Ontologies can deliver measurable value in our experience when they work as operational constraints, shaping how AI systems interpret data, make decisions and generate outputs. When ontologies are used merely as reference documentation, which is their traditional function, we usually advise clients that the cost of building and maintaining them is harder to justify.
What is an ontology?
An ontology is a formal, machine-readable model of the key concepts in a domain and the relationships between them. In enterprise settings, ontologies often underpin what we call an organisation’s ‘agreed source of truth’ by providing a shared semantic structure. Ontologies encode these meanings, enabling both humans and AI systems to interpret information consistently in different contexts (interoperability).
An enterprise ontology is usually agreed upon and defined by subject-matter experts (SMEs). Without an agreed source of truth, the intangible value of a business, including its tribal or tacit knowledge, often stays in people’s heads – and is lost when those people depart.
What are ontologies used for in 2026?
Ontologies have long been used in medical research, finance, defence and telecoms. In 2026, they are also being adopted across sectors that seek to automate and embrace agentic AI. The kinds of businesses we deal with – which include localisation businesses, global food cooperatives, travel companies and structural engineering firms – often have significant value hidden away in unstructured paper-based records. Even when this information is digitised and collected in one place, the hallucinations and errors typical of large language models (LLMs) often prevent them from adopting and scaling-up AI services.
With a bit of help from us, ontologies are being used to unify this often scattered and poorly maintained enterprise truth, providing AI with a reliable source of information within constraints that people are more likely to trust.
How are ontologies evolving?
Until recently, ontological approaches have been overlooked while companies experimented with grounding LLMs using methods such as hybrid RAG. These RAG methods focus more on improving the quality of data being ingested by AI, rather than imposing rigorous ontological rules and policies – which require more time and effort, but in our experience deliver higher levels of accuracy.
Combining both approaches in the form of neuro-symbolic AI, is a promising area of innovation that we are actively pursuing. A 2026 study by Zhu et al. argues that LLM agent stacks are flawed for enterprise use if they answer directly from an unrestricted knowledge space. Enterprise ontologies, on the other hand, are shown in the study to enable event-driven reasoning grounded in business context, improving reliability.
How do I create an ontology that delivers value?
A working ontology needs to be agreed upon and owned by the domain’s SMEs. Without SME ownership of the trust layer, systems often fail to deliver value because they are not accurate or explainable enough to be used on mission-critical tasks. Relying on IT engineers to fill in the gaps – an all-too-common shortcut – is no substitute for organised, consensus-driven decision-making by SMEs on what matters in a domain.
A good place to start in the building of a working ontology is to ask SMEs our three key questions, and potentially hire an ontology as a service company (OaaS) to help with the consensus-building approach needed to get an ontology delivering measurable value.
In what ways will ontologies measurably benefit my organisation?
Properly deployed, an ontology can govern AI systems so that audits and compliance checks become faster; automated decisions become explainable; and stakeholders can start to rely on the system’s outputs as a useful source of verified truth. But an ontology is not always required; simpler problems may only require a controlled vocabulary, for instance.