Research Links Semantic Data Context to Improved AI Agent Accuracy

Research presented at Gartner’s Data & Analytics Summit in London states that companies prioritizing semantic context in data infrastructure can improve AI agent accuracy by up to 80 percent and cut costs by up to 60 percent by 2027. Rita Sallam, distinguished VP analyst at Gartner, said agentic AI outcomes depend on context, including semantic representations of data.

Without context, she added, AI agents cannot operate accurately.

Gartner argues that traditional schema-based data models are no longer sufficient. The firm recommends adding a dedicated semantic layer at the core of enterprise data infrastructure. Skipping this layer, Sallam stated, will perpetuate data inefficiencies and expose companies to heightened financial, legal, and reputational costs.

Q1 earnings calls shows that at least 65 percent of S&P 500 companies cited the term “AI,” according to John Butters, VP and senior earnings analyst at FactSet. This figure is slightly below the previous quarter’s 68 percent but remains the second-highest share in at least five years.

Gartner says semantic coherence is becoming a cost-control and trust strategy rather than an optional feature, with potential implications for regulators and audit committees reviewing AI-generated outputs in financial reporting.