From Market Boom to Workflow Change in AI Analytics Platforms
AI analytics platform adoption is accelerating fast. Market analysts estimate that AI in analytics platforms will grow from US$ 28.1 billion (approx. RM129.3 billion) in 2025 to US$ 220.2 billion (approx. RM1,012.9 billion) by 2035, a 22.8% CAGR. Yet the most meaningful change for data teams is not the size of the market, but how new tools reshape everyday work. Vendors are going beyond bolt-on AI features to embed intelligence directly into modeling, governance and data preparation. Instead of focusing purely on dashboards or standalone copilots, platforms like Omni, VAST Data and Databricks are building AI-native experiences tied tightly to core data infrastructure. At the same time, Snowflake is mining query history to auto-suggest semantic models for AI assistants. Together, these moves signal a shift from isolated analytics projects to governed, AI-augmented data ecosystems that reflect how analysts actually write SQL and ship pipelines.
Omni’s Semantic Model: Turning AI Assistants into Trustworthy Analysts
Omni’s latest funding round underscores investor confidence in AI-native analytics. The company raised US$ 120 million (approx. RM552 million) in an Omni Series C at a US$ 1.5 billion (approx. RM6.9 billion) valuation, including a US$ 30 million (approx. RM138 million) employee tender offer. But the more important story for data teams is Omni’s architecture. The AI analytics platform is built on a semantic model—described as a governed context graph—storing metric definitions, business logic and permissions. Dashboards, spreadsheets, ad-hoc SQL and AI-generated queries all run against the same model, so AI answers are constrained by governance instead of bypassing it. That means AI assistants cannot invent metrics or ignore row-level security; they must reuse analyst-approved definitions. Customers like BambooHR and Cribl are using this to roll out self-service AI analytics at scale, turning the semantic layer into the shared brain that keeps human and machine-generated insights consistent.
VAST Data AI OS: Unifying Storage, Compute and AI at Infrastructure Scale
While Omni focuses on the semantic and BI layer, VAST Data targets the infrastructure foundation for AI workloads. Following a Series F round at a US$ 30 billion (approx. RM138 billion) valuation and roughly US$ 1 billion (approx. RM4.6 billion) in financing, the company positions its VAST Data AI OS as a unified operating system for large-scale AI deployments. Built on a shared-everything architecture, VAST’s platform aims to consolidate data, compute and real-time processing into a single system that can support millions of GPUs and global AI model lifecycles. For data engineers, this promises fewer fragmented storage tiers and less bespoke plumbing between analytics, training and inference environments. Instead of stitching together separate data lakes, feature stores and inference clusters, teams can run analytics and AI against a common, high-performance substrate, potentially simplifying governance and performance tuning for complex workloads.
Databricks Lakeflow Designer and Snowflake AI Assistants Rewire Daily Analytics Work
Databricks Lakeflow Designer and Snowflake’s AI-driven semantic modeling attack different parts of the same bottleneck: manual, repetitive data prep and modeling. Databricks Lakeflow Designer is a visual, no-code, AI-native experience for data preparation and analytics, embedded directly in the Databricks platform. Analysts and domain experts can drag-and-drop operators, use natural language and still generate production-ready code, which is operationalized via Lakeflow Jobs. This reduces handoffs to engineers for routine pipelines. Snowflake, meanwhile, is mining query history to infer metrics, facts, filters and relationships from real SQL usage. Frequently used expressions and joins are translated into candidate semantic model elements with human-readable names and descriptions. Combined, these approaches shift analysts away from writing yet another near-duplicate query or pipeline. Instead, they review AI-suggested models, validate transformations and focus on higher-value experimentation and storytelling.
What This New Wave Means for Data Teams and the Market Outlook
Across Omni, VAST Data, Databricks and Snowflake, a pattern emerges: AI is moving from a layer on top of analytics to something embedded into storage, modeling and workflow orchestration. For analysts, that means less time building fragile pipelines, more time curating semantic models and validating AI-assisted outputs. For engineers, unified platforms and AI OS strategies promise fewer bespoke integrations and more consistent governance. Still, adoption will not be frictionless. Organizations must align permissions, define trusted metrics and retrain teams to treat AI as a collaborator rather than a black box. With the AI in analytics platforms market projected to grow rapidly, competition will intensify around who can best balance self-service flexibility with enterprise control. Data teams that lean into semantic governance and AI-native workflows will be best positioned to turn this tooling wave into durable productivity gains.