Category: Retail & Logistics AI

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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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June 22, 2026
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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post SAP and Google Cloud deploy agentic commerce architecture appeared first on AI News.

June 19, 2026
Computer vision deployments drive retail productivity gains

Computer vision deployments are driving retail productivity gains as operators automate physical shelf tracking to protect eroding margins.

This hardware deployment directly addresses the persistent in-store execution failures currently costing the industry billions. A study authored by Coresight Research – in partnership with technology providers Simbe and RELEX Solutions – calculates the exact cost of these operational shortfalls.

Inefficiencies consume 6.4 percent of gross sales across the sector. Hardware, mass merchandise, and grocery categories will surrender $196.4 billion to these operational failures in 2026. The monetary value of these losses is jumping 21 percent over the previous year. This deficit vastly outpaces the three percent projected sales growth for the entire sector.

Nine in ten retailers report active difficulties managing their shop floors. Empty shelves and inaccurate pricing structures directly suppress operating margins. Margin erosion exceeds five percent for 89 percent of operating businesses.

Full-scale deployments of store intelligence platforms operate across 60 percent of enterprise footprints. This adoption rate represents an 18-percentage-point jump year-over-year.

Experimental pilot programmes account for a mere 18 percent of current market activity. The adoption curve skews heavily toward top-tier enterprises. 73 percent of retail companies generating over $5 billion in annual revenue maintain fully scaled deployments.

Mid-market operators lag behind, with only 42 percent of sub-$1 billion companies achieving similar deployment maturity. Treating physical stores as separate entities from digital channels degrades customer lifetime value. Capital expenditure directly targets out-of-stock tracking, automated pricing, planogram verification, and assortment planning.

Production deployments in hardware and grocery

BJ’s Wholesale Club provides a documented case study of applied shelf digitisation. The operator deployed Simbe robotics platforms to monitor inventory and price accuracy across its locations.

Management used this hardware foundation to generate digital twins of individual warehouse clubs. This application established real-time visibility systems previously absent from their physical operations.

BJ’s applied these digital models to route planning for online orders and curbside fulfillment. The engineering team recorded a 40 percent year-over-year improvement in picking efficiency through this data application. CEO Bob Eddy reported the technology enabled the company to elevate quality standards within fresh merchandise categories.

Grocery operator Albertsons applies AI to automate complex retail operations. The grocer targets $1.5 billion in productivity gains spanning three fiscal years. CEO Susan Morris explained: “We will be equipping our merchants with AI-driven insights and automated execution to optimise pricing, promotions, and assortment decisions, transforming category management and driving margin improvement.

“Our vision is the future where intelligent automation guides these decisions, freeing our people to focus on strategy and innovation.”

Flaws in deployment sequencing

Many organisations prioritise the installation of pricing software while ignoring foundational sensor infrastructure. 43 percent of surveyed technology leaders direct their capital toward pricing optimisation software.

Supplier collaboration platforms rank second in priority, attracting investment from 36 percent of operators. Only 33 percent of these organisations invest in the shelf digitisation hardware required to feed accurate data into those pricing models.

This hardware includes the sensors and cameras needed to verify physical stock availability. Store intelligence deployments require strict sequencing to function properly. Retailers must first digitise the shelf, deploy data analytics, install inventory tracking software, and finally execute pricing automation.

This inversion of the technology stack creates downstream data failures. Markdown algorithms process outdated inventory counts when physical tracking sensors are absent. Mispricing rates hit 13 percent in 2026, marking a four-point increase since 2024.

Pricing and promotional execution dominates the priority list, presenting an active difficulty for 92 percent of operators. Kim Anderson, VP of Store Operations at Schnucks Markets, states that shelf data must precede all other implementations. Without accurate physical inventory monitoring, downstream applications fail to meet their performance targets.

Out-of-stock events remain severely disruptive, with 52 percent of operators ranking inventory availability as highly demanding. Operators attempt to fix multiple problems simultaneously, with 40 percent directing capital toward three or more operational inefficiencies at once.

Labour reallocation and efficiency metrics

Lowe’s demonstrates the financial impact of automating the associate workflow through its ‘Perpetual Productivity Improvement’ initiative. Executive VP of Stores Joseph McFarland directed the deployment of workforce management tools and inventory solutions to eliminate redundant associate tasks.

The engineering rollout saved 80 non-productive labour hours per store on a weekly basis. Lowe’s advanced the initiative by deploying full shelf replenishment technologies powered by AI to track stock depletion in real-time.

Management distributed financial bonuses to the workforce based on documented productivity enhancements. The company issued $5,000 to associate store managers and varied payouts to hourly staff.

Broad industry data validates the performance metrics recorded by Lowe’s. The deployment of intelligence applications drives a 14 percent average reduction in time spent on manual store tasks. 86 percent of organisations record defined decreases in manual assignment hours.

Retailers report distinct performance disparities based on total revenue. 56 percent of operators generating over $5 billion report advanced reductions in task completion times, compared to only 36 percent of mid-market companies.

Organisations cite operational efficiency as their primary investment objective, followed closely by the unification of store data. Retailers expect these tools to generate new capital, with 40 percent of leaders seeking to establish alternative revenue streams like retail media networks.

Securing market competitiveness

Store intelligence technologies function as an interconnected ecosystem rather than standalone fixes for isolated problems. Deploying these systems without a coherent sequencing plan forces operators to build upon an unstable foundation.

Establishing real-time, shelf-level visibility proves strictly necessary before attempting to scale downstream software. Pricing automation, supplier collaboration platforms, and inventory forecasting applications require verified physical data to generate accurate outputs.

Customer behaviour responds directly to correct operational upgrades. Proper deployments increase customer lifetime value by 11 percent across the sector, while conversion rates improve for 50 percent of the operators executing physical automation frameworks.

48 percent of companies record increased enrollment in their loyalty programmes following system integration. Accurate pricing and consistent stock availability elevate online review metrics for 47 percent of surveyed operators.

Retailers compounding value through integrated, properly sequenced hardware and software capabilities possess a distinct market advantage over competitors accumulating disconnected applications.

See also: HSBC expands AI banking partnership with Google Cloud

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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June 18, 2026
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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Accenture: Consumers show growing trust in AI shopping agents appeared first on AI News.

June 15, 2026
Coinbase for Agents: Automating portfolio trading with AI

Coinbase for Agents connects AI to financial execution channels to automate trading and payments directly from user portfolios.

Large language models process vast quantities of data but lack direct integration with active financial portfolios. Individuals frequently employ these models to evaluate market developments or research investment opportunities. These software tools possess the capacity for complex reasoning but cannot execute financial transactions on behalf of the user.

Coinbase for Agents enables autonomous digital entities to execute trades, process payments, and manage balances within user-defined parameters.

Terminal-based systems use command-line interfaces to manage the connection. This route fits development environments such as Claude Code, Codex, or OpenClaw. The command-line architecture integrates directly into established local development toolchains. This implementation path lowers token expenditure during high-frequency tasks and accommodates extensive local customisation. Setting up this configuration involves installing specific skill packages via the Coinbase Developer Platform documentation and generating dedicated API keys.

Web-centric software arrangements, meanwhile, rely on the Model Context Protocol. MCP establishes a direct integration path for web-based agent environments like ChatGPT or Claude Web. It permits a rapid connection via a single account login procedure. This method functions without requiring manual API key creation or complex local configuration files. A remote MCP option will become available in the near future that will allow individuals to link their financial profiles using standard single sign-on features without writing code.

Portfolio allocation and execution

Account holders can program specific distribution rules, instructing an automated agent to establish or maintain targeted asset ratios.

As an example, a portfolio manager might select a target distribution consisting of 60 percent Bitcoin, 20 percent Ethereum, and 20 percent Solana. The agent executes this directive over extended timeframes spanning multiple months. It assesses real-time pricing data and positions limit orders to purchase assets when market valuations decline by five, ten, or fifteen percent. The software captures these brief market pullbacks to accumulate assets automatically.

Coinbase’s current system supports spot and derivatives trading but is working on expanding the protocol to include index funds, standard corporate equities, commodities, and prediction markets.

The autonomous assistant monitors available cash balances around the clock to keep funds productive. It distributes idle capital to generate rewards or highlights specific asset positions that require direct human attention.

Integrating the x402 protocol allows these agents to interact with external commercial systems. Coinbase introduced this agentic payment protocol last year to provide software agents with a standard method for economic interaction. Agents deploy capital via this protocol to purchase computing resources, analytical models, and proprietary market data to inform their decisions. Upcoming x402 integrations will standardise these automated purchases across web services.

Data collection determines the efficacy of automated trading logic. An agent assigned to execute a dollar-cost averaging plan into Ethereum uses historical metrics to optimise market entry. The system retrieves thirty days of hourly pricing statistics to pinpoint historical low points during the day and can then establish a recurring daily market purchase of $20 timed precisely to those optimal windows. The automated routine executes daily for two weeks based on a single initial command.

Security controls and compliance

Agents operate exclusively inside isolated portfolios to safeguard broader financial holdings. This design prevents the autonomous entity from viewing or accessing unauthorised balances.

Users already retain total control over the operational boundaries. However, upcoming platform updates will introduce explicit rulesets for fine-tuned governance. Users will dictate maximum transaction volumes, specific permitted assets, and absolute spending limits.

The platform subjects all agent-initiated payments to standard transaction monitoring and “Know Your Transaction” validation. Users receive automated compliance verification without building internal monitoring systems.

Coinbase’s latest product launch marks the expansion of a broader consumer product suite that began with the 2024 launch of AgentKit, which provided tools for embedding crypto wallets into software systems. The subsequent introduction of the x402 protocol and the release of Coinbase for Agents finalises the financial execution layer.

Alternative connection options exist for everyday investors who prefer simple interfaces. Coinbase Advisor operates natively inside the primary consumer application. This integrated agent provides automated recommendations and financial guidance directly to users. The assistant holds formal registrations with both the SEC and the CFTC as a financial advisor. For retailers, commercial merchants can deploy Coinbase Payments to accept automated transfers from these autonomous systems.

See also: Visa ChatGPT integration enables AI agent retail purchasing

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Coinbase for Agents: Automating portfolio trading with AI appeared first on AI News.

June 12, 2026
Visa ChatGPT integration enables AI agent retail purchasing

Visa has linked its payment infrastructure to ChatGPT, enabling AI agents to recommend retail products and execute financial transactions.

The deployment removes human intervention from the final stages of the retail funnel. Autonomous agents will now process user prompts, evaluate merchant catalogues, and complete the checkout process using Visa’s payment rails at any supporting merchant.

Previous retail AI integrations restricted automated purchasing to single-vendor environments. Retailers built proprietary chatbots confined entirely to their own inventory. Visa’s integration bypasses closed-loop architecture.

The payment giant connects the open-web reasoning capabilities of a large language model directly to a universal transaction network. Users simply command the agent to procure an item, and the model handles the vendor selection, product comparison, and financial settlement.

Enterprises should be aware that commercial transactions will increasingly execute without a human buyer ever seeing a retailer’s website, digital advertisement, or promotional email.

Restructuring retail data for AI agent buyers

Marketing departments design campaigns around human psychology, emotional triggers, and visual merchandising. AI agents operate on pure data evaluation.

When ChatGPT receives a mandate to purchase a specific product type, it parses technical specifications, aggregated sentiment scores, and pricing structures. Display ads and user interface optimisations hold zero weight in the model’s selection criteria.

Retailers will need to expose machine-readable inventory data. Search engine optimisation transitions into language model optimisation. The algorithms driving ChatGPT rely on structured data feeds, clear API documentation, and explicitly-formatted product attributes to evaluate whether an item meets the user’s parameters. Merchants failing to maintain high-quality, structured metadata will find their products invisible to the autonomous agent.

Personalisation occurs entirely on the user’s device or within the user’s secure LLM profile. The AI retains the consumer’s past preferences, sizing requirements, budget constraints, and brand affinities. Instead of the retailer attempting to guess the consumer’s needs through tracking cookies and site behaviour, the agent arrives at the digital storefront with a highly-specific procurement mandate.

Completing a transaction without human intervention requires a secure, automated handshake between the reasoning engine and the payment gateway. Visa provides the financial layer necessary to establish trust in an inherently untrusted agentic environment. Traditional checkout flows require manual data entry, CAPTCHA verification, and two-factor authentication loops. These mechanisms block autonomous agents.

Visa implements programmatic tokenisation to solve the authentication problem. The user pre-authorises the ChatGPT environment with specific spending parameters. When the LLM decides on a purchase, it generates a single-use payment token through the Visa network. The agent transmits this token via API to the merchant’s backend systems. The transaction settles exactly like a standard digital wallet payment, bypassing the visual user interface completely.

A digital storefront requiring multi-page navigation or mandatory account creation introduces failure points for the agent. Enterprises actively deploying headless commerce architectures possess an advantage. They can process the agent’s payload, confirm stock levels, and execute the payment token in milliseconds.

Enterprises track bounce rates, session durations, and cart abandonment to understand consumer behaviour. An AI agent does not browse—it queries an endpoint, extracts the necessary data, and either executes the payment or terminates the connection.

Retailers must develop new telemetry to measure agent interactions. Tracking the frequency of API queries from known LLM IP addresses replaces tracking unique human visitors. Understanding why an agent selected a competitor’s product will require analysing the structural differences in product data feeds rather than running A/B tests on website layouts.

Customer retention strategies also need adjustment. An autonomous agent evaluates the market fresh with every prompt unless explicitly instructed by the user to reorder a specific brand. Loyalty programmes must be engineered into the payment token or the user’s LLM profile. If the AI cannot automatically apply a loyalty discount during its background calculation, the merchant loses the pricing advantage intended to secure the repeat purchase.

Managing and securing the agentic AI supply chain

Prompt injection attacks could theoretically manipulate an agent into purchasing from malicious vendors or authorising inflated transactions. Visa’s network acts as the final validation layer, applying fraud detection models to the incoming token requests.

Businesses face the secondary challenge of managing automated returns and customer service queries initiated by the AI. If the delivered product fails to meet the parameters defined in the original prompt, the user can instruct the agent to reverse the transaction.

In this scenario, the AI will autonomously navigate the merchant’s return policy, initiate the refund request, and generate the necessary shipping labels. Retail customer service operations must deploy their own automated systems capable of negotiating directly with the consumer’s agent.

Visa’s ChatGPT integration confirms the enterprise transition from human-operated software interfaces to autonomous digital proxies. The customer is no longer necessarily a human navigating a web browser, but an algorithm executing a script.

See also: Aviva deploys AI to stop £230M in sophisticated insurance fraud

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

The post Visa ChatGPT integration enables AI agent retail purchasing appeared first on AI News.

June 11, 2026
McDonald’s tests Google-backed AI drive-thru ordering system

McDonald’s is testing a new AI system that can take drive-thru orders and support restaurant operations.

The system, called ArchIQ and nicknamed “Archy,” was introduced during the company’s Worldwide convention, according to Restaurant Business. It is being tested at five McDonald’s locations in the United States, though the company has not named the restaurants involved.

A video shared on X by a McDonald’s franchise owner showed the system greeting customers, processing order changes, displaying the final total, and asking customers to pull ahead for pickup.

A demonstration shared on X by the franchisee account McFranchisee showed the system taking orders in English and Spanish. The account said the system has processed more than one million transactions, with about 90% of orders completed without being escalated to staff.

The same account said ArchIQ can respond when repeat customers ask for their usual order. McDonald’s has not provided technical details on how that feature works.

ArchIQ is being developed with Google. According to McFranchisee, McDonald’s restaurants in the US are receiving Google Edge Cloud blades ahead of the rollout.

McDonald’s previous AI ordering test

ArchIQ is McDonald’s latest AI test for drive-thru ordering. The company previously worked with IBM on an automated ordering system across more than 100 restaurants.

McDonald’s ended that pilot in 2024 after customer complaints over order errors. The earlier IBM test was followed by customer videos showing incorrect orders, including one case in which the system reportedly added more than $250 worth of chicken nuggets.

After ending the IBM partnership, McDonald’s said it would continue exploring voice ordering technology.

Restaurant operations support

ArchIQ is not limited to customer ordering. McFranchisee said it can monitor restaurants and alert managers to possible issues.

According to McFranchisee, the system can alert managers if a freezer is down. It can also flag kitchen bottlenecks or other problems that need attention.

McFranchisee described ArchIQ as both an ordering tool and a management-support tool.

The test forms part of McDonald’s new growth plan, called “McDonald’s > NEXT.” The company said the plan is intended to improve restaurant operations and unit economics.

McDonald’s reported a large digital customer base in its 2025 results. The company said systemwide sales to loyalty members across 70 markets rose 20% to nearly US$37 billion in 2025, while 90-day active loyalty users rose 19% to nearly 210 million at year-end.

McDonald’s CEO Chris Kempczinski said in a press release that the strategy is aimed at the company’s next phase of growth and productivity.

The company has also referenced restaurant upgrades and possible menu changes under the same plan, but has not provided detailed information.

Automation and service

In a company memo, Kempczinski said more of the customer journey is becoming automated, leaving fewer chances for guests to interact with crew members. He said that it raises the standard for hospitality when customers interact with staff.

QSR Magazine’s 2025 Drive-Thru Report, citing Revenue Management Solutions, said drive-thru traffic remained negative month after month and hovered between minus 5% and minus 8% in 2025.

Other fast-food chains have also announced AI-powered drive-thru ordering systems, including Taco Bell and Wendy’s.

Jonathan Maze, editor-in-chief of Restaurant Business, told ABC News that companies often present drive-thru automation as a way to free employees for other tasks. The McFranchisee account said the system could reduce the need for workers to take orders in noisy drive-thru lanes.

Some X users responding to the ArchIQ demonstration said they preferred interacting with human workers. Others supported a more automated ordering process.

McDonald’s has not said when ArchIQ could be expanded beyond the five test locations. The company has said the system is intended to improve speed and accuracy while supporting customers and crew.

The company’s AI drive-thru system remains in limited testing.

(Photo by Boshoku)

See also: Walmart’s AI workflows meet the realities of the balance sheet

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

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June 10, 2026
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

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.

AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.

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

June 9, 2026