RPA vs. IDP: Technology Inflection that will Cannibalize a Whole Industry

Curious about what keeps experts, CEOs and other decision-makers in the Intelligent Document Processing (IDP) space on their toes? Get food for thought on IDP-related topics from the industry’s leading minds.

In this opinion piece, Brent Wesler, VP of Strategic Technology and Digital Automation at Records Management Solutions provider Vital Records Control (VRC), takes us through the early days of document management and explains where Intelligent Document Processing (IDP) originally came from, where he sees IDP as a standalone product and where he thinks Robotic Process Automation (RPA) is more suitable than IDP.

In the late 1990s, among the trends of Beanie Babies, Jelly Shoes, Pogs, and frosted tips stood Document Management Software. At the time, Document Management was a quickly growing industry where the simple concept of scan, index, and retrieve was something exciting and cool.

During the early aughts of Document Management, Cloud storage was non-existent, broadband access was limited to 1.5 Mbps, and T1 connections and search for documents based on metadata were new and exciting. Finance and Legal industries were particularly drawn to this solution, and we’d successfully implemented an average of 120 plus new ECM “systems” per year with many of our customers present in the mortgage and title attorneys use cases. Fifteen years later, most home and commercial loans are processed electronically through DocuSign, Dotloop, or the myriad of title attorney applications like TAM, AMS360, and the like added Document Management capabilities. In order to continue our success, organizationally we had to pivot. Evolving from basic Document Management to Workflow Automation solutions with high learning curve services was a logical next step, and one necessary to survive in an increasingly competitive technological landscape.

Now in 2025, the Document Management (also known as ECM) software space has been heavily cannibalized. Functionality comparable to a traditional ECM system is now available in inexpensive Cloud storage solutions as simple as Google Drive, OneDrive, or more capable solutions for compliance such as Box.com. Document Management has been commoditized, often without consumers realizing it, and demand for standalone ECM products has slowly been on the decline. Like other technological inflection points in history, the burgeoning Intelligent Document Processing (IDP) industry now faces a dilemma. How can they escalate development and drive key differentiators when technology like Robotic Process Automation (RPA) is bleeding into the IDP space?

Where did IDP come from?

IDP was a side effect of the advanced capture (Optical Character Recognition or OCR) market embracing and escalating the development of AI and ML tools. Before, companies like Kofax, ABBYY, Perceptive, Hyland, OpenText and others were stalwart tools which provided expensive, but capable document classification and extraction.

As recent as the 2010’s, neural modeling and using Large Language Model’s to classify, separate, and extract data from unstructured documents was unheard of. Circa 2021, as ChatGPT entered the fold alongside hypervisor engines like Azure, Google, and AWS, we saw escalation of development from IDP manufacturers, with now over 400 IDP players, (per Deep Analysis), competing for market share.

Now, legacy capture vendors like Kofax (Tungsten), Hyland, and OpenText now have old, monolithic tech that is unable to scale to future needs. Their products are heavily challenged by newcomers like Rossum, Base64, Hypatos, Klippa, and countless other platforms that started with an API-first approach and were built from the ground up with AI in mind.

IDP Industry Transitions to RPA? It’s Already Begun

Even with the 400+ newcomer IDP providers in the marketplace, we are seeing a great shift to Agentic AI and a huge push by IDP manufacturers to diversify their tech stack to include RPA or robotics. This can be seen with Rossum’s announcement of their specialized bots, KnowledgeLake’s purchase of RatchetSoft RPA to round out their IDP platform, as well as companies rooted in IDP, like Datamatics, having a full bot/RPA product set. Another indicator of this shift is that most IDP analytics companies, like IDC or Peak Matrix, now rate RPA vendors higher than most IDP products within an IDP specific matrix.

IDP as a standalone product is designed to typically front end a workflow automation process. It’s judged on its ability to monitor an email, classify the document, separate it, and then extract relative information. IDP’s value proposition was always data entry automation to remove hard cost labor from the process. Like the earlier anecdote about ECM and its decline as a leading technology, IDP is also feeling pressure from businesses that want to buy a single platform that can do more.

One of IDP’s best features is its ability to normalize data across unstructured documents. The problem is, IDP cannot take action, which is where Agentic AI comes into play. Using robots can take the normalized data and execute a number of post process actions such as entering the data into a line of business system or checking a web service to validate data and then transforming the data to be entered into another system. The premise to a good ROI is results, and when IDP can only carry the bucket to the end of the first step of a workflow automation it severely limits savings. Conversely, RPA can fulfill the entire lifecycle of the automation, making most complex use cases incredibly strong from a cost/savings benefit.

So where does IDP work as a stand alone product?

For the sake of discussion, let’s consider the industry’s biggest players, UiPath, Blue Prism, Pega, or Automation Anywhere. Depending on the RPA product, because there are many of them, the cost to get the platform and perform IDP use cases will be exponentially more expensive than a standalone IDP product. Before buying either tool, you should consider the cost and complexity of the solution.

#1: If your use case is simple procurement (invoices, sales orders, BOL) and transactional documents, then IDP is probably a better fit.

For instance, to use UiPath Document Understanding, UiPath’s IDP product, you need to buy an unattended bot, a named RPA developer license (~$13,000/year) and then pay $.20/page. Whereas if you were to use ABBYYVantage, a premier IDP solution you would pay just $0.20/page. If you look at lower cost RPA providers and mid-tier products like Electroneek, Ninetex, and even Microsoft Power Automate, that cost to acquire is significantly less than enterprise RPA players. Microsoft Power Automate only costs $200/month, and you can consume Azure AI Document Intelligence (AI Builder credits) for $0.016/page, making the IDP vs RPA equation a lot murkier.

#2: If your use case is very specific, will not grow to other areas of your business, and your budget is limited, IDP is a great standalone solution.

You can deploy IDP quickly with the new AI modeling features. The complexities of RPA, setting up queues for human in the loop data verification, triggers, automations and such do require more skills and likely more expensive to run. If you do not plan to expand the use cases then an automation platform through RPA may be overkill.

#3: Scanning and backfile – if your use case is batch capture, using desktop scanners then legacy IDP would be a great fit.

Many of the newcomer IDP solutions, along with RPA tools, don’t excel with handling large amounts of paper scanning, document manipulation, and human “in-the-loop” data verification user interfaces with document manipulation. Merging, editing, reordering, and splitting document sets will be better served in an IDP environment.

#4: If the data and documents you extract need to go to a popular line of business, like Microsoft Dynamics BC, there will likely be an output/export connector. But if there is not, then the cost and time to build bespoke application connectors will decrease profitability and long term support.

While most IDP players have an exposed REST API that you can use to develop the connector, many partners and customers don’t have the development resources to create system connectors. The IDP solutions themselves tend to offer pre-built connectors that require constant upkeep and can grow stale. Often, it’s system integration where the wheels fall off the proverbial bus and the IDP system grows ineffective to a point where end users face an inflection point.

Enter RPA, a tool designed to integrate with any application regardless of cooperation. Rather than requiring full stack development, RPA allows users to utilize its syntax and engine to perform the integrations in a fraction of the time. It uses the screens of the application or consumes the system’s APIs through a slick Integration Service, binding to hundreds of preconfigured tools like AWS Bedrock, Azure AI Document Intelligence, Azure OpenAI, and others without any coding.

Where does RPA do a better job than IDP?

#1: If your use case is complicated, where you have the volume of documents and the company initiative is to remove as much cost from the processes as possible, RPA gives you a single platform to build everything.

For example, insurance claims process and the difference between IDP and RPA. In this use case, the insurance company uses multiple Line of Business systems and 3rd party subscription services to manage underwriting, customer data, claims, risk management and costing. As new claims are received the document needs to be classified and data extracted. Based on its classification the data that’s extracted needs to now check 4-5 “systems” to validate the data and retrieve other related data, all culminating in a decision process without involving a human. While over simplified, the advent of Agentic AI is what’s driving adoption of RPA.

So what are the benefits of RPA over IDP?

#1: Deep Integration & Support for Legacy Applications – Generally, RPA is going to provide the ability to integrate with legacy desktop applications, such as mainframes where there are no API’s or webhooks. RPA utilizes linear or sequential workflow mapping to branch based on a bot’s action allowing for metadata to be extracted from a document or an attended bot process requiring a human to assist.

#2: Process Validation Involving Humans – RPA’s ability to engage humans when a bot cannot decide is critical to many complex workflows, placing the human in the bot’s queue to confirm or make a decision, only involving humans when absolutely necessary. RPA has the ability to self-heal when screens change, use learning to make predictive actions based on historical data, check multiple databases and systems synchronously to automate the processes. Ultimately, it comes down to ROI and results. If RPA can remove more labor in the process than IDP, and the Cost of Goods (COGS) doesn’t eclipse the labor savings, then RPA is the better approach.

#3: Flexible Consumption of AI Engines – RPA is designed to consume “connectors” as their intelligent document processing engine, whereas IDP manufacturers either embed a 3rd party AI/ML engine or develop their own engine. RPA allows use of almost any engine on the market, with certain workflows able to switch between AWS, Azure, Google, Claude, Grok or any of the other tools. IDP then becomes a plug and play connector to a larger workflow automation platform.

Conclusion

The tech industry follows trends, and much like ECM has been put at odds against built in Cloud Document Management systems, IDP is facing similar cannibalization from RPA. Such trends don’t mean instantaneous obliteration of legacy technology, instead a new view of applications for it. Like the last few successful remaining ECM use cases we see in K-12 schools, municipalities with long retention periods, and manufacturing quality control documents, IDP still has use cases that make sense. Particularly, use cases relating to procurement document processing, batch back file scanning with OCR assist, heavy document manipulation, and reduced cost and complexity. As price pressures on RPA reduce the cost, more systems integrators, customers and manufacturers will move towards robotic functionality. As AI tool sets get stronger, ChatGPT 1o Strawberry, DeepSeek, Claude, Grok 3 and others that now have reasoning built into the LLM, we will see more use cases to remove human labor costs from the process and in turn see more adoption of RPA platforms. With the ability to orchestrate and call any number of “connectors” from Box, DropBox, Asana, Coupa and the like, RPA replaces ECM, BPM (business process management) and reporting tools as just a step in the RPA workflow. It can be argued that RPA has cannibalized more than just IDP.

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