Why Trust in AI Outweighs Speed in Legal and Compliance Workflow

According to an Exterro blog post, while artificial intelligence (AI) is advancing at an unprecedented speed in legal, compliance, and regulatory workflows, trust in AI often outweighs speed. As AI becomes central to decision-making in sensitive contexts, its value depends on whether outputs are transparent, auditable, and controllable, which ensures defensibility under scrutiny from courts or regulators.

Unlike other legal department functions where minor errors may be tolerable, inaccuracies in legal or compliance contexts can dismantle entire cases or undermine regulatory submissions. AI must be explainable and accountable. This is reinforced by recent regulations such as the EU AI Act and new US executive orders. Legal teams must be able to trace how an AI reached its conclusions, verify sources, and retain oversight of final decisions. The true differentiator in enterprise AI, therefore, is not speed or performance but trust built on transparency, auditability, and control.

Transparency ensures AI systems “show their work.” By linking outputs to citations, references, or logs, teams gain confidence in AI-assisted findings and can defend results before courts or regulators. Auditability takes this further by documenting every action—what data was reviewed, who approved the results, and how exceptions were handled—creating a digital chain of custody for AI-driven processes. This record-keeping strengthens accountability.

Control keeps human judgment at the center. Legal professionals must retain control over AI outputs, ensure that sensitive data remains within the organization and is not shared with third parties, and apply organizational policies and regulatory obligations to maintain compliance with relevant laws and regulations. With these safeguards, legal professionals can focus on strategy, judgment, and defensibility.

While trust in AI may outweigh speed, it is not a checkbox; it’s a competitive edge. Organizations that build transparency, auditability, and control into their AI workflows can move faster with confidence and credibility, while those that do not risk losing both speed and defensibility.