Modern AI systems are only as effective as the information they consume. Unfortunately, much of that information is messy.
Taxonomies and other structured knowledge models have long helped organizations make sense of complex data. When paired with data management tools, they support everything from content discovery and analytics to personalization, integration, and lifecycle governance of both structured and unstructured data assets.
As AI becomes increasingly embedded in enterprise workflows, this foundational role hasn’t diminished. It’s evolved.
Most organizational data, as much as 80% according to some estimates, is unstructured. Tools powered by large language models (LLMs) offer a compelling way to tap into this data, something that, historically, has been difficult to do at scale. However, they come with risky trade-offs: hallucinated answers, inconsistent outputs, and limited explainability.
These gaps highlight the need for human-centered structures like taxonomies. By anchoring AI in standardized, well-modeled concepts, organizations make AI insights more trustworthy and useful.
The Role of Taxonomies and Related Models in a Modern Data Ecosystem
The simplest way to understand a taxonomy is as a structured hierarchy of concepts. But their value goes far beyond categorization.
A well-designed taxonomy provides standardized terminology that reflects how an organization talks about and defines its work, whether that’s products, processes, customer data, or content. It creates a shared language that can be applied across systems and departments and forms a consistent, reusable model for organizing information.
In modern data ecosystems, these models are machine-readable structures based on defined rules (often called schemas). This turns taxonomy from a reference tool into an operational asset.
Knowledge graphs build on this foundation. While implementations vary, they all share a core structure: nodes that represent entities or concepts and edges that define relationships between them.
Thanks to Ontotext for the knowledge graph example
These graphs are populated with real data, ranging from structured sources like product specs to unstructured ones like customer support call transcripts, making them powerful tools for linking and contextualizing information across domains.
Together, taxonomies and knowledge graphs provide structure, context, and meaning to enterprise data. Their standardized formats and defined schemas make them algorithm-friendly, giving AI and advanced analytics tools something solid to work with.
How Taxonomies and Knowledge Graphs Enhance AI Performance
Taxonomies and knowledge graphs improve AI performance by providing well-organized information and standardized terminology, critical tools for reducing ambiguity and improving accuracy. Their value plays out along two distinct paths: by enabling emerging AI techniques like GraphRAG and by supporting foundational data operations, including governance, quality control, and semantic consistency.
Graphs + Retrieval Augmented Generation, or GraphRAG, is a technique that improves LLM performance by combining it with an internal knowledge source. Instead of relying solely on what the model was trained on, GraphRAG retrieves information based on the relationships between concepts within a knowledge graph. This means AI outputs are guided by verified business concepts, so they generally hallucinate less and generate results that are more explainable and enterprise-relevant.
Historically, taxonomies have been used to organize and label information, bringing consistency to how data is classified, stored, and accessed. This traditional role is far from obsolete. In fact, data quality is more important than ever. As organizations navigate AI’s expanding influence, the ability to trace, audit, and validate data inputs through information architecture and its tools (including taxonomies) becomes a strategic necessity.
The core principle at play here is “things, not strings.” Labels (strings) may be identical, but they might represent dramatically different concepts (things). Generative AI models can’t inherently distinguish between identical terms used in different contexts. For example, without semantic scaffolding, “mercury” the element and “Mercury” the planet look the same.
Taxonomies and knowledge graphs provide this scaffolding, which embeds meaning into terms and surfaces the relationships that define them. This added layer of semantic intelligence helps reduce misinterpretation and improves the overall reliability of AI-generated outputs.
Customer Contact Data
Analyzing call transcripts, chat logs, and other customer touchpoints is a common use case for generative AI. These tools can surface valuable insights like where a customer is in their journey, what they’re trying to achieve, how they feel, and how satisfied or engaged they are.
But without structure, the output is messy.
Feeding raw contact data into a GenAI model often results in inconsistent or conflicting labels for key data points. That’s because these models are non-deterministic: the same input can generate unique outputs each time. Instead of streamlining operations, this creates extra work. Teams are left stitching together unstandardized data in labor-intensive, ad hoc ways, and that’s if usable analysis is even possible.
By contrast, when customer journeys and associated data fields are modeled using taxonomies, the output is standardized by design. This enables scalable, repeatable analytics that can power dashboards, surface trends, and guide decisions.
And this isn’t just a customer contact problem! It applies to analytics across the board. Wherever consistent labeling and terminology are enforced, the result is cleaner, more trustworthy data that’s ready for modeling, forecasting, and decision support.
Assessing Gaps in Enterprise Data and Enabling Governance
One of the most powerful benefits of the GraphRAG technique is explainability. Rather than just return answers, they point to the exact data sources behind them. This builds confidence in results and creates transparency around how information is used. That transparency can be extended even further when enterprise data is labeled with a consistent taxonomy. A well-developed subject taxonomy acts as the backbone of the information model, allowing organizations to track which concepts and content are being referenced by AI tools and which aren’t.
This makes it possible to identify high-demand topics, spot content gaps, and prioritize where new data or documentation is needed. In other words, user interactions with GenAI become a rich, passive feedback loop for understanding and improving the organization’s data landscape.
Beyond generating insights, this also supports stronger data governance. With taxonomy-labeled source data and explainable outputs, organizations gain the traceability and oversight needed to drive responsible AI use and compliance at scale.
Building Smarter Systems Through Structured Knowledge
Taxonomies, knowledge graphs, and related semantic models bring structure and clarity to enterprise data by standardizing terminology, contextualizing information, and organizing knowledge at scale. Far from legacy tools, they’re foundational to today’s AI-enabled ecosystems.
As AI capabilities continue to scale and shift, organizations need more than automation. They need alignment.
Semantic models offer a way to ensure that enterprise data isn’t just processed but understood, trusted, and applied with intent. They make it possible to build systems that are intelligent, consistent, responsible, and adaptable.
If your organization is investing in AI, it’s time to ask: Is your data ready to support it? Structured knowledge might be the missing piece.
Schedule a data strategy call with Factor to assess your semantic foundation and identify where taxonomy and knowledge models can unlock greater value.
