For us at ComplianceQuest, the value of AI in quality and safety management is tangible. We are leveraging intelligent automation and decision support capabilities to truly and practically improve various quality processes, including complaint handling, audit management, nonconformance (NC) and CAPA, safety incident handling, etc. Overall, using AI also plays a key role in enhancing the user experience, while driving effectiveness and efficiency into the entire QMS and SMS workflow.
The AI models and what lies “under the hood” can be complex, but for the end-user the question always is: What problem do we solve? What is the pain point we alleviate?
We built CQ.AI to help business leaders implement hyper-automation in their quality and safety processes. Formally, hyper-automation can be defined as an end-to-end approach to making use of an ecosystem of automation-related technologies to automate all business processes that can or should be automated.
When it comes to AI-enabled decision support, semi-autonomous decisions are ideal, considering that AI is used to save time yet having a human-in-the-loop takes care of any possible issues or AI-induced risks. It can be used to analyze trends, develop data consistency, and provide forecasts to improve business decisions. Additionally, using predictive analytics to garner insights from large data sets and unstructured data is extremely useful for quality (and safety) leaders.
In this whitepaper, we talk about:
- AI to enhance the complaints management process
- Nonconformance and CAPA made more efficient with AI
- Driving continuous improvement with AI-powered quality & safety audits
- Quicker investigations with AI-powered safety incident management
- AI-powered user experience to save time and increase adoption
- And more