A global tech leader reimagined how it managed control and risk—starting from the ground up.
What sparked the need for change?
Boards and regulators expect more than box-checking. They expect risk programs that can move at speed and scale. Our client set out to deliver just that.
Its goal? Address rising regulatory complexity and converging global risks—while supporting business growth into new markets and products.
To bring this vision to life, the company reimagined its risk management program from the ground up—rebuilding the risk architecture, foundational data, and taxonomies to create a stronger base for consistency, transparency, and scale. At the heart of the transformation was a connected system of technology tools powered by a single source of truth, giving the business the ability to streamline compliance and elevate risk management across the organization.
What solution did the teams unlock by working together?
The unified risk management system made it possible for PwC’s engineers to work with the client to quickly build suites of AI agents—developed in line with Responsible AI practices—that fit directly into the platform and redesigned how core processes, like defining controls and managing issues, were carried out.
The company’s priority was strengthening control descriptions—the foundation of the value chain. When controls don’t reflect reality, they can erode executive confidence, create regulatory risk, and may limit program effectiveness. PwC addressed this with agents that can check descriptions against standards, flag gaps or ambiguities, and help confirm controls align with how the business actually operates.
Building on this, PwC introduced an agentic issues workflow in which a set of agents standardize issue descriptions, flag duplicates early, and recommend severity, ownership, and remediation steps. By learning from past resolution times and control criticality, the agents help reduce backlog, improve consistency, and give the client greater confidence in risk signals.
Where did tech innovation meet human ingenuity?
This project wasn’t just about rolling out new tech. It was about rethinking how risk is managed and freeing people from tedious, manual work. Agentic AI cleaned up issue descriptions, translated them into clear, consistent language, and matched them to the right categories. It also flagged duplicates early to reduce backlog and improve data quality, then went further by suggesting ownership, risk severity, and remediation steps. But the real breakthrough came from combining AI’s speed with human judgment—freeing teams to focus on higher-value analysis and decisions. The result: a smarter, faster process that gave people confidence and raised the bar for managing controls.
What was the real-world impact of approaching things differently?
The impact was clear. Duplicate issues dropped by 40%, reducing noise and backlog for teams. Remediation timelines improved by more than 20%, so risks were addressed faster. Documentation became more consistent, making reviews and audits easier, while control alignment rose 25%, strengthening the link between issues and the right risk frameworks. Errors in documentation were cut in half, boosting accuracy and reliability. And manual effort to maintain data quality fell by about 60%, freeing teams for other work.
Leaders now have access to real-time dashboards with standardized, high-quality data, giving them sharper visibility into risk. Ultimately, the project sets a new standard for how control and regulatory risk can be managed at scale.
