While taxonomies can usually handle applications such as CRM and warehouse management in our experience, more sophisticated tasks using vertical AI agents, for example, may need an ontology. This is because a taxonomy on its own cannot enable AI to reason over data consistently, explain itself (XAI), or ensure reliable answers to natural language queries.
Used together however, taxonomies and ontologies can create a powerful AI trust layer, and be used in tandem with knowledge graphs, vector stores and controlled vocabulary for domain intelligent systems.
What’s the difference between an ontology and a taxonomy?
Both ontologies and taxonomies are machine-readable ways of structuring knowledge. The main job of a taxonomy is to organise things by classifying them in a hierarchy. A new document might get filed in the category ‘invoices’ for example, or a sub-category within that such as ‘supplier invoices’. Taxonomies make tagging, filtering and navigation easier – useful for simple product information management (PIM), for example.
An ontology is more complex and powerful than a taxonomy because it captures the relationships between concepts, which support machine reasoning. A taxonomy might tell you that a certain document is an invoice and that it belongs to accounts payable. An ontology can go further by telling your AI that the invoice belongs to a certain contract, that it refers to a specific purchase order, and that it contains payment terms that need to be taken into consideration. The AI can use these connections between things to make an enterprise system more domain intelligent and useful.
What are the practical benefits of an ontology?
With an ontology, an AI can flag when an invoice exceeds its 30 day payment terms. It can also help with cashflow forecasting by calculating when monies are due, while taking into account the named supplier’s record of prompt payment.
Properly deployed, an ontology can ground an LLM with encoded rules, definitions and policies, to make its responses more accurate and less prone to hallucination. It can clarify the fuzzy meanings of concepts such as ‘customer’ or ‘user’ to enhance semantic interoperability between departments, improving the contextual understanding of AI. It can also enable AI outputs and actions to be audited for compliance purposes, improving explainability by making relationships, rules and assumptions explicit.
Are ontologies and taxonomies really necessary?
Not all use-cases require ontologies and taxonomies. In some instances even a simple controlled vocabulary is enough in our experience. In recent years, however, many enterprises we’ve talked to have discovered that the outputs of their AI experiments with graphs and LLMs aren’t sufficiently accurate or reliable to be trusted with mission-critical tasks. This has caused an ‘AI lag’ where substantial investments in digital transformation are failing to deliver measurable returns.
In a speech at DAVOS in January 2026, Palantir’s Alex Karp argued that large-scale AI deployment requires strong ontological foundations. Karp said: “If you just buy LLMs off the shelf and try to do any of these things that are regulated, it’s not precise enough. What you’re going to see, especially in America, is people trying to do something like Ontology by hand. Once you build a software layer to orchestrate and manage the LLMs in a language your enterprise understands, you actually can create value.”
Some of the latest research backs him up, as do our own experiences in the field. The challenges, and unexpected benefits, of implementing a large-scale enterprise ontology for product catalogue and engineering data was described in a 2025 case study.
How do I build an ontology?
One drawback of ontologies is that they are harder than taxonomies to agree on, design and implement. However, ontology as a service platforms such as our own are now emerging that make the job of building and obtaining consensus on AI trust layers more accessible to non-technical subject-matter experts. A good place to start is by asking SMEs our three key questions.