Biological Scientists, All Other

Biologists often spend time doing experiments and analyzing data, and some of that work is already helped by machines and AI. For example, many biologists use custom software tools for research projects, and they routinely analyze large genetic or protein datasets using computer algorithms ^{[1] [1]}. In modern labs, robots and AI algorithms are speeding up routine steps.

One report noted that robotic lab systems “can perform experiments continuously without human fatigue, significantly speeding up research,” and that integrated hardware+AI setups are “enhancing the efficiency, accuracy and reproducibility” of lab work ^{[2] [3]}. In clinical labs, for example, automatic incubators and AI analysis let scientists handle many more samples faster and more safely ^{[3] [2]}. At the same time, experts emphasize that human insight remains crucial.

Researchers note that scientists still need to design the experiments, interpret results, and guide the AI tools ^{[2] [1]}. In short, AI today tends to augment biologists by doing tedious tasks or crunching big data, but biologists’ creativity and judgment are still needed for the hard problems.

Many life-science labs are eager to try AI because it can speed up discoveries and handle big data. In fact, one industry survey found that a majority of research labs plan to start using AI within the next few years ^[4]. These labs are upgrading their technology to make AI possible – for example, 81% of them now use electronic lab notebooks (up from 66% the year before) so they have clean digital data for AI tools ^[4].

Companies say the goal is no longer just saving time: digital lab tools are now seen as a way to “accelerate innovation” and new breakthroughs ^[4]. However, adoption is happening step by step. Advanced lab robots and AI software can be very expensive, and labs report needing more trained staff to use them.

For example, scientists often have to build custom software for each project ^[1], so one-size-fits-all AI tools don’t always exist yet. Regulation and ethics are not seen as big barriers (guidelines for lab work already exist) ^[4], but getting people up to speed is a challenge. Overall, most experts agree that AI will be a helpful “coworker” for biological scientists, not a replacement.

Students who keep learning science, data skills and problem-solving will remain in demand ^{[2] [4]}, while AI handles more routine analysis and gives scientists new insights.