Feature
The humble document is having its moment. For decades, invoices, contracts and forms were relegated to back-office busywork — filed away, manually processed and largely ignored by senior leadership. These paper trails took hours of human effort while generating little strategic value.
Today, artificial intelligence (AI) is turning these same documents into strategic assets. What once required armies of clerks and processing specialists is now handled by intelligent document processing (IDP) systems that not only extract data but understand context, generate insights and trigger automated actions across organizations.
The revolution isn't just about faster paperwork. It's about redefining how work itself gets done.
Table of Contents
- IDP Moves From Operations to Opportunity
- The Generative AI Breakthrough for Document Processing
- Architecture for Scale
- Measuring Beyond Efficiency
- Toward Autonomous Operations
- Window of Opportunity?
IDP Moves From Operations to Opportunity
Where IDP once lived in the IT department as a cost-saving measure, it's now earning C-suite attention as a way to compete.
"The industry has finally realized that IDP is more than just document capture and extraction. It’s an important part of larger enterprise transformations," said Adam Field, global head of product management at Tungsten Automation, recognized as a Leader in Gartner’s Magic Quadrant for IDPs. Rather than automating isolated tasks, "enterprises are plugging IDP into end-to-end workflows,” he said.
IDP is evolving, according to recent research commissioned by the SER Group and conducted by Deep Analysis and the Association for Intelligent Information Management. The study of 600 companies revealed an expansion in uses across industries.
"What's fueling this change is the surge of new IDP applications," explained John Bates, CEO of SER Group. "While invoice processing has long dominated the space, we're now seeing widespread adoption for licenses, permits, KYC onboarding documents, contracts and even HR workflows."
The implications extend beyond operational efficiency, Bates said. When a bank makes Know Your Customer processes faster, it builds customer trust as well as saves time. When a government agency shortens license approval processes, it doesn't just reduce costs, it makes citizens happier. When an HR department automates employee paperwork, it improves the employee experience as well as eliminates manual work.
The Generative AI Breakthrough for Document Processing
Behind this strategic shift lies technology revolution that has changed what's possible with document processing. Generative AI has moved IDP beyond simple data extraction into document understanding.
"Generative AI expands IDP capabilities beyond the basics to include summarization and question-answering,” Field said. “It allows organizations to manage greater document variability and deliver insights much faster than traditional OCR or machine learning approaches."
For organizations struggling with document processing complexity, this is good news. Previously, teams spent days building templates for each document type, said Suvrat Joshi, senior vice president of product at Nintex.. "Today, large language models are ready to use with no pre-training required. Generative AI enables contextual understanding of content, supporting summarization, translation and natural language querying in ways traditional methods never could," he said.
This changes what documents can become within an organization. Geoff Webb, vice president of product and portfolio marketing at Conga, frames the change in terms of fundamental utility. "Technology that cannot just ingest documents but actively review, compare and generate insights is a much more profound change than anything we've seen before,” he said. “GenAI is turning static documents into searchable, understandable data."
Early adopters are exploring applications that seemed impossible just years ago: summarizing contracts, generating compliance reports and translating onboarding materials. Each represents a way to make organizational knowledge more accessible.
Architecture for Scale
With expanded capabilities comes the challenge of enterprise-wide implementation. Success increasingly depends on building systems that grow and adapt alongside organizational needs.
"Organizations must evolve document processing from isolated tasks to an Enterprise Intelligence strategy," said Philip Brittan, CEO of Bloomfire. This means eliminating "ROT" — Redundant, Outdated, Trivial data — while "viewing documents as part of a connected knowledge ecosystem,” he said.
Of course, this requires technology. "Scalable IDP systems require modular, API-first architectures that can integrate any AI model needed, no matter the document type or data required,” Joshi said. This flexibility means organizations aren't locked into specific technologies or vendors as AI capabilities evolve.
Practical integration is also required, Field said. "IDP needs to be modular, connect to other systems easily and work in vendor or customer clouds,” he said. To do this, Tungsten said "APIs, SDKs and pre-built connectors that make this integration seamless.”
This architectural foundation distinguishes successful enterprise implementations from stalled pilot projects. Without robust integration, business can’t expect much from even the most sophisticated AI.
Measuring Beyond Efficiency
As IDP applications expand, traditional success metrics are proving inadequate for capturing their business value. Organizations are discovering that cost reduction and headcount elimination tell only part of the story.
Field's approach to measurement reflects this broader perspective: "We measure success by looking at accuracy, of course, but also speed, cost reduction and compliance,” he said. “Key metrics should include straight-through processing rates, exception reduction, time-to-value, compliance adherence and user adoption."
The shift in organizational priorities becomes clear in Bates' research findings. His survey revealed that traditional cost-cutting motivations are losing prominence. "Only 167 of the 600 organizations surveyed identified headcount reduction as a primary benefit," he reported. "Far more emphasized faster processing, improved ROI and greater business agility."
This evolution reflects understanding automation's role in today’s business. "Success should be judged against process KPIs like time to decision, compliance risk reduction and user adoption or satisfaction,” Webb said. “The technology is effective only if it accelerates the pace of business."
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The message is consistent: Automation's value lies not in replacing human workers, but in helping them focus on higher-value activities while helping the overall organization perform better.
Toward Autonomous Operations
Now, IDP is evolving with autonomous agents capable of initiating and orchestrating complex workflows.
"Over the next five years, IDP will evolve into a foundation for agentic AI,” Field predicted. “We are pioneering AI agents that orchestrate workflows, interact with systems and collaborate with humans. This will transform document processing from a support function into a central enabler of enterprise operations.”
However, the path to automation isn't without challenges. Despite technological advances, Bates' research reveals that most organizations still handle most documents manually. "Even organizations with 56% of processes automated handle tens of thousands of documents manually," he noted. "Full automation is technically possible, but systems must intelligently handle exceptions and escalate uncertain cases to human reviewers."
Eventually, agentic elements may instigate activity, not simply respond to employees, Webb predicted. “That's a profound change in how we work with technology,” he said.
Meanwhile, Joshi connects these developments to the broader automation landscape. "IDP will become a foundational part of automation platforms as agentic AI systems manage increasingly complex, document-driven workflows,” he said.
Window of Opportunity?
The convergence of advanced AI capabilities, enterprise architecture requirements and evolving business needs creates an opportunity. Organizations that recognize document processing as a strategic capability, rather than as an operational burden, position themselves to capitalize on this.
Success requires more than technology implementation. It demands leadership alignment, cultural adaptation and investment in scalable platforms. Human factors matter as much as technical ones, Brittan emphasized.
Moving toward an enterprise intelligence mindset requires preparing employees to work differently, with AI as a collaborator rather than as a mysterious black box. This requires change management, training and willingness to reimagine traditional roles.
Editor's Note: Read about how AI is pushing other back-end technology on to center stage:
- Enterprise Content Management Gets Disrupted, Again — Keeping up with all the name changes ECM has gone through over the years is hard. But the latest – intelligent content services – might just have holding power.
- AI-Driven Knowledge Management Turns Repositories Into Intelligent Ecosystems — Knowledge management is undergoing a profound transformation, driven by the rapid integration of artificial intelligence.
- What's Next for Enterprise Search? AI Knows — People don't want to search, they want to find information. A new breed of solutions are changing the boundaries of enterprise search.