How AI is Reshaping IDP and What to Look for in a Solution

AI is extending the capabilities of information capture and Intelligent Document Processing (IDP) to the point where it can now handle a much wider range of documents, including complex, unstructured formats such as emails, contracts, and PDFs. At the same time, IDP is becoming central to the success of AI initiatives, since organizations need to extract and use information from all kinds of documents to get real value from their AI investments.

But more powerful AI doesn't automatically mean better document automation. As these tools become more widely used, organizations must ensure reliable results, make new solutions fit with existing processes, and keep costs in check. Indeed, industry analysts at Gartner warn against the risks of applying advanced AI models indiscriminately:

"The brute-force application of large language models (LLMs) to every document creates unsustainable token inflation, increases the scope of error through hallucinations and unacceptable latency."

Manually checking every LLM output isn't feasible at scale. Therefore, modern IDP needs to automate routine processing accurately, decide on and orchestrate the next best steps in business workflows, and defer to human judgment when needed. Achieving that balance depends on how the underlying technologies work together, as IDC explains:

"AI technologies are not supplanting each other but rather contributing to a more comprehensive AI toolbox that enables the use of different tools and capabilities based on specific use cases and customer needs."

Viewing AI as a specialized toolbox empowers enterprises to automate complex end-to-end workflows, control costs, and minimize errors. With this framework in mind, this blog examines how AI is shaping the future of capture and IDP, what specific capabilities will define the industry's next phase, and how the right IDP solution can align with that evolution.

From reading documents to understanding outcomes

AI hasn't transformed IDP in one leap. Each wave has built on the last, from machine learning to LLMs to agentic AI. The technology began playing an increasingly important role in capture and IDP in the mid-to-late 2010s, when vendors introduced machine learning (ML) into their OCR, template, and rules-based systems to make document classification and data extraction more flexible and adaptive.

This foundation in data extraction paved the way for LLM breakthroughs in the early 2020s. LLMs can process unstructured content without document-specific training and support tasks such as summarizing content and answering questions about it.

As these technologies matured, they quickly branched into more specialized forms of generative AI (GenAI) to solve specific operational challenges. For instance, to handle complex visual layouts, Vision Language Models (VLMs) and other multimodal models can interpret combinations of text, images, audio, and video. Similarly, Small Language Models (SLMs) address the high costs and security concerns of LLMs by enabling organizations to run accurate, domain-specific AI securely on-premises.

Building on these advances, agentic AI is now extending document automation into broader business processes. Dedicated AI agents use document insights to make contextual decisions, trigger downstream actions, and learn continuously. For example, in insurance claims, agentic AI can take a claim from first notice of loss through document review, policy and coverage checks, and fraud flagging to settlement, directly connecting document intelligence to business outcomes.

As IDP expands to handle more types of information (including chat, audio, and video) and take on more steps in a process, organizations need to consider how to make automation reliable at scale. Cost, trust, visibility, and integration with existing systems all affect whether new capabilities deliver practical value.

That's where human expertise remains essential. People oversee automated workflows and handle the cases that require review or judgment, helping organizations maintain quality and accountability as they automate more work. In practice, successful IDP means applying the right capabilities to each process, with controls and human oversight suited to the work. For many organizations, that's prompting a fresh look at their capture and IDP platforms. IDC's 2026 survey found that 63% of organizations are at least likely to change their solution within the next 12 months, and 42% cite accuracy and quality as top factors in future deployment decisions. Buyers now want the right mix of accuracy, customization, governance, time-to-value, and AI for each use case.

Using Infosource's 2026 evaluation criteria to choose an IDP solution

For 40 years, Infosource has focused exclusively on the capture and IDP market. The independent analyst firm tracks vendor revenue to size the global market, and it combines that market view with a detailed look at the vendors themselves. For its 2026 Capture & IDP Vendor Matrix Report, every featured vendor completed an extensive questionnaire, answered follow-up questions, and, in many cases, briefed Infosource's analysts directly. The result is a structured comparison of more than 25 vendors from a firm that knows the category in depth, making it a useful guide for organizations weighing their options. Infosource evaluated each vendor across 18 categories grouped under two dimensions:

Tungsten Automation's strengths and star recognition in AI-Driven IDP

Infosource named Tungsten Automation a "Star" in its 2026 matrix, highlighting our strong vision and overall market impact, and we were positioned highest for Execution in the Market. Analysts evaluated Tungsten TotalAgility, our flagship platform for document automation, orchestration, knowledge discovery, and specialized AI. Designed for large, complex, and regulated industries, TotalAgility transforms unstructured documents into trusted decisions across any deployment model. Key advantages include:

• Flexible recognition technology: We combine an OmniPage OCR/ICR engine with ML, LLMs, rules, and other techniques. TotalAgility also offers pre-trained DocAI Accelerator models and Copilot for Extraction to speed up document AI model setup and configuration.
• No-code workflow design: TotalAgility's DocAI Studio gives users a web-based environment to assemble IDP pipelines visually. Its agentic workflow orchestration can combine document AI, Knowledge Discovery, and human-in-the-loop decisioning, helping organizations tailor automation to their unique processes.
• Flexible capture: Our solutions capture information from scanners, email, watched folders, mobile devices, and APIs, bringing it into a unified workflow.
• Global partner reach: We offer a broad global network of partners who actively resell and deliver Tungsten Automation products worldwide.
• Customer evidence: Infosource notes that our case studies demonstrate the versatility and scalability of our approach.

These capabilities position organizations to take full advantage of the latest advances in intelligent document processing.

Delivering trusted AI-ready data to every industry

Across all industries, the most important work almost always starts with a document. Our capture and IDP capabilities help organizations turn varied, often unstructured documents into reliable, AI-ready data that supports smarter decisions and streamlined operations. Here are some common TotalAgility use cases:

• Banking and financial services: Process invoices, loan applications, and onboarding documents to ensure timely and accurate review and approval.
• Insurance: Accurately classify claims documents, extract key details, and route cases promptly for efficient claims processing.
• Healthcare: Process forms and sensitive medical records, delivering relevant information to the right teams to enhance patient care.
• Logistics: Capture and monitor data from shipping documents, invoices, and receipts to improve supply chain efficiency.
• Public sector: Process applications, citizen correspondence, and supporting documents and deliver them efficiently to the appropriate department.

Looking ahead, we're continually investing in AI and agentic automation to expand document processing into broader, more connected workflows. Our ongoing commitment builds on 40+ years of proven capture and IDP expertise, with human oversight remaining a key part of automating processes responsibly.

Read the full Infosource 2026 Capture & IDP Vendor Matrix Report here to learn more and get in touch with one of our document AI and workflow automation experts to discuss solutions tailored to your organization.

FAQ

What is intelligent document processing (IDP)?

IDP uses technologies such as optical character recognition (OCR) and artificial intelligence (AI) to classify documents, extract information, and support the use of the information in business systems and workflows.

How is AI changing IDP?

AI is expanding the types of documents IDP can process and supporting capabilities such as working with unstructured content (emails, PDFs, voice recordings, etc.), as well as answering questions about documents and advancing workflow actions.

What is agentic IDP?

Agentic IDP uses AI agents to do more than extract information. Agents can make decisions and trigger downstream actions as part of workflows.

What did Infosource recognize about Tungsten Automation?

Infosource named Tungsten Automation a "Star" in its 2026 Capture & IDP Vendor Matrix Report. It highlights the company's strengths in recognition technology, workflow design, partner strategy, and customer references.