Why your AI will fail without a semantic layer

Why AI systems fail without constraints

Imagine a retail company using an AI system powered by an LLM to evaluate product sales performance. You ask the system to determine “What was the total adjusted revenue for Product X in 2023?” to inform strategic decisions.

Without proper safeguards, the AI system might query raw data from various tables across a database and attempt to generate a result that is likely inaccurate.

Why? Because “adjusted revenue” wasn’t clearly defined in your system. Does it account for discounts, returns, or currency fluctuations?

If the underlying data contains inconsistencies—like mismatched definitions of "revenue" or incomplete product details—the system might confidently generate recommendations based on flawed calculations. This could lead to misguided decisions, like overinvesting in a poorly performing product or underestimating demand for a successful one, costing the company time and money.

Now, consider a slightly different culprit. Instead of a vague question, the problem lies with an undefined metric in the data itself, like a column labeled "RevAdj_2023". Without clear metadata or context, the AI system cannot reliably interpret or use it. These ambiguities force the system to rely on incorrect assumptions, further compounding the risk of errors and unreliable outputs.

This isn’t just a minor inconvenience. It can result in poor decisions, wasted resources, and a loss of trust in AI systems.

We know that businesses are eager to integrate LLMs into their operations, whether for chatbots, smarter analytics, or creating entirely new innovations. But to realize the potential of these systems, they must be implemented with proper safeguards.

How a semantic layer solves this problem

That’s why AI systems need a semantic layer. It’s a centralized framework that defines key metrics and business logic, embeds metadata, and provides business logic and context for the data your AI system (or any other downstream system) queries.

The semantic layer enforces guardrails, ensuring the AI system queries only approved, governed, and contextualized metrics. It maintains consistency and ensures metrics and business logic are applied accurately across teams and systems.

In the earlier example, if the system receives a request like “What was the total adjusted revenue for Product X in 2023?” the semantic layer flags the query as invalid because no such metric exists. Instead, the semantic layer enables the system to steer the user toward valid, pre-defined metrics, for example:

• Revenue adjusted for discounts and returns for ProductX (2023)
• Adjusted revenue growth for Product X (2023 vs 2022)
• Revenue of active accounts for Product X (2023)

This ensures the AI system provides accurate insights while prompting clarifications like: “There isn’t a metric for ‘2023 total adjusted revenue for Product X.’ Did you mean total revenue adjusted for discounts and returns for Product X in 2023? Or something else?”

Similarly, as in the earlier example with "RevAdj_2023" the semantic layer provides important context by embedding metadata and clear definitions into the data pipeline. "RevAdj_2023" could include metadata and clear documentation that explains exactly what revenue adjustment covers—like the metric name, a description, calculation logic, and usage guidelines. This ensures there’s no ambiguity.

Without these constraints, even the most advanced AI systems can deliver inaccurate outputs. But with a semantic layer in place, you’re creating a single source of truth for your data—centrally managed and accessible to both business users and AI systems. This becomes the foundation for driving successful and trustworthy AI initiatives.

Why a semantic layer is key to AI success

To succeed, your AI systems require these critical essentials:

1. Consistency for reliable insights

Without a semantic layer, your data sources might have conflicting definitions or inconsistent calculations. For example, one team might define "revenue" as gross sales, while another subtracts discounts and returns. A semantic layer aligns metrics and business logic to a single, consistent definition, ensuring AI systems always query trustworthy data.

2. Governance to protect sensitive data and ensure consistency

Effective governance is about more than just securing sensitive data—it’s about ensuring consistent, accurate, and trustworthy insights across the organization. A semantic layer enforces governance by:

• Restricting access to sensitive metrics, ensuring the right people access the right data.
• Tracking changes to metrics and logic with a clear audit trail.
• Preventing unauthorized access to sensitive data.

For example, the semantic layer can prevent an HR team from accessing finance metrics or stop a customer-facing AI agent from exposing sensitive client data. Without these controls, your AI systems risk producing outputs that are not only inaccurate but also legally or ethically non-compliant.

Governance also ensures consistency when business logic or metric definitions change. Imagine the executive team decides to update the definition of "total adjusted revenue" to include discounts. Without a semantic layer, this definition change would have to be accounted for within any possible downstream system (a BI tool, an LLM, etc) that might query that metric, adding tedious overhead and unnecessary risk; it's just a matter of time before teams who query that metric get conflicting answers, resulting in confusion and compromised trust. A semantic layer gives organizations a place to define metrics once—centrally—and ensures updates are automatically applied across any and all connected systems, so AI interfaces, LLMs, and users always work with the latest, consistent, approved definitions.

3. Context for smarter decision-making

AI systems need metadata and logic to understand relationships between tables, interpret the meaning behind columns, and apply correct business logic. For example, does "customer churn" refer to a canceled subscription, or does it mean inactivity over a certain period? Without clear definitions, AI systems can make incorrect assumptions, leading to flawed outputs.

A semantic layer solves this by embedding these essential elements directly into the data pipeline, helping uncover the “why” behind the data by:

• Defining relationships between data elements: A semantic layer links tables (e.g., connecting "Customer ID" in "Customers" to "Transactions") so the AI system understands relationships like how purchases relate to customers or revenue to products. By defining these relationships explicitly, you can feel confident that joins between tables will always be performed correctly.
• Embedding metadata for clarity: Metadata defines what each field means, how it’s calculated, and how it should be used. For example Metric Name: customer_churn_rate Description: "The percentage of customers who canceled their subscription within the last 30 days." Calculation Logic: count(churned_customers)/count(total_customers) Usage Guidelines: "Only use for customers with active subscriptions in the last quarter."
• Standardizing business logic: It embeds rules and calculations like "'revenue'= price - discounts - returns" to prevent mismatched definitions across teams.

By embedding relationships, metadata, and logic, the semantic layer gives AI systems the context needed to deliver accurate insights. This makes it possible to handle complex queries like, "What’s the average revenue per customer who purchased a specific product last month?"

4. Speed and scalability for faster adoption

When data queries are slow and complicated, users abandon AI systems and turn to data teams for quicker answers, stalling AI adoption. The semantic layer changes this with smart caching and a centralized metric store. Instead of scanning raw tables for every query, AI systems pull precomputed, validated metrics, delivering faster results.

It also streamlines scaling by letting teams reuse standardized, governed metrics across projects. This removes the need to rebuild logic for every new initiative, speeding up AI adoption while ensuring accuracy and reliability.

Build AI the right way: Start with a semantic layer

Even the most advanced AI systems will fail without the right foundation. A semantic layer ensures your data is consistent, governed, and full of the context AI systems need to deliver meaningful results.

Before kicking off your next AI project, ask yourself: Is my data ready for AI? If not, it might be time to prioritize a semantic layer—because the success of your AI strategy depends on it.

The dbt Semantic Layer translates dbt models into well-defined business metrics that build the foundation for clean, reliable, and AI-ready data. It’s designed to seamlessly integrate with your dbt workflow, ensuring your data is not only accurate and governed but also aligned with your business goals.

Ready to build AI the right way? Schedule a demo of the dbt Semantic Layer today.