Good morning. In the race to deploy AI agents, many companies are overlooking a costly problem hiding in plain sight: data without context.
Companies that prioritize semantics in their AI-ready data will improve agentic AI accuracy by up to 80% and cut costs by up to 60% by 2027, according to new research released at the recent Gartner’s Data & Analytics Summit in London.
The implication for CFOs: a meaningful share of today’s agentic AI spend is at risk of being wasted on tools that hallucinate, introduce bias, and produce unreliable outputs—not because the models are flawed, but because the underlying data lacks context.
“Agentic AI outcomes depend on context, including semantic representations of data,” Rita Sallam, distinguished VP analyst at Gartner, said at the summit. “Without context—a clear understanding of the specific relationships and rules within an organization’s data—AI agents cannot operate accurately.”
Gartner argues that traditional schema-based data models are no longer sufficient, and that a dedicated semantic, or “context,” layer needs to sit at the core of enterprise data infrastructure. Skipping it, Sallam warned, will “perpetuate data inefficiencies” and expose companies to heightened financial, legal, and reputational costs.
