The chemical and biochemical sciences are undergoing a profound digital transformation that is giving rise to the emerging paradigm of the digital analytical sciences, driven by an increasing need for research data digitalization, structuring, and standardization. This Trends article provides a bird’s-eye view of how FAIR (Findable, Accessible, Interoperable, Reusable) data ecosystems are evolving from administrative guidelines into a critical enabler of modern analytical discovery. Because major advances in artificial intelligence (AI) depend fundamentally on structured and openly accessible scientific data, we discuss how the analytical sciences are now laying the corresponding infrastructural foundations needed to support the next generation of data-driven research. However, in domains such as biophotonics and advanced spectroscopy, large, comprehensively annotated experimental datasets remain limited, and algorithmic advances alone cannot fully compensate for data scarcity or poor standardization. To address this challenge, we examine the rapidly emerging field of physics-informed deep learning, in which physical laws and domain knowledge are incorporated directly into AI pipelines. By leveraging quantum-chemical simulations and transfer-matrix optics to generate synthetic pretraining data, these hybrid approaches can improve the robustness and generalizability of predictive models trained on limited experimental datasets. Finally, we discuss the emerging role of scientific representation learning and argue that realizing the full potential of AI in chemistry will require continued advances in hybrid algorithms alongside a sustained commitment to FAIR data governance and open scientific data infrastructures.