How AI Is Changing the Role of Taxonomists

Artificial intelligence (AI) is changing how organizations create, manage and use information and taxonomists are right in the middle of that transformation. While AI can automate portions of classification, tagging and metadata generation, it has not eliminated the need for taxonomy expertise. Instead, it is shifting the taxonomist’s role from primarily building and maintaining controlled vocabularies to designing, governing and evaluating the knowledge structures that increasingly power AI systems.

Traditionally, taxonomists have focused on organizing information through taxonomies, thesauri, ontologies, metadata schemas and other structured vocabularies. Their responsibilities often included identifying concepts, establishing preferred terms, creating hierarchical and associative relationships, maintaining consistency and helping improve search and retrieval.

Those responsibilities remain important. But AI has expanded both the tools available to taxonomists and the consequences of their work.

Machine learning and natural language processing can now analyze enormous collections of documents, identify recurring concepts, detect relationships, recommend tags and assist with classification. Generative AI can accelerate activities that once required significant manual review, including proposing synonyms, generating descriptions and mapping terminology across datasets.

That automation changes where taxonomists provide the greatest value.

Instead of spending as much time manually tagging individual assets or identifying every potential term, taxonomists can increasingly evaluate and refine machine-generated recommendations. Their expertise becomes essential in determining whether an AI-generated relationship actually makes sense, whether terminology reflects the language of the organization and its users and whether classifications are sufficiently accurate and consistent for their intended purpose.

In other words, the work is moving from simply creating structure to governing intelligent systems that use structure.

AI has also made taxonomy even more important beyond traditional search applications. Taxonomies and ontologies can provide semantic context for knowledge graphs, retrieval-augmented generation, enterprise search, recommendation engines, analytics and AI assistants. Organizations need reliable ways to define concepts and relationships so AI systems can better understand what their information means, not merely recognize patterns within it.

That puts taxonomists in a new position with a new perspective. Their responsibilities may now include evaluating AI-generated metadata, developing training and validation datasets, monitoring automated classification, identifying bias or inconsistencies and defining human-review processes.

Arguably the most important, AI has increased the importance of judgment.

An algorithm can suggest that two terms are related. A large language model can propose a definition. An automated classifier can assign a document to a category. But none of those outputs automatically makes the result correct, appropriate, unbiased or useful.

Taxonomists provide the human expertise needed to ask questions like: Does this relationship make sense? Is this terminology accurate? Whose language is represented? What context is missing? How will this classification affect retrieval? Should the AI be trusted to make this decision automatically?

As AI becomes embedded in more information systems, these questions become more consequential.

The future taxonomist, therefore, is unlikely to be someone replaced by AI. It is someone whose responsibilities have moved higher in the information value chain. Routine work may become increasingly automated, but the need for semantic expertise, governance, quality assurance and human judgment is growing.

AI can generate terms, suggest relationships and classify content at extraordinary speed. But speed is not the same as understanding. The taxonomist’s evolving role is to provide that understanding and to ensure that the intelligence organizations build on top of their information is grounded in structure, context and meaning.

Access Innovations helps organizations prepare their content for AI by preserving meaning, attribution, and trust before it ever enters a model. That foundation makes responsible, reliable AI not just possible, but sustainable.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, where smarter AI starts with structured, meaningful, well-governed knowledge.