The science industry is on the brink of another transformative year as artificial intelligence (AI) and machine learning (ML) continue to redefine the contours of research and development across life sciences, chemicals, and materials. In 2024, the fusion of digitalization and data is pushing AI to the next level as it continually enables faster innovation and deeper insights. In healthcare, AI is expected to leave the lab and enter the clinic. The first AI-discovered drug candidates are now undergoing clinical trials, a potential watershed moment for pharmaceutical development. AI tools from companies like PathAI are demonstrating how patient diagnostics and profiling can be dramatically improved through machine learning models trained on large, diverse datasets. According to Alister Campbell, VP Global Head of Science at Dotmatics, this year could mark the point when the life sciences sector begins to “see the fruits of digitalization.” Companies are starting to leverage previously siloed data for advanced drug repurposing—reclaiming compounds that may have failed clinical trials but still hold therapeutic potential. This more nuanced approach could both reduce R&D costs and improve patient outcomes. AI’s impact is also accelerating in the chemicals and materials industry, where it's being used to optimize material performance, support sustainable innovation, and reduce emissions in traditionally carbon-intensive sectors. As companies pivot to greener R&D processes, AI is a key enabler in speeding up the discovery of eco-friendly alternatives and improving return on investment. Driving much of this transformation is the maturation of large language models (LLMs) and generative AI, which are becoming more capable, multimodal, and scientifically literate. These tools are increasingly involved in early product design, testing, development, and even post-market analysis. However, this expansion is not without its hardships as a dramatic GPU shortage, evolving regulatory frameworks, and growing concerns around intellectual property protection are challenging the scalability of AI in science.