Your AI Agent Doesn't Know What "Revenue" Means

AI agents are fast, helpful, and increasingly embedded in enterprise workflows. But they still trip over a deceptively simple issue: definitions. Ask three teams what "revenue" means and you may get three valid, contradictory answers. Without a shared semantic layer, AI agents will pick the wrong definition, produce inconsistent outputs, and fail in production.

The Real Problem: Semantic Drift

In most organizations, business terms evolve across teams, tools, and time. That creates drift between:

Finance definitions (GAAP, net vs. gross)
Product analytics metrics (bookings, ARR, MRR)
Operational reporting (real-time, delayed, adjusted)

When an AI agent tries to reason across these systems, it faces ambiguous inputs and conflicting truths.

What a Semantic Layer Fixes

A semantic layer is not just documentation. It is a governed contract between data producers and consumers. It provides:

Canonical definitions for critical metrics
Versioning and lineage for changes
Machine-readable metadata for AI and analytics
Access control and policy enforcement

I often describe this as an Open Semantic Interchange (OSI): a shared, enforceable schema that keeps human and AI understanding aligned.

Practical Next Steps

Inventory your top 20 business terms and define owners.
Publish canonical definitions with versioning.
Enforce usage through metrics layers or semantic models.
Integrate definitions into your AI tooling and prompts.

If your AI is failing in production, it might not be the model. It might be your definitions.

Read the LinkedIn post: https://www.linkedin.com/feed/update/urn:li:activity:7424462930592673792/