In the current “SaaSpocalypse” environment, every enterprise technology investment is under pressure to prove real, material ROI. Technologies that don’t deliver can be replaced, consolidated, or shut off entirely. That makes the results of a new IDC Business Value study of Hyperscience customers particularly striking. Organizations achieved a 615% average ROI over three years, with payback in approximately seven months, demonstrating that transforming document-intensive work can deliver measurable business value today while modernizing the unstructured information estate for whatever comes next.
Additionally, the organizations interviewed by IDC reported an average of $8.6 million in annual business benefits, spanning claims and document processing gains, IT efficiency improvements, and business enablement. Within business enablement alone, customers reported an average of $4.3 million in additional annual net revenue, a signal that document automation is becoming an important growth driver while also improving operational efficiency.
We believe these results show that intelligent document processing is about far more than digitizing documents or automating data entry. It is helping organizations transform how work gets done by increasing automation and capacity, improving speed and accuracy, and creating a more modern, scalable foundation for the unstructured information that powers business processes today and the AI applications of the future.
The enterprise document challenge is bigger than extraction
The scale of the challenge becomes apparent when looking at the environments represented in the study. The organizations interviewed by IDC process an average of nearly 15 million pages annually, spanning more than 1,100 document types, 14 languages, across 27 countries.
At that scale, document processing is not an isolated back-office task. It is embedded in core business operations, from processing insurance claims and financial applications to managing supplier invoices and customer information. Every manual handoff, data entry task, processing delay, or error can introduce friction into the broader business process.
This is also why the role of Intelligent Document Processing (IDP) is changing. Historically, document automation was often viewed primarily as a way to extract data from documents and transfer it into another system. Increasingly, enterprises need something more comprehensive: technology that can understand complex documents, classify and extract information, validate results, route work, manage exceptions, and provide the governance and traceability required in production environments. As enterprises build out AI agents and generative AI applications, IDP is also emerging as the layer that makes those systems possible in the first place — transforming unstructured documents into the trusted, structured data that agentic workflows and LLMs depend on.
The findings of the IDC white paper demonstrate the value of that broader approach with interviewed organizations achieving:
- 214% higher levels of document and claims automation, and
- 89% higher document and claims processing accuracy,
Together, these gains let organizations process larger volumes of information without a corresponding increase in manual effort. But accuracy at this level is more than an efficiency gain — it’s a prerequisite. Agentic AI and generative AI applications are only as reliable as the data feeding them, and unreliable extraction becomes unreliable input, undermining the very automation these systems promise.
Automation creates capacity without simply adding headcount
Document-heavy operations often face a familiar dilemma: as business volumes increase, organizations need more people to process the corresponding increase in paperwork. A classic case of this is the administration of SNAP benefits (Learn more here). Automation can change that equation by allowing organizations to absorb greater volumes without proportionally increasing staffing requirements.
The IDC white paper found that organizations using Hyperscience were able to reduce manual effort and create capacity for growth while improving operational performance. One insurance organization interviewed for the study, for example, was able to avoid hiring additional processing staff despite increasing document volumes.
“Since implementing Hyperscience, we no longer need to bring in seasonal temps to handle the Medicare open enrollment peak. Our team is now able to process higher volumes without a corresponding increase in headcount.”
The value of that automation goes beyond reducing labor costs. When employees spend less time manually entering information, reviewing routine documents, and moving paperwork between systems, organizations can redirect that capacity toward work that requires greater expertise and judgment.
The impact was reflected in employee experience as well. The IDC white paper found that employees reported 24% higher career satisfaction, as automation reduced repetitive data-entry work and enabled teams to focus more heavily on higher-value activities.
“Career satisfaction has increased, probably around 20%. Staff still have their other responsibilities, but the cumbersome work of manually keying data from paper into the system has been removed by Hyperscience. That alone has made a meaningful difference in how people feel about their work.”
This represents an important evolution in the conversation around automation. The objective is not simply to replace manual work; it is to redesign how work gets done so that people can spend more of their time where human expertise creates the most value.
Faster processing can translate directly into better customer experiences
The operational benefits of document automation also extend beyond internal efficiency. In many industries, document processing is directly connected to the customer experience, which means delays and errors in back-office workflows can become delays and frustrations for customers.
In addition to the previously mentioned 89% higher document and claims process accuracy, the IDC Business Value study also found that organizations using Hyperscience experienced measurable improvements in processing speed – claims processing was accelerated by 61%.
One insurance organization interviewed by IDC reduced the time required to process Medicare supplement applications from approximately seven minutes to two minutes per application after implementing Hyperscience. While the operational gain is significant, the customer impact is equally important – applications can move through the process faster, reducing the amount of time customers spend waiting for decisions or outcomes. In fact, the study attributes its finding of 44% higher customer satisfaction directly to faster turnaround times and fewer errors in processing.
Similar dynamics apply across industries. Faster invoice processing can improve supplier relationships and reduce administrative bottlenecks. Faster financial application processing can shorten customer onboarding. More efficient processing of correspondence and forms can help organizations respond to customers more quickly.
In each case, document automation is working behind the scenes, but the resulting experience is visible to the customer.
“Hyperscience improved submitter satisfaction by accelerating document processing and the clearing of payments. It reduced the wait time for suppliers and other parties awaiting resolution.”
The value of automation increasingly depends on trust and governance
As organizations move from isolated AI experiments to deploying AI within mission-critical processes, efficiency alone is no longer enough. Enterprises also need to know that the information being processed is accurate, traceable, and governed appropriately.
This is particularly important for organizations operating in regulated environments, where decisions may need to be explained, information may need to be retained for specific periods, and auditors may require evidence of how data was processed.
The IDC study found that organizations using Hyperscience reported:
- 67% improvement in transparency into model decisions for explainability and trust
- 60% improvement in documentation and evidence workflows for internal or external audits
- 43% increase in adherence to data governance and retention policies
We believe these findings highlight an important distinction between simply automating a document workflow and operating on enterprise-grade document intelligence infrastructure. In production environments, organizations need visibility into not only what information was extracted, but also how it was processed and whether the resulting data can be trusted.
That becomes even more important as document-derived information increasingly feeds downstream AI systems.
AI is only as useful as the information it can access and trust
Generative AI has dramatically expanded what organizations can do with enterprise information, but it has not eliminated the underlying challenge of making that information usable.
Much of the data that enterprises want AI to reason over remains trapped in unstructured or semi-structured documents. If those documents contain inconsistent formats, complex tables, handwritten information, poor-quality scans, or other variations that make information difficult to interpret, downstream AI systems inherit those challenges.
This is why the quality of the information layer matters.
According to the authors of the study, “Generative AI has accelerated enterprise interest in automation, but our research found that organizations still require reliable mechanisms for transforming document-based information into trusted, structured data. The organizations we interviewed for this Hyperscience Business Value study demonstrated that intelligent document processing provides an important operational layer by improving accuracy, governance, and explainability before information is consumed by downstream AI systems or business applications.”
Intelligent Document Processing can serve as an important bridge between those two worlds by transforming information contained in documents into structured, validated, and governed data that can be used by business applications, automation workflows, and AI systems.
We believe the IDC white paper reinforces that connection. The organizations interviewed were not simply using Hyperscience to reduce manual document processing; they were using the resulting information to improve business operations while establishing stronger foundations for automation and AI.
The business case extends beyond productivity
Taken together, the findings illustrate why organizations are increasingly evaluating document automation in terms of business outcomes rather than simply processing volumes.
The benefits that customers experienced, including $8.6 million in average annual business benefits, while achieving 615% three-year ROI, and payback in approximately seven months, came from multiple sources, including reduced manual effort, increased operational capacity, faster processing, improved accuracy, additional revenue, and stronger compliance and governance.
That breadth is important because document processing touches so many parts of an organization. Improving one step in a workflow can create downstream benefits across operations, customer experience, employee productivity, and risk management.
For enterprises processing millions of pages across thousands of document types and multiple geographies, even incremental improvements can compound into significant business value.
Building the information layer for enterprise AI
This IDC Business Value study comes at a time when organizations are moving beyond the question of whether AI can deliver value and toward the more difficult challenge of operationalizing AI at enterprise scale. Doing that successfully requires more than powerful models. It requires reliable data, production-ready infrastructure, governance, and the ability to integrate AI into the processes where business decisions actually happen.
Documents remain a critical part of that equation.
By automating the intake, classification, extraction, validation, and routing of document-based information, organizations can reduce the operational friction associated with unstructured data while creating a more reliable information layer for decisioning and downstream systems.
The findings of the IDC white paper demonstrate that this foundational work can deliver substantial business value today, while also helping organizations prepare for what comes next.
The future of enterprise AI will depend not only on how intelligent models become, but on how effectively organizations can connect those models to the information and processes that run their businesses. For many enterprises, that journey begins with making the information inside their documents accessible, accurate, governed, and ready to act on.
Upcoming Webinar
To learn more about what’s driving ROI in Intelligent Document Processing, join our upcoming webinar ‘From Docs to Dollars: Unpacking the ROI of IDP & Hyperscience’ on Sept. 22 at 11 a.m. ET with experts from Hyperscience and guest speakers from IDC. Click here to register.