Enterprise AI: Trust Wavers Amid Context Gap

The Context Gap in AI

Trust Issues in Enterprise AI Organizations

In a sample of 101 companies, the infrastructure supporting artificial intelligence agents in a business context is rapidly expanding, but it is progressing at a pace that outstrips the trust it inspires. Retrieval-Augmented Generation (RAG) has emerged as the default method for providing context, and the native retrieval offered by providers has quietly overtaken specialized vector databases. However, a majority of these companies have already observed that their agents produce responses that seem confident but are actually incorrect, often due to missing or inconsistent context. A potential solution is emerging in the form of a governed semantic layer, but most companies are still in the process of developing it. The industry is moving towards hybrid retrieval; while provider-native tools are predominant, a plurality of companies wishes to retain the best specialized tools. This creates a context gap: agents appear authoritative, but rely on a foundation of trust that is not yet fully established.

Study Results

A survey conducted by VentureBeat Pulse explored the enterprise RAG infrastructure and the context layer: it examined the retrieval systems used by companies, how they acquire and evaluate them, and the direction in which the architecture is heading. Notably, the study highlighted the frequency with which this context is lacking. The main finding is a context gap: the difference between the confidence displayed by enterprise agents' responses and the reliability of the context supporting them. A majority of companies, 57%, reported that in the past six months, their AI agents produced confident but incorrect responses, attributed to missing or inconsistent business context. More than half of these companies stated that this occurred repeatedly. This is not a marginal issue: for 38% of companies, retrieval is the primary source of context. When this retrieval is weak or inconsistent, the resulting errors affect the agent's authority. To address this, 58% of companies are already using or developing a governed semantic layer, although for most, it is not yet in production.

Market State

The market is evolving in an unexpected direction. Provider-native retrieval, such as OpenAI's file search (40%) and Google's Vertex AI search (38%), is already surpassing each dedicated vector database. Companies expect hybrid retrieval to become dominant by the end of 2026 (34%). However, a plurality of them (36%) claims they want to retain top-tier standalone tools rather than rely solely on a provider's native context stack. A majority (57%) plans to change or add a provider in the coming year. This dichotomy between stated preference and actual usage shows that the market is purchasing provider-native tools while expressing a desire for independence.

Methodology

VentureBeat conducted this survey as part of its Pulse research series. The study focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses were filtered to include only organizations with more than 100 employees (n=101); no responses were received from organizations with 100 employees or fewer, thus ensuring the validity of the complete sample. All responses come from a single wave of Q2 2026 (June), meaning the report is a snapshot at a given moment and does not draw conclusions about monthly trends.

Key Findings

• Finding 1: Confident and Incorrect

  • More than half of companies attributed agent errors to poor context.
  • 57% of companies have had an AI agent produce a confident but incorrect response that they attributed to poor context.

• Finding 2: RAG is the Default Source of Context

  • Retrieval powers more agents than any other method.
  • For 38% of organizations, RAG on documents or a vector index is the primary means for agents to understand the business.

• Finding 3: Provider-Native Retrieval Already Dominates Vector Databases

  • OpenAI's file search (40%) and Google's Vertex AI search (38%) are leading.

• Finding 4: They Want to Retain the Best Specialized Tools

  • A plurality of 36% of companies states that they wish to retain top-tier standalone tools.

• Finding 5: Hybrid Retrieval is the Consensus Bet

  • 34% expect hybrid retrieval to dominate their production RAG systems by the end of 2026.