One Definition of Truth: Semantics as the Backbone of Trusted AI

AI-Ready Data Series — Part 2

In the first article, we made one point very clear: AI adoption doesn’t fail because models are weak. It fails because the answers are unreliable, untraceable, and unsafe to use in real decision-making.

That leads to the next practical question: what makes data “AI-ready” in day-to-day business reality? Not in theory. Not in a lab. In organisations where data lives across systems, teams, and tools.

One of the strongest messages from Lukas Zimmermann’s session (Strategy) at our recent AI Success Starts with AI-Ready Data event was that “AI-ready data” is not just about cleaning or moving data to a new platform. It’s about giving AI (and people) the missing business context, consistently, securely, and at scale.

And that is exactly where a semantic layer becomes a game-changer.

The real AI problem most organisations still have

Most enterprises are not one system. They are a patchwork:

• Multiple source systems (ERP, CRM, finance tools, operational apps, spreadsheets),
• Multiple analytics tools (Power BI, Qlik, Tableau, Excel, notebooks),
• Multiple teams defining metrics in parallel.

This creates a familiar kind of “structured chaos”: dashboards that look professional, but don’t agree; reports that are hard to reconcile; and definitions of key metrics that quietly drift over time.

In that environment, AI does not “fix” the mess; it accelerates it.

Because once you connect GenAI to fragmented, inconsistent, loosely governed data, you don’t just risk wrong answers. You risk:

• confident wrong answers (the most dangerous kind),
• unexplainable answers (no traceability),
• unsafe answers (privacy and access rules bypassed in practice).

So “AI-ready data” doesn’t stop at clean pipelines and governed access.
It also requires a controlled layer where business meaning and rules stay consistent across reports, dashboards, and AI use cases.

What a semantic layer actually does (in plain language)

A semantic layer is best understood as a governed translation and control layer between your data and the tools (or AI) that use it.

It does six practical things extremely well:

1. Turns technical complexity into business language
   It maps tables, joins, columns, and system-specific structures into concepts the business recognises (Revenue, Customer, Product, Region, Margin) with definitions that are not open to interpretation.
2. Creates a single source of truth for metrics
   If “Revenue” is defined once with the correct inclusions/exclusions, every tool and every user works from the same definition.
3. Provides universal access across tools
   Instead of each tool building its own logic, multiple tools can consume the same semantic definitions (BI tools, Excel, Python/R workflows, and increasingly AI-driven interfaces).
4. Enforces governance and security centrally
   Access becomes policy: who can see what, consistently, even when new interfaces (like GenAI) enter the picture.
5. Improves performance and cost predictability
   By reducing redundant logic and avoiding duplicated query patterns, teams get more stable performance and fewer “surprise” cost spikes.
6. Reduces vendor lock-in and increases agility
   Tech stacks change. A semantic layer helps maintain stable business definitions as technologies evolve.

If Part 1 was about why data readiness matters, Part 2 is about the mechanism that operationalises readiness: semantic consistency + governance.

“But we already have ETL and a data warehouse — isn’t that enough?”

ETL/ELT and data warehousing are essential. They make data available, structured, and often cleaner. But they typically do not solve these problems well:

• Business definitions live in too many places
  A calculation might exist in Power BI, another version in Excel, and a third version in a SQL view.
• Security rules are implemented inconsistently across endpoints
  Row-level access may be set in one tool, differently in another, and bypassed in ad hoc extracts.
• AI needs context that isn’t in the table schema
  GenAI doesn’t just need “columns”. It needs meaning: what is official, what is trusted, and what is restricted.

A semantic layer doesn’t replace ETL. It sits above it and answers:
“What does this data mean for our business, and how should it be used safely?”

Why semantic layers matter even more in the GenAI era

With classic BI, a user builds a dashboard, learns the tool, and usually follows some governance process.

With GenAI, the interaction changes:

• users ask questions in natural language,
• tools generate queries dynamically,
• outputs can look authoritative even when logic is wrong.

A semantic layer helps GenAI in two critical ways:

• Accuracy through context
  If business definitions and relationships are centrally modelled, AI has a stable structure to reason over.
• Trust through governance
  If access controls and approved metrics are enforced centrally, AI can’t “accidentally” expose restricted data or redefine KPIs on the fly.

That is the difference between “AI that can talk to data” and AI you can use in real business decisions.

A practical path to “semantic + AI-ready” (without boiling the ocean)

Most organisations shouldn’t try to model everything on day one. We suggest a better approach: