Data Analytics and Power BI Copilot Enablement

Prior to engaging Saints & Masters, the customer had activated Copilot for Power BI but was unable to derive reliable value from it. The underlying data and semantic foundation was not ready for AI-driven analytics, creating several compounding issues:

• •Inaccurate Copilot Reports: AI-generated reports were inconsistent and lacked business relevance — producing outputs that could not be trusted for decision-making.
• •Weak Semantic Foundation: An unstructured data model and undefined KPIs impacted Copilot's ability to understand business context, resulting in misaligned measures, broken relationships, and meaningless aggregations.
• •Low User Adoption: Users were unable to reliably generate reports using Copilot prompts, eroding confidence in the tool and reinforcing dependency on technical teams for manual reporting.

Saints & Masters adopted a structured, four-phase approach to diagnose the root causes of Copilot underperformance and rebuild the foundational layers required for AI-driven analytics:

• •Copilot Readiness Assessment: Assessed the existing Copilot implementation and identified gaps in data modelling, semantic layer, and report structure impacting output accuracy.
• •Semantic Model Optimisation: Refined data relationships, measures, and KPI definitions to provide clear business context and improve Copilot understanding of the underlying data.
• •Data Structuring & Standardisation: Organised datasets and metadata within OneLake to ensure consistent, governed, and business-aligned data for analytics.
• •Copilot Prompt Enablement for Reporting: Trained Copilot in Power BI to generate accurate, business-aligned reports using structured natural language prompts.

The data analytics and Copilot enablement engagement delivered measurable improvements across reporting accuracy, speed, and user adoption:

• •Accurate AI-Driven Reporting: AI-generated reports became more relevant, consistent, and aligned with business context through optimised models and prompts.
• •Adopted by Business Users: Business users confidently generate reports using natural language prompts, reducing dependency on technical teams for routine analytics.
• •Improved Copilot Accuracy: Copilot outputs shifted from inconsistent and unreliable to contextually accurate — directly attributable to the rebuilt semantic layer and standardised KPI definitions.
• •Faster Time to Insight: Significantly reduced time from query to report with prompt-driven analytics and AI-assisted reporting, eliminating the manual data gathering and formatting cycle.
• •Increased User Adoption: Confidence in Copilot-generated outputs grew as accuracy improved, driving organic adoption across business teams and reducing the backlog on technical reporting resources.