Life Scientists, All Other

Today’s life scientists are getting help from AI on many routine tasks. For example, some biotech labs use AI-driven robots to run experiments and gather data automatically. One company, Recursion Pharmaceuticals, built a “lab operating system” where robots do millions of biology tests every week and AI analyzes the results ^[1].

There are even “cloud labs” (like Emerald Cloud Lab) that scientists access online: researchers send in experiment designs, and AI-controlled robots run them 24/7 and return the data ^[1]. AI also helps with reading and writing work. It can quickly summarize new research papers or find patterns in huge datasets ^{[2] [2]}, so scientists spend less time on searches and more time on ideas.

Importantly, people still guide the work. Experts describe future labs as “self-driving” – machines handle boring, repetitive chores, while scientists focus on the creative and strategic parts of research ^{[1] [2]}.

Life sciences companies are eager to use AI, but adoption varies. Many big firms already invest in AI: a recent survey found 75% of life-science leaders started using AI tools in the last two years ^[3]. They know AI can speed drug discovery and cut costs.

In fact, McKinsey reports that about 75–85% of life-science workflows could use AI to handle parts of researchers’ jobs, potentially freeing 25–40% of scientists’ time for new ideas ^[4]. On the other hand, high-tech lab automation can be very expensive, so smaller labs may adopt it more slowly ^{[1] [3]}. Life-science work is also highly regulated for safety, so any AI must be carefully tested and overseen by humans ^[4].

Finally, because demand for life scientists is growing (the BLS projects this occupation will grow faster than average) ^[5], many companies will still hire human experts even as AI tools become common. In the end, AI is likely to augment life scientists — speeding up data tasks and experiments — while human skills like creativity, judgment, and teamwork remain very valuable ^{[1] [2]}.