Pharmacovigilance organizations are very interested in and moving rapidly with planning, piloting, and production implementation of intelligent automation solutions that automate tasks (rule-based automation) and/or mimic human-like data interpretation and decision making (machine learning [ML] and artificial intelligence).

Multiple technologies can be applied simultaneously to the same process step.

Implementation of intelligent automation solutions faces challenges regarding quality training data for ML models and regulatory guidance.