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In a recent REsource article, we explored how artificial intelligence has been impacting the corporate real estate market, with applications such as Laís AI making it easier to capture and process leads for both brokerage services and companies looking to lease or sell space in their buildings, among many other features.
But commercial real estate is not sustained by the office segment alone. Now it’s time to look at how these tools — when established in e-commerce — are impacting demand forecasting and the adaptation of warehouses in the logistics sector. To understand the consequences of integrating technology into the field, REsource spoke with LogFala CEO Patrick Nogueira.
The first step to understanding how the logistics sector is affected is to observe what changes, in practice, within e-commerce when these tools are adopted.
“When you adopt AI agents for sales and customer service, you can scale the business and increase performance while reducing costs. In practice, AI can handle much higher demand simultaneously, unlike a human team.”
According to him, automation absorbs level-zero and level-one demands, freeing human agents to handle more complex cases. In addition, the executive highlights the constant nature of AI-driven service:
“There is no fluctuation. When we talk about humans, especially in customer service, there are labor and operational rules: breaks, hour limits, schedules, and so on. AI is linear. If there are 1,000 customer interactions at the same time, it handles them. And it handles them well.”
REsource also spoke with Daniel Luz, CEO of Be Analytic, who emphasizes the impact on the sales front: beyond scale, AI increases conversion by making the buying experience more conversational and personalized. In his view, the trend is to reduce barriers especially for audiences with greater difficulty navigating traditional platforms, such as elderly consumers, by replacing interfaces built around menus and ads with chat-based interaction.
Nogueira comments on how demand has shifted with the growth of e-commerce:
“A few years ago, the boom was for larger warehouses. Today there is the promise of buying and delivering on the same day. What is the impact of that? It will increase the need for warehouses, but mainly for small warehouses, which we call small hubs — small urban hubs.”
According to him, as purchase volume rises, the channel grows, revenue grows, and service levels also need to grow.
Nogueira explains how the promise of same-day delivery and the demand for small hubs connects to AI:
“This is possible because, with AI, the customer can buy at 10 p.m., ask questions, request support, file a complaint, and be served quickly and efficiently.”
The idea, according to him, is to automate simple requests — such as delivery discrepancies, documents, and follow-ups — and shift only more advanced cases to human support.
The CEO points out that AI does not increase or reduce inventory on its own, since this depends on each shipper’s business model and risk policy. Still, he highlights that efficiency and margins depend on high turnover and leaner inventory.
Daniel Luz adds that, in the sales context, AI agents can also influence inventory indirectly: by suggesting strategic products with higher availability, they can steer demand and reduce stockouts. On the other hand, he notes that higher conversion rates also tend to increase storage needs, and cites market estimates in the range of 15% to 20% impact on sales.
In Luz’s view, the combination of AI and blockchain could also transform how logistics properties are searched and transacted. For him, conversational agents are expected to evolve to understand operational needs and recommend assets more efficiently than traditional filters. Tokenization, in turn, would bring traceability, auditing, and trust — especially in a market where decisions involve high investment and occupancy history weighs heavily in decision-making.
Another effect would be reduced fragmentation: instead of dealing with multiple intermediaries by state, companies could access a more unified and reliable chain.
For Patrick Nogueira, the expansion of last-mile logistics does not eliminate large warehouses, but it changes the design of logistics networks.
“Large warehouses will continue to exist because industrial production keeps increasing. But I see a slowdown in that growth and an acceleration of last-mile.”
With increasingly fast deliveries, models are emerging in which individuals offer space as micro-storage, following a logic similar to Zé Delivery. In the future, the key differentiator will be the speed of information. He projects that between 2026 and 2028, AI will take over repetitive tasks such as customer service, status updates, and monitoring.







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