CFOs could cut agentic AI costs up to 60% by fixing this overlooked data problem
Companies deploying AI agents are facing a significant issue with data lacking context, which can lead to wasted resources. Research indicates that improving the semantic quality of data can enhance AI accuracy and reduce costs substantially by 2027. CFOs are urged to reconsider their approach to AI investments, focusing on the importance of context in data management.
- ▪Companies that prioritize semantics in their AI-ready data can improve agentic AI accuracy by up to 80%.
- ▪A dedicated semantic layer is necessary for effective enterprise data infrastructure.
- ▪Skipping semantic coherence can lead to financial, legal, and reputational risks for companies.
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Good morning. In the race to deploy AI agents, many companies are overlooking a costly problem hiding in plain sight: data without context. Recommended Video 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.
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