The Case For Adding AI to Your Risk Management

Not many firms have unlocked the technology to help them model possible shocks and responses. Those that have say they’re reducing risk exposure — and it’s likely they’ll also see other long-term payoffs.
Oct. 6, 2026
5 min read

Key Highlights

  • AI tools help organizations automate risk detection, anomaly identification and scenario testing, leading to faster and more comprehensive risk management.
  • Leadership teams using AI for risk management are more likely to see reductions in exposure and gains in revenue and efficiency.
  • Effective AI deployment requires human oversight, cross-divisional collaboration and clear high-level goals to mitigate risks associated with AI misuse or errors.
  • A structured, incremental approach — setting goals, measuring progress and scaling — is recommended for successful AI integration in risk management.
  • Long-term benefits include cost savings, quicker response times, enhanced scenario modeling and improved employee morale as staff transition to more analytical roles.
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Artificial intelligence tools automate many tasks and speed up work that teams across your organization handle daily. But are you also using it to better manage risk and thus take potential work — the kind that brings with it the sharpest headaches — off the table altogether?

A recent study by consulting firm PwC on how companies are creating value from deploying AI showed that one-fifth of all firms are capturing about 75% of revenue and efficiency gains. One of their edges: Their leadership teams are up to 2.5 times more likely to use AI for risk management, be it to bolster their cybersecurity, help their financial teams or test their supply chains. And they’re more than twice as likely to say that doing so has reduced their exposure to risks.

Christopher Wright, the Global CFO Solutions and Business Performance Improvement leader at consulting firm Protiviti, says the blocking-and-tackling advantages of AI when it comes to risk management are the same as those of the technology broadly. It can run faster and cover more ground than individuals, including by generating synthetic data for finance teams to use in scenario planning. It can automate processes and unearth anomalies humans might miss or would catch much later. And it doesn’t need to sleep.

On the flip side, deploying AI in risk management brings with it the same risks as AI tools deployed anywhere else do. A bot could go rogue, venture beyond its tasks (internally or externally) or share sensitive information. Instead of monitoring risk, it could become one.

Crucial to avoiding such a nightmare scenario, said Wright: Having humans on the front end and the back end of the process and in between, checking and controlling the work. And those people should contribute from many different divisions of the company beyond finance. Wright said this is a clear instance where “having more up-front thinking by a larger group of people is better.”

The experts at PwC have the same advice. As crucial as great risk management is to organizations, their leaders can’t fully automate the task. Bringing AI to bear in finance and risk management is about enlarging the scope and depth of the work in that fuzzy area where reporting, forecasting and scenario planning meet. It’s about adding analysis and walking through possible responses at a scale that would be tricky to staff for and whose costs would be difficult to justify.

“Effective scenario planning requires domain expertise and judgment from business, policy, geopolitical, cyber, risk, and operational leaders, combined with AI-enabled models and digital twins,” PwC analysts wrote. “This combination can help companies see disruptions earlier, model effects on a key node, supplier, or input, and bring timely intelligence closer to the CEO and board.”

Small Bites and Honeycombs: How to Structure Your Approach

If you read that and thought your team is well behind in adoption, you’re not. PwC’s research shows that only 23% of CEOs say their teams have used AI to identify how possible changes in their supply bases, swings in commodity prices or changes in demand might affect their operations. Even in the still-new world of AI, this area still feels quite unexplored.

So how to embark on your AI journey in risk management? Wright says it’s important to set high-level goals first: Is the primary goal of any project to cut costs or speed up processes? From there, he said, the leadership teams getting this right are often following this three-step process:

  • Decide on key performance indicators
  • Quickly measure the progress being made
  • Just as quickly decide if you’ll run that process elsewhere and, if so, where

This “honeycombing” approach is not revolutionary, but it works well in the current AI environment, when returns on investment are tricky to calculate — a recent Protiviti report showed that 65% of finance organizations rate their ability to measure the ROI on their AI initiatives as “less than effective” — and new model iterations can substantively change what’s available and possible. In Wright’s view, the cycle is quicker than other investment decisions, and success comes in bite-sized portions.

You can go from reporting on the past to knowing in real time where things stand. That lets people move from being clerical to being analytical.

- Christopher Wright, Protiviti

The potentially far bigger payoffs to stronger risk management are down the road. Leadership teams that have more deeply modeled different scenarios and have a better handle on how they can absorb shocks and take advantage of upheaval will save money when those shocks arrive. Shorter response times, fewer strategic or tactical tweaks made on the fly and a generally higher speed of execution will produce substantial savings.

Another long-term benefit to keep in mind is the likely impact on morale and engagement as finance employees refine their use of AI tools while hitting broader risk management targets.

“You can go from reporting on the past to knowing in real time where things stand,” Wright said. “That lets people move from being clerical to being analytical.”

And that can grow into an important factor in ROI calculations: If you’re able to have team members graduate from clerical and administrative roles into higher-value analytical ones, it’s likely they’ll be happier and less likely to leave. Collect a few retention success stories and the hiring and training costs saved quickly add up. That’s an outcome every CFO will sign up for in a heartbeat.

About the Author

Geert De Lombaerde

Geert De Lombaerde

Contributor

A native of Belgium, Geert De Lombaerde joined EndeavorB2B in September 2021 to cover public companies, markets, and economic trends primarily for IndustryWeek, FleetOwner, Oil & Gas Journal, T&D World, and Healthcare Innovation. His work focuses on strategy, leadership, capital spending, and mergers and acquisitions, and he also works with Endeavor Business Intelligence on surveys and data projects.

Geert has been in business journalism since the mid-1990s. With a degree in journalism from the University of Missouri, he began his reporting career at the Business Courier in Cincinnati, initially covering retail and the courts before shifting to banking, insurance, and investing. He later was managing editor and editor of the Nashville Business Journal before being named editor of the Nashville Post in 2008. He led a team that helped grow the Post's online traffic by an average of more than 15% annually before joining Endeavor.

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