For years, artificial intelligence in retail was synonymous with basic chatbots and generic recommendations. That has changed. In 2025-2026, the conversation is no longer about whether to adopt AI, but how to operate it to solve concrete problems: seasonal peaks that saturate customer service, stockouts, resource-consuming returns, and teams unable to keep up with omnichannel demand.
The data confirms it: according to Eurostat, 19.95% of EU companies with more than 10 employees already use at least one AI technology, a figure that rises to 55% in large corporations. In Spain, the INE reports 21.1% adoption. And Gartner projects that 40% of enterprise applications will feature task-specific AI agents by the end of 2026. The shift is clear: we have moved from an AI that answers to an AI that executes.
What operational problems in retail does artificial intelligence solve?
AI applied to retail operations tackles recurring pain points: demand forecasting, inventory optimization, logistics automation, and dynamic pricing. However, its effectiveness depends on data quality and integration.
In demand forecasting, machine learning models anticipate sales by product, store, and season, reducing overstock and stockouts. Inventory optimization prioritizes replenishment based on turnover, margin, and seasonality. And logistics automation eliminates bottlenecks in picking, routing, and delivery promises.
As IBM points out in its 2026-2027 retail agenda, unlocking proprietary data and connecting it with inventory, forecasting, and personalization platforms is the critical step. Without integration with OMS, WMS, ERP, and CRM systems, AI amplifies errors instead of solving them.
A concrete example in Spain is Mercadona, recognized for its internal platform “Portal Tornillo,” which integrates real-time information for every product: sales, stock, suggestions, and operational comparisons. According to Cadena SER, it records over 250,000 monthly queries and generates annual productivity savings. This case illustrates something fundamental: AI in retail is built on data standardization; without that foundation, automations end up magnifying errors.
In logistics, Inditex invested in Theker Robotics, an AI-driven logistics automation startup, along with advances in the digitalization of the store ecosystem. This demonstrates that artificial intelligence in retail is not just marketing: it is an integrated supply chain and operation.
Does AI in customer service reduce costs or increase capacity?
Both, but not as many expect. Artificial intelligence in retail does not produce a magical staff cut; it allows existing teams to manage more volume with higher quality.
According to a Gartner survey, only 20% of customer service leaders report AI-driven headcount reduction. 55% maintain a stable workforce while managing more volume, and 42% hire specific AI-focused roles. The real model is people doing higher-value work while AI absorbs the repetitive tasks.
A case documented by KPMG in an electronics retailer showed a +20% increase in CSAT in 6 months, a 40% reduction in response time, and a 25% drop in operating costs, using assistants integrated with inventory and logistics plus human handoff. The key was not the AI model itself, but the integration with the back office: when the assistant accesses the real status of the order and stock availability, the answers are accurate, and the customer does not need to repeat their query.
Cases with the highest immediate return are concentrated in the areas that Gartner identifies as priorities:
- Low-effort self-service (tracking, exchanges, returns)
- Agent enablement (response suggestions, knowledge base search, QA)
- Omnichannel unification of customer context
How does “search & discovery” change when the customer uses AI to shop?
Shopping habits are changing. According to Bain & Company, between 30% and 45% of consumers in the US already use GenAI to research and compare products. Conversational search is gradually replacing category navigation.
Walmart is building “purpose-built” agents for specific retail tasks trained with proprietary data, while Amazon integrates generative and agentic AI to improve intent-based search and shopping journey assistance.
For mid-sized retailers, the lesson is clear: optimizing product sheets, enriching descriptions, and connecting the catalog with conversational assistants is no longer optional. The discovery experience is a business problem, not just a UX one.
What is slowing down adoption and why do so many projects stay in the pilot phase?
According to the Bank of Spain, most firms using AI are still experimenting. The barriers: lack of talent, costs, and data quality. Additionally, Gartner warns that over 40% of agentic AI projects could be canceled by 2027 due to costs, unclear value, or insufficient controls.
What separates projects that scale from those that die? 3 factors:
- Integration with the business backbone (inventory, orders, logistics)
- A defined ROI with clear metrics from the start
- Adequate controls for automation
The most effective approach is to advance in waves: high-impact quick wins (self-service, agent support), then integration with core systems, and finally controlled agentic automation. Carrefour chose Spain to launch ai.carrefour, its global AI solution, with progressive adoption and mass training for employees. Without training, AI is just another underutilized tool; with training, it transforms into a work system.
Regarding regulation, the EU AI Act will apply transparency and high-risk rules from August 2026. For retail, this involves inventorying AI uses, informing the customer when they interact with an automated system, and ensuring traceability. Anticipating these rules reduces reputational risk and rework.
How to turn AI into results during seasonal peaks and overwhelming demand?
The answer lies in a hybrid model from day one: AI that absorbs repetitive volume combined with human teams for exceptions, empathy, and complex cases.
During seasonal peaks, a well-integrated AI system in retail answers tracking queries, manages exchanges, and prioritizes incidents automatically. But when a customer has an emotional problem or a complex return, the handoff to a trained agent makes the difference between retaining or losing that customer. 24/7 coverage and flexible scaling are essential to avoid sales losses due to delays. In omnichannel post-sales, AI must work with real-time information (order status, availability, return policies) to provide consistent answers across any channel.
The differentiator is not just “implementing AI,” but operating the end-to-end experience: inbox, SLAs, returns, tracking, and retention. Having a partner that connects technology and operations accelerates results. At Xtendo Global, we combine generative AI solutions with specialized human teams to offer scalable omnichannel care, post-sales management, and operational support tailored to each business.
Conclusion
Artificial intelligence in retail is no longer a bet on the future: it is an operational tool with measurable impact. But its success depends less on the technological model and more on how it integrates with data, processes, and people. The retailers leading in 2026 start with clear ROI cases, integrate AI with their core systems, design human handoff from the beginning, and train their teams. They do not seek to replace people, but to amplify their capacity.
Frequently Asked Questions
How long does it take to see ROI from AI in retail? Quick wins in customer service (self-service, agent support) show results in 30-60 days. Structural initiatives like supply chain or agentic AI require 6-12 months. The important thing is to define metrics from the start and measure in waves.
What is the difference between generative AI and agentic AI in retail? Generative AI creates content, suggests responses, and assists in communication. Agentic AI executes actions within systems (replenishments, catalog updates, return resolution) under defined rules and permissions. It requires deeper integration and governance but offers deeper automation.
Is it mandatory to notify the customer when they interact with AI? The EU AI Act establishes transparency obligations that apply progressively. From August 2026, Article 50 rules come into force. It is advisable to anticipate this and design the experience with transparency from the start.
What minimum data do I need so that an assistant does not make mistakes in post-sales? Real-time order status, updated inventory, transport SLAs, current return policies, and a versioned knowledge base. Without this data, the assistant provides incorrect information and generates more tickets instead of resolving them.
How do I train the team so that AI is actually used? Training should include guidelines on when to intervene, playbooks by case type, adoption metrics, and regular QA sessions. The example of Carrefour in Spain, with its massive training program in 2025, demonstrates that the organizational approach is what turns AI into a real work system.
