For decades the data landscape was relatively static. Relational databases (hello, Oracle!) were the default and dominated, organizing information into familiar columns and rows. That stability eroded as successive waves introduced NoSQL document stores, graph databases, and most recently vector-based systems. In the era of agentic AI, data infrastructure is once again in flux — and evolving faster than at any point in recent memory. As 2026 dawns, one lesson has become unavoidable: data matters more than ever. RAG is dead. Long live RAG Perhaps the most consequential trend out of 2025 that will continue to be debated into 2026 (and maybe beyond) is the role of RAG. The problem is that the original RAG pipeline architecture is much like a basic search. The retrieval finds the result of a specific query, at a specific point in time. It is also often limited to a single data source, or at least that's the way RAG pipelines were built in the past (the past being anytime prior to June 2025).  Those limitations have led a growing conga line of vendors all claiming that RAG is dying, on the way out, or already dead. What is emerging, though, are alternative approaches (like contextual memory), as well as nuanced and improved approaches to RAG. For example, Snowflake recently announced its agentic document analytics technology, which expands the traditional RAG data pipeline to enable analysis across thousands of sources, without needing to have structured data first. There are also numerous other RAG-like approaches that are emerging including GraphRAG that will likely only grow in usage and capabilities in 2026. So now RAG isn't (entirely) dead, at least not yet. Organizations will still find use cases in 2026 where data retrieval is needed and some enhanced version of RAG will likely still fit the bill. Enterprises in 2026 should evaluate use cases individually. Traditional RAG works for static knowledge retrieval, whereas enhanced approaches like GraphRAG suit complex, multi-source queries. Contextual memory is table stakes for agentic AI While RAG won't entirely disappear in 2026, one approach that will likely surpass it in terms of usage for agentic AI is contextual memory, also known as agentic or long-context memory. This technology enables LLMs to store and access pertinent information over extended periods. Multiple such systems emerged over the course of 2025 including Hindsight, A-MEM framework, General Agentic Memory (GAM), LangMem, and Memobase. RAG will remain useful for static data, but agentic memory is critical for adaptive assistants and agentic AI workflows that must learn from feedback, maintain state, and adapt over time. In 2026, contextual memory will no longer be a novel technique; it will become table stakes for many operational agentic AI deployments. Purpose-built vector databases use cases will change At the beginning of the modern generative AI era, purpose-built vector databases (like Pinecone and Milvus, among others) were all the rage.  In order for an LLM (generally but not exclusively via RAG) to get access to new information, it needs to access data. The best way to do that is by encoding the data in vectors — that is, a numerical representation of what the data represents. In 2025 what became painfully obvious was that vectors were no longer a specific database type but rather a specific data type that could be integrated into an existing multimodel database. So instead of an organization being required to use a purpose-built system, it could just use an existing database that supports vectors. For example, Oracle supports vectors and so does every database offered by Google. Oh, and it gets better. Amazon S3, long the de facto leader in cloud based object storage, now allows users to store vectors, further negating the need for a dedicated, unique vector database. That doesn’t mean object storage replaces vector search engines — performance, indexing, and filtering still matter — but it does narrow the set of use cases where specialized systems are required. No, that doesn't mean purpose-built vector databases are dead. Much like with RAG, there will continue to be use cases for purpose-built vector databases in 2026. What will change is that use cases will likely narrow somewhat for organizations that need the highest levels of performance or a specific optimization that a general-purpose solution doesn't support. PostgreSQL ascendant As 2026 starts, what's old is new again. The open-source PostgreSQL database will be 40 years old in 2026, yet it will be more relevant than it has ever been before. Over the course of 2025, the supremacy of PostgreSQL as the go-to database for building any type of GenAI solution became apparent. Snowflake spent $250 million to acquire PostgreSQL database vendor Crunchy Data; Databricks spent $1 billion on Neon; and Supabase raised a $100 million series E giving it a $5 billion valuation. All that money serves as a clear signal that enterprises are defaulting to PostgreSQL. The reasons are many including the open-source base, flexibility, and performance. For vibe coding (a core use case for Supabase and Neon in particular), PostgreSQL is the standard. Expect to see more growth and adoption of PostgreSQL in 2026 as more organizations come to the same conclusions as Snowflake and Databricks. Data researchers will continue to find new ways to solve already solved problems  It's likely that there will be more innovation to help problems that many organizations likely assume are already: solved problems. In 2025, we saw numerous innovations, like the notion that an AI is able to parse data from an unstructured data source like a PDF. That's a capability that has existed for several years, but proved harder to operationalize at scale than many assumed. Databricks now has an advanced parser, and other vendors, including Mistral, have emerged with their own improvements. The same is true with natural language to SQL translation. While some might have assumed that was a solved problem, it's one that continued to see innovation in 2025 and will see more in 2026. It's critical for enterprises to stay vigilant in 2026. Don't assume foundational capabilities like parsing or natural language to SQL are fully solved. Keep evaluating new approaches that may significantly outperform existing tools. Acquisitions, investments, and consolidation will continue 2025 was a big year for big money going into data vendors. Meta invested $14.3 billion in data labeling vendor Scale AI; IBM said it plans to acquire data streaming vendor Confluent for $11 billion; and Salesforce picked up Informatica for $8 billion. Organizations should expect the pace of acquisitions of all sizes to continue in 2026, as big vendors realize the foundational importance of data to the success of agentic AI. The impact of acquisitions and consolidation on enterprises in 2026 is hard to predict. It can lead to vendor lock-in, and it can also potentially lead to expanded platform capabilities.  In 2026, the question won’t be whether enterprises are using AI — it will be whether their data systems are capable of sustaining it. As agentic AI matures, durable data infrastructure — not clever prompts or short-lived architectures — will determine which deployments scale and which quietly stall out.