Traditional RAG systems are evolving rather than becoming obsolete
Static data pipelines had their time. Retrieval-Augmented Generation (RAG), introduced to help language models fetch external knowledge, worked well in narrow use cases, when the information needed was structured or static. But as demands on AI shift, relying on a system that fetches data from a single point in time or source is becoming limiting. Still, that doesn’t mean RAG is done. It’s being rebuilt.
Vendors recognize the need to move beyond traditional RAG. In 2025, companies introduced new methods, like GraphRAG and Snowflake’s agentic document analytics. These tools don’t require clean, structured data and can access thousands of sources simultaneously. This takes us into a space where real-time, complex queries across varied data types are not only doable, but efficient.
So, the original idea of RAG might be basic now, but we’re seeing strong iterations rather than a total abandonment. For enterprises, this means RAG still has value, it just needs to be matched to the right use case. If you’re dealing with static knowledge bases, enhanced RAG works fine. For anything more dynamic or interconnected, look at solutions that use expanded RAG architectures or hybrid models like contextual memory.
If you’re overseeing AI deployment at scale, don’t toss RAG out simply because it feels old. These enhanced RAG systems can still deliver high-performance retrieval with far lower latency than more complex agents. Map the capabilities to your real data needs and operational constraints. Some old tools are still useful when upgraded properly.
Contextual memory is emerging as a critical component for agentic AI systems
Large language models are moving beyond simple Q&A bots. To become useful tools inside organizations, agents that evolve, adapt, and improve, these systems need memory. Specifically, contextual memory. It’s what allows AI to remember past interactions, track state over time, and adapt its actions based on feedback. That’s not an add-on feature, it’s foundational.
In 2025, several systems came online that pushed contextual memory into real enterprise use. Names like Hindsight, A-MEM, General Agentic Memory (GAM), LangMem, and Memobase led this space. All capable of giving AI long-term recall. It’s not just about remembering what a user said a minute ago, it’s about holding onto context through months of operation, enabling continuity in workflows and better decision support.
What this means practically is that contextual memory will become standard. In 2026, if your AI system doesn’t have a memory layer, it won’t be able to drive serious agentic use cases. We’re not talking about futuristic robots, we’re talking about assistants that can handle onboarding flows, customer follow-ups, and operational batch processes without losing coherence.
For executives, this changes how you think about integrating AI into your products or operations. Agentic systems require more than a fast inference. They need infrastructure that tracks long-term behavior, learning as they go. If you’re not building with memory-first architecture, you’re going to hit scaling limits fast.
Purpose-built vector databases will see a narrowing of use cases in 2026
At one point, vector databases appeared to be the bedrock for generative AI. They helped language models retrieve relevant information by converting text into vectors, a format AI understands. Purpose-built players like Pinecone and Milvus led the way. But 2025 showed that the value of vectors isn’t locked into specialized systems. Instead, vectors are becoming a core data type in general-purpose databases.
Major platforms responded quickly. Oracle added vector support. Google’s entire database suite integrated it. Amazon S3, known more for object storage than search, now lets companies store vectors directly. This means enterprises no longer need to deploy and manage separate vector databases, unless there’s a performance edge they can’t get elsewhere.
What’s happening now is a shift from narrow utility to integrated capability. General-purpose databases are absorbing vector functions, making dedicated vector databases less critical unless you’re pushing extreme performance or specialized indexing. In day-to-day scenarios, standard systems are often enough.
If you’re running AI systems at enterprise scale, re-evaluate your stack. Dedicated vector databases still have advantages in search accuracy and speed, but only in edge cases. You might be maintaining unnecessary complexity and cost by sticking with them. Look at whether your workloads can run efficiently on databases already in your core infrastructure. Most of them can handle vector storage and search well enough for production systems.
PostgreSQL is experiencing a resurgence as the backend of choice for generative AI
PostgreSQL turns 40 in 2026. It’s been around, but it’s not fading, it’s surging. In 2025, it became clear that PostgreSQL is where serious investment is landing. Snowflake acquired Crunchy Data for $250 million. Databricks spent $1 billion acquiring Neon. Supabase raised $100 million at a $5 billion valuation. No one throws that kind of money at tech unless it can scale and win.
PostgreSQL’s open-source model, performance profile, and developer ecosystem are all strong. GenAI systems need reliable, flexible databases to match the demands of real-time data, fast queries, and dynamic updates. PostgreSQL checks all those boxes. More importantly, it gives teams control without locking them into rigid vendor constraints.
It’s also the database of choice for vibe-based coding workflows, where developers move fast and integrate AI across the app stack. Neon and Supabase lean in heavily here, and their growing traction with enterprise buyers shows this is more than hype.
If you’re leading infrastructure or digital transformation planning, PostgreSQL deserves a hard look. It’s proven, it’s flexible, and it’s getting major R&D backing from serious tech players. The vendor ecosystem is strong. The performance benchmarks hold. More importantly, your engineering teams likely already know how to work with it. That lowers deployment friction and speeds up innovation. It’s not just old, it’s current, and most likely, future-proof.
Readdressing “solved” data challenges is becoming a continuous area of innovation
A lot of people assume foundational problems in data, like parsing unstructured documents or translating natural language into SQL, are solved. But 2025 proved that’s not true. While the basic tech has existed for years, making it scale well, work reliably in live environments, and produce consistent results across messy, real-world data is still tough.
Vendors are actively reworking these pipelines. Databricks now offers an advanced PDF parser designed to handle scale and noise. Mistral is doing similar work to boost performance and reliability in natural language interfaces. These advancements are happening because the current tools just aren’t robust enough for demanding enterprise environments. This is less about invention than execution, making foundational capabilities enterprise-grade.
In 2026, there will be continued breakthroughs in areas that many dismissed as handled. For companies already deploying AI, these updated tools could drastically improve reliability and performance. For those building new AI workflows, incorporating newer parsing and query translation layers could remove operational friction before it starts.
If you’re overseeing product, data, or AI strategy, revisit your assumptions about existing capabilities. Look beneath the surface, many “solved” tools break under pressure, especially when dealing with edge cases, multilingual datasets, or inconsistent formatting. New tools that upgrade core capabilities could lead to faster use-case deployment and lower error rates across your systems.
Continued acquisitions and investments in data infrastructure are shaping the agentic AI landscape
2025 was a big year for capital movement in the data world. Meta invested $14.3 billion into Scale AI to strengthen its data labeling capabilities. IBM agreed to acquire data-streaming vendor Confluent for $11 billion. Salesforce picked up Informatica for $8 billion. These aren’t opportunistic deals, these are long-term infrastructure bets tied directly to the future of agentic AI.
AI agents, especially those operating independently, need access to reliable, high-quality, real-time data. That means the systems feeding the AI need to be continuously modernized. The M&A wave we’re seeing is a direct response to that need. Big vendors realize intelligent systems aren’t just about model size. They’re about the data stack that powers them.
Consolidation will continue in 2026. It will bring tighter vertical integration across tools, possibly improving ease of use but also increasing the risk of vendor lock-in. At the same time, this financial activity will accelerate platform maturity, giving enterprises more complete toolkits from single partners.
As a C-suite executive shaping long-term AI programs, watch these moves closely. Acquisitions signal tech priorities and expose fragilities in the vendor landscape. Base your architecture on systems with strong support, clear direction, and integration flexibility. The tools you choose this year could determine whether your AI programs scale or stall over the next two. Don’t just plan deployments, plan for infrastructure resilience.
Key takeaways for decision-makers
- Evolving RAG systems remain strategically useful: Leaders should reassess Retrieval-Augmented Generation, not as outdated tech, but as evolving infrastructure. Enhanced RAG variants, like GraphRAG, are better suited for dynamic, multi-source data retrieval in high-context enterprise environments.
- Contextual memory is now foundational for agentic systems: Executives deploying adaptive AI must prioritize systems with long-context memory. Solutions like A-MEM and LangMem are now critical for enabling AI agents to maintain state, learn over time, and operate effectively at scale.
- General-purpose databases absorb vector capabilities: CIOs should review data stacks and eliminate unnecessary vector-specific systems. Multimodal platforms like Oracle, Google, and Amazon S3 now offer integrated vector support that meets most enterprise needs with lower complexity.
- PostgreSQL is a rising standard for AI-ready databases: Decision-makers should recognize PostgreSQL as a high-performance, open-source investment with strong enterprise backing. Major moves by companies like Snowflake and Databricks validate its long-term role in AI infrastructure.
- Foundational tools still require innovation to scale: Don’t assume parsing and natural language-to-SQL are solved, many tools still struggle under enterprise conditions. Leaders should regularly audit core components and stay current with new releases that offer greater reliability and scalability.
- Data infrastructure investment signals long-term AI value: Significant acquisitions by Meta, IBM, and Salesforce highlight where strategic AI value is heading. Executives must build with adaptability and vendor flexibility in mind, knowing that infrastructure, not just models, guides AI success.
