What is due diligence for IDP and why is it important?

Source: Dev.to

Introduction

Due diligence is the investigative process of vetting an investment or agreement to verify facts and make informed decisions. Good due diligence reduces risk and protects decision‑makers from costly mistakes.

With new intelligent document processing (IDP) vendors emerging monthly and technology iterating quarterly, the ability to analyze a crowded market and determine whether a solution fits your enterprise has become an extremely valuable skill. Analysts now track over 450 IDP entrants—a 15 % year‑over‑year increase—highlighting the need for tight decision‑making and software‑selection expertise.

• 62 % of IDP systems now involve external users.
• 66 % of new IDP projects are initiated to replace a previous IDP system.
• Proof‑of‑concept (PoC) evaluations are essential to verify AI accuracy, integration, and security before signing a contract.

Why are there so many IDP solutions?

Traditionally, intelligent capture served back‑office workflows such as mailroom automation, AP/AR invoice processing, and audit preparation—activities dealing mainly with structured and semi‑structured documents. Advances in machine learning (ML) and natural language processing (NLP) have expanded capabilities to handle unstructured documents, shifting focus to industry‑specific front‑office functions.

Today, over 60 % of IDP use‑cases support processes where external users create, access, or share unstructured documents/data, including:

• Customer service
• Employee onboarding
• Contract and agreement analysis
• Claims intake
• Licenses and permits processing

Given that 90 % of enterprise data is unstructured, data quality and quantity dramatically impact enterprise GenAI results, driving higher demand for IDP solutions. The market is growing at a 15 % annual rate, with 456 companies globally offering IDP as a standalone product or feature (Deep Analysis).

Differentiating among these vendors is challenging because product messaging is often similar. Spotting red flags can help filter out less‑suitable solutions.

Common red flags to watch for

• Unsubstantiated accuracy claims – Assertions of “99 %” or “near‑perfect” accuracy without proof are misleading. Ask for sample documents and validation results.
• Unclear data policies – Data privacy remains a top concern. Clarify who provides training data and how it is handled.
• Consumption or token pricing – Uncapped models can cause budget overruns; token pricing is unpredictable. Approximately 88 % of surveyed IDP purchasers prefer fixed‑price models.
• Human‑in‑the‑loop (HITL) sold as an upsell – Accuracy‑critical workflows require HITL verification. Vendors that treat it as an optional add‑on may be offering incomplete solutions.
• Polished demos that don’t reflect reality – Demos are often curated for specific scenarios. Conduct a PoC to evaluate performance on your actual document sets.

GenAI hype and its impact

GenAI advertising heavily influences IDP purchase decisions. Over 66 % of new IDP projects are started to replace legacy systems that lack promised GenAI capabilities. As of early 2025, more than 80 % of IDP vendors advertise GenAI features, sometimes positioning it as the primary differentiator—even though data quality, not GenAI, drives successful outcomes.

While large language models excel at zero‑shot/few‑shot learning and summarization, discriminative ML remains superior for raw data extraction at scale and lower cost.

Due diligence framework

Use the following checklist—derived from analyst criteria—to evaluate IDP vendors and avoid common pitfalls:

1. Purpose fit

   • Is the solution purpose‑built for our priority use cases?

2. Technical capability

   • Can it handle a wide variety of document types using modern ML/NLP?

3. Data security

   • Are data encryption, access controls, and a clear “no‑training‑on‑my‑data” policy verifiable?

4. Deployment & pricing

   • Is the platform easy to deploy and maintain?
   • Are pricing models predictable and transparent, with safeguards against unexpected cost spikes?
   • Does the vendor sell a single solution or an entire platform?

5. Vendor credibility

   • Does the vendor demonstrate a history of innovation and a clear product roadmap?

6. Model transparency

   • Can we see confidence scores and track model versioning?
   • Does the vendor provide end‑to‑end visibility of a document’s journey—from upload to storage and post‑processing handling?

Practical advice

Intelligent Document Processing has matured, but the market is crowded and not all vendors are equal. Prioritize:

• Case studies over demos – Real‑world results matter more than polished presentations.
• Measurable ROI – Focus on quantifiable benefits rather than vague GenAI promises.
• Data quality and model transparency – Ask vendors to demonstrate how their models work and how performance will be maintained over time.

In an era of pervasive AI, a healthy dose of skepticism is a virtue.

Sources

• Deep Analysis – The IDP field continues to expand
• AIIM – Market Momentum Index IDP Survey 2025
• Deep Analysis – Intelligent Document Processing Market Analysis 2025‑2028