Most Contract Work Time Isn't Review.
It's Finding Specific Fields.

Where the Hours Actually Go

Ask an in-house lawyer to list their biggest time drains and contract review will be near the top. But "review" is the wrong word. Most of those hours aren't spent assessing risk or negotiating terms. They're spent finding things.

The CLOC survey, based on responses from contracting professionals at organizations of all sizes, breaks the process into two measurable stages: locating the contract (45 minutes) and finding the relevant section or language (84 minutes). That's over two hours before any actual analysis begins — and for a single contract. Organizations generating 500 contracts a month log over 6,000 per year. The arithmetic is uncomfortable.

This pattern holds at the individual level too. In-house lawyers report spending 60–80% of their time on routine document review, much of which is retrieval and data entry rather than legal judgment. A 2025 survey by LegalOn and In-House Connect found that AI adoption in contract review grew 75% year-over-year, driven primarily by teams trying to claw back time from this exact problem.

The frustration is not abstract. On Reddit's r/legaltechAI, one in-house legal ops professional described the situation bluntly: "Clause extraction and deviation detection are table stakes. The harder problem is portfolio-level intelligence: obligation extraction across thousands of agreements, renewal exposure, change-of-control clauses before M&A, regulatory sweep analysis, etc. That's less about redlining and more about turning contracts into structured data the business can query." (source)

A paralegal in r/paralegal put it even more practically: "I'd trust clause extraction as a triage layer, not as a substitute for reading. The win is 'get me to the right page/section fast,' not 'tell me what it means.'" (source)

Why Ctrl+F Doesn't Scale to 50 Contracts

The instinctive solution is keyword search. Open each PDF, hit Ctrl+F, type "governing law" or "effective date," copy the result, paste into a spreadsheet. For two or three contracts, this works. For fifty, the cracks appear immediately.

Contract language resists exact-match search. The same concept appears under different labels: "Effective Date" in one agreement becomes "Commencement Date" in another, and "This Agreement shall become effective as of" in a third. A keyword search for "indemnification" misses contracts where the clause is titled "Hold Harmless." A search for "termination" returns every mention of the word across 80 pages, forcing you to manually separate the actual termination clause from passing references, defined terms, and cross-references.

Then there is the format problem. Some contracts are clean Word documents. Others are scanned PDFs — the product of a printer, a scanner, and enough compression artifacts to make the text barely legible. Keyword search fails entirely on image-based PDFs unless OCR has been run first, and even then, the quality varies.

The CLOC survey found that 65% of teams still use spreadsheets and email to manage contracts — no integrated tooling, no central repository, no automated extraction. Forty-six percent are sometimes unable to locate the right contract at all. Less than half feel confident the document they found is the most current version. These are not technology adoption failures as much as workflow design failures: the tools people reach for were never built for this job.

What Contract CLMs Do (and What They Don't Do for Extraction)

If you work in a mid-size or larger organization, you may already have a Contract Lifecycle Management system — DocuSign CLM, Ironclad, LinkSquares, or Juro. These platforms handle the full contract lifecycle: drafting, negotiation, approval routing, eSignature, storage, and post-signature obligation tracking.

CLMs are powerful at what they do. Ironclad automates approval workflows and keeps a central repository of negotiated agreements. LinkSquares applies AI to extract metadata and surface renewal risks across a portfolio. DocuSign CLM integrates contract generation with eSignature and post-execution analytics. Juro provides a browser-native workspace for collaborative contract creation and signing.

But here is the distinction that matters for extraction: CLMs are built around the contract as a unit. They manage the document, its status, its signatories, its deadlines. When you need to pull the same five data points from fifty contracts and land them in a spreadsheet — regardless of format, regardless of whether the contracts ever lived in a CLM — that's a different operation.

CLM platforms can extract metadata they were trained to find: party names, effective dates, contract values. But ask them to pull a non-standard field — say, the specific per-incident deductible in an insurance requirement clause buried in a vendor agreement — and you're either building a custom extraction model or doing it manually. The CLM's extraction scope is tied to its training data. If your field isn't in the pre-trained taxonomy, it doesn't come out.

This is not a failure of CLMs. It's a reflection of what they were designed to do. Contract lifecycle management and field-level data extraction solve adjacent but different problems. CLMs manage the contract's journey. Extraction tools pull specific data from the contract's pages — format-independent, template-free, and driven by what you ask for rather than what the tool was pre-trained to find.

How to Extract Specific Fields from Contracts, Step by Step

If you need to pull specific data points from a stack of contracts — parties, effective dates, governing law, liability caps, renewal terms, payment schedules — here is a workflow that works across contract formats without templates, without custom models, and without reading each document cover to cover.

This workflow is the opposite of traditional contract data extraction, where you train a model on sample contracts and hope it generalizes. The mechanism here is Custom Column Extraction: instead of training a tool to recognize where data sits on a page (the template/zonal OCR approach), you tell the AI what you want by typing column names — "Party A," "Effective Date," "Governing Law," "Limitation of Liability Cap." The AI reads each contract and locates the corresponding values by understanding what the terms mean, not by matching a predetermined position on the page. This is the difference between position-based extraction and semantic extraction: the format of the contract doesn't matter because the AI isn't looking at coordinates, it's looking at meaning.

The batch merge is what separates this from opening each file individually. Fifty contracts, one upload, one output table. The tedious part — opening every PDF, scanning for a field, copying the value, pasting it, checking for accuracy — is absorbed by the tool.

When Extraction Works, and When You Still Need a Lawyer

Field-level extraction solves a real problem, but an honest assessment means drawing the line between what it handles and what it doesn't.

What extraction handles well: pulling structured data points from contracts — parties, dates, dollar amounts, governing law, renewal terms, clause presence (does this contract contain an indemnification clause? yes/no). These are values that exist on the page in some form, whether explicit or inferrable. The AI locates them through semantic understanding, not position-matching, so format variation across contracts — different layouts, clause ordering, even handwritten annotations — doesn't break the extraction.

What extraction does not replace: legal interpretation of clause language. The AI can tell you that a contract contains an indemnification clause and extract its text. It cannot tell you whether that clause is unusually broad, whether it conflicts with another provision, or whether it exposes your organization to unacceptable risk under a specific jurisdiction's case law. That's legal judgment. Similarly, extraction tools don't replace CLM platforms — they don't manage negotiation workflows, route approvals, track signatures, or monitor post-execution obligations.

The mental model that works: extraction tools are to contract data what a paralegal's first-pass review is to a case file. They surface the information you need, organized and ready for your judgment. They don't make the judgment for you.

FAQ

Does this work with scanned contracts, or only digital PDFs?

It works with both. The AI reads the visual content of each page — scanned documents, phone photos of signed contracts, clean digital PDFs, Word files — through the same visual understanding pathway. There is no separate OCR step and no format-dependent accuracy drop for scanned documents, though very low-quality scans (under ~150 DPI or with heavy compression artifacts) may reduce accuracy.

Can it extract entire clauses or just single data points?

Both. You can name a column "Indemnification Clause" and the AI will extract the full text of that clause into the cell. You can also extract single values like "Effective Date" or "Contract Value." For long clauses, the extracted text can be several paragraphs of legal language.

Do I need a CLM to use this?

No. Extraction tools operate independently of CLM systems. You can use them on contracts stored anywhere — a shared drive, email attachments, a SharePoint folder. If you already have a CLM, extraction tools complement it by handling ad-hoc or non-standard field requests that fall outside the CLM's pre-trained extraction taxonomy.

How many contracts can I process at once?

There is no hard limit on contract count per batch, though practical throughput depends on your subscription plan's concurrency limits. The workflow is designed batch-first: upload all contracts together, and results land in one merged spreadsheet.

How accurate is the extraction?

For clearly stated data points (dates, party names, dollar amounts), accuracy is high — typically above 95% for clean documents. Accuracy can dip with extremely dense or unusual contract formatting, handwritten annotations, or poor-quality scans. The system outputs the source image alongside each extraction result, so verification is straightforward: you can spot-check any cell against the original document in seconds. For a deeper look at what affects extraction quality, see our guide on contract extraction accuracy.

From Search to Structure

The two hours the CLOC survey identifies — 45 minutes to find the contract, 84 to find the right clause — are not hours spent practicing law. They're hours spent navigating documents. The difference matters because it's fixable.

When you can name the fields you want and get them back as a structured table — across fifty contracts, across scanned PDFs and Word files and phone photos — the review itself becomes the work, not the preamble to the work. You sit down with a spreadsheet that already contains the data points you need. Your time goes to what the data means, not where it is.

Try it on a batch of contracts. Name the fields that matter to you. See what comes back.