For the past two years, the enterprise AI conversation has been dominated by models. Larger models. Better benchmarks. Faster inference. More agents.

And as organizations move from experimentation into production, a different reality is emerging: better models do not automatically translate into better business outcomes. This is the AI value gap we've talked about before—the growing disconnect between what foundation models are capable of and the value enterprises are actually realizing from them.

The reason is becoming increasingly clear: most business decisions are not made from documents alone; information alone is not enough if context is not executable.

In other words, pricing a product, allocating inventory, optimizing a supply chain, detecting fraud, managing network operations, or planning capital investments depends on whether AI systems can not only retrieve the knowledge, but access the structures that govern how a business actually works: relationships, calculations, policies, and decisions.

This is where decision intelligence enters the picture.

In this conversation with Paul Nashawaty of ECI and the AppDevANGLE podcast, RelationalAI founder and CEO Molham Aref discusses why the next phase of enterprise AI will be defined less by model size and more by the quality of the context surrounding those models.

The discussion explores:

• Why the AI value gap persists despite rapid advances in foundation models
• The limitations of treating context as documents alone
• Why structured, relational enterprise data remains central to business decision-making
• How semantics, ontologies, and reasoning systems help bridge the gap between information and action
• Why context is becoming as important to AI economics as it is to AI quality.

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Full conversation below --

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