In heavily regulated industries, compliance has shifted from a periodic checklist to a continuous challenge, driven by increasing complexity and regulatory demands. Enterprises face mounting pressure with the rise in regulatory workloads and compliance costs, as evidenced by significant GDPR fines across the EU. Traditional AI tools fall short in high-stakes compliance work due to their inability to provide structured reasoning, transparency, and integration with enterprise systems. Instead, a planning-based AI system offers a promising alternative by treating compliance as a structured process rather than a one-time task. This approach enables enterprises to parse regulations, map clauses to internal documents, score compliance gaps, propose specific updates, and maintain audit-ready traceability, all while significantly reducing manual effort and response time. As demonstrated in the aerospace industry, this system transforms compliance from a reactive to a proactive process, ensuring faster understanding, targeted reviews, and effective risk management, ultimately providing a competitive edge in regulatory response. The implementation of such systems is supported by long-context processing, intelligent planning, and built-in observability, offering deployment flexibility and ensuring data security.