Enterprise RAG: Shift to Hybrid Retrieval & the ‘Retrieval Rebuild’ in 2026

Enterprise adoption of Retrieval-Augmented Generation (RAG) is undergoing a significant shift, with a tripling of intent to adopt hybrid retrieval methods in the first quarter of 2026. This “retrieval rebuild,” as described by VentureBeat, signals a move away from initial RAG implementations and toward more sophisticated architectures capable of handling the demands of agentic AI infrastructure.

VB Pulse data, collected from organizations with 100 or more employees between January and March 2026, reveals that 33.3% of enterprises now intend to adopt hybrid retrieval, a substantial increase from the 10.3% recorded in the previous quarter. This surge in interest comes as 22% of respondents reported having no production RAG systems at all, suggesting a period of reassessment and refinement within the enterprise AI landscape.

Hybrid Retrieval Gains Traction as Initial RAG Architectures Fall Short

The shift towards hybrid retrieval is driven by the limitations of single-method RAG pipelines, which rely solely on vector similarity. Hybrid retrieval combines dense embeddings with sparse keyword search and reranking layers, offering improved retrieval accuracy and access control—critical features for production agentic workloads. This approach trades simplicity for robustness, addressing the challenges encountered when scaling RAG systems.

According to the data, the standalone vector database category is facing pressure, with Weaviate, Milvus, Pinecone, and Qdrant all experiencing a decline in adoption share during the quarter. This displacement is being absorbed by custom stacks and provider-native retrieval solutions, indicating a trend toward greater control and tailored infrastructure.

Interestingly, a growing number of enterprises are stepping back from RAG altogether. This suggests that the initial hype surrounding RAG may be moderating, and that organizations are carefully evaluating the technology’s suitability for their specific needs. Organizations that broadly implemented RAG in 2025 are encountering a common issue: the architecture designed for document retrieval struggles to maintain performance at agentic scale.

Investment Priorities Shift Towards Retrieval Optimization

The data also reveals a shift in investment priorities. While evaluation and relevance testing led budget intent in January at 32.8%, this figure fell to 15.6% by March. Conversely, retrieval optimization saw a significant increase, moving from 19.0% to 28.9% and becoming the top growth investment area for the first time. This indicates that enterprises are now focusing on improving the core retrieval capabilities of their RAG systems.

“Data teams are exhausted by fragmentation fatigue,”

Steven Dickens, vice president and practice lead at HyperFRAME Research

Steven Dickens, vice president and practice lead at HyperFRAME Research, highlighted the operational burden faced by enterprise data teams in a March interview with VentureBeat. He noted that managing separate vector stores, graph databases, and relational systems to power a single agent is a significant DevOps challenge.

The rise of custom stacks, reaching 35.6%, reflects this consolidation effort. Many organizations are now running both managed retrieval solutions and custom stacks, building retrieval infrastructure tailored to their specific requirements.

Reliability Emerges as a Key Factor in Vector Database Selection

While standalone vector databases are losing adoption share, they continue to be valued for their reliability. Enterprises are increasingly recognizing the importance of a dedicated vector layer for ensuring operational stability at scale. The reasons enterprises cite for needing a dedicated vector layer shifted significantly during Q1. In January, access control complexity (20.7%) and retrieval precision (19.0%) were the top concerns. By March, operational reliability at scale had surged to 31.1%, more than doubling and overtaking all other factors.

Two companies, &AI and GlassDollar, exemplify the value of purpose-built vector infrastructure. &AI, which builds patent litigation infrastructure, relies on Qdrant to ensure that every search result is grounded in a real source document, a critical requirement for patent attorneys. GlassDollar, a startup that evaluates other startups for Siemens and Mahle, uses a similar approach, prioritizing recall and trust in its search results.

“We measure success by recall,”

Kamen Kanev, GlassDollar’s head of product

Kamen Kanev, GlassDollar’s head of product, explained that their success is measured by recall—ensuring that the best companies are included in the search results. This emphasis on reliability is driving the demand for dedicated vector infrastructure.

Redefining “Good Retrieval” Beyond Correctness

The criteria used to evaluate retrieval systems are also evolving. In January, response correctness was the dominant factor, accounting for 67.2% of evaluations. However, by March, response correctness (53.3%), retrieval accuracy (53.3%), and answer relevance (53.3%) had converged. This suggests that enterprises are now looking beyond simply getting the right answer and are focusing on ensuring that the answer comes from the correct document and provides relevant context.

The increasing importance of answer relevance, which rose by five percentage points during the quarter, signals a growing sophistication in enterprise RAG evaluation. Measuring answer relevance requires purpose-built evaluation infrastructure, moving beyond simple pass-or-fail correctness checks.

the data suggests that RAG is not failing, but rather that the initial architectural approaches are proving inadequate for scaling. The “retrieval rebuild” is underway, with hybrid retrieval and custom stacks emerging as key components of the next generation of enterprise RAG systems. For 33% of enterprises, this rebuild is already the stated priority.