The document bottleneck holding up AI transformation

Organisations have invested heavily in AI in the last few years. Yet for finance and compliance teams, the most critical information often remains locked in documents that AI systems cannot reliably read or interpret.

 

Significant investment in AI is flowing into organisations of all sizes. Yet in boardrooms, finance departments, and compliance teams, a single issue is emerging repeatedly. It is not a lack of data, talent, or budget. It is a document.

 

For finance teams, a considerable amount of working time is spent reviewing dense, unstructured documents: contracts, board packs, ESG reports, and similar materials. When organisations ask whether AI can reliably extract the information that matters from those documents, the answer is frequently uncertain.

 

This represents a significant gap in enterprise AI adoption, and one that has not received the attention it deserves.

 

Research from IDC suggests that around 80% of enterprise data is unstructured, much of it held within documents. Separately, McKinsey has found that knowledge workers spend significant time searching for and processing information. This is a consistent finding across multiple studies.

 

 

Where AI has delivered results… and where it has not

AI programmes have delivered measurable results in areas where data is clearly structured and labelled: customer service automation, supply chain forecasting, financial modelling. These are areas where AI can make a genuine difference.

 

However, many business decisions depend on more than structured data. They rely on complex processes: a risk committee reviewing a supplier contract, a board receiving its quarterly report, a compliance team preparing an ESG disclosure, a legal team assessing liability across a portfolio of agreements.

 

These processes tend to be document-heavy, time-consuming, and largely manual. AI systems that have performed well elsewhere in the business often reach a clear limit when document-based workflows are involved.

 

In many organisations, critical information is not held in a structured database; it resides in a document. For the most part, AI has not yet been designed to engage with that information effectively.

 

 

The document problem is difficult to solve

The technical challenge is real. Documents are inherently complex: free-form text, tables, footnotes, and formatting conventions that do not translate easily into machine-readable data. And therefore, AI models built for structured data struggle with this kind of content.

 

Scanned PDFs present a further challenge. While modern PDFs are generally machine-readable, a significant volume of older contracts, filings, and board materials were digitised as image files rather than text, meaning their content remains invisible to most automated processing systems, including AI.

 

Regulatory developments are adding further pressure. The Corporate Sustainability Reporting Directive, ISSB standards, and growing scrutiny of ESG disclosures mean organisations are required to produce, interrogate, and stand behind a greater volume of critical documentation than before. Manual workflows that were adequate under a less demanding regulatory regime are increasingly under strain.

 

For finance and compliance leaders, the implications are practical. An overlooked clause in a supplier contract, an inconsistency in an ESG report, or a delay in assembling board materials can each carry real consequences, and as compliance requirements increase, so does the associated risk.

 

 

A more capable generation of AI is emerging

AI-powered document tools have existed for some time - from early search and OCR (Optical Character Recognition) applications through to today’s more advanced natural language processing systems. What has changed in recent years is that these tools have become significantly more capable and are now suited to corporate deployment.

 

Adobe Acrobat’s AI Assistant is one example. Built on the PDF format that Adobe introduced in 1993, it allows professionals to query complex documents in plain English: comparing versions of a governance document, condensing a board pack into an executive summary, or interrogating a regulatory filing, in a fraction of the time previously required.

 

For teams managing large volumes of documentation, capabilities such as automated summarisation and cross-language translation address operational bottlenecks that have historically required considerable manual effort.

 

 

The case for acting now

Where AI can support document review and extraction, compliance teams can redirect capacity from processing towards analysis and decision-making. When ESG reports can be automatically checked for internal consistency, governance risk can be managed more proactively. Where board materials can be interrogated quickly, the quality of executive decision-making improves.

 

Organisations that have not yet extended AI to their document workflows are leaving a productivity and risk-reduction opportunity unrealised, at a time when regulatory and competitive pressure to act is increasing.

 

Extending AI transformation to reach all information within an organisation (not only the data that is neatly structured and labelled) represents a meaningful shift. The organisations that derive the most value from AI in the coming years will not simply be those that have deployed the most models. They will be the ones who have carefully considered where their information actually resides and ensured their AI programmes can access all of it.

 

For many businesses, a significant share of that information is held in documents. The tools to access it are available. The question is no longer whether to act, but how quickly.

 

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This article is a team contribution from Adobe.

 

Main image courtesy of iStockPhoto.com and eric1513