Legal teams are often cast as the obstacle to AI adoption, but the resistance is usually rational: confidentiality obligations, privilege concerns, unclear data retention terms, and professional responsibility rules that were not written with generative tools in mind. This session reframes the conversation, offering a practical path for introducing AI capability into legal and contracting workflows without creating exposure. We'll examine which legal tasks are genuinely low-risk entry points — clause comparison, first-pass redlines against a playbook, summarizing negotiation history — and which require human control end to end. We'll also discuss vendor diligence questions legal should be asking, how to evaluate training-data and confidentiality terms, and how to write internal use guidance that people will actually follow rather than quietly ignore.

Takeaways

• Identify defensible starting use cases for AI within legal and contracting functions
• Evaluate AI vendor terms for confidentiality, retention, and training-data risk
• Draft usable internal guidance that balances enablement with professional obligations