Artificial Intelligence in Retail: What Walmart Is Really Doing
L'artificial intelligence in retail It's profoundly transforming the way major brands operate, and Walmart is one of the most concrete examples. Artificial intelligence in retail isn't just an experiment at Walmart: many solutions are already in production and generating measurable results in sales, logistics, and customer experience.
In 2024, the conversation around AI is dominated by hype, but Walmart has taken a pragmatic approach. The company is building an ecosystem of AI agents, generative models, and operational automation that aims to reduce friction along the entire value chain: from suppliers to physical stores to e-commerce.
This agentic vision of AI—in which intelligent systems collaborate with each other and with people—predicts the future of retail. And it offers practical insights for any company looking to use AI.’artificial intelligence in retail in a concrete way, going beyond spectacular but not very useful demos for business.
Artificial Intelligence in Retail: Walmart's Agentic Vision
At the heart of Walmart's strategy is an agentic model: not a single "super model" but a coordinated set of specialized AI agents. This approach to“artificial intelligence in retail It solves a wide range of problems: customer service, inventory optimization, employee support, and e-commerce personalization.
AI agents operate at three main levels. The first is the interaction level: conversational chatbots for customers and employees, voice assistants in stores, and internal helpdesks. The second is the operational level: systems that manage reorders, demand forecasts, and inventory allocation between stores and warehouses. The third is the decision-making level: recommendation engines, predictive sales analytics, and dynamic pricing.
Walmart combines generative AI models with traditional machine learning systems, using the former to understand natural language and create more human interfaces, and the latter to optimize processes and numbers. This integrated use of AI in retail demonstrates how GenAI is truly useful only when connected to the company's real data and workflows.
Internal use of AI: employee assistants and process optimization
A significant portion of Walmart's investments in artificial intelligence in retail It involves internal tools for employees. The company has developed AI assistants that help store staff with everyday tasks: consulting documentation, finding updated policies, and resolving issues in the field without having to contact a supervisor.
These internal AI agents are trained on Walmart's processes and connected to databases and operating systems. This way, AI in retail isn't just a front-end for the customer, but becomes a "co-pilot" for those working in the store or warehouse, reducing downtime and increasing service quality.
At the same time, Walmart uses advanced predictive models for its supply chain. Machine learning systems estimate demand for thousands of SKUs, integrating historical data, seasonality, promotions, local variables, and macroeconomic trends. This allows for improved fill rates, reduced out-of-stocks, and reduced waste, critical aspects in the’artificial intelligence in retail especially for food.
Generative AI for e-commerce and customer experience
A very visible area of application of the’artificial intelligence in retail At Walmart, it focuses on e-commerce and UX. The company is experimenting with semantic search and recommendations based on generative AI to improve product discovery. Instead of typing simple keywords, customers can formulate complex queries in natural language, which the system interprets and translates into targeted suggestions.
Walmart also integrates content generation capabilities: richer product descriptions, text variations for A/B testing, and micro-copy adapted to the user's context. This use of artificial intelligence in retail allows for scaling personalization without increasing content creation costs.
Another front is automated multi-level customer support. Chatbots and virtual agents handle a growing portion of requests, offering immediate responses, order tracking, and returns management. Complex cases are passed to the human agent with all the context already collected by the AI, improving handling times and satisfaction.
Data integration and governance: the foundation of AI strategy
L'artificial intelligence in retail requires a solid and well-governed database. In recent years, Walmart has invested in unified data platforms, data collection and cleansing systems, and governance tools to ensure quality, security, and privacy.
Walmart's AI strategy includes centralized data lakes, data catalogs, and internal APIs that allow AI agents to access information in a controlled manner. This reduces the risk of "blind models" making decisions based on incomplete or inconsistent data, a major challenge when scaling AI in retail.
Governance also covers ethical and compliance aspects: algorithm auditability, bias monitoring, and controls on automated decisions that impact pricing, promotions, and risk management. Large retail companies, such as Walmart, are following guidelines inspired by frameworks such as those of the’European AI Act, to reduce reputational and legal risks.
Comparison with global AI trends in retail
Walmart's move to focus on an agent ecosystem and full integration with business processes is in line with what many analysts see as the next phase of its’artificial intelligence in retail. No longer just isolated pilot projects, but extended and interconnected architectures.
According to research reported by McKinsey, The companies that derive the most value from AI are those that integrate it into their core business and invest in change management, training, and process review. Walmart appears to be moving in this direction, combining internal development, technology partnerships, and a strong focus on operations alignment.
Another key aspect is scale. AI in retail truly works when applied to large volumes of data and transactions, as in the case of Walmart: thousands of stores, millions of customers, tens of millions of SKUs. This allows for the training of more robust models and justifies significant investments in AI infrastructure.
Artificial Intelligence in Retail: Impact on Marketing and Business
L'artificial intelligence in retail It has a direct impact on marketing strategies and the way brands engage with consumers. In Walmart's case, data generated by AI and automation fuels more sophisticated segmentation, personalized campaigns, and tailored offers based on real behavior.
The combination of predictive models and generative AI enables the transition from mass-market campaigns to hyper-personalized journeys. Artificial intelligence in retail, for example, allows for the automation of relevant cross-selling and up-selling proposals, the adaptation of creatives in real time, and the optimization of media budgets based on expected performance.
From a customer experience perspective, conversational agents and intelligent interfaces reduce friction at every stage: product discovery, comparison, purchase, and after-sales service. This is particularly beneficial if digital channels—social media, messaging apps, e-commerce—are connected, creating a seamless flow between online and offline, as suggested by analytics. Walmart and major global operators.
For business, the’artificial intelligence in retail It translates into three main value drivers: increased revenue (improved conversion and average receipt), reduced costs (automation, inventory optimization, fewer errors), and risk mitigation (fraud detection, price control, reputation management). Companies that masterfully orchestrate these elements build a competitive advantage that is difficult to replicate.
How SendApp Can Help with Artificial Intelligence in Retail
The Walmart example shows that the’artificial intelligence in retail It becomes powerful when connected to customer relationship channels. In this context, WhatsApp Business is one of the most effective touchpoints for integrating AI and automation into everyday conversations.
With SendApp Official You can access the official WhatsApp APIs and connect your AI agents to messaging flows with customers and prospects. You can build intelligent retail chatbots that handle product inquiries, order status, returns, and personalized promotions, bringing AI logic to retail directly within the chat.
SendApp Agent It lets you organize your team's work on WhatsApp with a shared inbox, ticket assignment, and automated response. Human operators can only intervene on complex cases, while bots handle the rest, replicating on a smaller scale the hybrid human-AI model Walmart is adopting.
With SendApp Cloud You can orchestrate advanced marketing automation flows on WhatsApp: automatic sequences, transactional notifications, reminders, segmented campaigns. Integrate these flows with your marketing systems. artificial intelligence in retail – CRM, recommendation engines, pricing systems – means turning every conversation into a sales or loyalty opportunity.
If you want to bring the benefits of artificial intelligence in retail to your business, the first step is to build an intelligent conversation strategy on WhatsApp Business. Contact the SendApp team for a personalized consultation and find out how to activate a free trial of the platform, easily connecting AI, marketing, and automation across your messaging channels.
