CFOs could cut agentic AI costs up to 60% by fixing this overlooked data problem | Fortune

"Agentic AI outcomes depend on context, including semantic representations of data. 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."

"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."

"For CFOs, the takeaway reframes the AI conversation from a technology debate into a capital-allocation one. Semantic coherence, Gartner says, is becoming "a cost-control and trust strategy, not a nice-to-have," and potentially a focus for regulators and audit committees evaluating how AI-generated outputs flow into financial reporting and disclosures."

Agentic AI deployment is hindered by data that lacks context. Research indicates that prioritizing semantic representations in AI-ready data can improve agentic AI accuracy by up to 80% and reduce costs by up to 60% by 2027. Without context, AI agents can hallucinate, introduce bias, and generate unreliable outputs even when models are not flawed. Traditional schema-based data models are described as insufficient, requiring a dedicated semantic or “context” layer within enterprise data infrastructure. Skipping this layer can perpetuate data inefficiencies and increase financial, legal, and reputational risks. Semantic coherence is framed as a cost-control and trust strategy relevant to regulators and audit committees.

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