Microsoft has been rolling Copilot into every product in its portfolio, and Power BI is no exception. Since its gradual rollout through 2024 and wider availability in 2025, Copilot in Power BI has gone from a demo curiosity to something teams are actually evaluating for real workflows.
I have been using it in client environments and in my own work since early access. Here is an honest assessment of where it genuinely helps, where it still frustrates, and whether you should be enabling it for your team right now.
What Copilot in Power BI Actually Is
Before getting into the evaluation, it is worth being precise about what Copilot in Power BI does — because it is not one feature, it is several:
Report creation from natural language — describe what you want to see and Copilot generates a report page with visuals.
DAX measure suggestions — describe a calculation in plain English and Copilot writes the DAX for you.
Narrative summaries — Copilot reads a report page and generates a written summary of the key insights it finds.
Q&A improvements — enhanced natural language querying built on top of Power BI’s existing Q&A feature.
Data preparation suggestions — in Power Query, Copilot can suggest transformation steps based on your data.
These features have different maturity levels, and evaluating “Copilot in Power BI” as a single thing misses how uneven the experience currently is.
What Works Well
DAX Measure Generation
This is the strongest feature and the one I reach for most often. Writing DAX is genuinely hard for people who do not live in it daily — the evaluation context, filter propagation rules, and time intelligence syntax are all non-obvious.
Copilot handles common measures reliably. Year-to-date totals, same period last year comparisons, running totals, percentage of total, and basic CALCULATE patterns all come out correct or close to correct. For an analyst who knows what they want but struggles with DAX syntax, this is a meaningful productivity gain.
Where it gets less reliable is complex measures involving multiple CALCULATE modifiers, virtual tables, or iterator functions like SUMX with nested conditions. The output is often plausible-looking but subtly wrong — which is arguably more dangerous than obviously wrong. Always test generated DAX against known values before publishing.
Narrative Summaries
The narrative visual — where Copilot writes a plain-English summary of what a report page shows — works better than I expected. For executive-facing reports where the audience wants the headline before they look at the charts, auto-generated summaries save real time.
The summaries are factually grounded in the data, clearly written, and update dynamically when filters change. They are not going to win any awards for analytical depth, but for “here is what happened this month in plain English” they deliver.
Onboarding New Users
Copilot genuinely lowers the barrier for people new to Power BI. Being able to ask “show me monthly revenue by region as a bar chart” and get a working visual removes the initial friction of learning the interface. For organisations rolling out self-service BI to non-technical teams, this matters.
Where It Still Falls Short
Report Generation Is Inconsistent
The headline feature — describe a report and Copilot builds it — is the most inconsistent. Simple requests work reasonably well. Anything involving specific formatting, precise layout, custom colour schemes, or complex filtering logic produces results that need significant manual cleanup.
The bigger issue is that Copilot generates reports against your semantic model, which means a poorly structured model produces poor results regardless of how well you phrase the prompt. Copilot cannot fix a bad data model — it amplifies whatever is already there.
It Requires Premium or Fabric Capacity
Most Copilot features in Power BI require Power BI Premium Per User (PPU) or a Microsoft Fabric capacity. This is a significant cost barrier for smaller teams or organisations on standard Power BI Pro licences. For many businesses evaluating whether Copilot justifies the upgrade cost, the honest answer right now is probably not yet — unless DAX generation is a daily pain point for your team.
Context Window Limitations
Copilot works best with focused, well-structured semantic models. Feed it a complex model with dozens of tables, hundreds of measures, and ambiguous naming conventions and the quality drops noticeably. It struggles to resolve which measure or column you mean when there are several plausible candidates, and the errors it makes in those situations are harder to spot.
Q&A Is Still Unreliable for Production Use
The natural language Q&A feature has been in Power BI for years and Copilot has improved it, but it is still not reliable enough for business-critical workflows. It handles straightforward questions well and fails unpredictably on others. Until it is consistently right, I would not surface it to end users without heavy curation of the synonym and linguistic schema settings.
The Broader Picture: AI in the Data Stack
Copilot in Power BI sits within a broader Microsoft Fabric vision where AI is embedded across the entire data pipeline — from data ingestion in Data Factory, through transformation in notebooks, to reporting in Power BI. The ambition is significant.
In practice, the integration is still maturing. The features that work today — DAX generation, narrative summaries, basic report creation — are genuinely useful additions to a Power BI workflow. The features that are still rough — complex report generation, Q&A, cross-tool intelligence — need another year of development before they are production-ready for most teams.
Should You Enable It?
My current recommendation depends on your situation:
Enable it and use it actively if your team writes a lot of DAX, has Power BI Premium or Fabric capacity already, and has a clean, well-documented semantic model. The DAX assistance alone pays for the time investment of turning it on.
Enable it cautiously if you have a mixed-skill team of analysts and engineers. The narrative summaries and basic report generation help analysts, but set expectations clearly — Copilot is an assistant, not an expert. Wrong DAX that looks right is a real risk.
Hold off if you are on standard Power BI Pro, have a messy data model, or are in a regulated environment where AI-generated content in reports requires additional governance. Clean the model first, then revisit Copilot when the licensing economics make more sense for your organisation.
Copilot in Power BI is genuinely useful today in specific scenarios. It is not the revolution the marketing suggests, but it is also not vapourware. The DAX generation and narrative features alone have saved me hours on projects in the past year.
If you are evaluating it for your organisation and want to work through the specifics of your setup, feel free to get in touch.