For many years the information panorama was comparatively static. Relational databases (hiya, Oracle!) had been the default and dominated, organizing info into acquainted columns and rows.
That stability eroded as successive waves launched NoSQL doc shops, graph databases, and most just lately vector-based techniques. Within the period of agentic AI, information infrastructure is as soon as once more in flux — and evolving quicker than at any level in latest reminiscence.
As 2026 dawns, one lesson has turn out to be unavoidable: information issues greater than ever.
RAG is useless. Lengthy reside RAG
Maybe essentially the most consequential development out of 2025 that may proceed to be debated into 2026 (and possibly past) is the position of RAG.
The issue is that the unique RAG pipeline structure is very similar to a primary search. The retrieval finds the results of a particular question, at a particular cut-off date. It is usually typically restricted to a single information supply, or no less than that is the best way RAG pipelines had been constructed previously (the previous being anytime previous to June 2025).
These limitations have led a rising conga line of distributors all claiming that RAG is dying, on the best way out, or already useless.
What’s rising, although, are different approaches (like contextual reminiscence), in addition to nuanced and improved approaches to RAG. For instance, Snowflake just lately introduced its agentic doc analytics know-how, which expands the normal RAG information pipeline to allow evaluation throughout 1000’s of sources, with no need to have structured information first. There are additionally quite a few different RAG-like approaches which might be rising together with GraphRAG that may doubtless solely develop in utilization and capabilities in 2026.
So now RAG is not (solely) useless, no less than not but. Organizations will nonetheless discover use circumstances in 2026 the place information retrieval is required and a few enhanced model of RAG will doubtless nonetheless match the invoice.
Enterprises in 2026 ought to consider use circumstances individually. Conventional RAG works for static data retrieval, whereas enhanced approaches like GraphRAG swimsuit complicated, multi-source queries.
Contextual reminiscence is desk stakes for agentic AI
Whereas RAG will not solely disappear in 2026, one method that may doubtless surpass it when it comes to utilization for agentic AI is contextual reminiscence, also referred to as agentic or long-context reminiscence. This know-how permits LLMs to retailer and entry pertinent info over prolonged durations.
A number of such techniques emerged over the course of 2025 together with Hindsight, A-MEM framework, Normal Agentic Reminiscence (GAM), LangMem, and Memobase.
RAG will stay helpful for static information, however agentic reminiscence is important for adaptive assistants and agentic AI workflows that should be taught from suggestions, preserve state, and adapt over time.
In 2026, contextual reminiscence will not be a novel approach; it should turn out to be desk stakes for a lot of operational agentic AI deployments.
Goal-built vector databases use circumstances will change
Originally of the trendy generative AI period, purpose-built vector databases (like Pinecone and Milvus, amongst others) had been all the trend.
To ensure that an LLM (typically however not solely by way of RAG) to get entry to new info, it must entry information. One of the simplest ways to try this is by encoding the information in vectors — that’s, a numerical illustration of what the information represents.
In 2025 what grew to become painfully apparent was that vectors had been not a particular database sort however somewhat a particular information sort that might be built-in into an current multimodel database. So as an alternative of a corporation being required to make use of a purpose-built system, it may simply use an current database that helps vectors. For instance, Oracle helps vectors and so does each database supplied by Google.
Oh, and it will get higher. Amazon S3, lengthy the de facto chief in cloud based mostly object storage, now permits customers to retailer vectors, additional negating the necessity for a devoted, distinctive vector database. That doesn’t imply object storage replaces vector search engines like google — efficiency, indexing, and filtering nonetheless matter — but it surely does slender the set of use circumstances the place specialised techniques are required.
No, that does not imply purpose-built vector databases are useless. Very similar to with RAG, there’ll proceed to be use circumstances for purpose-built vector databases in 2026. What’s going to change is that use circumstances will doubtless slender considerably for organizations that want the best ranges of efficiency or a particular optimization {that a} general-purpose answer would not assist.
PostgreSQL ascendant
As 2026 begins, what’s previous is new once more. The open-source PostgreSQL database might be 40 years previous in 2026, but will probably be extra related than it has ever been earlier than.
Over the course of 2025, the supremacy of PostgreSQL because the go-to database for constructing any sort of GenAI answer grew to become obvious. Snowflake spent $250 million to amass PostgreSQL database vendor Crunchy Information; Databricks spent $1 billion on Neon; and Supabase raised a $100 million sequence E giving it a $5 billion valuation.
All that cash serves as a transparent sign that enterprises are defaulting to PostgreSQL. The explanations are many together with the open-source base, flexibility, and efficiency. For vibe coding (a core use case for Supabase and Neon particularly), PostgreSQL is the usual.
Anticipate to see extra development and adoption of PostgreSQL in 2026 as extra organizations come to the identical conclusions as Snowflake and Databricks.
Information researchers will proceed to seek out new methods to unravel already solved issues
It is doubtless that there might be extra innovation to assist issues that many organizations doubtless assume are already: solved issues.
In 2025, we noticed quite a few improvements, just like the notion that an AI is ready to parse information from an unstructured information supply like a PDF. That is a functionality that has existed for a number of years, however proved more durable to operationalize at scale than many assumed. Databricks now has a complicated parser, and different distributors, together with Mistral, have emerged with their very own enhancements.
The identical is true with pure language to SQL translation. Whereas some may need assumed that was a solved drawback, it is one which continued to see innovation in 2025 and can see extra in 2026.
It’s important for enterprises to remain vigilant in 2026. Do not assume foundational capabilities like parsing or pure language to SQL are absolutely solved. Maintain evaluating new approaches that will considerably outperform current instruments.
Acquisitions, investments, and consolidation will proceed
2025 was an enormous yr for giant cash going into information distributors.
Meta invested $14.3 billion in information labeling vendor Scale AI; IBM mentioned it plans to amass information streaming vendor Confluent for $11 billion; and Salesforce picked up Informatica for $8 billion.
Organizations ought to anticipate the tempo of acquisitions of all sizes to proceed in 2026, as huge distributors notice the foundational significance of knowledge to the success of agentic AI.
The influence of acquisitions and consolidation on enterprises in 2026 is difficult to foretell. It could result in vendor lock-in, and it may additionally probably result in expanded platform capabilities.
In 2026, the query gained’t be whether or not enterprises are utilizing AI — will probably be whether or not their information techniques are able to sustaining it. As agentic AI matures, sturdy information infrastructure — not intelligent prompts or short-lived architectures — will decide which deployments scale and which quietly stall out.
