Walmart continues to make strides in cracking the code on deploying agentic AI at enterprise scale. One of the retailer’s primary objectives is to consistently maintain and strengthen customer confidence among its 255 million weekly shoppers. Walmart’s AI architecture rejects horizontal platforms for targeted stakeholder solutions. Each group receives purpose-built tools that address specific operational frictions. Customers engage Sparky for natural language shopping. Field associates get inventory and workflow optimization tools. Merchants access decision-support systems for category management. Sellers receive business integration capabilities. The segmentation acknowledges the fundamental need of each team in Walmart to have purpose-built tools for their specific jobs. Store associates managing inventory need different tools from merchants analyzing regional trends. Generic platforms fail because they ignore operational reality. Walmart’s specificity drives adoption through relevance, not mandate. Walmart’s Trend to Product system quantifies the operational value of AI. The platform synthesizes social media signals, customer behavior and regional patterns to slash product development from months to weeks. The system creates products in response to real-time demand rather than historical data. The months-to-weeks compression transforms Walmart’s retail economics. Inventory turns accelerate. Markdown exposure shrinks. Capital efficiency multiplies. The company maintains price leadership while matching any competitor’s speed-to-market capabilities. Every high-velocity category can benefit from using AI to shrink time-to-market and deliver quantifiable gains. Walmart’s approach to agent orchestration draws directly from its hard-won experience with distributed systems. The company uses Model Context Protocol (MCP) to standardize how agents interact with existing services. Walmart leverages decades of employee knowledge, making it a core component of its growing AI capabilities. The company systematically captures category expertise from thousands of merchants, creating a competitive advantage no digital-first retailer can match. The strategic advantage compounds. Walmart’s 2.2 million associates represent proprietary intelligence that algorithms cannot synthesize independently. Their framework applies across industries. Financial services organizations balancing customer needs with regulatory requirements, healthcare systems coordinating patient care across providers, manufacturers managing complex supply chains are all facing similar multi-stakeholder challenges. Walmart’s approach provides a tested methodology for addressing this complexity.
Sam’s Club AI-enables omnichannel ad measurement for brand partners using deterministic data from the Sam’s Club closed-loop ecosystem
Sam’s Club is releasing a solution called Omni-Impact, an AI-based tool for Sam’s Club Member Access Platform (MAP) retail media network to track ad performance. Omni‑Impact uses deterministic data from the Sam’s Club closed-loop ecosystem to let advertisers see what is driving incremental sales across all MAP on-site and off-site channels and over time. It also offers a 12-month longitudinal view of campaign performance across MAP channels. To help ensure consistency, Omni‑Impact applies a single, unified methodology across all MAP ad solutions. As a result, participating brands can use standardized metrics to compare performance across tactics. Omni‑Impact also simulates media mix strategies and delivers predictive budget guidance tailored to each advertiser’s historical performance and category dynamics. Omni‑Impact surfaces performance trends across a wide range of audience segments, such as age, household size or membership tiers. The retailer also recently began using first-party data to create personalized experiences through an offering called MAP’s Omni Experiences. This offering combines digital performance tactics like search and display with immersive experiences in and around media activations via channel, including online and in-store. These experiences can promote large-scale seasonal events, such as back-to-school, as well as smaller brand-led localized events.
Afresh grocery tech reduces shrink by 25% and lifts sales by an average of 3% digging down to the granular level on products that are trickier to track
AI-powered software company Afresh made to the THRIVE Top 50 FoodTech Companies list for the second year in a row. “The platform leverages artificial intelligence to ingest and digitize large volumes of fresh data, enabling accurate item-level forecasts, inventory understanding, and perishability predictions,” THRIVE said. “This empowers grocers to make margin-boosting decisions across their supply chain, reducing food waste and increasing product freshness.” Afresh positions itself as a mission-driven operation “driving both environmental and business results for grocery retailers” through its proprietary software that eliminates food waste through technological efficiency. While initially focused on reducing waste in the produce section, Afresh expanded its reach in 2023 to include the meat, seafood, deli, and foodservice departments. The company said its technology reduces shrink by a whopping 25% and lifts sales by an average of 3%. Afresh has developed a system that digs down to the granular level on products that are trickier to track, such as foods from the salad and hot bars. Rather than forecasting out a few days in advance, Afresh’s tech can estimate demand more than a month in advance. That longer time horizon helps grocers more efficiently plan orders. Once Afresh’s store ordering system is combined with its distribution ordering system “you can create this true demand signal across the chain and truly optimize your decisions.”
Simon Data’s marketing AI agents analyze the full spectrum of customer interactions and identify buying patterns to surface high-impact campaign opportunities and instant access to signals directly inside Snowflake data environment
Simon Data has launched Composable AI Agents for marketers, built natively on Snowflake Cortex AI and powered by Claude from Anthropic. They introduce a new operating model that provides marketers with direct, governed access to explore, identify, and activate data for personalization without requiring code or relying on data teams. The power of Composable AI Agents built on Cortex AI isn’t only that they execute—it’s where they execute. Simon’s agents run entirely inside the Snowflake environment. That means data, prompts, and activation all happen in place, under full enterprise governance, observability, and control. There’s no data movement, no black-box logic, and no compromise on speed or scale. Simon’s Composable AI Agents reveal hidden customer signals, integrate real-world context, and automate activation directly inside the data environment to unlock new capabilities. Imagine reaching customers who mentioned competitors in support calls. Or triggering offers to parents buying for teens who follow niche influencers. Or launching weather-based promotions to browsers in storm-affected regions. These use cases are no longer hypothetical. With Composable AI Agents, marketers gain hands-on control over signals and execution logic. Even small teams that once waited weeks for data pulls or SQL queries can now operate with the speed and precision of organizations with far more resources. With Simon, the marketing workflow doesn’t start with a segment; it starts with a goal, such as retaining customers, driving upsell, or increasing engagement in a specific region. Simon AI Blueprints turn these goals into agent-ready workflows—predefined strategies that guide how data is used, which signals to prioritize, and what activation logic to apply. From there, Simon’s Composable AI Agents work in concert to surface the right data, model the precise audiences, and activate campaigns. Three types of Composable AI Agents work alongside marketers to launch dynamic campaigns: Insights Agents analyze the full spectrum of customer interactions to surface high-impact campaign opportunities. Using Claude’s advanced reasoning capabilities, they identify patterns such as “customers buying kids’ products who mention back-to-school stress in support calls are 4x more likely to respond to convenience-focused messaging.” Marketers gain instant access to signals that would otherwise require custom analysis—no dashboards, no SQL, no handoffs. Data teams reduce exploration requests while enabling governed, self-service discovery inside Snowflake. Data Agents use breakthrough AI-generated “Smart Fields” technology to transform rich contextual data into actionable customer attributes. Instead of basic demographics, marketers now have access to insights like “weather sensitivity score” or “family purchase influence level.” Marketers can activate context without engineering lift or manual tagging. Data teams maintain full control over structure and access with zero data movement. Automation Agents execute campaigns using this deep contextual understanding, automatically creating and optimizing hundreds of adaptive micro-targeted campaigns. Marketers launch personalized campaigns in hours, not weeks. Data teams no longer export data or manage fragile reverse ETL pipelines.
Walmart’s transformation into a digital retail powerhouse is driven by narrowing e-commerce losses stemming from economies of scale, catchment area densification, and the ramp-up of high-margin membership and advertising revenue streams
Walmart’s transformation into a digital retail powerhouse has reshaped its earnings trajectory, according to a detailed e-commerce “Block” analysis from Morgan Stanley. The firm highlighted how Walmart has evolved over the past decade “from an old-economy brick-and-mortar retailer being disenfranchised by AMZN” into “an eCommerce, Retail Media and supply chain disruptor.” Morgan Stanley breaks Walmart’s digital evolution into three distinct phases: a “modest growth” stage from 2009 to 2012; a heavy investment phase from 2013 to 2019; and the current era marked by narrowing e-commerce losses and operating margin expansion. “E-commerce losses have narrowed to the point of near-breakeven,” analysts noted, as economies of scale, catchment area densification, and the ramp-up of high-margin Membership and Advertising revenue streams take hold. According to Morgan Stanley, the eCommerce “Block” includes three synergistic revenue sources: online merchandise sales (1P and 3P), Walmart Connect advertising, and Walmart+ membership fees. Though online merchandise is still “loss-making,” Morgan Stanley sees “narrowing losses” as scale lowers delivery costs. Advertising income is estimated at ~3.5% of GMV, with “~70% flow through to Adjusted Operating Income,” and Walmart+ now boasts ~15 million subscribers contributing ~$1.3 billion in revenue for 2024. With these elements working together, Morgan Stanley estimates Walmart’s U.S. e-commerce unit could generate ~$6 billion in incremental adjusted operating income over the next three years, with margins potentially in the “~10% to ~12% range.” Digital GMV has surged from $59 billion in 2022 to $89 billion in 2024, while ad revenue has increased from $1.9 billion to $3.2 billion over the same period. “Walmart’s digital flywheel supports long-term earnings growth,” Morgan Stanley concluded, forecasting continued strength as high-margin revenue streams outgrow digital sales.
DoubleVerify’s solution on Snapchat combines impression-level ad exposure metrics with eyes-on-screen data, delivering an unprecedented level of attention insight
DoubleVerify has launched DV Authentic Attention® for Social. The new offering combines DV’s scalable ad exposure data, including key metrics such as viewable time and screen share, with eyes-on-screen ad signals from Lumen Research, creating the most holistic attention solution available for advertisers seeking to measure their performance on Snapchat at scale. This is the first solution on Snapchat to combine impression-level ad exposure metrics with eyes-on-screen data, delivering an unprecedented level of attention insight across their platform. The solution enables advertisers to assess media performance on Snapchat with greater precision, helping inform budget decisions, validate campaign impact, and drive stronger ROI. DV Authentic Attention offers three main metrics to provide brands with actionable insights into their campaign performance: Ad Focus – Evaluates the ad’s ability to capture eye gaze, helping marketers understand the likelihood of an ad reaching users. Dwell Time – Measures how long an ad holds a user’s attention, quantifying in seconds the focus each ad receives to inform creative optimization. Attention Index – Offers an overall measure of attention on Snap and enables advertisers to benchmark their results against peer performance within their vertical. By measuring at the impression level, DV captures granular data that reveals the specific drivers of attention within each campaign, which powers insights that surpass the aggregated reporting used by other attention offerings on Social. For Snap advertisers, this enables a deeper understanding of how creative, placements, and activation strategies ultimately impact user attention.
Better Mortgage’s voice AI loan assistant can seamlessly transition to human originators by surfacing key borrower insights, tracking outstanding questions, anticipating next steps and replacing them
Better Mortgage’s technological advancements are anchored by two proprietary tools: TinMan, the company’s end-to-end loan origination system, and Betsy, a voice-based AI loan assistant. At the core of Better Mortgage’s AI strategy is a clear conviction: automation should elevate, not eliminate, human expertise. Betsy was built to work in tandem with loan officers—not in place of them. Every interaction she has with a borrower is fully visible within the Tinman dashboard, giving loan officers complete transparency and the ability to jump in with full context at any point. Her warm hand-off capabilities, including real-time summaries and status notes, ensure a seamless transition from machine to human. The shift from AI to human feels intuitive, not abrupt, reinforcing the trust borrowers place in their loan officer while still benefiting from around-the-clock digital support. Importantly, loan officers aren’t being sidelined by this technology — they’re being elevated. Betsy surfaces key borrower insights, tracks outstanding questions or documents, and anticipates next steps, allowing originators to step into each conversation already informed. Betsy allows loan officers to focus their energy on building relationships and driving decisions forward. The scalability of this hybrid model is already visible through Better’s NEO Powered by Better initiative. Partner companies like NEO Home Loans are now able to serve significantly more families without increasing headcount—proof that tech-human collaboration isn’t just efficient, it’s expansive. Ultimately, Betsy and Tinman aren’t replacements. They’re reinforcements. Together, they enable a concierge-level mortgage experience where accuracy, speed, and human empathy converge.
Syncfusion customer service platform’s copilot can cancel orders, update licenses, and call external APIs directly from a ticket or chat
Syncfusion, an enterprise technology provider, has released significant updates to its customer service and help desk ticketing platform, BoldDesk. The new features include AI-driven automation, reducing response time and manual work, and allowing developers more control over customer-support data. The enhancements aim to leverage AI for everyday support tasks and streamline agent workflows without adding complexity. The release centers on action-oriented AI, frictionless ticketing, and unified, omnichannel data. New features include: AI actions execute tasks for you: Copilot can cancel orders, update licenses, and call external APIs directly from a ticket or chat. AI-suggested replies in notifications: The AI can insert a context-aware suggestion into the automatic email sent to customers when they submit a ticket. Live chat speeds up conversations: AI-written summaries and subject lines and service-level agreement (SLA) timers help agents close chats faster. Drag-and-drop ticket forms: Group fields, preview attachments, and share links so agents reach the correct info faster. No-code workflows gain safeguards: New business-hour conditions and execution logs improve workflows. A new draft mode lets admins test automations before launch. Deeper integrations: Two-way Salesforce sync, new voice apps, and ticket automerging bring omnichannel context into a single view. Usage dashboards and new languages: AI-specific analytics, persistent layouts, and six additional UI languages show ROI and support global teams.
InComm Payments taps NCR Atleos API solution to let consumers conduct cardless cash withdrawals in retail environments via ATMs with a simple and secure code delivered through the applications they already use and trust
The InComm NCR Atleos partnership is launching a new wave of self-service ATM solutions across the United States. This strategic move aims to increase cash accessibility for consumers by integrating modern ATM technology into retail environments. InComm Payments delivers enhanced end-to-end payment solutions and emerging financial technology solutions that help businesses grow across a wide range of industries including retail, healthcare, tolling & transit, incentives, mobile payments, digital currencies and financial services. By enabling omnichannel connections and alternative payment methods, InComm Payments enables businesses to deliver seamless and valuable commerce experiences to customers worldwide. Atleos’ ReadyCode solution empowers InComm Payments to further bridge the digital-to-physical divide with a scaled ATM network integrated with an API solution that allows consumers to conduct cardless cash withdrawals with a simple and secure code delivered through the applications they already use and trust. ReadyCode is enabled at ATMs inside leading retail locations across more than 40 states, serving over 70 of the top population centers in the U.S.
Remark’s AI product expert personas are trained on the anecdotes and knowledge of subject-matter experts and embedded directly in brand storefronts to offer shoppers store-like experience with trusted and personalized guidance
Remark, the company building human-trained AI product experts for commerce, announced a $16 million Series A funding round. Remark’s language models are trained on the knowledge, tone, and preferences of Olympic athletes, stylists, estheticians, new parents, and more. These insights become always-on digital advisors embedded directly in brand storefronts. Unlike generic chatbots or large language models fine-tuned on public data, Remark’s personas are trained on the anecdotes and personal knowledge of subject-matter experts. The result is warm, informed guidance that helps customers find the right products and delivers the kind of personalized, high-touch experience shoppers expect in store. Theo Satloff, CEO and co-founder of Remark says “By working with real product experts to train AI personas, we’re creating guidance that’s trusted, helpful, and deeply personal. Our goal is to make online shopping feel less like a transaction and more like being guided by someone who truly understands what you need.” Remark gives merchants a competitive edge by combining the revenue driving power of human retail sales associates with the speed and scale of AI.