AI & Copilot Readiness

To get reliable results from Microsoft Copilot, Fabric and enterprise AI, your data model, KPI definitions, relationships and security model need to be ready for AI-driven analytics.

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Data Quality

Reliable and consistent underlying data

Semantic Model

Relationships · measures · clear structure

Business Language

Shared terminology and KPI definitions

Security

RLS · permissions · governed access

AI Readiness

Copilot and AI on a trusted foundation

Trusted data + clear business logic + governance = better AI outcomes

The Right Foundation

AI Is Only as Reliable as the Data Model It Works With

Incomplete business definitions, ambiguous KPIs, incorrect relationships and uncontrolled access can lead to unreliable AI-assisted analysis.

Semantic Model Review

Assess whether tables, relationships, measures and business logic are understandable and usable by AI.

Business Terminology

Align KPI, field and measure definitions with the terminology used by the business.

Security & Governance

Review RLS, access boundaries and data sensitivity in the context of AI use cases.

Assessment Scope

Prepare Your Data Platform Before Scaling AI Usage

Technical Readiness

Review data quality, model relationships, DAX measures, performance, naming, descriptions, RLS and source dependencies.

Business Readiness

Define which questions AI should answer, which KPIs matter and which business terminology users rely on.

1. Current State

Review the existing BI and semantic model architecture.

2. Readiness Analysis

Identify data, modelling and governance gaps that limit reliable AI usage.

3. Implementation Plan

Prioritise improvements and define pilot AI use cases.

How Ready Is Your Data Platform for AI?

Let’s assess your current Power BI and data platform to identify what needs to change before AI and Copilot adoption.

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