P2P

P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 6 27 and populate multiple summary spreadsheets. Quality control would then rely on supervision and spot-checking. The process would be time- and labor- intensive, expensive, and prone to multiple secondary reviews. An AI-native workflow incorporates AI extraction followed by paralegal and attorney validation against source documents, with clear escalation protocols for ambiguous provisions. The budget also reflects this architecture: reduced hours for manual extraction, increased hours for validation and analysis, and explicit technology costs. The staffing model emphasizes attorneys with subject matter expertise who can evaluate AI outputs rather than armies of reviewers performing initial extraction. And quality control checkpoints are embedded throughout: sampling protocols for extraction validation, escalation criteria for complex provisions, and documentation standards for AI-assisted conclusions. This builds feedback loops where corrections to AI outputs improve future performance. And it produces deliverables that are structured for ongoing maintenance rather than one- time analysis, because AI-native architecture recognizes that these reviews are a continuous process, not a distinct project. To provide a practical takeaway, the following sample intake checklist demonstrates the key questions legal teams should address when designing an AI-native matter. • What AI tools are permitted for this matter under client guidelines and confidentiality constraints? • What is permissible AI use (extraction, summary drafting)? • What data sources will be used, and how are version conflicts reconciled? • What structured outputs are required (fields, taxonomy, format), and who will review them first? • What validation approach will be used (spot-checking, multi-tier review, issue escalation protocol), and who signs off? • How does AI change matter staffing (validation reviewers, escalation counsel, legal project management teams, knowledge management and innovation attorneys, AI training specialists)? • What documentation artifacts must exist at close (error and verification logs, review workflow charts)? • How will AI use be communicated to the client, including billing expectations and required disclosures?

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