5 ways we’re transforming artificial intelligence into impact
Merck put out a useful “AI-in-the-real-world” listicle with five concrete places it’s applying AI across the company. It starts with drug discovery: using AI/ML foundation models to uncover disease patterns, identify better drug targets, and speed up molecular design and early testing before moving candidates into clinical trials.
The rest of the list is about operational leverage: using predictive models to improve clinical trial site selection, patient matching, and retention (including flagging likely dropouts); deploying internal generative/agentic AI tooling (a proprietary platform used by most of the workforce) to automate and simplify workflows; and modernizing manufacturing via faster event-based supply chain risk assessments plus computer-vision inspection of vials/syringes to catch defects.
Finally, it describes applying AI to medical education and field engagement: embedding AI across the content lifecycle (including medical/legal/regulatory review) and using a generative-AI chatbot to help field reps summarize relevant info and respond in real time to provider needs. The closing section is a good reminder that none of this works without high-quality data pipelines: the “boring” work of structuring, organizing, and governing data is what makes higher-level AI applications reliable.