AI concepts
AI-ready data (data ready for an AI deployment)
Data that meets Gartner's five technical conditions, making it possible to train models and keep them running in production. Alignment with a specific use case, clear ownership of the asset, automated pipelines with quality gates, maintained metadata, and continuous quality control. Without all five conditions, AI learns chaos and replicates it at scale.
Primary source: Gartner Data & Analytics Summit 2024, Informatica CDO Insights 2025
Gartner introduced the AI-ready data category in 2024 as a counter to the phenomenon where firms treat their own operational data as ready for an AI deployment simply because it is catalogued in a warehouse. The definition is deliberately strict: most enterprise data today does not meet this standard.
The five conditions
Use case alignment. The data is not generic, it is matched to a specific business problem. A set of ERP transactions is not AI-ready for a churn prediction model until it is enriched with behavioural signals.
Asset-level ownership. Every data asset has an owner accountable for its quality. Without this, the data belongs to no one, meaning accountability disappears.
Automated pipelines with quality gates. Data flows between systems through automated processes that reject faulty records before they move on.
Live metadata. Schema, lineage, and column semantics update in real time, not once a year in Excel.
Continuous quality monitoring. Quality measured across five dimensions (completeness, consistency, accuracy, timeliness, uniqueness) is reported continuously, not during an audit.
The scale of the problem
Informatica CDO Insights 2025, on a sample of 600 data leaders: 43 percent name data quality as the main barrier to AI deployments. This is the single largest technical cause of GenAI project failures, larger than model choice, larger than infrastructure, larger than talent availability.
Polish context
Mid-sized and large Polish corporations have three characteristic deficits. First, ERP systems implemented four to seven years ago with thirty percent of master-data fields empty. Second, CRM without integration with operational systems. Third, key commercial data in Excel files on local drives rather than in databases.
Diagnosing the level of AI-readiness for specific use cases in your company is an element of the AI Readiness Audit.