In today’s ever changing digital environment, AI has the potential to completely change how businesses function and handle risks. JUMO’s Enterprise Risk Manager Conrad Wilson says that to guarantee AI strengthens rather than undermines a risk management framework, businesses must adopt a strategic, scientifically-minded strategy for incorporating AI into the organisation. This goes beyond simply utilising cutting-edge technology. Here are some principles to consider for integrating AI into risk management frameworks while fostering a culture of risk awareness.

Be aware of how AI affects your company

Artificial Intelligence can transform operations, increase productivity, and create new opportunities. But its influence on a company extends beyond these advantages. AI can change the identification, evaluation, and management of risks with a more scientific approach . It’s a great way to also consistently strive to improve your comprehension of the risks associated with AI. Each industry and company will be differently impacted so flesh these nuances out. 

Understanding how AI-related risks affect Operational, Financial, Strategic, and Compliance risks is critical, as is the need to improve current procedures without starting from scratch. AI shouldn’t be seen and treated as a completely distinct risk category, but rather as a theme within the current risk environment that you should be cognisant of. Include a diverse range of stakeholders (senior and junior) to ensure everyone is on the same page in terms of AI’s potential impact on the business.

Risk identification

Understanding how AI fits into business operations and the unique vulnerabilities it could present is essential to identifying any risks associated with it. This necessitates a thorough strategy that views AI as a risk factor for every facet of business. AI, for instance, can have an impact on operational procedures, financial models, regulatory frameworks, and strategic decisions, to name a few. Why not handle AI risks with the same rigour as conventional risks? 

If your first step in the risk management process fails, you will struggle managing any type of risk. Mix things up by considering different measures for risk identification e.g:

• Facilitating brainstorming sessions
• Sending out risk questionnaires
• Dissecting industry trends and events
• Performing root cause analysis of operational incidents
• Solidifying the combined assurance model

Risk assessment

Risks associated with AI need to be identified and then their respective likelihood and impact needs to be considered. This should be a practical process that improves on current risk assessment techniques rather than developing brand-new frameworks. Your risk appetite and tolerance statement should include Key Risk Indicators (KRIs) connected to artificial intelligence to make sure the company is ready to manage these risks within reasonable bounds. The combined assurance model should seamlessly collaborate to assess and respond to AI-related risks in real-time, making risk management more dynamic and responsive.

You need to understand it, to manage it. Challenge risk assessment findings and what they mean for the business.

Risk treatment

Developing mitigations that address AI-related issues requires making sure they operate well with your current risk management system. Enhancing your present processes is the aim, whether that means putting data governance procedures in place to reduce the possibility of biassed AI outputs, bolstering cybersecurity measures to safeguard AI systems, or making sure 3rd party risks are well understood. AI-related risks should be handled as part of the entire risk management strategy. There’s no need to reinvent the wheel.

Be realistic about risk treatments, some risks need to be accepted, whilst some AI risk factors can’t be left unattended and need to be mitigated.

Risk monitoring and reporting

Risks associated with AI must be continuously monitored, which necessitates supervision of AI systems in order to identify abnormalities in behaviour and new threats. Keep a clear perspective on how AI is affecting your risk landscape by incorporating AI-related KRIs into your risk appetite and tolerance statement. Frequent reporting guarantees that the organisation can react quickly to any changes and that stakeholders are kept informed. In addition to strengthening the incorporation of AI-related risks into your current procedures, this proactive monitoring makes sure that your risk management system is resilient and adaptable.

Risk culture

The effective integration of AI into a risk management framework requires a strong culture that understands risk. This entails creating an atmosphere where staff members are prepared to handle threats associated with AI and are aware of their significance. Creating a risk-aware culture requires education and awareness initiatives that emphasise the role AI plays in risk management and promote candid discussion of possible threats. It’s pivotal to ensure that everyone in the company, from junior staff to senior management, is on the same page when it comes to managing risks associated with artificial intelligence. Preach the ‘everyone is a risk manager’ phrase and instil accountability. Talk openly and freely about the risks and even more exciting – the opportunities!

Final thoughts

It takes more than just implementing new technology to integrate AI into risk management – you also need to improve current procedures in a practical, cohesive manner. A company can leverage AI’s potential benefits while avoiding any negative effects by comprehending how AI affects it, recognising and evaluating risks, and putting in place efficient treatment and monitoring plans. To stay ahead of the curve in this new frontier, any organisation can further ensure that it is well-prepared to navigate the growing risk landscape by embracing a scientific mentality and cultivating a strong culture of risk awareness.

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