💁 About this article
This article is part of Fugo’s Power BI knowledge base: a collection of resources answering common Power BI questions. We include notes throughout where Fugo’s integration may be helpful for displaying dashboards on digital signage.
Table of contents
A quick framing: “AI in Power BI” is not one thing
When people ask about “AI in Power BI,” they’re usually lumping together several very different capabilities that sit at different layers of the product.
Some features are:
built directly into Power BI visuals
available only in Power Query
gated behind Fabric capacity
or not actually part of Power BI at all, but commonly used alongside it (for example, ChatGPT for DAX help)
This is part of why community discussions often sound frustrated or dismissive. Expectations are rarely aligned with what’s actually shipping.
Copilot in Power BI
Copilot is Microsoft’s generative AI layer inside Power BI and Fabric. It is the most visible “AI” feature, and also the most controversial.
What Copilot can currently do:
generate report pages from natural language prompts
suggest visuals based on a semantic model
help explain or summarize report content
assist with simple DAX or transformations
generate narrative summaries for reports
enhance natural language querying (Q&A)
What it cannot reliably do:
refactor or modify existing visuals in place
apply formatting or styling rules consistently
handle complex DAX without errors
understand business logic without strong model metadata
This gap between demos and day-to-day use shows up clearly in community feedback.
Most experienced users describe Copilot as:
useful for exploration
acceptable for junior analysts
unreliable for production logic
Copilot also requires Fabric capacity (F64 or higher) or Power BI Premium (P1). It is not available on trial SKUs.
Natural language Q&A (and its transition to Copilot)
Power BI has supported natural language querying for years through the Q&A visual. This feature does not rely on generative AI.
Q&A works by:
mapping user questions to fields and measures
using manually defined or suggested synonyms
interpreting intent through structured rules
Copilot extends this by:
auto-suggesting synonyms
helping interpret ambiguous phrasing
generating explanatory text alongside answers
Microsoft has announced that traditional Q&A experiences are scheduled for deprecation in December 2026, with Copilot positioned as the long-term replacement.
In practice:
well-trained Q&A visuals often outperform Copilot for precision
Copilot reduces setup effort but increases review effort
This tension comes up repeatedly in end-user discussions.
Built-in AI visuals
Power BI includes several visuals that are often overlooked in “Copilot-centric” discussions.
These include:
Key Influencers
Identifies drivers behind a selected outcome by statistically analyzing contributing factors.
Decomposition Tree
Allows users to break down a metric across multiple dimensions interactively.
Anomaly Detection
Flags unusual data points in time-series visuals using built-in statistical models.
These features:
do not require Copilot
do not use generative AI
are deterministic and explainable
They tend to be more trusted in production environments because they behave predictably.
AI features in Power Query
Power Query includes AI-assisted features under AI Insights, such as:
text analytics
sentiment detection
key phrase extraction
image tagging (via Azure Cognitive Services)
These operate during data preparation, not reporting.
They are typically used to:
enrich raw datasets
extract structure from unstructured text
prepare data before modeling
These features are closer to “classic ML services” than conversational AI, and they are generally more stable.
External AI tools used alongside Power BI
Many practitioners rely on AI outside of Power BI rather than inside it.
Common patterns mentioned across Reddit threads:
using ChatGPT or Claude to draft DAX
asking LLMs to explain existing measures
exporting semantic models via Tabular Editor to provide context
using AI to refactor Power Query M code
This shows up oftne in user community threads.
These workflows are not officially supported, but often outperform Copilot for experienced users.
Data privacy and governance considerations
Copilot does not operate on “just column names.”
According to Microsoft’s documentation, Copilot may process:
model schema
selected data points
contextual information needed to answer a prompt
All access is scoped to the authenticated user’s permissions, but this still requires review by security and compliance teams.
This concern comes up frequently in community discussions, especially in regulated environments.
What AI in Power BI is actually good at today (2025)
Across documentation and real-world usage, AI features in Power BI are strongest when used for:
exploration
summarization
onboarding non-technical users
reducing manual setup effort
They are weakest when asked to:
encode business logic
replace modeling discipline
generate production-ready DAX or visuals without review
This gap explains why many experienced practitioners remain skeptical, even while continuing to experiment.
A note on Power BI delivery and visibility
None of Power BI’s AI features solve how insights reach people.
Copilot can help create or explain a report, but it does not:
schedule unattended viewing
ensure dashboards stay visible
manage display hardware
For teams using Power BI in shared or passive environments like office TV screens, platforms such as Fugo sit downstream of AI features. Power BI (with or without Copilot) produces the content. Fugo handles how that content is reliably displayed at scale.
Practical takeaway
Power BI’s AI features are real, but uneven.
They span:
deterministic analytics tools
classic machine learning services
generative AI via Copilot
Used appropriately, they reduce friction. Used as a replacement for modeling or BI expertise, they create more work than they save.