AI-Ready Data Infrastructure: From Siloed Storage to Query-Ready Assets
AI-ready data infrastructure is the set of software and architectural patterns that turn fragmented enterprise data into immediately queryable, governed and AI-consumable assets without rebuilding pipelines or physically relocating files. In most enterprises, data bottlenecks have replaced algorithmic limits as the main barrier to scaling AI. Data lives in silos, ingestion frameworks and ETL chains that add latency, complexity and cost, especially for unstructured data that lacks a consistent schema. This slows AI agents, real-time analytics and machine learning, even when budgets and models are in place. Vendors are now targeting the infrastructure layer, automatically organizing data as it lands, extending data lakehouse architecture to raw files and exposing query-ready unstructured data through open table formats. The emerging goal is clear: unlock existing storage for AI at scale while avoiding bulk copying, re-platforming and constant pipeline maintenance.
AdTech as a Stress Test for AI-Ready Data Lake Infrastructure
AdTech has become a proving ground for AI-ready data infrastructure because advertising platforms handle extreme data volumes and low-latency demands. Eon reports that advertising systems routinely process hundreds of billions of events daily across bidding, attribution, audience and reporting pipelines, where any delay directly affects campaign performance. Traditional stacks respond with layers of ingestion, transformation and quality tools that leave teams maintaining infrastructure instead of improving AI models. Eon’s approach converts operational cloud data into an open Apache Iceberg-based data lake as it lands, while it optimizes storage, keeps metadata current and validates quality. Rise, which processes more than 200 billion events and over a petabyte of data every day, uses this model to gain sub-minute freshness, automated data quality checks, and significant compute savings without adding new pipelines. This shows how AI-ready data infrastructure can remove enterprise data bottlenecks in one of the most demanding sectors.
Query-Ready Unstructured Data Without Moving a Single File
Unstructured content is the largest and least used data class in many enterprises, and the need to move it has been a major barrier to AI. Komprise notes that although unstructured data is over 80% of an enterprise footprint, less than 1% is used in AI. The cost and complexity of copying petabytes from NAS and multi-cloud storage, combined with missing schema and poor quality, have kept most of this data dark. Komprise Transparent File Tables address this by indexing files into a global metadatabase and presenting a structured Apache Iceberg table that contains enriched metadata plus pointers to the original files. AI and analytics platforms such as Snowflake and Databricks can query this table as if the data were local, while Komprise Transparent Move Technology dynamically loads only the files needed, when they are needed. As a result, query-ready unstructured data becomes available without bulk transfer.
Extending Data Lakehouse Architecture to Operational and File Data
Both Eon and Komprise show how data lakehouse architecture is evolving from a static analytics destination into an AI-ready fabric that spans operational and file-based data. Eon turns streaming operational workloads into open Iceberg tables as data lands, making AI agents and analytics tools consumers of a continuously organized lake instead of downstream batch outputs. Komprise, in parallel, allows IT teams to export subsets of its global metadatabase as Transparent File Tables into lakehouses, where data experts can query them without needing direct access to the underlying management layer. In both cases, the lakehouse becomes the logical query hub for structured and unstructured data from many systems. This reduces the need for separate ETL tooling, semantic layers and bespoke ingestion for each AI use case, and shifts focus to governance, metadata enrichment and shared open table formats.
From ETL Drag to AI at Scale Across Data-Intensive Industries
The pattern emerging from AdTech and unstructured data management is relevant to any data-intensive sector, from SaaS and financial services to e-commerce and media. Enterprises want to expand AI without multiplying ETL jobs, duplicating storage or accepting weeks-long ingestion projects before pilots can start. By organizing operational data into open table formats at ingest and exposing file estates as query-ready unstructured data, AI-ready data infrastructure turns existing storage into a live foundation for analytics, machine learning and AI agents. One quotable insight captures the shift: “Every enterprise we talk to has the same problem in a different costume – the AI initiative is funded, but the data isn’t ready or readily accessible,” said Ofir Ehrlich, Eon’s CEO and co-founder. As more vendors follow this model, AI bottlenecks are likely to move from data plumbing back to where they belong: model design and business outcomes.
