Why intelligent document processing solutions often miss the mark

01 Mar, 2024 / 3 minutes read

Every business, regardless of size or industry, deals with vast amounts of documents and information on a daily basis. From invoices and receipts to contracts and emails, the volume of unstructured data can be overwhelming. To tackle this challenge, many businesses turn to Intelligent Document Processing (IDP) solutions, powered by cutting-edge technologies like machine learning and natural language processing. However, despite their promise of efficiency and accuracy, many IDP solutions often fall short of delivering on their clients' expectations. In this blog post, we'll delve into the reasons behind this phenomenon.

Complexity of document formats

One of the primary challenges faced by IDP solutions is the sheer variety and complexity of document formats. Documents come in all shapes and sizes, ranging from structured forms to unstructured narratives. PDFs, emails, images, handwritten notes – each presents its own set of challenges for extraction and processing. While some IDP solutions excel at handling structured data, they struggle with unstructured or semi-structured documents, leading to inaccuracies and incomplete extraction.

Lack of contextual understanding

Despite advancements in natural language processing, many IDP solutions still lack the ability to understand context effectively. While they may accurately extract data from individual fields, they often fail to grasp the overall meaning or intent of the document. For instance, an IDP solution might correctly identify the amount and date on an invoice but fail to recognize the currency or understand the relationship between the parties involved. Without contextual understanding, the extracted data may lack relevance and reliability.

Poor quality of input data

Garbage in, garbage out – the adage holds true for IDP solutions as well. The accuracy and effectiveness of these solutions heavily depend on the quality of input data. If the documents are poorly scanned, contain smudged text, or have inconsistent formatting, it can significantly impair the performance of the IDP system. Moreover, IDP solutions may struggle with handwritten or cursive text, leading to errors in data extraction.

Lack of an efficient validation tool

Often, the extracted document data needs to be validated by the user. This mostly applies to cases where document structure greatly varies or the input document has low quality. In both of these scenarios it is extremely difficult to extract the desired data with very high accuracy. That’s why it is imperative for the IDP solution to provide an extremely efficient tool the user can validate this data very fast.

Limited training data

Machine learning algorithms power many IDP solutions, and their performance improves with exposure to more training data. However, acquiring and labeling large volumes of diverse training data can be a daunting task. As a result, many IDP systems are trained on limited datasets, which may not adequately capture the nuances and variations present in real-world documents. This limitation can hinder the system's ability to generalize and adapt to new document types or formats.

Overreliance on automation

While automation is a key feature of IDP solutions, excessive reliance on it can be counterproductive. Some businesses deploy IDP systems with the expectation of complete automation, neglecting the need for human oversight and intervention. However, no AI system is infallible, and errors are inevitable, especially in complex document processing tasks. Without human validation and correction, these errors can propagate downstream, leading to costly mistakes and compliance issues.

Integration challenges

Implementing an IDP solution often requires integration with existing systems and workflows. However, legacy systems, disparate data sources, and complex IT environments can pose significant integration challenges. Incompatible formats, security concerns, and data privacy regulations further complicate the integration process. As a result, businesses may struggle to seamlessly incorporate IDP into their existing infrastructure, limiting its effectiveness and adoption.

Maintenance and upkeep

Like any other software solution, IDP systems require regular maintenance and updates to stay effective. However, some businesses underestimate the ongoing maintenance costs associated with these solutions. As document formats evolve, new regulations emerge, and business processes change, IDP systems need to be continuously refined and optimized. Failure to allocate sufficient resources for maintenance can result in performance degradation and obsolescence over time.


While Intelligent Document Processing holds immense potential for streamlining document-centric processes and unlocking valuable insights, its widespread adoption and effectiveness are hindered by a myriad of challenges. From the complexity of document formats to the limitations of machine learning algorithms, addressing these challenges requires a holistic approach that combines technological innovation, domain expertise, and strategic planning. By understanding the root causes of shortcomings in IDP solutions and taking proactive measures to mitigate them, businesses can unlock the full potential of intelligent document processing and drive meaningful improvements in efficiency, accuracy, and compliance.

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