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Category: AI in Action

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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.

  • How C3 AI agents will automate predictive maintenance for Shell

    Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance.

    The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean heavily into autonomous AI agents, putting them in charge of the entire maintenance lifecycle.

    Going from that first warning sign all the way to a completed repair, this level of automation strips away the need for constant human oversight and makes sure the company’s resources are pointed exactly where they are needed most.

    “This expanded partnership with Shell proves what’s possible when enterprise AI is fully operationalised at global scale for predictive maintenance—reducing unplanned downtime and delivering hundreds of millions of dollars in economic value,” said Stephen Ehikian, President of C3 AI.

    “Shell has built mature AI predictive maintenance programs on our platform, and together we’re now pushing into agentic AI, advancing how this technology can further transform reliability, safety, efficiency, and operational performance.”

    C3’s AI agents help Shell move past basic anomaly detection

    In the beginning, Shell used machine learning simply to spot odd patterns in sensor data, giving engineers an early heads-up before things broke. To pull this off, the system ingests a massive amount of real-time operational technology (OT) data and mixes it with business context from ERP platforms such as SAP.

    The next step introduces AI agents built for actual reasoning and independent action. While older systems stopped at pinging an engineer when things looked unusual, this next-generation framework independently investigates why an alert fired in the first place.

    Once it pinpoints the root cause, the agent steps up to draft precise work orders, confirm part availability in the inventory, and generate procurement requests.

    C3 AI’s platform handles the heavy lifting, providing a model-driven space to easily integrate high-frequency sensor feeds with structured financial and maintenance logs. These AI capabilities are trained to learn the normal operating baselines for specific gear, like pumps, turbines, and compressors.

    The agentic layer sits on top of this foundation. Operators configure an individual agent for a given piece of equipment by defining its objectives and permitted responses. If the core machine learning models detect a deviation from normal operations, this agent activates, gathering extensive contextual data to build a complete picture of the situation. This context usually includes recent maintenance history, environmental conditions, and upstream process variables.

    Using all that information, it suggests a fix backed by solid evidence. Human operators can then easily approve or override the plan. As the system proves itself over time, Shell can fully automate its responses to certain types of alerts. Connecting straight into systems like SAP is critical here, allowing the agent to work inside the exact same workflows that human planners already use.

    The real impact of agentic AI for predictive maintenance

    Putting agentic AI to work at this scale tackles the classic “last mile” headache in predictive maintenance. Many industrial companies can predict failures just fine, but turning those insights into fast, efficient action remains a challenge. Usually, engineers still have to manually dig through alerts, investigate the causes, and write up the work orders themselves.

    Shell wants to shrink that timeline. By letting AI handle root cause analysis and work orders, the delay between a predicted failure and the actual fix drops. That directly improves equipment uptime and protects production.

    Moving to a model where repairs only happen when the equipment condition actually demands it naturally saves money, simply because nobody is wasting time tinkering with perfectly fine machinery. Leaving healthy hardware alone also means it lasts much longer.

    On top of the cost savings, stepping in before a catastrophe hits makes the whole operation much safer and cuts down on environmental risks, which is always top of mind in the energy sector.

    “What Shell and C3 AI have built on Azure over the past several years is exactly what enterprise AI should look like—real applications, running in production, delivering measurable value at global scale,” commented Sandy Gupta, VP GISV, Software Development Companies at Microsoft.

    This expanded rollout shows that we are finally talking about practical industrial AI production workflows instead of just algorithms. Rather than just the prediction itself, the real value comes from the system’s ability to act on it with barely any human oversight.

    See also: Meta Business Agent drives AI-powered conversational commerce

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    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 How C3 AI agents will automate predictive maintenance for Shell appeared first on AI News.