Automation of the Risk Management Lifecycle with AI and Analytics

Every business in the world today needs a risk management strategy that is proactive, data-driven and collaborative. It must be designed to make life easier for Quality Management and/or risk management leaders – presenting data, risk trends, patterns and risk mitigation “suggestions” at the click of a button.

However, many companies continue to use spreadsheets and emails to manage risks – thinking it will work. It doesn’t. In fact, even companies that are using digital and automated risk management solutions to improve quality metrics are still struggling to spot risks in time to avoid product recalls, tackle regulatory risks and so on. There is a crying need for a more advanced solution.

But before we dive into the requirements of what a more advanced risk management solution looks like, let’s start with the basics. Risk management involves four fundamental steps:

• Identifying the risk
• Assessing the risk
• Mitigating the risk
• Monitoring and reporting the effectiveness of risk control measures

As a business scale, so does the complexity of assessing the risks to prioritize based on frequency and impact. This prioritization is essential for businesses to mitigate the major risks, establish metrics and measures to monitor them, then track and report the effectiveness of these measures.

While there are many tools and solutions available to facilitate risk management, they often fall short and do not deliver on the promise of preventing major quality or safety events, leaving businesses vulnerable.

In this whitepaper, we talk about:

• Common mistakes made when it comes to risk management
• Why risk and compliance must be in sync
• The journey from risk assessment to risk management
• Risk identification and controls