Year-end sales continue to be a barometer of the digital maturity of retail, revealing the gap between companies that have evolved their strategies and those that still face structural and operational limitations. In an increasingly competitive market, investing in technology has ceased to be a trend and has become a basic requirement to guarantee performance, stability, and personalization at scale.
Artificial intelligence (AI) has taken on a central role in this advancement. When applied strategically, it allows for the identification of purchase intentions in real time, the adjustment of prices according to customer behavior, and the delivery of more relevant offers. Among the most transformative applications are dynamic pricing, guided suggestions, and search engines supported by LLM models.
According to Alexsandro Monteiro, Head of Retail at FCamara, a Brazilian multinational technology and innovation company, this combination is redefining the buyer experience. “AI is eliminating the traditional funnel. The journey, which used to be linear, has become a continuous system where each click, search, or interaction feeds the next step and maximizes conversion,” he states.
In large consumer sector operations monitored by FCamara, the results are already tangible. In a dynamic pricing project, for example, a retailer began to predict price elasticity, stock depletion, and regional consumer behavior. Within a few months of implementation, it recorded a 3.1% increase in net margin on end-of-season collections – equivalent to R$ 48 million in one year. In another e-commerce operation, AI solutions accelerated platform development by 29%, increasing responsiveness during periods of high demand.
Based on these experiences, Monteiro highlights four pillars that explain why AI has established itself as crucial for increasing efficiency and profitability in the market:
- Contextual recommendations and increased average order value: models that interpret intent in real time are replacing traditional systems based solely on history. AI reads micro-signals, browsing patterns, and relationships between items, boosting discovery, expanding conversion, and increasing the average order value.
- Search with LLM and semantic understanding: search engines supported by language models understand what the audience means – not just what they type. Natural queries, such as "comfortable shoes for working all day," generate more accurate results, reducing friction and bringing the user closer to making a purchase.
- Conversational assistants focused on conversion and efficiency: AI-driven chatbots and co-pilots act as digital salespeople. They answer complex questions, suggest compatible products, offer sizes, and apply sales rules, while simultaneously reducing operational costs by relieving human customer service.
- The seamless and invisible journey: the integration of dynamic pricing, contextual recommendations, intelligent search, and conversational assistants creates a fluid ecosystem where each interaction feeds back into the next. The result is a continuous, targeted journey that is virtually imperceptible to the visitor.
According to Monteiro, these pillars show that AI has moved beyond being an operational accelerator and has established itself as a competitive differentiator for retail.
“As more companies mature their data and intelligence structures, more opportunities arise for sustained growth, efficiency gains, and the creation of much more precise shopping experiences – especially during critical periods such as end-of-year sales,” he adds.
"Evolution now depends on the ability of organizations to transform technology into practical decisions, connected to the business and focused on real results," concludes Monteiro.
