In eCommerce logistics, the real bottleneck is neither warehousing nor transportation, but the last mile. Up to 65% of total logistics costs are concentrated in this phase, the stage in which the parcel moves from the distribution network to the final recipient.
The reason is structural. The last mile is the only segment of the supply chain where the logistics system encounters human unpredictability: absent recipients, incomplete addresses, missed delivery windows, urban traffic peaks. Every unexpected variable generates additional attempts, extra miles, vehicle congestion, and increased unit costs.
It is therefore no surprise that the adoption of Artificial Intelligence systems in logistics is accelerating rapidly. 85% of logistics companies are already investing in AI to improve efficiency and performance, because as the number of variables grows, only a predictive system can optimize operational decisions.
In this article, we analyze one of the most critical steps in eCommerce logistics: the selection of the delivery point. This is where much of last-mile efficiency is determined today, and it is precisely in this decision-making process that Artificial Intelligence is demonstrating its greatest operational impact—automatically identifying the optimal destination for each individual order.
A Premise: Pickup Points and Lockers Are Strategic Infrastructure
For years, out-of-home deliveries were considered merely an ancillary option, mainly designed for customers who could not receive parcels at home. Today, their role has changed.
Pickup Points and Lockers are strategic logistics nodes capable of increasing delivery density and drastically reducing operational uncertainty.
From a logistics standpoint, the difference is clear: delivering ten parcels to ten different addresses means ten stops, ten probabilities of failure, and ten variable dwell times. Delivering ten parcels to a single consolidated point means one stop, predictable timing, and a 100% success rate. This shift transforms delivery from an unpredictable activity into a plannable process. And this is where Artificial Intelligence comes into play.
A widespread network of pickup points creates a range of options from which an algorithm can select the most logistically efficient solution.
Here is the essential premise: without a distributed and extensive infrastructure of possible destinations, AI has no real alternatives to compare and, consequently, no concrete margins for optimization.
How AI Automatically Selects the Best Delivery Point
The most powerful function of AI applied to delivery is not volume forecasting or route optimization. It is the ability to determine, for each individual order, the delivery point with the highest probability of operational success and the lowest overall cost.
To achieve this, models simultaneously analyze historical data and real-time information. They do not limit themselves to the recipient’s geographic location, but reconstruct behavioral patterns: usual pickup times, average distance traveled for collection, frequency of failed home deliveries. These data are overlaid with logistics parameters such as pickup point saturation, carrier performance on that route, average transit times, and residual network capacity.
The result is a probabilistic assessment of each available option. The algorithm does not select the point closest to the end customer in absolute terms, but the one that maximizes the balance between operational cost, delivery speed, and likelihood of successful pickup.
The Advantages of Predictive Decision-Making Over Manual Customer Selection
When delivery point selection is left exclusively to the customer, the system effectively gives up decisive information. Recipients typically choose based on subjective criteria—such as proximity to a pickup point or locker, personal habits, or convenience, but they lack visibility into operational factors like future locker saturation, projected driver workload, or carrier performance in that area.
A predictive system, on the other hand, calculates the expected outcome of each alternative and can adapt the choice in real time. The impact of AI in logistics is already measurable. Algorithm-based route optimization can reduce delivery times by up to 20%, while predictive planning tools have been shown to cut delays by up to 12% and reduce delays caused by inefficient or risky routes by 25%. In some documented implementations, a 28% decrease in delays and on-time delivery levels of up to 98% have been observed.
Implementing AI in Logistics: What You Actually Need in Practice
One of the main obstacles to adopting Artificial Intelligence in delivery management is the perception that introducing it requires long projects and complex infrastructure.
In reality, in most cases, integration occurs via APIs that connect predictive modules to systems already present within the eCommerce digital logistics infrastructure, such as shipping platforms, order management systems, and tracking solutions.
The factor that truly impacts timelines, however, is not the technology, but the quality of available data. Data quality is the primary critical success factor in decision automation projects. Clean and consistent datasets allow reliable models to be trained even if they are not particularly large. Conversely, very extensive but heterogeneous archives slow down implementation and reduce forecasting accuracy.
When data foundations are solid, initial operational tests can be launched quickly and progressively integrated into real workflows without disrupting existing operations.
The Role of an Orchestration Platform Like GEL Proximity
Remember the initial premise?
For a predictive system to truly work, it must operate on a broad and already active physical network. This is where the infrastructure component becomes decisive.
GEL Proximity provides access to a Network of over 500,000 Pickup Points and Lockers that can be integrated via API. This means AI does not operate on a limited number of options, but on an extensive and widely distributed Network. The broader the network, the higher the probability that the system will identify an optimal solution for each individual order.
And this is the strategic point: when the distribution infrastructure is already available and easily integrable, adopting predictive logic stops being a complex project and becomes an operational evolution.
The efficiency of eCommerce deliveries no longer depends solely on shipping speed, but on the quality of decisions made before the parcel leaves the warehouse. In this scenario, GEL Proximity becomes your logistics performance lever, enabling you to apply advanced decision-making models on an already active network, reducing implementation complexity and activation time.
Turn delivery point selection into a competitive advantage.
Contact the GEL Proximity team today and discover how to integrate our Network of over 500,000 pickup points and lockers into your eCommerce business.
