According to Gartner, 40% of Agentic AI projects will be cancelled by 2027.
Enterprise AI doesn’t fail because the model can’t produce an answer. It fails because nobody told it what the business means by “on time,” “active customer,” or “at risk.” Ask the same question to two teams, and you’ll get two different numbers, not because the AI is broken, but because the context it needed never made it past the analyst’s head.
While human analysts can bridge this gap through their domain experience, AI agents need the underlying context to act upon any request. In the face of growing challenges, a Semantic Layer is essential for delivering the organizational context that AI systems can work upon. It has emerged as the “connective tissue” connecting enterprise data with LLMs to enable decision-making and analytics.
Here’s a deep dive into why Enterprise AI systems need a semantic foundation.
Why AI systems need a Semantic Layer
Most enterprises spent the last two years doing exactly what they were told to: building the data infrastructure to give AI models real-time access to enterprise data. Lakes, pipelines, APIs, all of it. What nobody built alongside it was a way to tell the model what any of that data means.
IDC’s “Future Enterprise Resiliency and Spending” survey (conducted in October 2025) found that 93% of survey respondents believe that AI agents are more focused on Semantic Layers in BI and data analytics. Enterprise AI investments are gradually shifting from “which model to deploy” to “which context infrastructure the model is likely to use.”
From multiple failures in enterprise AI, the trend is getting clear. Business context is the missing infrastructure that needs to be set correctly before submitting any query to the AI model. The context infrastructure informs the AI model about the following:
- Which metrics were called and how were they calculated?
- Which data entities is it applicable for?
- Which users have the permission to see the results?
- How is it scoped to the specific business decision being made?
In short, the semantic foundation integrates various pieces for AI and also enforces the decisions.
Implementing the context layer with Onix’s Semantic Twin
Powered by its Wingspan platform, Onix’s Semantic Twin delivers the “missing” Semantic Layer for AI initiatives. Positioned as an enterprise intelligence fabric, this Agentic AI platform provides the shared business context in any existing data environment.
Here’s how Onix’s Semantic Twin helps enterprises implement the contextual layer:
- Provides operational context by monitoring the exact data lineage, thus creating a single source of truth.
- Maps every data relationship and KPI, thus enabling any AI output to be traced to its precise data source.
- Deploys its integrated Eagle tool to autonomously profile and map the enterprise systems and data environments to update the Semantic Twin continuously.
Conclusion
With Onix’s Semantic Twin, AI-adopting enterprises can build a semantic foundation, which is necessary to scale modern AI systems. Through solid context governance, enterprises no longer have to depend on effective AI models to address their business problems.
In our latest white paper, we delve deeper into the context gap and why enterprise AI stalls during implementation. Here are some of its highlights:
- Root cause analysis of enterprise AI projects in modern enterprises
- Importance of a context layer along with its 3 essential components
- The 4-layer architecture of Onix’s Semantic Twin, along with insights into its 5 inbuilt components
- Role of Onix’s Birds suite in building the Semantic Twin engine
- How Onix’s Semantic Twin differs from competitive solutions
Ready to build an intelligent enterprise? Download our white paper today and book a free product demo.
Reference links:
The Semantic Layer Is Becoming Context Infrastructure for AI
The Golden Age of the Semantic Layer: Why AI Can’t Work Without Context
