Use AI to prepare the review packet, not decide the risk
Contract review preparation is a useful AI workflow when the system extracts clauses, summarizes obligations, compares terms against an approved playbook, and flags missing context. It becomes risky when AI interprets legal risk or suggests acceptance without review. NIST AI Risk Management Framework is the right frame because contract workflows need context mapping, risk measurement, control management, and ongoing governance.
PwC Responsible AI survey is relevant because responsible AI requires practical controls, not just policy. In contract workflows, those controls include privilege boundaries, approved clause libraries, escalation rules, and a named legal owner for risk interpretation.
Protect permissions and source authority
Microsoft 365 Copilot data protection architecture is relevant because enterprise AI depends on identity, permissions, data protection, and auditing. Contract review adds another layer: sensitive documents, negotiation strategy, customer terms, vendor terms, and privileged analysis should not be treated as one general search corpus.
IBM Institute for Business Value AI capabilities research reinforces the capability foundation. The system needs reliable data, operating model, adoption, and measurement. AI can highlight clause differences, but the business still needs a maintained contract playbook and review process.
Measure review preparation, not legal replacement
Track extraction accuracy, clause-library match rate, legal correction rate, escalation frequency, and cycle-time reduction. The first goal is a better-prepared human review, not automated legal judgment.
Use AI governance and training to define review boundaries and a QuickStart AI Audit to inspect permissions before contract data enters an AI workflow.
