The AI advantage hiding in risk management

The value from AI is concentrated. In PwC’s recent AI performance study of more than 1,200 companies, spanning regions around the world and 25 sectors, 20% capture roughly three-fourths of AI-driven revenue and efficiency gains. One overlooked contributor to their success: using AI for risk management. These top performers are 2.3 times as likely as others to report that AI has helped reduce their exposure to risk.

Their edge comes in part from where they’re applying it. In areas like cybersecurity, financial and supply chain risk management, and modelling threats and responses to climate risk, these companies were 1.5 to 2.5 times as likely as others to be using the technology. Many of these processes can be time- and labour-intensive, requiring teams to consolidate and make sense of information from a wide array of sources. With the right level of oversight, AI can make those processes far more efficient and accurate. 

For example, AI-enabled solutions already help organisations monitor risk and compliance more effectively by tracking unusual patterns, collecting data and evidence, and generating reports. AI-enabled solutions can scan the horizon to anticipate risks, synthesizing external data with internal risk indicators and insights to spot where a company's business or physical operations may face threats, down to the level of individual clients, locations, products, or transactions. 

The technology can also generate synthetic data to make scenario-planning richer by, for example, modelling policy or geopolitical shifts and their potential impact on company operations. And it can make regulatory submissions more accurate and consistent. 

In all these applications, reviewers still need to confirm findings and investigate exceptions, but the combination of automation and human judgement helps teams cover more ground faster, with clearer audit trails and better insights. 

Here’s how other companies can capitalise on AI for risk management:

Think end-to-end. Many companies start by automating existing workflows. But the biggest gains happen when teams reimagine their processes and integrate AI across the full set of risk processes, from alert generation and investigation to decision-making and reporting. 

Build trust at the beginning. Many companies use a parallel-testing approach, running AI side-by-side with traditional methods and keeping experienced reviewers in the loop. This approach builds trust, produces the compliance documentation that regulators require, and helps teams pinpoint exactly where automation adds value. 

Overcome barriers to adoption. Teams can be hesitant about AI for many reasons: cost, data, privacy, environment readiness, trust. It’s essential to overcome these barriers. Some organisations partner with secure tech providers, test in controlled pilots, and scale only when the results are verified—all with the aim of balancing speed with accountability. 

Accelerate, don’t just automate. Many risk teams never saw the kind of digital investment or transformation that customer-facing functions experienced. AI now gives these teams a way to catch up by reengineering processes for speed, quality, and consistency. The goal is more than just greater efficiency. It’s integrating risk functions more directly into business operations so they can help organizations manage higher-risk activities, spot opportunities, and operate confidently in complex markets.