Findings from a global survey on AI adoption and document infrastructure by Apryse reveal a striking paradox: while AI has gone mainstream, most enterprises are still struggling with the document data governance needed to scale it.
Traditional data shared between documents tends to be messy, inconsistent, and hard for AI to interpret. While humans can manually vet the data, this is unsustainable for most companies. Without intelligent pre-processing, these documents remain unstructured, making automation and accurate insights increasingly challenging.
The September 2025 survey of 465 organizations across North America, Europe, Australia and New Zealand uncovered key insights:
- 64.5% of organizations already run AI in production; largely for enhancing operational efficiency (63%), improving the customer experience (51%), and making data-driven decisions (41%).
- 76.6% store between 25–75% of their data in documents, yet only 38.1% rate that data as “excellent” for AI use.
- 67.3% say keeping document processing in-house is “extremely important.” 54% cite data security concerns as the top barrier to scaling AI, and 49% cite data quality.
- 82.8% plan to invest in document automation within the next 12 months—but nearly half lack confidence in their current pipelines.
- 62.8% experience document quality issues “occasionally” or “frequently.”
“AI is no longer experimental, it’s operational,” said Andrew Varley, CPO, Apryse. “But enterprises are discovering that the infrastructure behind it, especially around document data quality, hasn’t evolved fast enough.
“Surging data growth without governance, a lack of visibility into what content already exists, and fragmented tooling are now the biggest barriers to intelligent processing at scale.”
Asia-Pacific quietly leads in AI maturity
While North America leads in AI deployment (77.7%), Australia and New Zealand quietly outpaced the West in infrastructure maturity. These respondents reported the highest adoption of generative and predictive AI, hybrid cloud usage, and OCR technologies, signaling a global shift in innovation.
“Oceania is outpacing the West in several key areas of AI infrastructure,” added Varley. “The region has been an early-adopter of data residency rules and regulatory mandates, pushing organizations to embrace hybrid cloud and advanced document processing.
“With highly-regulated industries like healthcare, government, and financial services dominating the market in Oceania, this urgency has created a solid model for accurate document-to-data workflows.”
From chaos to context: the need for structured document data
The survey also highlights a growing demand for tools that go beyond digitization. Organizations need solutions that extract meaning and structure from documents, not just text. When asked about the most critical capabilities in document automation, respondents ranked:
- Table/form recognition (59.6%) to solve the problem of understanding layout and relationships in documents like invoices, contracts, and forms.
- Developer-friendly SDKs to lower technical barriers to automated document workflows.
- Metadata tagging to enable context-aware data classification for compliance, searchability, and governance, reducing risk and improving AI accuracy.
