Gartner predicts that by 2027, organizations that prioritize semantics in AI-ready data will increase agentic AI accuracy by up to 80% and cut costs by up to 60%. For life sciences data leaders, that prediction points to a specific architectural choice: a semantic layer and knowledge graph, not just more data or a bigger model.
Gartner's AI agent predictions: accuracy fails without context, not data
Most life sciences organizations don't have a data shortage. They have internal research databases, clinical systems, regulatory submissions, and licensed datasets, alongside public sources. The constraint on agentic AI isn't access to that data; it's whether an agent can understand what the data means well enough to act on it correctly.
That is the gap Gartner called out at its Data & Analytics Summit in London in May 2026. Speaking on the summit stage, Rita Sallam, Distinguished VP Analyst at Gartner, said agentic AI outcomes depend on context, including the semantic representations of data, and that without a clear understanding of the relationships and rules inside an organization's data, agents are far more likely to hallucinate, introduce bias, and produce unreliable results. Gartner's conclusion was direct: by 2027, organizations that prioritize semantics in AI-ready data will see agentic AI accuracy improve by up to 80% and costs fall by up to 60%.
That is a striking spread for a single architectural decision. Semantics, not model size or data volume, is the lever Gartner is pointing to. Here's why that's the case.
What does "semantics" mean in an agentic AI context?
A semantic layer is a layer of meaning placed over raw data: the business definitions, relationships, and context that let a system, human or AI, understand what data represents rather than just where it's stored. Gartner's point is that traditional schema-based data models don't carry this. A table of compound identifiers or a field labeled "diagnosis code" tells a system almost nothing about what that value means, how it relates to other concepts, or which of several coexisting naming conventions it follows.
A knowledge graph is the structure that typically carries this meaning at scale. It models data as entities and the relationships between them, for example a target linked to a pathway, linked to a disease, linked to a compound, so information can be traversed and reasoned over instead of retrieved as isolated rows. Gartner is describing the same underlying requirement from the AI-governance side, sometimes called context AI: agents need a context layer with semantic coherence, or they will keep failing in expensive, hard-to-audit ways.
Two problems that make life sciences semantics challenging
Life sciences data has two properties that make the semantics gap worse than in most industries, and both map directly onto what Gartner is warning about.
The language problem: The same concept is routinely described differently across sources. One system records "EGFR," another "ErbB1," a third "HER1," all referring to the same target. Disease classifications vary between ICD-10, MedDRA, and internal terminologies. This isn't inconsistent data management; multiple naming conventions coexist by design across a fragmented vendor and standards landscape. An agent querying across sources without semantic mediation will either miss the connection entirely or, worse, treat two records of the same entity as unrelated facts.
The relationship problem: A gene links to a pathway, which links to a disease, which links to a compound, which links to an adverse event. These relationships cross domains, systems, and organizational boundaries. Relational databases can store each piece, but they don't make the connections between them naturally traversable. An agent asked to reason across that chain needs the relationships modeled explicitly, not left for it to infer from disconnected tables.
Gartner's warning is that agents operating without this kind of context don't just underperform; they produce answers that look plausible and are wrong, which is a governance and cost problem as much as an accuracy one. Sallam framed it plainly: "Context with semantic coherence will become a cost-control and trust strategy, not a nice-to-have."
What a context layer looks like in practice
Gartner advises data and analytics leaders to establish a context layer as a core part of their infrastructure, built on semantic representations of data rather than schema alone. In life sciences, that context layer typically needs to do two things at once:
- Resolve terminology: Recognize that "EGFR," "ErbB1," and "HER1" are the same concept, and that a search for a broad category like "neoplasm" should reasonably include subcategories such as "carcinoma" or "lymphoma."
- Model cross-domain relationships: Capture the links between genes, pathways, diseases, and compounds as a queryable graph, so an agent can traverse from one to the next instead of stopping at the boundary of a single source.
Gartner also expects regulators to demand greater semantic transparency over time, and boards to treat semantic governance as both a strategic risk and a competitive opportunity. For a regulated industry already accountable for provenance and traceability in submissions and safety reporting, that raises the stakes on getting this right rather than retrofitting it later.
A look into exploring in DISQOVER, checking provenance, and exporting data
Where DISQOVER fits
This is the specific gap DISQOVER is built to close. DISQOVER is ONTOFORCE's AI-ready semantic data platform for life sciences, built on a knowledge graph, that connects internal and external data silos into a single, searchable ecosystem for both human researchers and AI agents. It addresses the language problem through semantic mediation, which resolves synonyms and hierarchy within concepts, and the relationship problem through cross-domain relations captured in its knowledge graph, so an agent or a researcher can move from target to pathway to disease to compound in one traversal. Every result carries traceable provenance back to its source, version, and context, which supports the citation and verification agentic systems need to reduce hallucination risk, and the audit trail regulated environments require.
The bottom line
Gartner's prediction reframes semantics from a data-quality nicety into a budget line with a measurable return: up to 80% more accurate agentic AI, up to 60% lower cost, by 2027. For life sciences organizations already contending with fragmented terminology and cross-domain relationships that don't fit neatly into relational tables, that return depends less on which model an agent runs on and more on whether the data it retrieves carries meaning it can trust. Building that context layer now, rather than after an agent deployment underperforms, is the practical takeaway from Gartner's warning.
