The Challenge

A global life sciences company was experiencing long cycle times in its quality investigation (QI) escalation process, the time between receiving a product complaint and resolving the issue. This process is critical for identifying potential adverse events and implementing corrective actions to ensure patient safety.

However, the escalation process was becoming increasingly strained due to:

• A high volume of daily quality records

• Complex cross-team communication

• Large amounts of unstructured data to review

In the context of fast-moving global health challenges like COVID-19, delayed escalations could have severe consequences for patients, regulators, and the company’s reputation.

The Solution

Common Sense AI developed an intelligent automation solution powered by Natural Language Processing (NLP) to accelerate decision-making in the QI process.

Our system analyzed historical quality records and learned to predict, at an early stage, which cases were likely to escalate. This enabled teams to focus attention earlier and take timely action on high-risk events.

The model was embedded in a fully automated pipeline, ensuring fast, scalable, and consistent processing across all incoming records, while reducing human workload and improving traceability.

The Result

The company achieved a dramatic reduction in cycle time:

• From 3–4 weeks down to less than 1 week

• Improved early detection of critical issues

• Reduced manual effort through intelligent triage

• A more agile and responsive QI process, crucial for safeguarding product quality and public health

This solution demonstrates how targeted AI, when paired with automation, can deliver measurable impact in regulated, high-stakes environments.