The company said e-commerce grew by double digits in 2025 and passed 30% of sales. Several L’Oréal brands are also involved in OpenAI’s global ChatGPT advertising pilot. They include SkinCeuticals, CeraVe, and Garnier. The programme focuses on ads within AI-assisted consumer interactions.

    L’Oréal described the pilot as focused on AI-native advertising at moments of consumer intent and commerce. The company has not provided further operational details on how the ad placements will appear inside ChatGPT.

    AI use in research and formulation

    The partnership also extends to L’Oréal’s research work. The company said it is using GPT-Rosalind, OpenAI’s life sciences reasoning model, to map the skin microbiome.

    OpenAI launched GPT-Rosalind as a model for life sciences research tasks, including evidence synthesis and experimental planning. L’Oréal said it is applying the model to skin microbiome research, starting with La Roche-Posay. The skin microbiome refers to the community of microbes that live on the skin. L’Oréal said the work is aimed at identifying beneficial bacteria that can support the development of new skincare products.

    L’Oréal’s 2025 Annual Report also cited AI work in formulation science. L’Oréal Research & Innovation and IBM are developing a Formulation Foundation Model for beauty formulation.

    L’Oréal has also worked with NVIDIA on AI development and deployment. The company has said the partnership covers areas including 3D product rendering and predictive formulation science.

    Internal AI tools

    OpenAI’s latest model will also be used in CreAItech, L’Oréal’s internal generative AI content platform. The platform is designed to create images and videos while reflecting the visual identity and history of L’Oréal’s brands.

    CreAItech is used by L’Oréal teams for beauty content creation. The OpenAI model support will apply to image and video generation.

    Asmita Dubey, L’Oréal’s chief digital and marketing officer, said the company wants to use AI to support consumers and employees. She also cited its use across marketing and research.

    Emmanuel Marill, OpenAI’s managing director for EMEA, said the work with L’Oréal covers research and employee tools, as well as consumer-facing services.

    The collaboration forms part of L’Oréal’s wider AI programme. The company said the programme covers consumer tools and internal work across marketing and research. L’Oréal said 73,000 employees have already been trained in generative AI. The company has also introduced internal tools including L’OréalGPT and personal AI companions.

    The announcement coincides with L’Oréal’s 10th year at VivaTech.

    (Photo by Helio E. López Vega)

    See also: Microsoft sells OpenAI models in China. OpenAI and Anthropic won’t.

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  • SAP and Google Cloud deploy agentic commerce architecture

    SAP and Google Cloud are deploying agentic commerce architecture to automate multi-agent marketing and retail operations at enterprise scale.

    SAP research indicates 78 percent of businesses consider AI essential for retaining customers in 2026. However, the same data reveals fewer than two in five companies share customer data across customer experience (37%) or CRM (39%) platforms. 

    Addressing this structural data failure requires direct infrastructure intervention. SAP and Google Cloud expanded their partnership to build an agentic customer experience architecture, connecting data, AI, engagement, and commerce operations.

    The deployment relies on restructuring how AI interacts with backend commercial platforms. Most digital commerce infrastructures rely on fragmented APIs. SAP Commerce Cloud adopts the Universal Commerce Protocol to standardise data exchange among retailers, payment gateways, and autonomous agents. This framework allows software to independently execute the full retail sequence, spanning initial search, transaction processing, and post-sale resolution.

    Deploying the Universal Commerce Protocol

    Engineering teams integrating the Universal Commerce Protocol facilitate direct interactions between intelligent agents and commerce platforms. The standardisation lowers integration costs and accelerates onboarding into AI-driven channels.

    SAP plans to collaborate with Google to ensure merchant products surface organically across the Gemini application and Google Search, specifically incorporating AI Mode functionalities. Consumers interact with these interfaces while the backend architecture processes inventory checks, cart management, and payment processing without requiring retailers to rebuild existing infrastructure.

    SAP Commerce Cloud integrates Google Gemini capabilities to power a designated Shopping Assistant. Brands deploy the assistant directly to their consumers to facilitate chat, voice, and text engagements. State retention remains active throughout the complete shopping cycle. The deployment ingests live behavioural inputs, current warehouse capacities, and active marketing data to assemble distinct merchandise pairings, including full event configurations. By continuously refining recommendations, the application ensures high relevance and strict physical fulfilment capability.

    Enterprise systems often fail when promotional campaigns trigger demand that physical inventory cannot satisfy. Frontend interfaces failing to synchronise with backend warehouse systems frequently halt digital purchases. Users regularly click promotional emails, load the associated mobile application, and face sudden out-of-stock notices during checkout. Fulfilment updates experience severe delays, leaving support agents without a complete operational picture. SAP and Google Cloud engineered their joint solution to correct these specific systemic customer experience failures.

    Instead of managing disconnected points of contact, the architecture unifies the entire sequence. Traditional commercial setups require consumers to repeatedly input previously shared information. Support staff frequently lack access to unified records, preventing them from resolving issues efficiently. The integration targets these operational breakdowns, ensuring the system recognises the user and their precise context instantly across all digital properties.

    Bidirectional data flows

    Marketing execution demands highly accurate data pipelines. SAP Engagement Cloud partners with Google Cloud to formulate an autonomous multi-agent framework. The technical foundation relies on SAP Business Data Cloud Connect for Google BigQuery. The deployment relies on bidirectional, zero-copy data linking secured by strict administrative controls. Leaving vast data stores in place rather than duplicating them drops storage expenses and network latency.

    BigQuery ingests live variables like weather conditions, precise locations, and active advertising interaction rates. SAP Customer Experience solutions supply the internal behavioural context, tracking customer profiles, exact transaction histories, specific service interactions, and consented engagement records. SAP Engagement Cloud activates the combined intelligence, deploying autonomous agents to orchestrate personalised interactions throughout the customer lifecycle.

    Routing information through the Business Data Cloud while BigQuery handles the logic forces immediate inventory synchronisation. The Shopping Assistant actively queries live warehouse records before displaying any product. Software checks physical supply against consumer requests, verifying availability prior to making the suggestion.

    Generative execution in production environments

    Advanced generative models dictate the localised output of the marketing campaigns. Google Gemini models, specifically including the Nano Banana 2 iteration, provide specialised agentic skills. The models dynamically generate localised messaging, customised imagery, and campaign variations based on the exact specifications provided by the bidirectional data flow.

    The deployment upgrades standard text messages into immersive and interactive interfaces via Google Rich Communication Services. Advertising creatives evolve continuously based on incoming engagement data. The system processes the interaction, evaluates the response against the user profile, and instructs the Nano Banana 2 model to adjust the subsequent communication.

    Marketing departments achieve high efficiency by abandoning manual execution. Instead of configuring rigid campaign parameters, teams establish business goals and provide enterprise data access to the SAP Engagement Cloud. The autonomous agents coordinate the necessary steps, segmenting audiences based on Google BigQuery analytics and generating specific content variations through Google Gemini models.

    Evaluating the infrastructure impact

    Deploying the architecture restructures standard commerce operations. Consumers dictate their purchasing intent to search engines and conversational interfaces. The embedded AI agents process the intent, navigate the Universal Commerce Protocol connections, and complete the purchase directly against the enterprise backend.

    Retailers retain full ownership of the customer relationship despite the transaction occurring within a third-party environment. The architecture captures the consented engagement data, feeding the transaction history back into the SAP Customer Experience solutions. The system updates the localised customer profile, providing the Google Gemini models with fresh context prior to the next engagement cycle.

    The system continuously improves campaign performance without requiring direct human intervention. The multi-agent framework evaluates the success of a generated Rich Communication Services text message, adjusting the variables prior to the next automated dispatch.

    See also: Computer vision deployments drive retail productivity gains

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  • Accenture: Consumers show growing trust in AI shopping agents

    Consumers are showing a willingness to let AI agents take on more shopping-related tasks, according to new research from Accenture.

    The company’s 2026 Consumer Pulse Research, based on a survey of 25,590 consumers across 16 countries, found that 74% of respondents would trust a personal AI agent more than their best friend to make a purchase on their behalf.

    The report described this as a move beyond the use of chatbots or search tools. In this context, an AI agent refers to software that can act on a consumer’s behalf within set permissions. It can shop, negotiate, resolve complaints, manage subscriptions, and, in some cases, complete purchases.

    Consumers are ready to delegate

    The survey found that 74% of consumers would allow an AI agent to handle routine tasks. These include deal negotiation, complaint resolution, subscription renewals, and product reorders.

    Accenture said this level of delegation does not mean consumers are ready to hand over every decision. Instead, the findings suggest that consumers are more open to delegating parts of shopping that feel repetitive, time-consuming, or low-risk.

    The report also found that 32% of consumers would ask an AI agent to make a purchase decision on their behalf within defined limits. These limits could include budget and brand preferences, with other conditions set by the user.

    In that scenario, the AI agent would choose the best available option, but the consumer would still review and approve the purchase before payment. The report categorised this as delegated decision-making, separate from task execution and autonomous purchasing.

    Autonomy still has limits

    A smaller group of consumers is open to AI agents completing purchases without final approval. The report found that 9% of respondents would allow an agent to initiate and complete purchases within defined boundaries.

    The payment stage recorded lower openness to autonomous agent decisions. Accenture said only 12% of consumers are open to agents making purchase decisions autonomously at the payment stage.

    The report identified several conditions that affect consumer willingness to delegate more control. These include data safeguards, configurable permissions, and instant override options. Clear recourse, platform reputation, and perceived neutrality also affect trust.

    Consumers are more comfortable with AI agent autonomy in parts of the journey where effort is high and emotional stakes are lower. The report pointed to negotiation and post-purchase support as areas where consumers showed greater openness.

    The report said recurring services ranked highest across stages of delegation, while lifestyle and travel purchases showed a sharper drop as autonomy increased.

    It also said consumers are more likely to keep control over choices linked to identity or personal enjoyment. A consumer may delegate routine grocery restocking but still want to choose a hotel room, clothing item, or experience directly.

    What it means for brands

    The report said AI-assisted shopping requires brands and retailers to make product information clear and machine-readable. If consumers use agents to compare options, pricing, availability, policies, and claims will also need to be easy for agents to assess.

    AI agents can compare brands using structured attributes and verified claims. They can also weigh price-to-value ratios and fulfilment records. The report said this affects how brands appear across digital channels, including search engines, marketplaces, and social platforms.

    The report found that 56% of all consumers would tell their AI agent which brands to consider. Among behaviorally loyal consumers, 37% said they would allow an agent to switch brands if it found a better fit.

    The report linked brand switching to factors such as fit, price, availability, and service performance.

    Accenture also found that consumers are interested in agents that can work across providers. The report said 61% want an agent that can shop across multiple grocery retailers on their behalf, while 71% want an agent that can plan and book a complete trip across airlines, hotels, and activities.

    Brands and retailers need product data, pricing, availability, policies, and claims to be readable by the systems agents use to evaluate options, according to the report.

    The main reasons cited were existing knowledge of shopping preferences, trust built through service and support, and access to a broad selection of products and services.

    The report listed several possible roles for brands and retailers in AI-assisted commerce. Some may build their own agents, while others may integrate data, inventory, and services into platforms that consumers already use.

    The report cited verified information, clear inventory, transparent pricing, and reliable fulfilment data as factors that can help agents evaluate brands more easily.

    It also found that 71% of consumers expect generative AI to influence at least half of their spending decisions over the next 12 months.

    The report also found that 63% of consumers want agents to shop for their “idealised self.” Examples include helping them make healthier choices or stay within budget. Some respondents also want agents to support more intentional upgrades.

    Among active generative AI users, 26% said they had already bought a more expensive item because AI increased their confidence in the decision. The same proportion said AI had led them to increase their basket size.

    Stores still matter

    The survey also asked consumers how AI could affect stores. It found that 87% believe AI will affect the role of stores. Another 31% said stores will become more important for creating moments of enjoyment.

    The findings show lower openness to full automation than to routine task delegation. It shows a more selective pattern, with consumers delegating routine or lower-risk tasks while retaining control over purchases that involve personal preference, risk, or emotional value.

    The report said some brand evaluation could take place inside agent-led comparison systems before consumers visit a website, app, or store.

    (Photo by Growtika)

    See also: Visa ChatGPT integration enables AI agent retail purchasing

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  • Weis Markets adds Instacart AI-powered shopping carts to stores

    Weis Markets is adding Instacart’s AI-powered shopping carts, Caper Carts, to select stores in Pennsylvania, bringing digital coupons, loyalty features, and repeat-purchase recommendations into the grocery aisle.

    The Pennsylvania-based grocery chain is working with Instacart to deploy the smart carts, which include cameras, certified scales, location systems, and a touchscreen.

    According to Instacart, Caper Carts use basket-facing camera sensors, outward-facing cameras, certified scales, and location-tracking systems to support item recognition and checkout functions. The system combines edge computing on the carts with cloud AI trained on more than 1.6 billion online grocery orders.

    Shoppers can use the cart screen to monitor spending during their trip. They can also access location-based digital coupons directly from the cart.

    Weis customers can sign up for a Weis Rewards account through the cart and redeem loyalty benefits while shopping. Customers who link their accounts can also use a Buy It Again feature, which shows items they have previously purchased.

    Weis and Instacart already work together on online grocery services. In 2023, Weis partnered with Instacart to offer same-day delivery from 133 locations in Pennsylvania, New York, and Delaware.

    Instacart expands Caper Cart rollout

    The Weis rollout adds to Instacart’s wider Caper Cart deployment. The company says the carts now span more than 100 cities across 15 states.

    Caper Carts are available across more than a dozen retail banners, including Kroger, Schnucks, and Wakefern banners such as ShopRite and Fairway Market.

    Earlier deployments have produced some store-level usage data. Retail Dive reported that Schnucks data showed Caper Carts handled more than 10% of sales on busy days at one store. That store had 10 Caper Carts and around 160 traditional carts, according to the report.

    Greg Zeh, senior vice president and chief information officer at Weis Markets, described the carts as part of the company’s effort to improve the shopping process. He pointed to real-time spend tracking and on-cart coupons as key features.

    Instacart described the partnership as an extension of Weis Markets’ use of digital tools inside stores. David McIntosh, Instacart’s chief connected stores officer, said Caper Carts bring together in-store and online data.

    Weis adds AI to checkout operations

    Weis has also been adding AI to self-checkout. Toshiba Global Commerce Solutions said Weis completed a chainwide deployment of its ELERA Security Suite across self-checkout lanes.

    The system includes produce recognition and loss prevention tools. Toshiba says the technology uses edge AI for on-device processing.

    At the time of Toshiba’s December 2025 announcement, the system was operational across self-checkout lanes in all 199 Weis locations. Weis also reported that more than 94% of customers selected the produce recognition feature at self-checkout.

    Grocers test AI beyond checkout

    Albertsons Companies has also introduced an AI-based quality control tool for produce inspection. The system is designed to help identify moldy or damaged fruit before it reaches store shelves.

    The tool initially focuses on strawberries and red and green grapes. Albertsons says it is intended to improve quality rating consistency and support faster decision-making.

    The company also says the tool expands quality data and helps align inspections with company standards.

    Albertsons operates more than 2,000 stores, including Safeway, Jewel-Osco, and ACME. The system supports quality inspectors working in its distribution centres.

    The quality control system uses computer vision to support produce inspections across Albertsons’ store brands. It was developed in-house by the company’s technology and supply chain teams.

    Albertsons built the tool on Google Cloud’s Gemini Enterprise platform, including Vision AI and Gemini models. Google Cloud said it advised on the AI component used in the supply chain process.

    (Photo by Franki Chamaki)

    See also: Amazon brings AI shopping assistant to retailers with Kate Spade

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    The post Weis Markets adds Instacart AI-powered shopping carts to stores appeared first on AI News.