Is Your Data Ready for AI? A 5-Step Checklist for Copilot in Power BI

• December 23, 2025
• Sameer Mohammed
• no comments

Artificial Intelligence (AI) is transforming how organizations analyze and act on data. With Copilot in Power BI, businesses can generate insights faster, ask natural language questions, and automate reporting. But here’s the catch: AI is only as good as the data it works with.

If your data is incomplete, inconsistent, or insecure, Copilot’s recommendations may be misleading. That’s why preparing your data is the most critical step before adopting AI. In this blog, we’ll walk through a 5-step checklist to ensure your data is truly AI-ready.

Step 1: Establish Strong Data Governance

Governance is the foundation of trustworthy AI. 

• Define Policies: Set clear rules for data ownership, access, and compliance. 

• Align with Standards: Ensure you meet industry regulations like GDPR, HIPAA, and SOX. 

• Secure Access: Use role-based access control (RBAC) to ensure only authorized users can view sensitive dashboards. 

• Track Lineage: Use tools like Microsoft Purview to understand exactly where your data—and your AI’s insights—originate. 

Without governance, AI insights risk being inaccurate or non-compliant.

Step 2: Ensure High Data Quality

AI thrives on clean, consistent, and complete data. 

• Cleanse Data: Use Power Query to remove duplicates, fill missing values, and standardize formats. 

• Monitor Health: Continuously check for outdated or irrelevant records. 

• Set KPIs: Establish data quality metrics (accuracy, completeness, timeliness) to measure readiness. 

High-quality data ensures Copilot delivers reliable recommendations.

Step 3: Integrate Data Sources Seamlessly

Disconnected data silos limit AI’s potential. 

• Unify Sources: Connect ERP, CRM, and cloud sources into a unified model using Microsoft Fabric pipelines for ingestion and transformation. 

• Leverage IoT: Ingest high-volume data from connected devices. (See how to handle this in our guide: Power BI and IoT: Real-Time Insights).  

• Stream Data: Enable real-time streaming datasets for instant updates. (Read more on Top Use Cases for Real-Time Streaming). 

• Build Semantic Models: Create a “single source of truth” in Power BI so metrics are consistent across every dashboard. 

Integration ensures Copilot has a holistic view of your business.

Step 4: Secure Your Data 

AI adoption must prioritize security. 

• Encryption: Encrypt sensitive data streams both in transit and at rest. 

• Row-Level Security (RLS): Apply RLS to protect confidential information based on the user’s role. 

• Audit Logs: Monitor activity logs to see who is asking Copilot what questions. 

• Authentication: Implement multi-factor authentication (MFA) for all dashboard access. 

Security builds trust in AI-driven insights.

Step 5: Scale for Growth 

AI workloads demand scalable infrastructure. 

• Optimize Performance: Use aggregations and incremental refresh to keep reports snappy. 

• OneLake: Utilize OneLake in Microsoft Fabric to store massive datasets without duplication. 

• Future-Proofing: Design models that can handle growing IoT and transactional data volumes. 

Scalability ensures Copilot can handle tomorrow’s data challenges.

Is Your Business AI-Ready?

Copilot is a powerful engine, but your data is the fuel. Using low-quality fuel in a high-performance engine will only lead to breakdowns. Investing in your data foundation today doesn’t just enable Copilot—it enables the advanced predictive capabilities we discussed in our previous post on Generating Predictive Insights with AI.

Not sure where your data stands?

Partner with AQL Technologies for a Data Readiness Assessment:

• Audit your current architecture
• Identify gaps in your semantic models
• Provide a roadmap to a Fabric-ready environment
• Start your AI readiness journey with AQL Technologies today.

[Contact Us to Get Started]