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Vendor Selection
Feb 17, 2022
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5 Key Considerations for Selecting Intelligent Document Processing (IDP) Solutions

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Manish Rai
VP of Marketing

With the global intelligent document processing (IDP) market projected to reach $4.1B by 2027, there is no denying that IDP is one of the fastest-growing software segments out there. It is also a critical component of the hyperautomation toolkit companies need to take the next step in their automation journey.

As VP of Product Marketing at Automation Anywhere, I helped launch IQ Bot—one of the leading intelligent document processing (IDP) solutions on the market. Later, I helped Appian evolve their IDP solution as well. These experiences involved evaluating a wide variety of IDP offerings, and helped me form a clear idea of their shared limitations. This article offers insight into the five most important characteristics to look for when selecting an IDP vendor.

#1: Future-proof open architecture

Ensure any IDP solution under consideration does not rely on a fixed set of optical character recognition (OCR) or document intelligence (AKA “document AI”) solutions. The commoditization, and rapid evolution, of artificial intelligence (AI) models makes selecting a tool that offers an open architecture paramount. Doing so ensures the platform you invest in today will be capable of leveraging the best artificial intelligence models of tomorrow.

#2: “Assembly line” task processing

Documents can be incredibly complex and unpredictable–a classic example of unstructured data, or information that doesn’t follow a predefined manner of organization. This means that different types of AI and machine learning (ML) may be required to accurately extract and analyze data from varied document datasets. For example, multiple models might be needed to detect and extract key information from an invoice, such as company names, logos, billing or shipping addresses, watermarks, approval stamps, handwritten notes, line items, and balances due. It is essential that your IDP solution can break complex tasks down into smaller components, and then use the best AI model for extracting details from each section of a document. This will prevent the IDP tool from missing important details, and help ensure the highest level of data accuracy possible.

#3: Robust human-in-the-loop (HITL) workflows

Most IDP solutions provide split-view, human in-the-loop capability.  However, most lack the ability to define sophisticated human-in-the-loop workflows that ensure high output quality.  Make sure your solution supports resource crowdsourcing, task routing to third-parties, escalation to in-house experts, and rerouting to meet service-level agreements (SLAs).

#4: Guaranteed data output quality

IDP providers often tout the high quality of output their service provides, but are reluctant to give quality guarantees. Ask vendors about the quality control mechanisms they have in place to safeguard output, and ensure it is possible to achieve your desired accuracy threshold before making a significant financial commitment. Additionally, it is important that tasks can be intelligently routed between AI and human workers, including the flexibility to optimize the system for speed, cost, or quality.

#5: Continuous learning based on human feedback

Continuous learning and human feedback are fairly standard talking points among major IDP vendors; however, incorporating supervised or active learning can prove difficult in practice. Most existing intelligent document processing systems fail to deliver on this promise, so ask to see this capability in action before making a purchasing decision. Product demos should be customizable, and based on your company's data. Ask to work through a sample project that involves human feedback informing and refining the model.

Consider unstructured data processing (UDP), a powerful alternative to IDP

Emerging UDP solutions were designed to overcome the limitations of existing IDP systems, and in some cases are a better fit for not just automating document-centric processes, but any business process that relies on unstructured data—which includes things like images, text, video, and audio files. UDP platforms are built to serve as a centralized hub for storing, processing, and analyzing unstructured data, allowing users to combine technologies and worker types in a way that best suits their goals.

Fortunately, there is a no-code, unstructured data processing ‘tech stack’ available to integrate with your existing investments. Check out the following resources on unstructured data processing to learn more about the technology, and don’t hesitate to reach out to super.AI to learn how UDP can benefit your specific application scenario.

Additional intelligent data processing resources

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