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Context is the make-or-break layer for AI in production

Your AI model might not be the reason it fails in production. Missing or unclear context throws off the model, leading to untrustworthy outputs that the business can't act on.

Stephen Catanzano
By
  • Stephen Catanzano, Senior Analyst
  • Omdia

    Intelligence and advice powered by decades of global expertise and comprehensive coverage of the tech markets.
Published: 04 Jun 2026

As AI moves from contained pilots into production and agentic systems, enterprises have learned a hard lesson: the model is rarely the problem. Meaning is, and the missing ingredient is context.

Most enterprises have plenty of data. What they lack is a shared, governed layer of meaning that travels with that data wherever it lives. That business context layer becomes the foundation for everything else.

Why context matters

Context is the business meaning wrapped around raw data: the definitions, relationships, rules and lineage that tell a model what a column, a metric, or an entity signifies. An AI system that does not understand what data represents produces fast, fluent and confidently wrong answers at a production scale that erodes trust faster than any accuracy benchmark can rebuild.

Context is the difference between a number that signifies customers and a number that signifies quarterly net revenue under the current recognition policy. Without it, "churn" means one thing to finance, another to product and a third to sales. An agent asked about churn will blend those contradictions into outputs no one can act on.

Context expands scope

Context used to sit quietly in a catalog that analysts consulted by hand. Now it must do the following:

  • Be delivered at runtime to queries, models and autonomous agents acting on data without a human in the loop.
  • Span fragmented environments across clouds, lakes, SaaS applications and operational systems.
  • Stay governed. An agent that can open tickets, update records and trigger workflows is only as safe as its inherited business rules and guardrails.

An example of a company building for this reality is Starburst. Rather than forcing organizations to centralize data before AI can use it, its Enterprise Intelligence Platform brings AI to the data. Its AI-ready data products combine governed data, metadata and business definitions into reusable, trusted assets that provide consistent business context at runtime regardless of where data resides. Starburst's AI Data Assistant then delivers that context where business users work -- inside applications, workflows and agents -- so meaning follows the question instead of rebuilding it for every model.

But this strategic point is bigger than any one vendor. Context, not compute, is becoming the gating factor for production AI. Organizations continuing to layer models on fragmented, undefined data will keep receiving fast answers they cannot trust, while those investing in governed, distributed context will scale AI with confidence.

Stephen Catanzano is a senior analyst at Omdia, where he covers data management and analytics.

Omdia is a division of Informa TechTarget. Its analysts have business relationships with technology vendors.

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