Did IDC’s 20% Generative AI and IDP Prediction Hold?
IDC ·
In October 2023, IDC FutureScape: Worldwide Future of Work 2024 Predictions forecast that by 2026, enterprises that directly tie generative AI to intelligent document processing (IDP) would see a 20% increase in new use cases, along with gains in productivity, scale, and customer experience. Three years later, Amy Machado, Research Director at IDC, says the […] The post Did IDC’s 20% Generative AI and IDP Prediction Hold? appeared first on IDC .
In October 2023, IDC FutureScape: Worldwide Future of Work 2024 Predictions forecast that by 2026, enterprises that directly tie generative AI to intelligent document processing (IDP) would see a 20% increase in new use cases, along with gains in productivity, scale, and customer experience. Three years later, Amy Machado, Research Director at IDC , says the number held and was probably conservative.
The bigger change is that document work once split across separate products is starting to run as a single workflow.
Watch the full conversation with IDC’s Amy Machado below, or keep reading for the highlights.
IDP was not new in 2023. What changed was the work it took to get value from it. Machado says earlier tools needed models trained on specific document types. Foundation models reaching the market added a more intelligent layer of extraction and classification, especially for unstructured content.
Machado separates documents by how predictable they are. A tax form is structured: it looks the same every time, so the software knows where to find a name, an address, or a Social Security number. A contract is long-form text with no fixed layout, and multimodal models now extend the same extraction to images and audio, which opens document types that were out of reach before.
It also changed who benefits. IDP use cases had been operational and transactional, at high volume. Generative AI pushed document AI toward ad hoc, text-heavy tasks that used to demand hours of manual reading from knowledge workers.
Machado says IDC held the number at 20% rather than higher because enterprises adopt more slowly than software ships. IDP has long lived in regulated, secure workflows such as tax and healthcare, where documents carry PII and PHI.
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Just because a solution can do something doesn’t necessarily mean that you just want to immediately deploy it at scale.
Yes, and Machado says 20% is probably low. She sees 2023 as the cusp of the shift. She points to IDP market revenue growing at roughly 19 to 20%, which she says lines up with the forecast. She adds that some of that spend may now be booked under copilot and assistant products, with document AI running underneath, so the revenue is harder to attribute.
She stands by the number as written. In her words, 20% was provocative enough to push buyers to look for new use cases, and “it could have been fifty percent.”
The surprise was less about which use cases appeared and more about how the surrounding process came together. Outbound document generation and customer communication management were once their own silo. Processing and extraction were another, and enterprise content management a third, each with its own vendors.
Once a company can read what is inside its documents, it can connect those pieces. Machado says content services vendors now pitch coverage of both outbound and inbound work, which means fewer products to stitch together. The use cases themselves are familiar: accounts payable, invoice processing, customer onboarding, and healthcare statements.
It also changes how modernization works. Many content services customers sit on legacy applications with custom integrations and assume the only path is a lift-and-shift migration. Machado says AI now lets them understand what they have and why they have it, then move only what they need. She also points to MCP, an integration protocol, as part of how agentic workflows will connect applications.
Cost is the other correction. Machado notes that many document tasks never needed a large language model, and traditional OCR still handles them. With companies watching AI spend and token usage, she says the question is no longer which model to pick: “Do we even need a large language model or a small language model for this?”
Accuracy, according to Machado. “Accuracy is still very much a moat,” she says. If teams can’t trust the output of their AI or agentic tools, they stay stuck in proof of concept without reaching ROI, and take on risk that compounds once agents act on the results.
She ties that to context. Business context lives in SOPs, process documentation, customer information, emails, and the record of who does what and why, along with what sits in the CRM or ERP. Companies that treat unstructured data as an afterthought are leaving that context out of their AI.
That is the thread running into her FutureScape 2027 prediction on metadata automation. Machado says historic metadata, such as file type, author, last opener, or SharePoint site, is not enough to drive agentic workflows. Her prediction centers on attaching ontologies, taxonomies, and tags so content can be related to structured data, with AI generating that metadata at scale. She wants subject matter experts, not only data scientists, to help maintain it, because the business keeps changing.
Her comments point to a few checks before scaling a document workflow:
Machado’s closing advice is to remember why the business exists. A healthcare company is there to deliver care, so the question is how AI improves that care and the employee experience around it.
Employee buy-in is the practical test. In her words, “if you really don’t have employee buy-in, then it’s kind of a waste,” so the decision belongs to subject matter experts and line-of-business teams as much as to IT or the C-suite.
Customers are watching too. Her annual customer communication survey shows people growing less tolerant of inaccurate information, poor self-service, and chatbots that don’t help.
The post Did IDC’s 20% Generative AI and IDP Prediction Hold? appeared first on IDC .