AI-Powered Contract Extraction & Obligation Management: A Strategic Implementation Guide

Contracts govern business relationships: vendor agreements, customer contracts, partnership deals, employment terms, real estate leases, service agreements. Each contract contains commitments, obligations, rights, and terms that organizations must track and manage. Renewal dates approach. Service level agreements require monitoring. Payment schedules must be followed. Termination rights expire. Auto-renewal clauses trigger unless action is taken. Insurance requirements need verification. Compliance obligations demand attention.

Yet most organizations manage contract obligations through manual, ad-hoc processes. Legal teams maintain spreadsheets tracking renewal dates. Procurement professionals set calendar reminders for key contracts. Department heads try to remember which vendors have committed to specific SLAs. Finance manually tracks payment terms and schedules. This fragmented approach creates persistent problems: obligations get missed, renewals happen by default when renegotiation would have been better, favorable terms go unenforced, commitments aren’t monitored proactively, and critical contract knowledge lives only in individuals’ memories.

The consequences are measurable. Contracts auto-renew at unfavorable terms because no one noticed the deadline. Vendors miss committed SLAs without consequence because tracking is inconsistent. Payment terms favorable to the organization go unenforced. Insurance or compliance requirements aren’t verified, creating risk exposure. Intellectual property protections expire unnoticed. Termination rights that would allow exiting poor relationships go unexercised because the window passes without awareness.

Traditional contract management approaches help but don’t solve the core problem. Contract management systems store documents centrally but still require manual entry of key terms and obligations. Legal teams manually read contracts to extract important provisions. Creating and maintaining spreadsheets of contract obligations consumes time and becomes outdated as contracts are amended or new contracts are signed. The fundamental challenge remains: extracting structured data from unstructured contract text and proactively managing obligations over time.

LLM-powered contract extraction and obligation management systems can address these challenges comprehensively by automatically extracting key terms, dates, and obligations from contracts, organizing commitments into trackable obligations, proactively alerting teams before deadlines or required actions, enabling strategic analysis across contract portfolios, and creating searchable contract knowledge bases. But this use case requires careful implementation to ensure extraction accuracy for legally binding documents, maintain appropriate human oversight for high-stakes contract decisions, and build trust among legal and business stakeholders.

Is This Use Case Right for Your Organization?

Identifying the Right Business Problems

This use case makes strategic sense when your organization faces specific, measurable contract management challenges:

Contract obligations are missed or managed inconsistently. If your organization regularly discovers missed renewal deadlines, forgotten commitments, or untracked obligations after the fact (often when a problem arises or through audit findings) you have systematic contract management gaps. Calculate the cost: How many times in the past year were contracts auto-renewed when renegotiation would have been preferable? How often did vendor performance issues go unaddressed because SLA commitments weren’t being tracked? What has inconsistent obligation management cost in missed opportunities, vendor disputes, or compliance issues?

Contract portfolio visibility is limited. If leadership cannot quickly answer questions like “What are our total contract commitments across vendors?” “Which contracts have auto-renewal clauses?” “What SLAs have we committed to customers?” or “Which contracts expire in the next 90 days?”, you lack strategic visibility into contract portfolios. This limited visibility prevents strategic decision-making about vendor consolidation, contract standardization, risk management, or spend optimization.

Manual contract review and data extraction consume excessive time. If legal, procurement, or operations teams spend substantial time reading contracts to find specific terms, manually entering contract data into spreadsheets or systems, or researching what your contracts say when questions arise, you’re bearing high operational costs. Calculate attorney time spent on contract review for extraction purposes, procurement time maintaining contract spreadsheets, and business team time hunting for contract terms when needed.

Contract knowledge is fragmented and person-dependent. If understanding your contract obligations depends heavily on specific individuals (the attorney who negotiated the deal, the procurement manager who handles that vendor, the business leader who signed the partnership) you have knowledge concentration risk. When those people are unavailable or leave, critical contract knowledge disappears. If onboarding new team members requires extensive contract review because institutional knowledge isn’t captured systematically, you’re paying repeatedly for the same information.

Strategic contract opportunities are missed. If you discover favorable negotiating positions too late (bulk pricing thresholds you nearly reached, competitive alternative vendors, or leverage points from multi-contract relationships) you’re missing strategic opportunities. Without aggregate view of contract portfolios, strategic decisions about vendor consolidation, renegotiation timing, or contract standardization lack adequate information.

Compliance and audit requirements create burden. If demonstrating contract compliance (for customer audits, regulatory reviews, or internal audits) requires substantial manual work compiling information from contracts, you face operational burden and potential compliance risk if obligations weren’t tracked comprehensively.

When This Use Case Doesn’t Fit

Be realistic about when this approach won’t deliver value:

Contract volume is genuinely minimal. Small organizations with 10-20 simple contracts can manage obligations manually without sophisticated systems. Don’t over-invest in automation for truly simple contract portfolios.
Contracts are extremely simple and standardized. If all contracts follow identical templates with minimal variation, simple database entry may suffice without extraction automation. Automation adds most value when contracts vary and contain complex terms.
Negotiation and drafting are your priority, not management. Some organizations focus contract effort on negotiation and drafting with minimal ongoing obligation management. If contracts are largely “sign and forget” with few ongoing obligations, management automation delivers less value than negotiation or drafting assistance.
You lack legal/procurement expertise to validate. Contract extraction must be validated by people with sufficient expertise to assess whether AI captured terms accurately. If you lack this expertise, automation is premature.
Regulatory constraints prevent automated extraction. Some industries or contexts have restrictions on how contracts can be processed or analyzed. Understand regulatory requirements before implementing automated extraction.

Measuring the Opportunity

Quantify the business case before proceeding:

Missed obligation costs: Calculate the cost of missed renewals that should have been renegotiated, untracked vendor SLA breaches, forgotten commitments discovered later, or expired rights and protections. Even preventing a few significant missed obligations annually can justify substantial investment.
Manual extraction and tracking time: How many hours monthly do legal, procurement, and business teams spend manually reviewing contracts for specific terms, maintaining tracking spreadsheets, or researching contract provisions? Calculate at loaded rates. For legal teams especially, this time is expensive.
Contract portfolio optimization value: What would better contract visibility enable? Estimate the value of vendor consolidation opportunities, better negotiation leverage, strategic renegotiation timing, or more favorable terms achievable with comprehensive contract intelligence.
Risk reduction: What would proactive obligation management be worth in reduced compliance risk, avoided vendor disputes, protected intellectual property, and maintained insurance coverage? Calculate potential losses prevented.
Faster contract research: When business questions require contract review (“Can we do this under our partnership agreement?” “What are our payment terms with this vendor?”) how much time is currently spent finding answers? Faster contract research enables faster business decisions.

A compelling business case shows ROI within 18-24 months (longer than some use cases given implementation complexity) and demonstrates clear connection to risk reduction, strategic contract management, and operational efficiency rather than just time savings.

Designing an Effective Pilot

Scope Selection

Choose a pilot scope that proves value while managing complexity:

Select a specific contract category for initial extraction. Don’t try to extract data from all contract types simultaneously. Pick one category:

Vendor or supplier contracts (service agreements, purchase agreements)
Customer contracts (SaaS agreements, service contracts, licensing agreements)
Partnership or channel agreements
Real estate leases
Employment or consulting agreements
Specific high-value contract category (top 20 vendors by spend)

Choose contracts with clear business value in tracking. Ideal pilot candidates:

Contain obligations requiring ongoing monitoring (SLAs, deliverables, payment terms, renewal dates)
Represent meaningful business relationships (significant spend, critical services, important customers)
Have sufficient volume to demonstrate value (20+ contracts minimum)
Currently cause pain through manual tracking or missed obligations
Have clear stakeholders who will use extracted information

Define specific data elements to extract. Be precise about what information matters:

Critical dates:

Contract effective date and term length
Renewal dates and notice requirements
Termination dates and rights
Payment due dates
Milestone or deliverable dates

Financial terms:

Payment amounts and schedules
Pricing terms and escalation clauses
Volume discounts or tier pricing
Penalties or late fees
Incentive or bonus structures

Performance obligations:

Service level agreements and metrics
Deliverables and timelines
Quality standards or specifications
Response time commitments
Uptime or availability guarantees

Rights and restrictions:

Intellectual property ownership
Confidentiality obligations
Non-compete or exclusivity terms
Audit rights
Termination rights and conditions

Risk and compliance terms:

Insurance requirements
Indemnification provisions
Limitation of liability
Regulatory compliance obligations
Data protection and security requirements

Establish validation approach. Extraction accuracy must be verified:

Have legal or procurement professionals review all extracted data initially
Compare extraction to manual review for subset of contracts
Identify extraction errors and patterns
Validate that nothing critical is missed
Assess whether confidence scores accurately reflect extraction certainty

Document current state baseline. Before implementing anything, measure: time spent manually tracking contract obligations, number of missed obligations in past year and their cost, time to find specific contract information when questions arise, and contract portfolio visibility (can leadership answer strategic questions about contracts?).

Pilot Structure

A typical pilot runs 10-14 weeks with clear phases:

Weeks 1-3: Contract Collection and Data Definition

Gather pilot contracts in various formats (PDFs, Word documents, scanned images)
Finalize specific data elements to extract with legal/procurement input
Document what “correct” extraction looks like for each data element
Identify sample contracts representing range of complexity
Set up extraction system with requirements and examples

Weeks 4-8: Extraction and Validation

Run extraction on all pilot contracts
Have legal/procurement experts review all extracted data
Track extraction accuracy by data element type and contract characteristics
Identify patterns in extraction errors or missed information
Refine extraction approach based on findings
Test extraction on contract amendments and related documents

Weeks 9-11: Obligation Management Testing

Load extracted obligations into management system
Set up alerts and notifications for upcoming deadlines
Test stakeholder workflows (who gets notified when)
Gather feedback on usefulness and accuracy
Validate that obligation tracking meets business needs
Measure time savings versus manual tracking

Weeks 12-14: Assessment and Stakeholder Review

Analyze extraction accuracy across data elements and contract types
Calculate time savings and business value realized
Review complete findings with legal, procurement, and business stakeholders
Assess whether extraction quality meets standards
Identify requirements for scaling
Make go/no-go decision

Success Criteria

Define clear metrics before starting:

Extraction accuracy: AI should achieve 90-95%+ accuracy on critical data elements like dates, parties, and financial terms. Less critical elements might accept 85%+ accuracy. Different data elements have different accuracy requirements: dates must be nearly perfect, while general obligation descriptions can tolerate more variation.

Completeness: Extraction should capture all material terms and obligations. Missing critical obligations is worse than extraction errors because missing information creates risk exposure. Target 95%+ completeness for critical obligations.

Time savings: Contract data extraction and entry should be 70-90% faster with automation than manual review and data entry. A contract taking 45 minutes for manual extraction should require 5-10 minutes for review and validation of AI extraction.

Usability of extracted data: Extracted information must be actionable: stakeholders can use it to manage obligations, make decisions, and answer business questions. If extraction is technically accurate but not practically useful, the system fails.

Obligation management effectiveness: The pilot should demonstrate that proactive obligation management works: alerts arrive with adequate notice, stakeholders take appropriate action, missed obligations decrease measurably.

Stakeholder confidence: Legal, procurement, and business teams must trust extracted data enough to rely on it for contract management. If stakeholders feel compelled to verify everything manually, automation doesn’t deliver value regardless of technical accuracy.

The pilot succeeds when it demonstrates high extraction accuracy, substantial time savings, and genuine stakeholder adoption for contract obligation management in the pilot category.

Scaling Beyond the Pilot

Phased Expansion

Scale deliberately based on pilot learnings and contract complexity:

Phase 1: Expand to all contracts in pilot category. If you piloted with top vendor contracts, extend to all vendor contracts. Process higher volumes while maintaining validation sampling. Build operational processes and stakeholder confidence.

Phase 2: Add similar contract types with comparable structure and terms. From vendor service agreements, expand to other commercial agreements. Similar contracts share terminology, structure, and obligation types, making expansion more predictable.

Phase 3: Extend to different contract categories with distinct characteristics. Customer contracts differ from vendor contracts; employment agreements differ from both. Different categories may require adapted extraction approaches and separate validation.

Phase 4: Add contract amendments and modifications. Once base contract extraction works reliably, extend to tracking amendments, addenda, and modifications that change original terms. This requires more sophisticated tracking of how terms evolve over time.

Phase 5: Build advanced analytics and intelligence. Move beyond extraction to strategic insights:

Portfolio analysis (aggregate spend, risk exposure, obligation concentration)
Contract comparison (benchmarking terms across similar contracts)
Negotiation intelligence (what terms are typically negotiated, leverage points)
Risk identification (unfavorable terms, missing protections, concerning patterns)
Optimization opportunities (vendor consolidation, standardization, renegotiation priorities)

Technical Requirements for Scale

Production contract management systems require robust capabilities:

Document processing. Handle diverse contract formats:

Native PDFs (text-based documents)
Scanned PDFs (requiring OCR)
Word documents (various versions)
Image files (scanned paper contracts)
Email attachments and embedded documents
Multi-language contracts if applicable

Extraction sophistication. Production systems need:

Understanding of legal terminology and contract structure
Relationship extraction (which obligations apply to which parties)
Conditional term extraction (obligations triggered by specific conditions)
Cross-reference resolution (terms defined elsewhere in contract)
Amendment and modification tracking (how terms change over time)
Confidence scoring (how certain is the extraction for each element)

Obligation management capabilities. Beyond extraction:

Calendar and alert system for upcoming deadlines
Workflow automation (routing obligations to responsible parties)
Status tracking (obligation fulfilled, pending, missed)
Escalation logic (what happens if obligations aren’t addressed)
Documentation of obligation fulfillment
Reporting on obligation performance

Integration requirements. Production systems should connect with:

Contract management platforms (storage, versioning, access control)
Financial systems (payment tracking, invoicing)
Procurement systems (vendor management, purchase orders)
CRM systems (customer contract terms)
Calendar and task management tools
Legal practice management systems

Search and retrieval. Enable fast contract research:

Natural language search across contract portfolio
Clause or provision search (find all contracts with specific terms)
Comparative search (which contracts are most favorable on particular terms)
Relationship search (all contracts with specific party)
Semantic search (find similar provisions even with different wording)

Organizational Requirements

Technology enables extraction, but organizational processes determine value:

Establish clear ownership and accountability. Define who’s responsible for:

Validating extraction accuracy for different contract types
Managing obligations extracted from contracts
Updating system when contracts are amended
Ensuring new contracts are processed
Acting on alerts and notifications

Build stakeholder workflows. Different teams need different information:

Legal teams need contract terms, compliance obligations, and risk exposure
Procurement needs vendor obligations, pricing terms, and renewal dates
Finance needs payment terms, billing schedules, and financial commitments
Business units need SLAs, deliverables, and performance obligations
Create appropriate views and notifications for each stakeholder

Maintain data quality and currency. Establish processes for:

Regular validation sampling to ensure extraction accuracy doesn’t degrade
Updating extracted data when contracts are amended
Flagging contracts requiring re-extraction after system improvements
Archiving or removing data from terminated contracts
Quality assurance reviews by contract type

Create contract intake processes. Ensure all contracts get extracted:

Integrate extraction into contract execution workflow
Route newly-signed contracts for extraction automatically
Track which contracts have been extracted versus pending
Handle backlog of historical contracts systematically
Process amendments and modifications promptly

Legal, Compliance, and Risk Considerations

Contract extraction and management involve legally binding documents requiring appropriate handling:

Accuracy and Liability Considerations

Extracted contract data informs business decisions with legal and financial consequences:

Extraction errors can have serious consequences. Wrong dates lead to missed deadlines. Incorrect financial terms cause payment disputes. Missed obligations create contractual breaches. Implement appropriate safeguards:

Human validation of critical extracted data (especially for high-value contracts)
Confidence scoring highlighting uncertain extractions
Clear disclaimers that extraction assists but doesn’t replace legal review
Regular accuracy audits and quality assurance
Incident processes for when extraction errors are discovered

Liability for reliance on extracted data. Organizations must determine:

What level of reliance is appropriate on extracted data
When human verification is required before taking action
What approval processes exist for contract-based decisions
How errors are identified and corrected
Whether vendors providing extraction technology have appropriate liability limitations

Professional responsibility. In some contexts:

Lawyers must exercise independent professional judgment
Reliance on AI extraction must be competent and reasonable
Understanding of AI limitations and appropriate oversight is required
Professional liability insurance may have questions about AI use

Access Control and Confidentiality

Contracts contain sensitive business information:

Contract confidentiality. Contracts often include:

Proprietary business terms and pricing
Strategic partnership information
Confidential deal structures
Sensitive customer or vendor information
Information subject to non-disclosure obligations

Appropriate access controls. Implement:

Role-based access (not everyone should see all contracts or extracted data)
Need-to-know restrictions for sensitive contracts
Audit logging of who accesses which contract information
Data protection appropriate to sensitivity
Compliance with confidentiality obligations in contracts themselves

Third-party service provider considerations. If using external extraction services:

Understand where contract data is processed and stored
Ensure confidentiality protections in vendor agreements
Consider on-premises deployment for sensitive contracts
Validate vendor security and confidentiality practices
Maintain audit trail of external data processing

Regulatory and Industry-Specific Requirements

Different industries have specific considerations:

Healthcare organizations must handle contracts containing protected health information (PHI) under HIPAA with appropriate safeguards.

Financial services firms have specific requirements around contract documentation, record retention, and regulatory examination readiness.

Government contractors may have classified or controlled unclassified information (CUI) in contracts requiring specific handling.

Public companies must consider whether contract information is material non-public information (MNPI) requiring special protection.

Data Retention and Records Management

Contract data has specific retention requirements:

Legal retention requirements. Contracts and extracted data must be retained according to:

Statute of limitations for contract enforcement (typically 3-10 years after expiration)
Industry-specific regulations (some sectors require longer retention)
Litigation hold requirements (preservation when litigation is reasonably anticipated)
Regulatory examination requirements

Version control and audit trails. Maintain:

Original contract documents with extraction
Extraction version history (how extracted data changed if re-extracted)
Amendment tracking (how contracts evolved over time)
Decision records (actions taken based on extracted obligations)
Audit trail of who accessed what information when

Monitoring, Observability, and Continuous Improvement

System Performance Tracking

Monitor extraction quality and business value delivery:

Extraction accuracy metrics:

Accuracy by data element type (dates, financial terms, obligations, parties)
Accuracy by contract type and complexity
Completeness (percentage of material terms captured)
Confidence score calibration (do high-confidence extractions prove more accurate?)
Error patterns (what types of contracts or terms cause difficulties?)

Operational metrics:

Contracts processed successfully
Processing time per contract
Contracts requiring manual intervention
Extraction validation time (human review required)
Backlog of contracts awaiting extraction

Obligation management metrics:

Obligations tracked and alerts generated
Obligations fulfilled on time
Missed obligations and their causes
Alert response time (when stakeholders act on notifications)
Obligation fulfillment documentation

Business Impact Measurement

Connect contract management to business outcomes:

Risk reduction:

Decrease in missed contract obligations (renewals, deadlines, commitments)
Reduction in vendor disputes from untracked SLA breaches
Improved compliance with contractual commitments
Better protection of contractual rights and remedies

Financial impact:

Contract cost savings from better negotiation leverage
Avoided auto-renewals at unfavorable terms
Enforcement of favorable payment terms
Identification of volume discount opportunities
Penalties recovered for vendor non-performance

Operational efficiency:

Time savings on contract extraction and data entry
Faster contract research when business questions arise
Reduced time in contract negotiations with better visibility to existing terms
Improved onboarding efficiency with accessible contract knowledge

Strategic capability:

Portfolio visibility enabling strategic decision-making
Vendor consolidation opportunities identified
Contract standardization informed by portfolio analysis
Better risk management through aggregate obligation visibility

Dashboards for Different Audiences

Create appropriate views for different stakeholders:

Legal teams need extraction accuracy monitoring, contracts requiring review, obligation tracking for legal commitments, and research capabilities across contract portfolio.

Procurement teams need vendor contract terms, renewal dates and notice requirements, pricing terms and opportunities, SLA tracking, and vendor performance against commitments.

Finance teams need payment terms and schedules, financial commitments and exposure, billing terms, and contract values for budget planning.

Business unit leaders need obligations relevant to their area, upcoming actions required, contract terms enabling or constraining business activities, and strategic contract portfolio insights.

Executive leadership needs high-level contract portfolio metrics, risk exposure, strategic opportunities, and program ROI.

Continuous Improvement Process

Establish regular cadences for enhancement:

Weekly operational monitoring ensures smooth operation: contracts processed on time, extraction quality maintained, obligations tracked appropriately, stakeholders receiving alerts.

Monthly accuracy reviews examine recent extractions: validation sampling results, error patterns identified, difficult contract types, areas needing improved extraction, user feedback on accuracy.

Quarterly strategic reviews assess:

Business value delivered (obligations prevented from being missed, strategic insights enabled)
Extraction coverage and gaps (what contract types should be added)
Process improvements (workflow optimization, integration opportunities)
Stakeholder satisfaction and adoption

Annual contract portfolio analysis uses extracted data for strategic insights: terms benchmarking, risk concentration, standardization opportunities, negotiation strategy improvements, vendor relationship optimization.

Adaptation Strategies

Contract extraction must evolve with business needs:

New contract types and terms. As business evolves:

New products or services create new contract types
Evolving business models introduce new terms
Changing regulations create new compliance obligations
Strategic shifts emphasize different contract provisions
Extraction must adapt to capture newly-important information

Improved extraction accuracy. Continuously refine:

Address extraction errors through improved examples or logic
Enhance handling of difficult contract language or structures
Improve extraction of specific data elements showing lower accuracy
Extend extraction to additional contract provisions as priorities expand

Enhanced obligation management. Evolve beyond basic tracking:

More sophisticated alert logic (multiple notices, escalation)
Better workflow integration (automatic task creation, approvals)
Predictive analysis (obligations at risk based on patterns)
Automated fulfillment tracking and documentation

Connecting to Your AI Strategy

This use case delivers maximum value when integrated with your broader AI strategy:

It should address documented strategic priorities. Contract management affects risk exposure, vendor relationships, customer commitments, and financial obligations. If contract visibility, obligation management, or risk reduction are strategic priorities, this use case directly supports them.

It builds organizational capability for document intelligence. Successful contract extraction teaches how to extract structured data from complex legal documents, handle domain-specific language and concepts, maintain appropriate accuracy for high-stakes information, and build stakeholder trust in AI-extracted data. These capabilities transfer to other document-intensive processes.

It creates contract intelligence infrastructure. Once contracts are systematically extracted and organized, you can build additional capabilities: contract drafting assistance using institutional knowledge, negotiation playbooks informed by portfolio analysis, risk scoring across contract portfolios, or automated contract generation for standard agreements.

It demonstrates AI’s value in legal and procurement. Successful contract management shows AI can handle complex legal documents, support rather than replace professional judgment, deliver measurable business value in traditionally manual domains, and build confidence in AI for other legal or procurement applications.

It generates insights about contracting practices. Aggregate contract data reveals patterns: commonly negotiated terms, standard vs. custom provisions, favorable vs. unfavorable terms, risk concentration, and relationship dynamics. These insights inform contracting strategy, template development, and negotiation approaches.

It enables proactive contract management. Organizations shift from reactive firefighting (responding to missed obligations) to proactive management (systematically tracking and managing commitments), from fragmented knowledge to institutional memory, and from individual dependence to organizational capability.

Conclusion

AI-powered contract extraction and obligation management deliver clear value when they address genuine challenges around missed obligations, limited contract visibility, time-consuming manual extraction, or fragmented contract knowledge. The technology enables systematic extraction and tracking that manual approaches cannot match at scale, but success depends on high extraction accuracy for legally binding documents, appropriate human oversight for high-stakes contract decisions, and genuine stakeholder adoption for obligation management.

Before pursuing this use case, confirm it addresses documented business challenges: costs from missed obligations, lack of strategic contract portfolio visibility, substantial time spent on manual contract review and tracking, or knowledge concentration risk. Define success criteria emphasizing both extraction accuracy and business value: time savings AND reduced missed obligations. Run thorough pilots with comprehensive validation that prove extraction meets accuracy standards for contract data and that obligation management workflows serve actual business needs. Scale deliberately with continuous quality assurance.

Most importantly, view this use case as part of your broader contract management and AI strategy. Contract extraction should enhance professional contract management, not replace legal or procurement judgment. The contract intelligence infrastructure you build, the institutional knowledge you capture, and the proactive management capabilities you establish should create compounding value beyond immediate extraction efficiency. Done well, automated contract extraction and obligation management becomes a strategic capability that enables better risk management, superior vendor and customer relationships, and proactive contract portfolio optimization: differentiating your organization through contract management excellence that protects business interests, optimizes commercial relationships, and ensures commitments are systematically tracked and fulfilled rather than discovered through costly failures or missed opportunities.

AI-Powered Contract Extraction & Obligation Management: A Strategic Implementation Guide