Contract volume isn't shrinking — it's just changing shape. For legal teams drowning in supplier agreements, NDAs, and due diligence document sets, AI-powered contract review promises relief, but the gap between a polished vendor demo and a working production system is wider than most firms expect. This episode of Law draws on this in-depth guide to automating contract review with AI agents to map out what a real, end-to-end deployment actually looks like — friction, tradeoffs, and all.

The episode walks through each stage of building an AI contract review workflow, from initial scoping through long-term maintenance, covering what firms consistently get wrong and what separates a system people actually use from an expensive tool that gets abandoned:

• Define the use case before touching a vendor. Contracts aren't interchangeable — the narrower and more specific your starting objective (flagging indemnity clauses, extracting governing law provisions, identifying change-of-control triggers), the more likely the deployment is to succeed.
• Evaluate platforms on your documents, not their pitch decks. Accuracy on realistic samples, processing speed at genuine volume, clause-level customization, and data security all matter more than feature lists.
• Training data quality is non-negotiable. Feeding a model years of mixed-quality, poorly annotated archives produces a liability generator, not a force multiplier. Clean, current, carefully labeled documents are the foundation everything else depends on.
• Calibration is iterative, not a one-time setting. Tuning sensitivity too high floods reviewers with false positives; tuning it too low lets real problems slip through. Finding the right threshold is an ongoing process, not a launch milestone.
• Integration determines adoption. An AI review tool disconnected from existing document management, matter management, and approval workflows will be abandoned. The system has to live where the work already happens, with intelligent alert thresholds so it reduces noise rather than adding to it.
• Post-launch maintenance is an operational commitment, not an afterthought. Models drift as drafting practices and regulatory requirements evolve. Retraining schedules, validation gates, staged testing environments, and careful feedback management are what keep a well-launched system performing over time.

The through-line of the episode is a useful corrective to the "plug-and-play" framing that surrounds much of the AI-in-law conversation: the firms seeing real results aren't treating this as a product installation — they're treating it as a system that requires design, sustained attention, and genuine operational discipline. For listeners interested in the broader question of how AI fits into legal workflows, the episode AI Orchestrators in Law: Smarter Workflows Without Losing the Human Edge is a natural companion listen.

Law