Enterprise AI: Build or Buy?
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Enterprise data extraction often falls short not because the technology is lacking, but because implementations focus on accuracy instead of how extracted data supports real business decisions. Teams that see lasting value design extraction as an integrated, trusted pipeline that connects documents directly to the systems and workflows where decisions are made.
Enterprises sit on a goldmine of information locked in documents. Contracts contain pricing terms and renewal dates that finance never sees. Regulatory filings hold compliance obligations that legal cannot track at scale. Even operational reports reveal patterns that would transform risk management if anyone could access them consistently.
The technology to extract this information exists. Believe it or not, it’s actually existed for years. Yet according to Gartner, 80% of enterprise data remains unstructured, trapped in emails, documents, PDFs, and spreadsheets that never feed into the systems where decisions actually happen.
This disconnect defines the current state of enterprise data extraction. The capability gap is not technical, rather it’s operational. And the distance between extracted data and actionable intelligence remains vast for most organizations.
Teams that’ve implemented extraction projects recognize the pattern. A promising pilot demonstrates impressive accuracy metrics on a controlled document set. Leadership approves expansion. Then integration challenges emerge, edge cases multiply, and the project stalls somewhere between proof-of-concept and production deployment. MIT's NANDA initiative found that 95% of enterprise AI pilots fail to deliver measurable ROI, with flawed enterprise integration cited as the primary cause.
The organizations succeeding with extraction are not buying better technology. They’re designing better pipelines that connect documents to the decisions those documents should inform.
Three failure patterns derail most enterprise extraction implementations, and none of them are primarily technical.
Treating extraction as a standalone capability rather than a workflow component represents the most common mistake. Teams optimize for accuracy metrics while ignoring how extracted data reaches decision-makers. A contract extraction system that achieves 98% accuracy on key terms delivers zero operational value if those terms never reach the systems where procurement decisions happen. The extraction is technically successful and practically useless.
Underestimating document variability compounds the challenge. Pilots operate on clean, representative document samples. Production environments surface the messy reality, like scanned documents with poor image quality, non-standard formats, handwritten annotations, documents that combine multiple languages, and edge cases that no training set anticipated.
A study analyzing 500,000 document-processing transactions found that traditional rule-based systems experience significant failure rate increases when processing documents with variable layouts or inconsistent structures.
Integration debt finishes what the first two patterns start. Extracted data must flow into existing systems. Each integration requires connectors that someone must build and maintain. According to Menlo Ventures' enterprise AI research, implementation costs were cited in 26% of failed pilots, frequently catching organizations off guard even when the extraction technology performed as expected.
Technical accuracy and organizational trust are not the same thing. As highlighted in the previous section, a system can achieve 99% extraction accuracy and still be useless. In this case, it’s when business users refuse to rely on its outputs. The trust gap kills more extraction projects than the accuracy gap.
Trust requires explainability. When an extracted value appears in a dashboard or triggers a workflow, users need to understand where it came from. Which document? Which page? Which specific text passage? Systems that present extracted data without provenance create a black box that compliance teams cannot audit and business users will not adopt. Every AI response should be traceable, with metadata tracking lineage, authorship, version, and time to satisfy enterprise regulations like HIPAA, SOX, and GDPR.
Trust requires confidence transparency. Not all extractions carry equal certainty. A clearly printed date in a standard format extracts with near-perfect confidence. A handwritten annotation on a scanned document extracts with significant uncertainty. Systems that present both with equal authority undermine their own credibility.
Trust requires graceful exception handling. Outliers are destined to arise. For example, corrupted files, unusual formats, and documents that do not match expected templates. Systems that fail silently on these exceptions create data gaps that users discover at the worst possible moments. Systems that flag uncertain extractions for human review build confidence by acknowledging their own limitations.
Trust requires feedback loops. When a user corrects an extracted value, that correction should improve future extractions. When a document type consistently produces low-confidence results, the system should surface that pattern.
The knowledge fabric approach addresses trust at the architectural level by tagging every element with lineage and maintaining complete audit trails. This is not a feature, rather it’s a requirement for enterprise adoption in regulated industries.
Different document categories require different extraction approaches and deliver different value profiles. Understanding these distinctions helps teams prioritize and design appropriate pipelines.
Contracts and agreements represent the largest untapped data source in most enterprises. A PwC report found that large organizations manage upwards of 40,000 active contracts at any given time. Key terms, obligations, renewal dates, pricing structures, and liability provisions remain locked in PDFs while teams make decisions without visibility. Research indicates that organizations lose approximately 9% of revenue to contract value leakage. Things that attribute to this figure are forgotten discounts, untracked rebates, and missed renewal opportunities. For a mid-market enterprise with $100 million in annual contracts, that represents roughly $9 million in avoidable losses.
Financial documents drive cash flow, compliance, and planning. Invoices, statements, purchase orders, and financial reports arrive in high volume and require rapid processing. Organizations using document automation reduce invoice processing cycle time from 12 days to under 3 days on average. The value is not just speed. It’s automated processing that reduces human error rates by up to 90% compared to manual data entry. In finance, automated document processing reduces invoice errors by up to 37%, directly impacting profitability.
Regulatory filings and correspondence carry compliance implications that require tracking, response, and audit trails. The volume in regulated industries overwhelms manual review. Healthcare providers using document automation have reduced patient record processing time by 50%. Insurance companies using automation have cut claims processing times by an average of 60%. The extraction requirement here extends beyond accuracy to include auditability. Literally every extracted field must trace back to its source document with complete provenance.
Operational records from service tickets to inspection reports contain patterns that inform risk detection and monitoring. Extraction enables pattern recognition across previously siloed documents. The value in operational documents is often aggregate rather than individual. No single document contains the insight, but the pattern across thousands of documents reveals what no human reviewer could see.
Before selecting technology or launching pilots, teams should evaluate their readiness across these dimensions:
Organizations that can’t answer these questions clearly are not ready for extraction technology. They are ready for extraction strategy work.
Enterprise data extraction succeeds when teams design for decisions rather than documents. Organizations that treat extraction as a pipeline to build, integrated with existing systems and aligned with specific decisions, will capture the value that remains locked in their document repositories. Those that treat extraction as a capability to acquire will continue struggling through pilots that never reach production.
If you’re still in the second camp, the Unframe platform can help you work across both structured and unstructured data sources. Everything from PDFs, emails, spreadsheets, and databases, to SaaS applications, and APIs. This unified approach means extracted document intelligence connects directly to the structured data it relates to, without requiring separate integration projects for each data type.
When your extraction pipeline can pull from any source and route to any destination, the gap between documents and decisions closes significantly.
