How AI Is Changing Corporate R&D in 2025 and Beyond

What AI Has Changed

Speed and scale at the front end. Tasks that previously required weeks — surveying literature, identifying researchers, analyzing large experimental datasets — can now be compressed to hours or days.

Hypothesis generation in structured domains. In drug discovery and materials science, generative AI models are proposing novel molecular structures, predicting material properties, and reducing experimental search spaces.

Knowledge management. AI tools can now index, surface, and synthesize institutional knowledge from past research programs — making findings from three-year-old engagements accessible.

What AI Has Not Changed

The need for domain expertise. AI tools augment researchers; they do not replace the scientific judgment required to design meaningful experiments, interpret ambiguous results, or navigate the boundary between what is known and what is not.

The value of the research question. AI raises the productivity of research, but only if the underlying program is well-designed. The quality of the question still determines the quality of the answer.