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26 AI WORKFLOW PLANNING: TAKING THE RISK UPFRONT For sophisticated matters, legal teams should begin to design matters with defined verification checkpoints. Incorporating predetermined checkpoints in the matter workflow where designated reviewers validate AI outputs before they advance to subsequent stages. This concept reflects established principles of quality control in complex operations. These checkpoints help maintain professional responsibility standards, ensuring that attorneys exercise the supervision and competence obligations that bar authorities increasingly emphasize in AI contexts. They may also provide the necessary feedback that improves AI use and builds internal playbooks over time. The key architectural insight is that these checkpoints must be designed into the workflow from the outset, not added reactively when concerns arise. An architecture that clearly defines where AI operates, where human validation occurs, and how AI-driven and manual processes interact is more risk-averse than an ad hoc approach. AI MATTER DATA GOVERNANCE: THINK TWICE Perhaps no aspect of AI-era legal technology use receives less attention than it deserves: the role of matter-level data governance in enabling effective AI use. AI tools require clean, consistent, well-structured data to function effectively. When fed duplicated files, inconsistent metadata, or poorly classified documents, the output becomes unreliable; or worse, it creates rework and write- offs. Poor matter data governance does not just increase the likelihood of errors; it undermines the budget, timeline, and the quality of matter delivery overall. For legal matter architecture, this means that AI data governance principles must inform how documents are collected, organized, and maintained throughout the engagement. For example, data centralization by creating one reliable source of truth for matters becomes essential. When teams operate from a shared foundation, duplicate tracking disappears, reporting becomes faster, and verification becomes cohesive. Standardization of metadata, naming conventions, and classification approaches enables AI tools to process matter information consistently and create consistent, easily verifiable outputs. And retention and data stewardship policies that were once primarily about risk management now become prerequisites for effective matter management. EXAMPLES FROM PRACTICE: DOCUMENT REVIEWS The easiest way to see the proposed matter architecture at work is to examine a common legal matter example. Consider a large-scale document review. Traditional intake focuses on file volume, deadline, review team size, and billing structure. An AI-native approach leads with the team defining structured data fields that AI will extract: termination provisions, assignment restrictions, change of control clauses, and governing law. The document collection process ensures consistent file naming and organization that enables AI processing. A traditionally structured matter would assign teams of attorneys and paralegals to review each agreement, extract key terms, Standardization of metadata, naming conventions, and classification approaches enables AI tools to process matter information consistently and create consistent, easily verifiable outputs.

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