Generative AI work is underway at most organizations, yet data foundations are uneven. Couchbase commissioned an independent market survey from UserEvidence of 619 product, engineering, data, and AI professionals that shows strong confidence and rising activity, especially in developer productivity, data analysis, and chatbots. At the same time, many teams are relying on a single model and lack a unified multi model database, while concerns about hallucinations and data privacy remain widespread. Read on to uncover what the data displayed regarding how enterprises are adopting GenAI, where bottlenecks appear in RAG and data pipelines, and how a modern NoSQL data layer can help teams move from pilots to production.
AI adoption and the data foundation
62 percent of organizations surveyed are already working with GenAI, but most lack a unified data platform. Only 29 percent report using a multi model database to feed AI, which can slow innovation as projects scale. Fragmented stacks make it harder to trace errors, govern sensitive information, and meet latency expectations.
Model usage is early and concentrated, with 49 percent of respondents using only one LLM, typically ChatGPT. Meanwhile, 77 percent worry about falling behind the AI curve, underscoring the need for faster, repeatable, and secure data pipelines that are ready for AI. Current usage is practical and data hungry, with 72 percent being used for development, 65 percent for data analysis, and 59 percent for chatbots. All of these workloads depend on a database optimized for speed, schema flexibility, and AI integration.
What does this mean? Without a unified, developer friendly data layer, it seems that teams may struggle to move from promising pilots to durable, production grade experiences.
Top concerns from respondents
Respondents’ top concerns center on trust. 85 percent worry about GenAI hallucinations, and 83 percent worry about sharing proprietary data with LLMs. Addressing both requires RAG architectures backed by databases that can store and search vectors while keeping trusted source data close, so answers are grounded and reliable. It also demands secure, controlled data layers that enforce access rules and prevent leakage while still enabling useful retrieval. In short, trust is the gating factor for customer facing AI, and grounded retrieval with clear governance must be designed from the start, not added later.
RAG and data pipeline challenges
Respondents flagged several challenges that map directly to core data responsibilities. 67 percent cite privacy and compliance concerns with public LLMs, underscoring the need for a database with built in access controls, vector search, and multi model storage to enforce security policies. 49 percent struggle with data pipelines and prompt engineering, which a platform that supports JSON, native vector embeddings, and ETL integrations can simplify by reducing brittle glue code. 47 percent report difficulty managing unstructured data; document and vector features that handle PDFs, text, and metadata together can close this gap and make retrieval reliable.
What good looks like is a disciplined approach to the RAG data lifecycle. Consolidate priority sources, standardize chunking and metadata, co-locate vectors with source data, and enforce access controls. Instrument latency end to end, and add validation steps that check responses before actions are taken so accuracy and trust are measurable.
Putting it together: where a NoSQL database helps
A modern NoSQL platform can unify structured, semi structured, and unstructured data with JSON first models, store and query vectors next to source truth, and provide real time retrieval across regions. This reduces complexity in RAG, strengthens governance, and supports the sub second performance that user facing AI demands.
Conclusion
Confidence in AI is high, but maturity is early. Most work remains pilots such as copilots, chatbots, and single model tests rather than complex agents or enterprise applications. This ambition and reality gap combined with low trust is where a NoSQL database can help by simplifying data, scaling RAG retrieval, and delivering production performance so organizations can move from experimentation to real results. Unlock the full findings in The State of Enterprise AI Development: Implementation Insights & Architectural Realities.
