How does the quality of logistics data affect the effectiveness of artificial intelligence in shipping processes?

Companies are increasingly implementing AI, automatic carrier selection, and advanced shipping rules, hoping for faster order handling and lower operating costs. In practice, however, the effectiveness of automation rarely depends solely on algorithms — much more often, success is determined by the quality of the logistics data the system works with. An outdated product weight, an incomplete delivery address, or an incorrect customs code can lead to costly mistakes, delays, and additional work for the team. As a result, even the best-designed AI solution will not make accurate decisions if it receives incomplete or inconsistent information. That is why, before automating shipping, it is worth asking one question: is your logistics data ready to work with AI?

AI does not make better decisions than the data it receives

Automatic carrier selection, intelligent shipping rules, and systems supporting order handling are designed to streamline logistics and increase operational efficiency. However, even the best solutions will not achieve the expected results if they work with incomplete or outdated data. This is why experts emphasize that the biggest challenge in AI projects is not the model itself, but data preparation. According to industry estimates, as much as 80–90% of the time spent on AI and analytics projects is devoted to collecting, organizing, and cleaning data. Gartner reports have also been pointing for years to poor data quality as one of the most common reasons for the failure of automation initiatives.

AI does not see the parcel – it sees data

For a human, a parcel is a physical product that can be weighed, measured, and assessed. For a shipping system, it is only a set of information stored in a database. It is on this basis that decisions are made regarding carrier selection, transport pricing, or document generation. The most important data used by logistics systems includes:

• shipment weight,
• parcel dimensions,
• delivery address,
• destination country,
• order value,
• HS customs code,
• requirements of a specific courier service.

If even one of these pieces of information is incorrect or incomplete, automation may operate according to its technical assumptions, but at the same time lead to incorrect business decisions.

What are AI hallucinations in logistics?

The concept of AI hallucinations is most often associated with language models generating false answers. In logistics, the mechanism works in a similar way, although the effects are more noticeable operationally. A hallucination occurs when the system does not have a complete set of data and tries to fill the gaps with the most likely scenario. Instead of relying on facts about a specific order, it bases its decision on historical data, average values, or default rules. The problem is that “most likely” does not always mean “correct”.

GIGO – the principle that still governs automation

In the world of AI, advanced algorithms are often discussed, but one of the most important principles of computer science remains unchanged. GIGO (Garbage In, Garbage Out) means that the quality of the result will never be better than the quality of the input data. In a manual handling model, many errors could be caught by an employee. In an automated environment, there are far fewer such checkpoints. The system works faster, but it scales mistakes just as quickly. That is why the question should not be only: “Are we using AI in logistics?”. Much more important is: “Is our logistics data good enough for AI to make the right decisions?”. The answer to this question determines whether automation becomes a source of savings or another area generating costs and operational exceptions.

Why does data quality become so important as e-commerce scales?

If a store processes several dozen orders a day, an employee can catch a missing apartment number, an unusual product weight, or an incorrectly selected courier service. The problem appears when the business starts to grow. Automation becomes a necessity, but at the same time it significantly increases the importance of data quality.

50 parcels a day versus 5,000 parcels a day – scale changes everything

The situation looks completely different with hundreds or thousands of shipments per day. In such a model, there is no room for manual control of every order. The process must be repeatable, predictable, and based on automated decisions. Therefore, as scale increases, the importance grows of:

• correct product weights and dimensions,
• up-to-date carrier selection rules,
• validated address data,
• consistent product data across all systems.

It is at this stage that data quality stops being a technical problem and becomes a matter of operating costs and the company’s ability to continue growing.

International sales increase data requirements

The development of cross-border sales means additional responsibilities related to documentation, product classification, and the requirements of individual carriers and customs authorities. In the case of international shipments, the following become particularly important:

• HS customs codes,
• declared shipment values,
• product descriptions,
• recipient data required by local regulations,
• commercial and customs documents.

An error that, in domestic shipping, ends with the need to correct a label may, in international sales, result in the shipment being stopped at the border, additional costs, or delivery delays. That is why companies developing international sales should treat the quality of logistics data as one of the elements of preparation for scaling the business.

A single source of truth as the foundation of automation

One of the most common problems in growing organizations is data fragmentation across multiple systems. Product information is stored in the online store, ERP, marketplaces, and the shipping platform, and each of these databases may contain a different version of the same data. This is why the concept of a single source of truth, meaning one central source of truth for logistics data, is becoming increasingly important. Thanks to this, all systems use the same up-to-date information, and the risk of errors resulting from data inconsistency is significantly reduced. This is especially important in e-commerce, where, according to customer experience reports, most customers expect full transparency in the delivery process and the ability to track their shipment at every stage.

Delivery-related problems remain one of the most frequently indicated causes of negative shopping experiences. Therefore, the greater the scale of operations, the more data quality affects not only operating costs, but also customer satisfaction and brand perception.

The concept of a single source of truth does not mean that all data must physically be located in one system. The key is to provide one place where it is aggregated, analyzed, and presented in a consistent way. For this purpose, companies use analytics tools that integrate data from the store, ERP, marketplaces, and carriers, which makes it easier to control costs, data quality, and the efficiency of logistics processes.

Check how analytics works in Alsendo Business Pro

How to prepare logistics data before implementing AI and shipping automation?

Effective automation begins not with choosing a tool, but with organizing the data on which it will work. Before implementing AI, it is worth checking whether the organization has complete, consistent, and up-to-date information about products, addresses, and shipping processes. Even the best algorithm will not compensate for missing input data.

Prepare a data audit

A good starting point is to conduct a simple data readiness audit. It is worth answering a few basic questions:

• Do all products have assigned weights and dimensions?
• Are addresses validated already at the order placement stage?
• Are HS codes assigned to products?
• Are declared values automatically retrieved from orders?
• Is carrier data updated regularly?
• Does the organization have a single source of truth for logistics data?

Sources:

https://www.pragmaticinstitute.com/resources/articles/data/overcoming-the-80-20-rule-in-data-science

https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says

https://www.gartner.com/en/data-analytics/topics/data-quality

https://www.dhl.com/global-en/microsites/ec/ecommerce-insights/insights/reports/2026-ecommerce-trends-report.html

Anna Sztyk