TL;DR

• Vendor claims vary wildly in accuracy: While JPMorgan reduced review time from thousands of hours to seconds with COIN, typical enterprise gains are 20-40 percent efficiency improvements; demand proof with your actual documents before believing 95 percent+ accuracy claims.

• Five core AI jobs to test systematically:

  • OCR & Metadata Extraction: Target 85 percent+ accuracy for mixed documents, test with scanned PDFs, phone photos, and complex layouts.

  • Risk Flagging: Expect 85 percent recall for critical clauses, use precision/recall metrics rather than overall accuracy claims.

  • Repository Q&A: Achieve 80 percent accuracy for factual queries, 60 percent for analytical questions with proper document indexing.

• What "good" evaluation looks like: Use public datasets like Stanford's ContractNLI for testing, demand vendor demonstrations with your document types, verify accuracy claims with independent evaluation scripts, and test edge cases before deployment.

• Red flags to avoid: Claims of 95 percent+ accuracy without specifying test conditions, no precision/recall breakdowns by task type, inability to demonstrate with customer documents, and no human-in-the-loop for low-confidence predictions.

• Why Concord delivers measurable results: Concord's automatic metadata extraction, AI-powered repository search, and integrated clause analysis provide transparent performance metrics you can verify, helping teams achieve real efficiency gains with clear ROI measurement.

The AI revolution in contract lifecycle management has arrived. But separating genuine capability from marketing hype requires rigorous evaluation.

According to Microsoft Azure's technical documentation, their Document Intelligence service achieves 80 percent+ accuracy targets for custom models, yet many CLM vendors claim 95 percent+ accuracy across all document types.

Meanwhile, Stanford's ContractNLI research demonstrates that even sophisticated AI models struggle with contract-specific challenges, particularly negations and exceptions that make legal language uniquely difficult.

This guide provides specific tests, benchmarks, and evaluation scripts to help you cut through AI marketing claims and identify solutions that deliver measurable value with your actual contracts.

Executive summary: the testing imperative

The gap between AI marketing and reality is widening. While breakthrough applications exist, most enterprise implementations fall short of vendor promises.

The reality check on AI claims

Industry success stories provide context for realistic expectations. However, these represent best-case scenarios with optimized data and workflows.

Most organizations experience more modest gains. Research indicates that companies adopting AI-driven contract automation typically see 20-40 percent efficiency improvements, not the 90 percent+ reductions often promoted.

Testing philosophy: treat AI like software purchases

Demand proof with your actual documents. Marketing demos with clean, simple contracts don't reflect real-world complexity.

Your evaluation should include scanned documents, poor-quality images, complex multi-party agreements, and edge cases that break standard patterns. Only testing with your document mix reveals true performance.

Preview: five core AI jobs to evaluate

This guide focuses on five essential AI capabilities in CLM:

1. OCR and metadata extraction from various document types

2. Clause risk flagging with precision and recall metrics

3. Fallback language generation for rejected terms

4. Playbook automation and compliance checking

5. Repository Q&A with natural language queries

Each section provides specific accuracy thresholds, testing protocols, and red flags to identify before making purchasing decisions.

AI job #1: OCR and metadata extraction

OCR technology forms the foundation of AI contract analysis. Without accurate text extraction, downstream AI capabilities fail regardless of sophistication.

What it should do

Convert scanned contracts to structured data including parties, dates, financial amounts, and key terms. Extract metadata consistently across document types and quality levels.

Modern systems should handle phone-captured images, scanned PDFs, and native digital documents with comparable accuracy.

Reality check on accuracy claims

Microsoft Azure Document Intelligence provides transparency on performance expectations. Their documentation recommends targeting 80 percent+ accuracy scores for custom models, with close to 100 percent for critical applications.

However, accuracy varies significantly by document type and quality. Real-world testing shows that even advanced systems struggle with complex layouts, poor scan quality, and handwritten annotations.

OCR Performance Benchmarks by Document Type:

Testing protocol for OCR accuracy

Create a representative test set that matches your actual document mix. Avoid testing only with clean, simple contracts.

Sample Test Dataset:

• 15 native PDF contracts (your actual agreements)

• 15 scanned PDFs at 300 DPI resolution

• 10 phone-captured contract images

• 10 complex multi-party agreements with tables

• 5 documents with handwritten annotations

Accuracy Evaluation Criteria:

• Party names and addresses: >95% accuracy required

• Contract dates (effective, expiration, renewal): >95% accuracy required

• Financial amounts and payment terms: >90% accuracy required

• Clause categorization and indexing: >80% accuracy acceptable

Test each document type separately. Many vendors achieve high accuracy on native PDFs but fail dramatically on scanned or phone-captured images.

Red flags in OCR claims:

Vendors claiming 99 percent+ accuracy without specifying document quality and type are likely overstating capabilities. Real-world accuracy depends heavily on input quality.

Lack of confidence scoring for individual extractions indicates less sophisticated technology. Modern OCR systems provide confidence levels for each extracted field.

Implementation with Concord

Concord's AI automatically extracts key terms immediately upon document upload. The system provides transparency into extraction confidence levels and allows manual verification of uncertain fields.

Test Concord's performance with your document mix during evaluation. The platform handles mixed document types and provides clear feedback on extraction accuracy.

AI job #2: clause risk flagging

Risk identification represents one of the most valuable AI applications in contract management. However, accuracy varies significantly by clause type and training data quality.

What it should do

Identify problematic clauses including unlimited liability, auto-renewal provisions, IP assignment clauses, and termination restrictions. Flag deviations from company standards and highlight terms requiring legal review.

Advanced systems should provide risk scoring with explanations rather than simple binary flags.

Reality check on precision and recall

Stanford's ContractNLI research demonstrates the complexity of contract language analysis. The dataset includes 607 contracts with 17 different hypotheses, revealing how AI systems struggle with legal language nuances.

The precision versus recall problem

Most users prefer high recall (catching all risks) over high precision (fewer false positives). Missing a critical liability clause costs more than reviewing several flagged clauses that aren't problematic.

However, too many false positives reduce user adoption. The optimal balance depends on your risk tolerance and review capacity.

Clause Analysis Performance by Type:

Testing protocol for risk flagging

Use standardized contract datasets for consistent evaluation. The Stanford ContractNLI dataset provides a reliable benchmark with expert legal annotations.

Risk Flagging Test Cases:

Evaluate both recall (did it catch the actual risks?) and precision (how many flags were false positives?). Create a confusion matrix for each clause type.

Success criteria for clause analysis:

• High-risk clauses: >85% recall rate required

• Standard commercial terms: >70% precision acceptable

• Complex legal language: >80% recall for liability and IP clauses

• False positive rate: <30% for practical usability

Advanced testing methodology

Test the system's handling of negations and exceptions. Legal language often uses "except," "unless," and "provided that" constructions that reverse clause meaning.

Example test: "Liability is unlimited except for acts of gross negligence" should not trigger unlimited liability alerts.

Contract language variations pose another challenge. "Net 45 days" and "forty-five days after invoice date" should both flag against a "Net 30" standard.

Red flags in risk flagging:

Claims of 95 percent+ accuracy across all clause types without domain-specific training indicate unrealistic expectations.

Inability to explain why clauses were flagged suggests less sophisticated analysis. Modern systems should provide reasoning for risk assessments.

Implementation considerations

Most effective systems combine AI flagging with human expertise. AI identifies potential issues; legal professionals make final risk determinations.

Concord's clause analysis provides risk scoring with explanations, enabling informed review decisions rather than blind acceptance of AI recommendations.

AI job #3: fallback language generation

Generating alternative clause language represents the most complex AI application in contract management. Most systems offer template substitution rather than true contextual generation.

What it should do

Suggest contextually appropriate alternative language when standard clauses are rejected. Generate fallback positions that maintain legal validity while addressing business concerns.

Advanced systems should understand negotiation context and propose language that addresses specific counterparty objections.

Reality check on generation capabilities

Current AI systems excel at pattern recognition but struggle with creative language generation that maintains legal precision.

Most "fallback generation" consists of template substitution from pre-approved clause libraries. True contextual generation remains challenging for legal language.

Fallback Language Quality Assessment:

Testing protocol for language generation

Create realistic negotiation scenarios that require fallback language. Test the system's ability to maintain legal coherence while addressing business needs.

Sample Test Scenarios:

Evaluate generated language for legal soundness, business appropriateness, and contextual relevance. Have legal counsel review AI-generated alternatives.

Quality assessment framework

Generated language should maintain legal validity while addressing the specific business context. Generic template language often fails to address negotiation nuances.

Generation Quality Criteria:

• Legal validity: Does the language create enforceable obligations?

• Business alignment: Does it address the specific concern raised?

• Contextual appropriateness: Does it fit the overall contract structure?

• Risk balance: Does it appropriately allocate risk between parties?

Human-in-the-loop requirements

Best practice requires legal review of all AI-generated language. The AI should flag low-confidence suggestions for mandatory human review.

Systems should provide reasoning for language choices and highlight areas of uncertainty rather than presenting generated text as definitive.

Concord's approach focuses on clause library integration with fallback options rather than open-ended generation, providing more reliable results for contract negotiation.

AI job #4: playbook automation

Playbook automation compares contracts against internal standards and flags deviations. Implementation requires substantial setup but provides significant value for high-volume contract processing.

What it should do

Automatically compare incoming contracts against company playbooks and flag deviations from standard terms. Provide specific references to violated standards and suggest approved alternatives.

Advanced systems should handle semantic variations of standard language and understand business context for exceptions.

Reality check on setup requirements

Playbook automation requires extensive upfront configuration. Most implementations need 40-80 hours of initial setup plus ongoing maintenance as standards evolve.

The system must learn company-specific language patterns and understand when deviations are acceptable versus problematic.

Playbook Implementation Complexity:

Testing protocol for playbook automation

Create a comprehensive playbook with 10-15 key standards that represent your most important contract requirements.

Sample Playbook Standards:

Governing Law Standard: Must be [Your State] law
Liability Cap Standard: Maximum 1x annual contract value
Payment Terms Standard: Net 30 days maximum
Termination Notice Standard: Minimum 30 days written notice
IP Ownership Standard: Client retains all pre-existing IP
Confidentiality Standard: Mutual obligations required
Auto-renewal Standard: Maximum 1-year renewal terms
Indemnification Standard: Mutual indemnification only
Force Majeure Standard: Standard clause language required
Dispute Resolution Standard: [Your preferred method]

Test the system's ability to identify violations of each standard using contracts that deviate in 3-5 areas.

Advanced semantic testing

Sophisticated systems should catch semantic variations that violate standards. "Net 45 days" should flag against a "Net 30 days" standard even though the exact phrase doesn't match.

Test the system's understanding of business context. Some deviations may be acceptable for strategic partnerships while problematic for vendor agreements.

Semantic Analysis Evaluation:

• Exact match detection: Should be 100% accurate

• Semantic variation detection: Target >90% accuracy

• Context-aware exceptions: Target >80% accuracy

• False positive management: <20% of flagged items

Implementation reality

Most successful playbook implementations focus on a limited set of high-impact standards rather than trying to automate every possible contract variation.

Start with clear, objective standards (dates, dollar amounts, governing law) before attempting subjective judgment automation (reasonableness standards, business appropriateness).

Concord's playbook automation provides systematic deviation flagging with specific references to violated standards, enabling efficient contract review and approval processes.

AI job #5: repository Q&A

Natural language queries against contract repositories represent one of the most user-friendly AI applications. Quality depends heavily on document indexing and semantic search capabilities.

What it should do

Answer natural language questions about your contract portfolio using plain English queries. Handle both factual questions (which contracts expire next quarter?) and analytical questions (what's our average liability cap?).

Provide specific contract references and confidence levels for answers rather than unsupported assertions.

Reality check on query capabilities

Query accuracy depends on document quality, indexing completeness, and question complexity. Factual queries perform better than analytical or comparative questions.

Microsoft's approach to document intelligence emphasizes the importance of structured data extraction as the foundation for effective querying.

Repository Q&A Performance Expectations:

Testing protocol for repository queries

Upload 100+ contracts to create a realistic test repository. Include various contract types, date ranges, and counterparty relationships.

Sample Test Queries by Category:

Verify answers manually for a subset of queries to establish accuracy baselines. Track response times and user satisfaction with answer quality.

Accuracy evaluation methodology

Use the ContractNLI methodology with human-verified answers as ground truth. Create a test set of questions with verified correct answers.

Evaluate not just accuracy but answer completeness and relevance. A technically correct but incomplete answer may not provide practical value.

Query Evaluation Criteria:

• Factual accuracy: >80% for simple queries, >60% for complex analysis

• Answer completeness: Includes relevant contract references and dates

• Confidence scoring: System indicates uncertainty for low-confidence answers

• Response time: <10 seconds for most queries under normal system load

Advanced query testing

Test the system's ability to understand business context and legal nuances. "High-risk contracts" should return agreements with problematic terms, not just high-value contracts.

Evaluate handling of ambiguous queries and follow-up questions. Can the system clarify what you mean by "problematic clauses" or "recent contracts"?

Complex Query Scenarios:

• Multi-part questions requiring information synthesis

• Queries requiring legal interpretation or risk assessment

• Time-based analysis requiring historical comparison

• Cross-contract relationship analysis

Implementation best practices

Repository Q&A quality depends on consistent metadata extraction and document indexing. Poor underlying data quality makes even sophisticated AI systems ineffective.

Focus on clean, well-structured data ingestion before implementing advanced querying capabilities. Garbage in, garbage out applies especially to AI systems.

Concord's repository intelligence provides transparent query responses with contract references, enabling users to verify AI answers and build confidence in system capabilities.

The evaluation scorecard: what good looks like

Establishing clear performance benchmarks helps distinguish genuine AI capability from marketing claims.

Minimum viable performance standards

These benchmarks represent the minimum performance levels required for practical AI deployment in contract management:

Core AI Performance Benchmarks:

Red flag indicators to avoid

These warning signs indicate vendors making unrealistic claims or lacking transparency about system capabilities:

Major Red Flags:

• Claims of 95%+ accuracy without specifying test conditions or document types

• No precision/recall breakdowns by specific task type

• Inability to demonstrate capabilities with customer's actual documents

• No human-in-the-loop options for low-confidence predictions

• Refusal to provide evaluation datasets or testing methodologies

Technical Red Flags:

• No confidence scoring for AI predictions

• Claims of perfect accuracy on all document types

• No discussion of edge cases or system limitations

• Generic demos that don't reflect customer document complexity

Testing best practices for procurement

Always test with your actual contract types and document quality. Marketing demos with clean, simple agreements don't reveal real-world performance.

Demand vendor demonstrations using your documents, not their cherry-picked examples. This reveals true system capabilities and limitations.

Procurement Testing Checklist:

• [ ] Test with actual document mix (scanned, native, phone-captured)

• [ ] Verify accuracy claims with independent evaluation

• [ ] Test edge cases (poor quality, unusual formats, complex language)

• [ ] Evaluate confidence scoring and human review workflows

• [ ] Confirm performance metrics match vendor claims

• [ ] Test integration with existing document management systems

Performance monitoring post-deployment

Establish baseline metrics before deployment and monitor performance over time. AI systems can degrade as document types evolve or data patterns change.

Track user adoption and satisfaction alongside technical performance metrics. High accuracy means little if users don't trust or use the system.

Ongoing Performance Metrics:

• Processing accuracy by document type

• User adoption rates and satisfaction scores

• Time savings and efficiency improvements

• Error rates and manual intervention requirements

• System uptime and response performance

Concord provides transparent performance analytics that enable continuous monitoring and optimization of AI capabilities within your specific contract workflows.

The path forward: implementing AI that works

Successful AI implementation in contract management requires realistic expectations, systematic evaluation, and focus on measurable business outcomes.

Start with clear success metrics

Define specific, measurable goals for AI implementation. "Improve efficiency" isn't specific enough to evaluate success or failure.

Examples of clear success metrics:

• Reduce initial contract review time by 30%

• Identify 90% of liability limitation clauses automatically

• Answer 80% of contract portfolio queries without manual research

• Flag contract deviations within 24 hours of upload

Pilot with limited scope

Begin with a narrow use case where AI can demonstrate clear value. Successful pilots build confidence and provide data for broader implementation.

Focus on high-volume, repetitive tasks where AI can provide immediate efficiency gains. Avoid complex, judgment-intensive processes for initial deployment.

Plan for human-AI collaboration

The most successful implementations combine AI efficiency with human expertise. AI identifies issues and opportunities; humans make final decisions.

Design workflows that leverage AI strengths (speed, consistency, pattern recognition) while preserving human judgment for complex decisions.

Measure and optimize continuously

AI performance can vary over time as document patterns change or new contract types are introduced. Regular monitoring and retraining ensure sustained performance.

Track both technical metrics (accuracy, speed) and business outcomes (time savings, risk reduction, user satisfaction).

Continuous Improvement Framework:

• Monthly performance reviews with accuracy trending

• Quarterly user feedback sessions and workflow optimization

• Annual system evaluation and vendor performance assessment

• Ongoing training data updates and model refinement

Why Concord delivers measurable AI value

Concord's approach emphasizes practical AI implementation with transparent performance metrics and clear ROI measurement.

The platform provides automatic metadata extraction with confidence scoring, AI-powered repository search with verifiable results, and integrated clause analysis with explainable risk assessment.

Rather than promising unrealistic accuracy levels, Concord focuses on delivering consistent, measurable improvements to contract workflows that teams can verify and optimize over time.

Most importantly, Concord's AI capabilities integrate seamlessly with human expertise, providing the efficiency benefits of automation while preserving the judgment and oversight that complex contracts require.

The future of contract management lies not in replacing human expertise with AI, but in combining human judgment with AI efficiency to achieve better outcomes faster. Concord delivers this balance with transparent, measurable results.

Bibliography

• Koreeda, Yuta, and Christopher D. Manning. "ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts." Findings of EMNLP 2021, Stanford University. https://stanfordnlp.github.io/contract-nli/

• Microsoft Learn. "Interpret and improve model accuracy and confidence scores - Azure AI services." https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/concept/accuracy-confidence

• Microsoft Learn. "What Is Azure AI Document Intelligence?" https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/overview

• Microsoft Learn. "Contract data extraction – Document Intelligence." https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/prebuilt/contract

• Microsoft Learn. "Transparency note for Document Intelligence." https://learn.microsoft.com/en-us/legal/cognitive-services/document-intelligence/transparency-note

• Mulyadi, Didik. "Azure AI Document Intelligence Deep Performance Analysis (Extraction Speed and Accuracy)." Medium, March 1, 2025. https://didikmulyadi.medium.com/azure-ai-document-intelligence-deep-performance-analysis-extraction-speed-and-accuracy-bfb22ffcb114

• Stanford Law School. "Professor-Student Collaboration at Stanford Law School Results in the Largest-Ever Public Dataset of Corporate Contracts." https://law.stanford.edu/press/professor-student-collaboration-at-stanford-law-school-results-in-the-largest-ever-public-dataset-of-corporate-contracts/

• Stanford Report. "Stanford Law creates largest-ever public dataset of corporate contracts." https://news.stanford.edu/stories/2025/04/law-school-dataset-sec-material-contracts-corpus