# Lessons from Processing 35,000 Contracts **URL:** https://genserv.ai/blog/lessons-from-35k-contracts **Published:** December 12, 2025 **Author:** Chris Hand, CEO & Co-Founder **Category:** Business --- ## Summary Processing over 300,000 pages of contracts for a customer taught us a lot about how AI compares to human accuracy. --- ## Full Article # When AI Outperforms Human Review: Lessons from Processing 35,000 Contracts When a client approached us about implementing an AI agent to review contracts and extract key data, they made what seemed like a reasonable request: they wanted to benchmark our AI's performance against their team's human review process. We were nervous. Really nervous. ## The Challenge: More Than Simple Data Extraction This wasn't a straightforward data extraction task. While some data points were relatively simple—names, dates, amounts—the client needed approximately 100 different data points extracted from each contract. Many of these required genuine understanding: - Payoff program structures - Conditional terms that depended on other clauses - Cross-referenced provisions spread across multiple sections - Complex relationships between different contract terms The agent needed to read, comprehend, and synthesize information across entire documents to accurately capture these nuances. And not just for a few contracts—we were looking at processing upwards of 35,000 contracts, each running 100-200 pages. That's hundreds of thousands of pages of legal text. ## The Benchmark Surprise When we completed our initial implementation and ran our manual review of the results, we discovered something surprising: the human review process we were benchmarking against was only 70-80% accurate on many of the key data points, especially the more complicated ones. Meanwhile, our AI agent was consistently performing in the mid-to-high 90% accuracy range. This wasn't what we expected. And it taught us several critical lessons. ## Lesson 1: Human Review Isn't a Perfect Baseline We tend to treat human performance as the gold standard—the benchmark that AI needs to reach. But this assumption breaks down when the task is: 1. **Highly complex**: Requiring cross-referencing multiple sections and understanding interdependent terms 2. **Done at volume**: Processing hundreds or thousands of similar documents 3. **Cognitively demanding**: Maintaining focus and accuracy across repetitive analysis Humans are remarkable, but we're also prone to fatigue, inconsistency, and cognitive load limitations. When you're the 10th person reviewing your 50th contract that week, accuracy naturally degrades. ## Lesson 2: Scale Changes Everything Could a human team review 35,000 contracts with 100-200 pages each? Technically, yes. Practically? Not in any reasonable timeframe or budget. You'd need: - A large team working full-time for months - Significant management overhead to ensure consistency - Quality control processes to catch errors - The budget to fund all of the above And even then, you'd likely see accuracy issues due to the sheer cognitive load and repetitiveness of the work. This was the perfect use case for AI: a task that's cognitively demanding, highly repetitive, and needs to be done at scale with consistent accuracy. ## Lesson 3: AI Excels at Consistent, Complex Pattern Recognition What makes AI agents particularly effective for this type of work isn't just speed—it's consistency. The agent applies the same analytical framework to contract #1 and contract #35,000 with equal precision. It doesn't get tired. It doesn't lose focus. It doesn't cut corners when faced with the 200th confusing clause structure. For tasks requiring: - Cross-referencing multiple document sections - Recognizing complex patterns and relationships - Maintaining accuracy across high volumes - Consistent application of extraction rules A well-trained AI agent can actually be *more reliable* than human review. ## The Real Benchmark Question This experience fundamentally changed how we think about AI benchmarking. The question isn't "Can AI match human performance?" The real questions are: - What is actual human performance on this specific task at this specific scale? - What level of accuracy does the business actually need? - What's the cost-benefit tradeoff between different approaches? Sometimes, when you measure honestly, you discover that the AI isn't trying to match human performance—it's already exceeding it. ## When to Consider AI for Contract Review Based on this experience, AI-powered contract review makes sense when you have: 1. **Volume**: Dozens, hundreds, or thousands of contracts to process 2. **Complexity**: Data points that require reading and understanding relationships across the document 3. **Consistency requirements**: Need for uniform application of extraction rules 4. **Time constraints**: Need results faster than a human team can deliver 5. **Cost sensitivity**: Budget limitations that make large human review teams impractical ## The Bottom Line We went into this project worried about whether our AI could match human accuracy. We came out realizing we'd set the bar too low. When you're dealing with complex, high-volume document review, a well-implemented AI agent doesn't just match human performance—it can significantly exceed it. The key is being honest about what human performance actually looks like at scale, rather than assuming humans are perfect reviewers. If you're drowning in contracts that need review, the question isn't whether AI can do it as well as humans. The question is: can you afford not to use AI? --- *Interested in learning more about AI transformation for your business? At GenServ, we help mid-market companies identify and implement AI solutions that deliver real business value. [Get in touch](https://genserv.ai/schedule) to discuss your challenges.* --- ## About GenServ AI GenServ AI is an AI transformation consultancy helping mid-market companies ($10M-$100M revenue) implement AI solutions with measurable ROI. - **Website:** https://genserv.ai - **All Blog Posts:** https://genserv.ai/blog - **LLM Content Index:** https://genserv.ai/llms.txt - **Schedule a Call:** https://genserv.ai/schedule