AI Instead of Manual Data Entry: How Intelligent Order Entry Eases the Workload in Logistics

A professional manages data using dual monitors, showcasing advanced entry management systems with flowing digital lines.

A quick glance at the screen on the left, copy the order details, click into the form on the right, paste the data – logistics clerks and administrative staff still spend far too much valuable time copying information from one system to another. Yet manual order entry is a process that can easily be automated and handed over to software.

Why Order Capture Quickly Becomes a Bottleneck

To err is human. When routine sets in or our attention drifts because the task at hand is repetitive and unexciting, mistakes become much more likely. Typos, transposed numbers and incorrect clicks are almost inevitable when orders are entered manually – not to mention the amount of time the process consumes.

On top of that, there are often media disruptions and format differences between documents and systems. All of this can be avoided by automating order capture. In the following sections, we explain why it pays to use AI-powered software and how the process works, from an incoming email to a structured dataset.

Context Matters: What Modern Data Capture Needs to Do Today

For a tool to genuinely save time, it must be able to perform a task independently. If every second document still requires manual intervention, the benefit is limited. That is why modern data capture tools should no longer rely solely on Optical Character Recognition (OCR).

OCR has been around for decades and has served many applications well over the years. However, it has one major limitation: OCR recognizes letters and numbers, but it does not understand context. If the layout of a form changes, values appear in different positions or handwritten notes are added, OCR quickly reaches its limits and can no longer deliver the desired results.

This is where artificial intelligence comes in. AI-powered data capture recognizes content, document structures and relationships. It understands what the data represents. For example, if it detects a euro symbol, it knows that the corresponding value belongs in a price field – regardless of where that field appears on the form compared to the original layout. AI can also read handwriting, provided it is reasonably legible; much like a human reader.

How Intelligent Order Entry Works in Practice

So what does the process look like in day-to-day operations? There are different workflows for AI-powered order capture depending on whether it operates as a standalone tool or as part of a larger platform where multiple modules access the same database. In integrated environments, some steps are fully automated, but the basic principle remains the same:

The file containing the order document is either inserted into the AI portal via drag & drop or forwarded to the associated email address. Alternatively, an email rule can be configured to automatically forward emails with transport orders attached to the AI portal.
The so-called “pipeline” is started. If an order consists of several documents, such as an order, a delivery note and a customs document, these can be processed either separately or together.
The AI classifies the documents, processes them and structures the extracted data.
Before the captured order is finally stored, a human review is required. The logistics clerk checks which data has been extracted from the PDF and verifies that it has been transferred correctly into the appropriate data fields.
The result is a machine-readable format that can be used by downstream systems. In most cases, the output is provided as a JSON file, which can then be downloaded and imported into the target system. Alternatively, all order documents are stored in a designated folder that can subsequently be accessed by systems such as the Transport Management System (TMS).

Choosing the Right Tool: Why Industry Expertise Makes the Difference

There are countless software solutions available for automated data capture. So how can companies choose the right one? One of the most important principles when working with AI also applies here: the quality of the input determines the quality of the output. The more detailed the instructions, the richer the contextual information and the better the examples provided to the AI, the more accurate the results will be.

This is why it makes sense to choose a software provider with in-depth industry expertise and the ability to train the AI accordingly. How do logistics processes work? Which fields appear repeatedly in transport documents? A standard solution designed specifically for the logistics industry can already capture around 80 % of previously unknown order forms correctly.

Ideally, company-specific knowledge should also be incorporated. For example, if an order mentions “pallets”, does that refer to flat pallets or Euro pallets? Which pallet types does the freight forwarder actually use? Such details make a significant difference.

Highly Customizable: How AI-Powered Order Capture Adapts to Your Business

No matter how extensively AI software has been trained, certain customization requirements only become apparent during day-to-day operations. Naturally, development teams remain available after the rollout to implement necessary adjustments. However, it is even more practical if customers can define rules themselves using a prompting function.

Take the example of Meier Brewery. This customer regularly places transport orders for collecting barrels. What the logistics clerk knows from years of experience is that the barrels are always placed on pallets. This is an important detail for transport planning. Without additional background information, the AI cannot know this. However, if users can define their own rules, the AI will apply this knowledge in future. Employees simply enter prompts in plain language, such as: “For customer Meier, barrels should always be treated as pallet shipments.” In day-to-day business, this becomes extremely valuable because the AI gradually incorporates the knowledge that previously existed only in the employees’ minds.

Speaking of learning: data protection naturally takes top priority. The AI therefore does not learn automatically from processed datasets. Instead, it can only be trained using explicitly defined rules. This ensures that sensitive business data remains protected and does not end up in the wrong hands.

Conclusion

Automated Order Capture: Less Copying, More Time for Value-Adding Tasks

Modern AI solutions reduce manual work, minimize sources of error and accelerate the entire order processing workflow. Their full potential is realized when technological innovation is combined with deep logistics expertise. When AI-powered order capture not only extracts information from documents but also understands logistics-specific relationships and can be flexibly adapted to each company’s individual requirements, businesses benefit from smoother processes, relieved employees and significantly higher data quality.