IDP: Taking Document Automation to the Next Level

The earliest examples of what we now call Optical Character Recognition (OCR) date back to the early 20th century, including machines that converted printed characters into telegraph code. One of the most charming early versions was the Octophone.

 This was a handheld device designed to help blind users “hear” printed text through audible tones.

 

Since its early days, OCR has become so embedded in enterprise document management that it is often taken for granted and seen as a bedrock technology for back-office automation in the form of invoice processing, form capture, and basic records digitisation.

 

But recent research and real-world customer experience show that OCR is undergoing a fundamental transformation. This is no mere incremental improvement, but a shift into something far more powerful: truly Intelligent document processing (IDP).

 

In fact, according to 2025 research from the Association for Intelligent Information Management (AIIM), co-sponsored by SER, OCR is storming out of its traditional confines and moving very swiftly from the back-office into customer-facing and compliance-critical workflows.

 

At the heart of this transformation are new OCR use cases such as Know Your Customer (KYC) checks, contract management, HR onboarding, claims processing, medical records and many more. This shift is reshaping how enterprises think about their business documents—turning them from static artefacts into dynamic sources of genuine and timely business intelligence.

 

 

From OCR to true document understanding

What is driving this change is the convergence of OCR with AI in all its many guises, including machine learning, deep learning, and large language models (LLMs)). While AI has played a role in IDP for years, today’s models can handle far greater complexity: they can interpret handwriting, low-quality scans, inconsistent layouts, and ambiguous data. Crucially, they can now extract proper meaning from a business document, not just text.

 

This signals the beginning of a new era of document understanding that is faster, more accurate, and context-aware than anything we’ve seen before. The contrast between legacy OCR and modern IDP is profound: we are no longer talking about simply recognising characters, but about understanding documents in ways that enable end-to-end automation.

 

 

What the AIIM research tells us

Let’s return to that independent study to see why. AIIM commissioned  independent researchers Deep Analysis to survey over 600 enterprise users across the UK, the US, and Europe, all in organisations with more than $10 million in annual revenue and at least 500 employees. These organisations span a wide range of sectors, including government, energy, financial services, healthcare, and manufacturing. For anyone familiar with legacy OCR, the findings are very striking.

 

For example, a very high 78% of respondents say they already have AI operational in the form of IDP, often delivered via the cloud by hyperscalers such as Amazon or Microsoft. Just a year ago, stalled projects and concerns about new AI caused many to predict this was just another hype cycle with limited real-world impact. Data from real-world enterprise users suggests that the trend has shifted dramatically.

 

Interestingly, despite all the AI and automation, paper remains central. 61% of modern IDP users say paper documents are still a key part of their processes. Far from disappearing, paper continues to fuel automation efforts, reflecting both regulatory realities and regional habits, such as the continued use of paper cheques in the US.

 

 

The demand for future-proof IDP platforms

Despite widespread adoption, most organisations are still early in their AI maturity. Many existing IDP solutions are still rooted in first-generation OCR and rules-based automation, and they struggle with flexibility, scalability, and accuracy.

 

The fact that 68% of new IDP projects are replacing existing systems signals growing dissatisfaction—an extraordinary figure for such a mature industry. The rise of GenAI and LLMs has certainly disrupted the market. Innovative startups and scale-ups will continue to take market share, while legacy vendors loaded down by technical debt will struggle to catch up.

 

There’ll be demand for future-proof, AI-agnostic platforms—systems that allow new AI models to be integrated as they emerge, without repeated rip-and-replace cycles. Organisations want composable architectures that combine traditional rules-based tools with modern machine learning and deep learning, tailored to specific workflows. 

 

And what about all those familiar back office OCR use cases? Invoice processing remains a strong use case, but AI is pushing IDP into far more complex territory. AIIM’s research highlights its use in Licenses and Permits, Know Your Customer (KYC) onboarding docs, Contracts/Agreements, and Human Resource Files. Take a KYC process, for example: it may involve parsing ID cards, detecting fraud, validating data, and archiving records in compliance-ready formats—tasks that would have been impossible with older OCR systems.

 

Now customers can tackle these complex workflows, it looks like the IDP fuse has been well and truly lit. 65% of respondents have implemented or are considering a new IDP project. Of that group, 45% plan to automate a new use case, an extraordinary result for a mature technology. This finding supports the thesis that GenAI capabilities are unleashing new use case potential, especially in front-office or customer-facing applications.

 

This evolution also points to a broader shift towards what I call intelligent content automation, which denotes the convergence of AI-powered document understanding with enterprise workflow automation, tightly integrated with ERP, CRM, and service platforms.

 

 

Why orchestration still matters

While hyperscalers play a critical role by providing infrastructure and native document AI services, these services alone are not turnkey enterprise solutions. Organisations still need platforms that can orchestrate multiple AI components, integrate them with internal systems, and compose intelligent pipelines aligned with business processes.

 

This is where partners can add real value as IDP integrators and orchestrators, combining hyperscaler capabilities with proprietary tools to deliver flexible, business-centric solutions. Increasingly, it is becoming clear that intelligent automation is not a single cloud service, but can only be built through careful composition.

 

IDP is about empowerment, not job cuts

AI is often seen solely as a job destroyer.  Interestingly, only 167 of the 600 organisations surveyed cited headcount reduction as a primary benefit of IDP. Far more emphasised speed, ROI, and agility—highlighting a shift away from cost-cutting narratives towards human empowerment.

 

It’s clear that IDP is not about eliminating people, but about freeing knowledge workers from repetitive tasks so they can focus on judgement, exceptions, and higher-value decision-making.

 

That said, jobs will evolve. Routine document-handling positions may decline, while demand grows for skills in exception management, process optimisation, and oversight. To support this transition, organisations need user-friendly tools—visual workflow builders, low-code configuration, and integration with familiar platforms like Microsoft Teams or Office—so business users, not just IT, can shape automation effectively.

 

The IDP future will affect us all

IDP today is no longer just about documents but about the convergence of content, AI-driven understanding, and workflow automation. As these capabilities become more accessible and composable, document workflows will evolve into intelligent business pipelines.

 

This is not just a renaissance for IDP—it is a transformation. While we have come a long way since the charming 1913 Optophone, it could be that we are only at the beginning of what intelligent content automation will make possible.

 

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John Bates is CEO of SER Group, a global leader in ECM and Intelligent Content Automation

 

Main image courtesy of iStockPhoto.com and wakila