Addressing the context gap in AI execution with the Semantic Twin

Addressing the context gap in AI execution with
a Semantic Twin

How to bridge the missing context gap in AI infrastructure

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77%
of executives report that less than 20% of their enterprise data is sufficiently contextualized for AI agents.

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80%
improvement in agentic AI accuracy by 2027 for organizations that prioritize semantics in AI-ready data.
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40%
of future Agentic AI failures can occur due to lack of semantic coherence.
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Why enterprise AI projects require the context layer

As investments in enterprise AI solutions increase, there’s a growing need for a semantic foundation that can provide contextual relevance to AI-optimized data. Without this foundation, AI systems are likely to produce a lot of “noise” and inaccurate outputs. The 2026 Gartner prediction states that “by 2027, enterprises that prioritize semantics in AI-ready data will increase their accuracy by 80% and reduce costs by 60%.”

 

Onix’s latest white paper presents the relevance of a semantic foundation in modern AI initiatives. The white paper also gets into why the context layer is usually the hardest part of enterprise AI and how Wingspan solves it by building a Semantic Twin that operates as an Enterprise Intelligence Fabric.

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What you’ll learn:

  • Why most enterprise AI produces confident answers that are wrong — and the one gap responsible.
  • The three components Gartner says every production AI system needs, and why none can be bought.
  • How Wingspan builds a Semantic Twin that makes every AI initiative compound rather than reset.
  • Why data catalogs, BI tools, and MLOps cannot fill this gap and what does.
  • How Onix delivers AI that reaches production, not just pilots, across three programs at once.
  • How a top-5 US health insurer cut query costs by 85% and response time by 70%.
  • Why the Semantic Twin built for initiative one makes initiative ten faster, cheaper, and more accurate. 
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