Enterprise AI surges as real-time data and token cuts collide

By late 2025, it became clear that high-quality, well-structured enterprise data is the indispensable foundation for agentic AI to truly augment human productivity. Current telemetry data, often dismissed as 'mostly garbage,' is underutilized, prompting the development of advanced techniques such as vector embeddings and simulation environments to manage the exploding volume and complexity of data. Companies like Resolve AI and ServiceNow are pioneering agentic AI solutions, while infrastructure providers race to deliver performant, cost-effective data plumbing essential for these agents to function effectively.

By early 2026, industry leaders like Julie underscored that disciplined data practices and robust data foundations are critical prerequisites for scaling AI beyond isolated projects, citing Aramco and McDonald's as exemplars who invested early in their data strategies. Despite this, over 90% of the necessary global data work remains undone, highlighting a massive ongoing challenge. Moreover, companies are creating investment capacity for AI by streamlining fragmented processes and reducing management layers, focusing on operational basics as a key enabler of data readiness.

The urgency of resolving enterprise data crises became even more pronounced by March 2026, as experts like Jacco van der Kooij warned that broken or fragmented data infrastructures not only hinder AI adoption but accelerate costly mistakes. Four critical data crises—unstructured, incompatible, disconnected, and incomplete data—undermine AI’s ability to generate actionable insights, with 95% of customer conversations remaining unusable by AI. Building a real-time, closed-loop data infrastructure is essential to enable AI systems to learn and respond within minutes or hours, rather than months, ensuring timely market and customer responsiveness.

Agentic AI’s promise hinges on integrating diverse enterprise data sources—from human-generated tickets and Slack channels to wikis and telemetry data—to enhance troubleshooting and operational efficiency. This shift demands not only better data quality but also sophisticated data integration strategies that marry disparate inputs into coherent, actionable intelligence, setting the stage for agentic AI to move from experimental to enterprise-grade application.

In mid-2026, Databricks unveiled a transformative approach to data architecture with its LTAP (Lake Transactional/Analytical Processing) and Lakehouse//RT innovations, effectively collapsing traditional multi-layered data pipelines into a unified storage layer. By shifting unification from the query engine to the storage layer, LTAP allows specialized engines—Postgres for transactional workloads and Spark for analytics—to operate on the same governed Delta or Iceberg data formats without data duplication or ETL pipelines. This architectural leap eliminates brittle synchronization processes and enables real-time, low-latency access to unified data, a critical capability as AI agents increasingly generate and consume vast volumes of operational data at scale.

Lakehouse//RT complements LTAP by delivering millisecond query latencies directly on governed Delta Lake tables, achieving sub-100ms response times even under high concurrency—up to 12,000 queries per second—and outperforming existing dedicated serving layers by as much as 16 times. This real-time compute engine, powered by Reyden, removes the need for separate serving databases or proprietary storage formats, allowing applications to query fresh, unified data within a single governance framework. Preview customers have demonstrated the system’s ability to handle tens of thousands of dashboard queries in seconds, underscoring its readiness to support the demanding operational workloads of AI agents.

Databricks’ introduction of Lakebase, a serverless Postgres database integrated into the Lakehouse platform, further advances this unified architecture by providing a high-velocity, low-latency 'working memory' tailored for AI agents. Lakebase supports instant branching and separates compute from storage, enabling elastic scalability aligned with modern app and agent workloads. This integration not only collapses traditional silos between transactional and analytical data but also accelerates enterprise adoption of agentic AI through partner-developed accelerators, positioning Databricks as a pivotal enabler of next-generation AI-driven applications.

A key technical breakthrough underpinning LTAP is its ability to reconcile the conflicting storage requirements of transactional and analytical workloads by supporting both row-oriented storage for OLTP and columnar storage for OLAP within the same open data formats. This innovation, achieved within the year prior to mid-2026, eliminates the need for fragile change data capture (CDC) pipelines—often humorously dubbed 'continuous data corruption' internally at Databricks—and ensures that transactional tables appear natively in the lake for up-to-date analytics. This blurring of traditional database boundaries marks a fundamental evolution in data infrastructure, enabling AI agents to operate on fresh, consistent data without the overhead of maintaining multiple database copies or synchronization layers.

By early 2026, CData's Connect AI platform has set a new standard for enterprise-grade AI deployments by integrating advanced agent tooling with robust security measures such as SCIM 2.0 and Custom OAuth Applications. This evolution addresses a critical industry challenge where many AI initiatives falter due to immature data infrastructure, as mature, governed platforms like Connect AI provide the necessary governance and granular control over agent actions and data access to move beyond experimentation into reliable, production-grade applications.

Benchmark tests underscore Connect AI’s superior performance and reliability, achieving an impressive 98.5% accuracy in real-world tasks—outperforming competing multi-cloud platform providers by over 25 percentage points. This leap highlights how CData’s emphasis on deep connectivity to over 350 business systems, combined with contextual awareness and stringent control mechanisms, is pivotal for enterprises demanding trustworthy and scalable AI solutions.

By mid-2026, database innovators like Pinecone and TigerData spearheaded a shift toward token optimization and cost-efficient data management tailored for AI agents. Pinecone’s Nexus knowledge engine, now generally available, revolutionizes data structuring by organizing enterprise information into queryable layers that reduce AI token consumption by an impressive 9-15 times while enhancing query accuracy. Meanwhile, TigerData’s Ghost offers isolated, disposable PostgreSQL databases for each AI agent, introducing a novel billing model based on computing time rather than resource count, effectively controlling operational costs in dynamic AI environments.

Recognizing the escalating costs and complexity of AI deployments, major cloud and database providers such as Snowflake, Oracle, and Microsoft have integrated token and cost management features directly into their platforms. This strategic move complements innovations from specialized firms by embedding token optimization and consumption-based billing into mainstream infrastructure, addressing the critical challenge of balancing AI agent performance with sustainable operational expenses across enterprise-scale deployments.

Pinecone’s Nexus not only slashes token waste but also surpasses traditional Retrieval-Augmented Generation (RAG) systems in demanding domains like legal research and enterprise data management. Its advanced capabilities—including workspaces, contexts, manifests for curated data, and the KnowQL querying language—enable AI agents to navigate complex datasets with precision and efficiency, setting a new standard for knowledge engines that marry cost savings with superior functional depth.

In July 2026, UNNet, under CEO Kim Joo-seop, launched the Tarantula AI Vector DB, a pioneering vector database that seamlessly integrates structured and unstructured corporate data—ranging from documents to images—by converting them into numerical vectors to enhance AI answer accuracy and relevance. Built atop PostgreSQL’s vector extensions, Tarantula facilitates easy adoption without requiring enterprises to replace existing database systems, while also embedding critical features like high availability and security. UNNet’s broader vision aims to expand this AI Data Platform by merging traditional database capabilities with AI vector search to support comprehensive data storage, search, analysis, and performance management, thereby advancing practical AI applications within enterprises.

Just days later, EnterpriseDB (EDB) unveiled its unified Postgres AI platform, which dramatically outperforms specialized vector databases and other AI data platforms in speed, accuracy, and cost-efficiency for agentic AI workloads. Demonstrating median query latencies as low as 50 milliseconds at 50 million vectors—80 times faster than Databricks and 21 times faster than MongoDB Atlas—EDB’s solution completes complex multi-step agentic loops in mere milliseconds, slashing latency from hundreds to just 27 milliseconds. This breakthrough stems from embedding AI capabilities directly within the operational Postgres database, eliminating the inefficiencies and risks associated with separating AI processing from primary data storage, and resulting in up to a 51% reduction in total cost of ownership over three years compared to DIY cloud AI stacks.

EDB’s integrated approach also tackles critical challenges such as data drift, latency, and governance by removing the need for complex ETL pipelines and multiple disparate systems, ensuring AI agents operate on live, consistent data. Their philosophy that 'intelligence belongs next to the data' enables transactional, analytical, and agentic AI workloads to coexist within a single system, streamlining operations and enhancing reliability. This innovation arrives at a pivotal moment, as IDC forecasts agentic AI will command over 25% of global IT spending by 2029, and Gartner predicts that a third of enterprise applications will embed agents by 2028, positioning EDB’s unified AI data platform as a crucial competitive advantage in the rapidly evolving AI infrastructure landscape.

By mid-2026, Databricks has strategically positioned its Lakebase serverless Postgres database as the cornerstone for agentic AI adoption, integrating it within the Lakehouse platform to unify transactional and analytical workloads under a single governed system. This Lake Transactional Analytical Processing (LTAP) vision, supported by Unity Catalog’s unified governance, enables AI agents to seamlessly read and write operational data in real time without relying on separate serving stacks, thus simplifying complex data pipelines and enhancing trust in AI-driven enterprise operations.

Databricks is accelerating enterprise adoption of agentic AI through a collaborative ecosystem of global consulting partners—including Capgemini, Avanade, and CI&T—who leverage Lakebase’s innovative features like zero-storage database branching and intelligent autoscaling to deliver industry-specific accelerators. These ready-to-deploy solutions target sectors such as finance, marketing, cybersecurity, and customer service, effectively streamlining data modernization and reducing time-to-market for AI-powered business processes.

The serverless architecture of Lakebase, characterized by the separation of compute and storage and instant branching capabilities, aligns with broader industry trends toward elastic, usage-based data infrastructure that optimizes costs while scaling to meet AI-intensive workloads. This design not only deepens Databricks’ footprint beyond traditional analytics and data warehousing into operational databases but also positions the company to challenge incumbent cloud database providers by fostering platform lock-in and boosting revenue per customer as developers increasingly build AI agents and high-scale applications on Lakebase.