Meet the maker: Aoife May on AI, regulatory agility, and the future of compliance

Human expertise improves AI-driven compliance by adding regulatory context, business judgment, applicability assessment, and accountability. AI can summarize information and suggest patterns, but compliance professionals determine whether the output is accurate, relevant, and appropriate for the institution.

Expert review is especially important when AI supports regulatory change management, policy updates, control changes, risk assessments, or audit evidence. Combining AI speed with human expertise helps institutions improve productivity without weakening compliance governance.

Compliance teams should use AI responsibly by applying it to defined use cases, grounding outputs in trusted information, requiring human review, and documenting decisions. AI should help compliance professionals work faster and more consistently, but it should not independently make regulatory interpretations or final risk decisions.

Responsible AI use in compliance requires clear governance, data protection, role-based access, output validation, and accountability. Teams should start with controlled workflows such as regulatory research, change monitoring, summarization, obligation analysis, task routing, and documentation support.

Regulatory agility means the ability of a financial institution to identify, assess, and respond to regulatory change quickly and effectively. It requires more than monitoring new rules. Institutions also need to determine applicability, assign ownership, update policies and controls, track implementation, and document completion.

AI and automation can support regulatory agility by reducing manual research, summarizing changes, identifying potential obligations, and improving workflow visibility. Strong governance and human oversight ensure that speed does not come at the expense of accuracy or accountability.

Trusted AI for compliance is AI designed to support regulated compliance workflows with reliable, explainable, and reviewable outputs. It combines AI-enabled assistance with authoritative content, expert oversight, workflow controls, evidence capture, and human accountability.

The goal is to help compliance teams improve speed, consistency, and documentation while keeping final decisions with qualified professionals. Trusted AI is especially useful for regulatory change management, obligation management, policy review, compliance monitoring, and risk management.

AI can support regulatory change management by helping teams monitor regulatory updates, summarize changes, identify possible obligations, assess likely impacts, and organize implementation tasks. This can help compliance teams reduce manual effort and respond more efficiently to regulatory change.

Human judgment remains necessary because regulatory change decisions require interpretation, business context, risk evaluation, and accountability. Compliance professionals should validate AI outputs, confirm applicability, approve next steps, and document the rationale before acting.

Human oversight is important in compliance AI because regulatory decisions often involve legal interpretation, operational impact, customer outcomes, and examiner expectations. AI can assist with research and analysis, but it cannot replace professional judgment or institutional accountability.

A human-in-the-loop process helps ensure AI outputs are reviewed, corrected if needed, approved, and documented. This reduces the risk of inaccurate conclusions and supports stronger audit readiness, regulatory confidence, and governance.