How to migrate from Power BI to an AI-native BI tool without losing your reports

Migrating from Power BI to an AI-native BI tool like Querio can seem daunting, but it’s manageable with a clear plan. The process focuses on preserving report logic, ensuring data accuracy, and improving workflows by leveraging live data and centralized business metrics. Here’s how to approach it:

• Inventory Power BI Assets: List all dashboards, reports, datasets, and dataflows. Prioritize those critical to business needs while retiring unused ones.
• Design Querio Architecture: Shift to a live data model connected directly to your data warehouse (e.g., Snowflake, BigQuery). Centralize business logic in Querio's context files, kept in a git repo you own, as your semantic layer for consistency.
• Migrate Logic and Data Models: Rewrite Power Query and DAX logic into SQL or dbt models. Validate calculations and ensure alignment with legacy reports.
• Rebuild Dashboards: Use Querio’s agents to recreate dashboards with live data and consistent metrics, then promote each one through a pull request.
• Roll Out and Retire Power BI: Start with a pilot phase, train users on natural-language querying, and gradually phase out Power BI.

This step-by-step migration ensures minimal disruptions while transitioning to a more efficient, real-time analytics platform.

Step 1: Take Stock of Your Power BI Assets

Before diving into migration, take the time to inventory all your Power BI assets. Skipping this step often leads to overlooked dependencies and underestimating the project’s scope, which can bring everything to a standstill.

Build a Full Inventory of Your Power BI Environment

Your inventory should include every type of asset: dashboards, reports, datasets (semantic models), dataflows, and datamarts. For each asset, record key details such as the owner, business purpose, target audience, refresh schedule, and gateway configuration. To save time, leverage tools like Power BI Admin APIs and PowerShell cmdlets for automating data collection. This can help you avoid hours of manual spreadsheet work [5][6][7].

Compile all this information into a single master reference document - essentially your migration map. This document should include every workspace, semantic model, and permission set, providing a comprehensive guide for the rebuild phase.

"Every legacy report must be evaluated for its data sources, refresh model, semantic structure, and dependency chain. Any migration plan built without this architectural awareness will eventually run into bottlenecks." - Dossier Analysis [8]

Sort Reports by Complexity and Priority

Once your inventory is complete, the next step is to organize it. Not all reports are worth migrating. Begin by categorizing them based on how often they’re used and their complexity. For reports that haven’t been used in over a year, consider retiring them.

Here’s a helpful way to group the remaining reports:

Report Category	Complexity	Priority	Key Characteristics
Executive Scorecards	Medium–High	Critical	High-level KPIs, used by leadership [9][2]
Operational Dashboards	Medium	High	Interactive monitoring, frequent use [9][2]
Financial Statements	High	High	Rigid hierarchies, complex formatting [2]
Self-Service/Sandbox	Low–Medium	Medium	Ad-hoc exploration by analysts [2]
Redundant/Legacy	Low	Low	Low usage, outdated sources [10]

This tiered approach ensures you focus on the most important reports first, while also identifying opportunities to consolidate or eliminate unnecessary ones. As Melissa Coates, Data Platform MVP, explains: "A migration can provide an opportunity to deliver improvements... It can include consolidating multiple reports into one, or eliminating legacy items that haven't been used for some time." [4]

Document Data Sources and Transformations

For each report slated for migration, map out its entire data chain. This includes identifying:

• Source systems: SQL Server, APIs, flat files, etc.
• Connection modes: Import, DirectQuery, or Live Connection.
• Power Query (M) transformations: Every transformation applied before the data reaches the semantic model [11][12].

Pay extra attention to DAX measures and calculated columns, as they represent critical business logic. Separate your calculation logic (e.g., defining metrics like Gross Margin %) from your presentation logic (e.g., formatting choices like bold text or red font). Calculation logic should reside in the semantic layer, while presentation logic belongs in the visual layer [2][11]. This distinction, made early on, can prevent significant rework later.

Additionally, flag any Row-Level Security configurations to ensure they’re validated during migration.

With your Power BI asset map complete, you’re ready to move on to designing Querio’s target architecture.

Step 2: Design Your Target Architecture in Querio

Now that you’ve got your Power BI asset inventory ready, it’s time to map out how everything will fit into Querio. The goal here is to create a cleaner, scalable structure that supports live data workflows. By building on your Power BI inventory, this step ensures a smooth transition to real-time analytics.

Move to a Warehouse-First Model

One of the key shifts in this migration is understanding how Querio handles data differently. Unlike Power BI, which relies on data imports, Querio queries your data warehouse directly, whether it’s Snowflake, BigQuery, Amazon Redshift, ClickHouse, or another supported platform. This approach removes extracts and the worry about outdated data. Live connections mean your reports always reflect the most current insights, which is critical for real-time decision-making.

Rebuild the Semantic and Metrics Layer

Consistency across reports is crucial, and Querio achieves this with shared context: table and column docs, metric definitions, and business terms, stored as plain files in a git repo your team owns. You define them once, and every dashboard, ad-hoc query, and agent answer uses the same definitions. Changes open a pull request under the repo's own reviewers. If you use dbt, Querio reads your MetricFlow metrics directly, so there's no second copy. For example, renaming technical fields like TR_AMT to something clearer like Total Revenue not only improves readability but also helps AI tools interpret data more accurately using an AI BI engine.

If you’re dealing with complex DAX logic that can’t be expressed as simple metrics, move that logic into the warehouse using SQL models (via dbt) and link it to Querio. During the migration, run a pilot phase to validate Querio’s query results against your Power BI reports. Comparing figures line by line is the most reliable way to ensure calculations match before decommissioning old reports.

Decide Where Transformations Should Live

To keep your architecture streamlined, assign transformations based on their purpose:

• Core transformations like joins, filters, and aggregations belong in the warehouse, ideally as dbt models.
• Business metric definitions should live in Querio’s context layer.
• Exploratory or ad-hoc logic fits best in Querio’s notebook environment.
• Formatting for presentation stays in the visual or dashboard layer.

Transformation Type	Where It Should Live
Joins, aggregations, filters	Warehouse SQL model (e.g., dbt)
Business metric definitions	Querio context layer
Ad-hoc or exploratory logic	Querio notebook
Presentation formatting	Visual/dashboard layer

This clear separation keeps your models clean and makes future updates faster. It’s the same principle you applied in Step 1: separating calculation logic from presentation logic, but now applied to the entire architecture.

"Calculate in the model, present in the report. When calculation and presentation remain separated, models stay cleaner and easier to evolve." - Inforiver [1]

With your architecture mapped out, the next step is to migrate your data models and reporting logic to Querio.

Step 3: Migrate Data Models and Report Logic

With the target architecture from Step 2 ready, it’s time to move your data models and report logic into Querio.

Repoint Data Sources to the Warehouse

Before diving into report logic updates, make sure your warehouse is fully prepared for the migration. This means ensuring core tables are clean, dbt models are properly configured, and your data is organized according to the plan from Step 2. Once everything checks out, connect Querio. On Snowflake, BigQuery, and Databricks, per-user OAuth means queries run as each person, so the warehouse's access rules still apply. On Postgres, Redshift, and ClickHouse, Querio uses a service account plus its own permission policies.

Next, revisit the Power BI inventory you created in Step 1. Map each Power BI dataset to the corresponding warehouse table or dbt model. To make deployment across environments smoother, parameterize your data sources.

Rewrite Power Query and DAX Logic

Start by converting core transformations into SQL or dbt models. Move business metric definitions into Querio's centralized semantic layer to maintain consistent metrics across your reports. Querio’s agent can help here. Ask it to translate a DAX measure into SQL, then open the notebook behind the answer and check the output before you save any metric.

Keep an eye on dependency chains during this process. Even a small error in base measures can cascade into larger issues. Audit these chains early and simplify wherever possible. Querio's CI check helps too. Renaming a dbt model flags every dashboard, query, and context file that uses it before the pull request can pass. That only works if the foundational logic is solid.

Once the logic is rewritten, validate the new models to ensure they align with your legacy reports.

Validate Data and Logic Accuracy

After migrating and rebuilding your logic, double-check that everything works as expected. Run identical reports in both systems, using the same date ranges and filters, and compare totals line by line. If discrepancies pop up, trace them back to their source - whether it’s a transformation mismatch, a metric definition issue, or an incorrectly applied filter.

Don’t stop at the numbers. Test interactive features like filters, drill-downs, and date slicers to confirm they behave correctly. Pay attention to load times too - most visuals should load data within 10 seconds. If they don’t, the problem is likely in the warehouse model, not Querio. Address it at the source.

Validation Aspect	Querio Method
Logic verification	Inspectable SQL/Python in a notebook you can rerun
Metric consistency	Shared metric definitions in the git repo
Error correction	Fixes to context and dashboards go through pull requests
Data accuracy	Live warehouse connection (real-time)
Security validation	SOC 2 Type II + row-level security

Finally, update any external automation or API calls tied to Power BI report IDs to reflect the new Querio artifact IDs.

Step 4: Rebuild Dashboards and Workflows in Querio

Now that your data models are validated and your logic is successfully migrated, it’s time to rebuild the dashboards and workflows your team relies on daily.

Recreate Dashboards with Live Data

Start by focusing on high-priority dashboards. Map each Power BI visual to its equivalent in Querio - features like trend line charts, KPI cards, and summary tables have direct counterparts, reflecting the essential features of modern business intelligence tools. Querio uses inspectable SQL for all queries against the live warehouse, so there’s no need for data extracts. This ensures that dashboards always reflect the most up-to-date data.

The shared context you set up in Step 2 comes into play here, applying the same metrics and definitions across dashboards. This eliminates the risk of inconsistent numbers across reports. Each dashboard is marked experimental, team, or trusted, so users know which ones have been reviewed.

Use AI Agents to Speed Up Migration

Querio's agents can make the dashboard reconstruction process faster and more accurate. Describe what you need in plain English, and the agent writes the SQL and Python in a notebook you can check and rerun. Claude Code and Cursor can do the same work through Querio's CLI, which helps when you're rebuilding many reports. If you’re working on complex reports that previously used multi-step DAX calculations, open the notebook and confirm the logic before you add the result to a dashboard. Promoting the finished dashboard opens a pull request your reviewers approve, so you maintain the integrity of your report logic and data throughout the migration.

Set Up Scheduled and Recurring Workflows

Querio automations run on a schedule. Each one is a scheduled prompt or SQL query that delivers results daily, weekly, or monthly to email or Slack. For monitoring, a scheduled prompt can check a metric on live warehouse data and report back, so stakeholders stay informed without additional effort. Automation runs use no AI credits.

Workflow Type	How to Replicate in Querio
Scheduled report delivery	Automation that sends a scheduled SQL query to email or Slack
Recurring deep-dive analysis	Scheduled prompt, with notebooks stored as files in the git repo
Threshold-based alerts	Scheduled prompt that checks a live warehouse metric and reports to Slack
Automated data refresh	Live warehouse connection; refreshes use no credits

Once your dashboards and workflows are in place, you’ll be ready to move on to the rollout phase and complete the transition from Power BI.

Step 5: Roll Out Querio and Retire Power BI

Once models and dashboards have been successfully migrated, the next step is to roll out Querio while phasing out Power BI. By approaching this transition in carefully planned stages, you can minimize disruptions to daily operations.

Start with a Pilot and Gather Feedback

Kick things off by running a pilot in one business area, such as finance, marketing, or operations. During this phase, operate Querio and Power BI side by side for 4–8 weeks. This overlap gives users time to compare results, ensuring key metrics align while also identifying any issues or edge cases before fully switching platforms.

Use this period to engage your internal champions - the power users who are responsible for most of the analytical activity. Their early involvement is crucial for refining the system and gaining broader team support. Once the pilot team verifies that Querio can handle at least 85% of critical queries[3] accurately, you can confidently move to a wider rollout. This initial phase sets the stage for a smooth transition across the organization.

Train Teams on AI-Native Workflows

Querio introduces a new way of working, moving from traditional pre-built reports to using plain English queries. To help teams adapt, structured training is key.

Focus on three main workflows during training:

• Natural-language querying: Teach users how to ask questions directly in Querio.
• Notebook-based analysis: Show how to create and share deeper analyses.
• Governed self-serve access: Emphasize how the shared semantic context layer ensures consistent results.

Make sure to demonstrate how users can review the SQL behind their queries and save these for future use. Additionally, Querio's context files (business terms plus table and column docs) guide the agent toward your business rules, which cuts down on repeated clarifications during the adjustment period. Updating them opens a pull request, so the data team stays in charge of what the agent learns.

Once your teams are comfortable with these workflows, you can begin decommissioning Power BI assets.

Decommission Power BI Assets Safely

Before retiring Power BI, conduct an audit to understand which assets are actively used. Studies suggest that about 40% of Power BI dashboards go unused, making them safe to retire without affecting operations. Use Power BI Service metrics to identify the most-viewed dashboards and workspaces, focusing your efforts on decommissioning the rest.

For dashboards that are still critical, document key DAX measures and redirect URLs to avoid broken links. After confirming system stability, cancel Power BI Pro licenses and decommission Premium capacity.

Here’s an example timeline for a phased migration:

Phase	Weeks	Key Activities
Audit & Scope	1–2	Identify top workspaces by usage; export DAX; map dataflows
Migration, Rebuild & Training	3 to 10	Translate DAX to SQL and MetricFlow metrics; rebuild top reports; validate in parallel; train users; redirect URLs
Retirement	11–12	Decommission Power BI licenses; archive final report versions

To prevent dashboard sprawl, implement a formal retirement policy where outdated dashboards are removed on a set schedule. This approach helps maintain user trust and keeps your system clean and efficient[14].

Conclusion: Key Steps for a Smooth Migration

Migration isn’t just flipping a switch - it’s a carefully planned process. The five phases - auditing your existing assets, designing your target architecture, migrating data models, rebuilding dashboards, and rolling out to users - are designed to ensure accuracy and keep your teams running smoothly during the transition.

Phase	Key Activity	Goal
Preparation	Audit Assets	Identify critical assets and business logic
Integration	Connect warehouse (Snowflake/BigQuery)	Query live warehouse data
Modeling	Build context in the git repo	Define key metrics and relationships
Recreation	AI Agents	Reproduce Power BI visuals in Querio
Rollout	Pilot program & parallel testing	Validate accuracy and train users

The migration process revolves around a warehouse-first approach, which queries your data warehouse directly, with each user's own warehouse permissions where the warehouse supports it. Reports stay accurate during and after the migration.

Another critical element is the centralized semantic layer, kept as files in your git repo, which standardizes metrics like Gross Margin or MRR across all queries, dashboards, and agents, eliminating confusion and ensuring consistency.

"At scale, consistency is a design decision - not an outcome." - Inforiver [2]

Finally, a successful pilot program - able to handle most essential queries - signals readiness for a full rollout. By following these steps, your migration process can maintain continuity, accuracy, and efficiency from start to finish.

FAQs

How do I translate complex DAX measures into SQL without changing results?

To convert complex DAX measures into SQL while maintaining the same results, start by carefully examining the logic behind the DAX expressions. Then, replicate that logic using SQL's built-in functions and expressions. It's crucial to test the SQL queries thoroughly to confirm they produce identical outputs to the original DAX measures. You can also use migration tools to simplify and validate the process. These tools not only ensure precision but can also minimize runtime translation efforts and boost overall performance.

What should stay in the warehouse vs Querio’s semantic layer?

When deciding what belongs in the warehouse versus Querio’s semantic layer, it all comes down to how the data is utilized.

• Warehouse: This is where raw data, live connections, and stable models reside. It ensures consistency and delivers strong performance for foundational data needs.
• Semantic layer: Ideal for managing business logic, metrics, and KPIs. It helps maintain governance, minimizes duplication, and streamlines maintenance.

To put it simply: store raw data in the warehouse and handle business logic in Querio’s semantic layer, which lives as files in your git repo (or inside your dbt repo, so a model change and its context go in one pull request).

How can we validate parity between Power BI and Querio during the pilot?

To check for parity, focus on comparing data accuracy, report consistency, and performance between the tools. Start by running the same queries in both Querio and Power BI to confirm that their outputs align. Ensure dashboards display identical metrics and visualizations to avoid discrepancies. Test performance using live data connections, and double-check governance settings such as permissions and compliance protocols. These steps will help determine if Querio meets or surpasses Power BI in terms of data reliability and usability during the pilot phase.