AI lease abstraction accuracy depends on the tool, document type, and fields being extracted. The honest benchmark for purpose-built tools like Lextract:
Accuracy Benchmarks by Document Type
| Document Type | AI Accuracy | Notes |
|---|---|---|
| Typed NNN lease (standard) | confidence-scored | Clean scans, consistent language |
| Full service gross lease | confidence-scored | More complex operating expense language |
| Modified gross lease | confidence-scored | Variable structure |
| Ground lease | lower confidence | Complex cross-references |
| Lease with multiple amendments | lower confidence | Requires amendment reconciliation |
| Poor quality scan | lower confidence | Low scan resolution limits accuracy |
Manual first-pass accuracy varies by reviewer, document complexity, and QA process. AI extraction on standard commercial leases is useful because it pairs extracted values with confidence scores that focus human review on uncertain fields.
What Field-Level Accuracy Means
In a 126-field extraction, some fields can still require manual verification even when the overall result is high confidence. Not all errors are equal. An error in base rent amount has greater consequence than an error in building year built.
Confidence scoring addresses this: Lextract provides a 0-100 confidence score on every extracted field. Reviewers can immediately identify which fields are uncertain and verify only those, rather than re-reading the full document. A typical 126-field extraction has a focused set of low-confidence fields reviewable in 10 to 20 minutes.
Where AI Is Most Accurate
- Numeric fields (rent amounts, percentages, dollar figures): high confidence on clearly stated values
- Date fields (commencement, expiration, option deadlines): Consistently high accuracy on typed leases
- Party names (landlord, tenant, guarantor entities): High accuracy on standard formatting
- Binary clause presence (personal guarantee yes/no, audit rights yes/no): typically high confidence on clearly drafted clauses
Where AI Is Less Accurate
- Ambiguous defined terms: Operating expense inclusions/exclusions requiring full-document context
- Amendment hierarchies: Superseded vs. current provisions in multi-amendment leases
- Complex percentage rent calculations: Variable breakpoints with sales definition carve-outs
- Handwritten annotations: AI handles printed text reliably; handwriting accuracy is low confidence
The Right Workflow
AI extraction as first pass with targeted human review is designed to be much faster than full manual abstraction while preserving human review for consequential fields:
- AI processes lease in 5-15 minutes (vs. 4-8 hours manually)
- Confidence scores identify 8-15 fields needing review
- Human verifies flagged fields in 15-25 minutes
- Total time: Under 30 minutes per lease vs. 4-8 hours manually
This workflow is not "trust AI blindly" - it is "use confidence scoring to separate clearly extracted fields from the fields that warrant verification."