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The unescapable rise of machine learning (ML) and artificial intelligence (AI) challenges the role of existing text analytics techniques such as Named Entity Recognition and Natural Language Processing in extracting information from scientific text. Often these rely on underlying ontologies to provide the semantic foundation for more complex linguistic and statistical analysis. This paper investigates how ontologies and ontology-led text analysis fits with emerging ML/AI algorithms and the synergies brought by combining the two approaches. We highlight real-world use-cases from across the Pharmaceutical and Life Science sector where SciBite’s text analytics systems have been employed to create next-generation enterprise data infrastructure for many of the world’s leading companies.