AI Adoption Surges, But Document Infrastructure Struggles to Keep Up
The adoption of artificial intelligence (AI) has reached a significant milestone, with a global survey from Apryse revealing that 64.5% of enterprises are now utilizing AI in their operations. Despite this surge, there is a notable gap when it comes to the quality of document data management, with only 38.1% of organizations rating their document data as "excellent" for AI use. This disparity raises concerns about the ability of organizations to fully leverage AI technologies effectively.
In a survey conducted in September 2025, involving 465 organizations across North America, Europe, Australia, and New Zealand, the key findings highlighted a glaring issue: while AI has become mainstream, the document infrastructure that supports it hasn't evolved at the same pace. The traditional model for document data has often proven to be disorganized, inconsistent, and challenging for AI systems to interpret accurately. Manual vetting of such data may work for smaller companies but is not a sustainable solution for scaling enterprises.
As many as 76.6% of respondents reported that they store a significant portion of their data (between 25-75%) in document formats. However, the challenges of scaling AI integration come from a lack of document quality and control, with 54% citing data security as the primary obstacle to implementing scalable AI solutions. Additionally, 49% of organizations pointed to data quality issues as a significant barrier.
The urgency to invest in document automation is prevalent, with 82.8% of respondents planning to allocate resources in this area within the next year. Yet almost half of these organizations expressed a lack of confidence in their current document processing pipeline, suggesting a critical need for improvement in document handling processes.
One notable observation from the survey was the superior AI maturity exhibited by participants from the Asia-Pacific region, particularly Australia and New Zealand. These respondents demonstrated higher adoption rates of generative AI, predictive AI, hybrid cloud systems, and optical character recognition (OCR) technologies, indicating a significant shift in global innovation toward AI.
"AI is no longer in the experimental phase; it's operational and intertwined with daily business processes, yet the infrastructure necessary for its support has not kept pace," stated Andrew Varley, Chief Product Officer at Apryse. The rapid increase in data generation coupled with inadequate governance and fragmented tools has hindered organizations from processing document data intelligently at scale.
Moving from chaos to context emerges as a crucial need in today's document automation landscape. Organizations are seeking not just basic digitization tools, but sophisticated solutions capable of extracting meaning and structure from various documents, including invoices, contracts, and forms. In the survey, respondents identified several critical features necessary for effective document automation:
Apryse has recognized these needs and is addressing them through its embeddable SDKs and intelligent pre-processing technologies, which transform unstructured documents into structured, AI-ready data. This capability not only facilitates scalable processing but also ensures that enterprises maintain control over their data governance.
For those keen on exploring the complete findings of Apryse's survey, further details are available at their official website. As the landscape of AI continues to evolve rapidly, the importance of robust document infrastructure cannot be understated, as it forms the foundation upon which effective AI systems are built.
In conclusion, while AI's presence in enterprises is expanding, the journey toward fully leveraging its potential is hindered by existing document infrastructure challenges. Addressing these weaknesses will be crucial for organizations striving to achieve successful digital transformation and to harness the full capabilities of AI technology.
In a survey conducted in September 2025, involving 465 organizations across North America, Europe, Australia, and New Zealand, the key findings highlighted a glaring issue: while AI has become mainstream, the document infrastructure that supports it hasn't evolved at the same pace. The traditional model for document data has often proven to be disorganized, inconsistent, and challenging for AI systems to interpret accurately. Manual vetting of such data may work for smaller companies but is not a sustainable solution for scaling enterprises.
As many as 76.6% of respondents reported that they store a significant portion of their data (between 25-75%) in document formats. However, the challenges of scaling AI integration come from a lack of document quality and control, with 54% citing data security as the primary obstacle to implementing scalable AI solutions. Additionally, 49% of organizations pointed to data quality issues as a significant barrier.
The urgency to invest in document automation is prevalent, with 82.8% of respondents planning to allocate resources in this area within the next year. Yet almost half of these organizations expressed a lack of confidence in their current document processing pipeline, suggesting a critical need for improvement in document handling processes.
One notable observation from the survey was the superior AI maturity exhibited by participants from the Asia-Pacific region, particularly Australia and New Zealand. These respondents demonstrated higher adoption rates of generative AI, predictive AI, hybrid cloud systems, and optical character recognition (OCR) technologies, indicating a significant shift in global innovation toward AI.
"AI is no longer in the experimental phase; it's operational and intertwined with daily business processes, yet the infrastructure necessary for its support has not kept pace," stated Andrew Varley, Chief Product Officer at Apryse. The rapid increase in data generation coupled with inadequate governance and fragmented tools has hindered organizations from processing document data intelligently at scale.
Moving from chaos to context emerges as a crucial need in today's document automation landscape. Organizations are seeking not just basic digitization tools, but sophisticated solutions capable of extracting meaning and structure from various documents, including invoices, contracts, and forms. In the survey, respondents identified several critical features necessary for effective document automation:
- - Table/form recognition (59.6%): This capability solves layout and relationship comprehension in complex documents.
- - Developer-friendly SDKs: These help minimize technical barriers to developing automated document workflows.
- - Metadata tagging: This feature enables context-aware classification of data, improving compliance, searchability, and overall governance to enhance AI accuracy.
Apryse has recognized these needs and is addressing them through its embeddable SDKs and intelligent pre-processing technologies, which transform unstructured documents into structured, AI-ready data. This capability not only facilitates scalable processing but also ensures that enterprises maintain control over their data governance.
For those keen on exploring the complete findings of Apryse's survey, further details are available at their official website. As the landscape of AI continues to evolve rapidly, the importance of robust document infrastructure cannot be understated, as it forms the foundation upon which effective AI systems are built.
In conclusion, while AI's presence in enterprises is expanding, the journey toward fully leveraging its potential is hindered by existing document infrastructure challenges. Addressing these weaknesses will be crucial for organizations striving to achieve successful digital transformation and to harness the full capabilities of AI technology.
Topics Consumer Technology）
【About Using Articles】
You can freely use the title and article content by linking to the page where the article is posted.
※ Images cannot be used.
【About Links】
Links are free to use.