Content Writer
In this post, we’ll walk you through the process of evaluating data entry automation opportunities for businesses who manually extract data from PDFs, helping you understand where it can add real value and how to navigate the key challenges along the way.
Manual data entry remains a challenge because businesses frequently handle third-party data in inconsistent formats beyond their control. As a result, PDFs emerged as the go-to solution for data exchange, becoming the unofficial standard across industries. While PDFs are ideal for human readers, they are unstructured and unreadable by machines. As digitalisation accelerates, the demand for manual data extraction continues to rise.
While many businesses struggle with manual data entry, identifying workflows that are well-suited for automation is key to maximising impact and efficiency. Not all manual document processes are ideal candidates for automation, but those that are often share these common characteristics:
Repetitive information extraction: For example, pulling the same types of data (e.g., dates, totals) from invoices across different suppliers.
High volume of repetitive actions: Tasks with predictable patterns, such as extracting order details from purchase orders, can save significant time and effort.
Error-prone processes: If human involvement frequently results in errors or delays, automation can improve accuracy and efficiency.
Clear input / output structure: Workflows where "documents come in, data comes out" are straightforward to automate. Avoid processes with delays and external dependencies.
Once the right workflows are identified, selecting the appropriate technology is the next step in ensuring that automation delivers real, measurable benefits.
OCR engines with Regex: OCR (Optical Character Recognition) engines extract every word from a PDF into a machine-readable format. After extraction, regular expressions (RegEx) are used to filter out unwanted values." Tesseract is an example of an established open-source OCR engine alternative.
Openly available AI tools: Popular large language models (LLMs) like ChatGPT and Claude Sonnet are effective at extracting data from diverse document layouts. Their ability to understand semantic context makes them particularly useful for tasks such as classifying documents into predefined categories. Since LLMs sometimes generate inaccurate information, they pose risks for critical business tasks. Therefore, it is essential to incorporate a data validation step into LLM data extraction workflows.
Custom-built ML solutions: Custom-built machine learning (ML) solutions can be customised to handle specific business documents, ensuring high extraction accuracy. This approach is well-suited for organisations with in-house technical expertise and large-scale processing needs. Beyond developing accurate models, building such a solution requires establishing supporting infrastructure for tasks like model retraining, error management, and integrations.
Specialised OCR AI tools: Specialised OCR AI tools are designed specifically for document data extraction and classification. These tools range from API-only solutions to end-to-end platforms with features like integrated error handling, pre-built integrations, and no-code workflows. Tools such as Cradl AI exemplify this category, offering robust integrations and ongoing support for seamless implementation and maintenance. They are ideal for businesses seeking an efficient, ready-to-use solution without the complexities of custom development.
Data extraction tools are not immune to occasional errors, particularly when dealing with complex document layouts. This makes effective error handling a critical component of data entry automation solutions. Without dedicated data validation interfaces to identify and correct AI mistakes, automation can lead to escalating inaccuracies or require excessive manual intervention, undermining its benefits.
A well-designed human-in-the-loop system ensures that errors are flagged early, efficiently resolved, and integrated seamlessly into the overall data entry workflow.
Because documents change over time, extraction tools must adapt to these changes. For AI tools, having adaptable AI models that are easy to re-train and monitor are essential for ensuring that a solution can handle new document types and scale with the business.
A lack of adaptability will often require intervention from automation specialists to make adjustments. This ongoing need for manual intervention can undermine the overall efficiency of the automation process.
For data entry automations to truly reduce human effort, they should integrate seamlessly with existing systems like ERPs, automation tools, and document management platforms. A well-integrated solution ensures that data is extracted, processed, and routed to the right systems automatically, reducing errors and improving overall workflow efficiency.
Identify workflows that are ideal for data entry automation and leverage the right data extraction technologies to cut costs, reduce manual effort, drive operational efficiency and improve digitalisation. Key considerations include error handling, adaptability, and model retraining to ensure long-term effectiveness. Additionally, seamless integration with existing systems is essential for maximising the benefits of automation.
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