Forum Discussion
Copilot, limitations?
Thank you for the follow-up — and I genuinely understand your frustration. You’re not wrong that, from a human perspective, this is a very simple arithmetic problem, and yes, ChatGPT can solve it easily.
However, the key point is that Copilot in Power BI is not failing at math — it is refusing to cross semantic boundaries it is not allowed to cross.
What you are describing is a cross-report reconciliation problem, not a calculation problem.
When you say:
“Report A says 100,000 passengers in November
Report B splits November into 80,000 NS and 20,000 SN
How many of the 100,000 are NS vs SN?”For a human (or ChatGPT), the assumption is obvious: they refer to the same population.
For Copilot:
These are two separate semantic models
There is no guaranteed relationship
There is no shared grain or lineage
There is no enforced business rule that NS + SN = total passengers
Copilot is designed to not assume equivalence, even when it seems obvious.
That’s why it asks for:
The exact measure
The exact table
The exact relationship
All data inside the same model
It’s not intelligence vs stupidity — it’s governance vs inference.
Why ChatGPT “works” here
ChatGPT:
Assumes the numbers are compatible
Ignores data lineage
Doesn’t care if the logic would be invalid in a governed BI system
Copilot:
Must avoid generating potentially wrong business answers
Cannot reconcile two reports unless they are backed by the same dataset
Cannot “peek” into another report or model
The real limitation (and you’re right to call it out)
Where I agree with you 100%:
Copilot does not currently help enough with reconciliation-style reasoning, even when:Measures exist
Names match
Logic is obvious to a human
That does limit its usefulness today for analysts doing validation, reconciliation, or sanity checks across reports.
So yes — for this use case, Copilot is not helpful. That’s a fair and valid conclusion.
The practical takeaway
Copilot works best when:
All numbers live in one semantic model
Relationships are explicit
Measures are already defined
For:
Cross-report summaries
Reconciliation
“Explain the difference between report A and report B”
Today, external reasoning tools (like ChatGPT) are still better.
You’re not misunderstanding Copilot — you’re simply hitting a real product boundary. Calling that out is fair, and it’s exactly the kind of feedback Microsoft needs to hear.
If this explanation helped clarify why this happens (even if you still disagree with the design), please consider giving Kudos 👍 and marking as the Accepted Answer ✔ so others don’t hit the same frustration without context.
Hi Alexx22,
This is a very common confusion, and you’re not wrong to question it — but the issue here is expectations, not that Copilot is “bad AI”.
Copilot in Power BI is not the same thing as ChatGPT or other general-purpose AI tools.
Why Copilot behaves this way (important distinction)
Copilot for Power BI is:
Context-bound
Model-aware, not data-exploratory
Strictly constrained by the semantic model
It cannot invent logic, infer business meaning, or “guess” calculations unless:
The measure already exists, or
The model is clearly defined in a way Copilot can reference.
So when Copilot says:
“I need the measure in the report”
That is actually expected behavior.
Your example: % evolution between months
From a human perspective, this is “easy”.
From Copilot’s perspective:
It does not know which measure represents “Total Passengers”
It does not know which date column defines “month”
It does not know whether you want:
MoM %
YoY %
Rolling comparison
Same month last year
Cumulative vs discrete
Unlike ChatGPT, Copilot will not assume.
Copilot can:
✔ Explain existing measures
✔ Modify existing measures
✔ Generate DAX if the intent is unambiguous and grounded in the model
Copilot cannot:
❌ Infer missing measures
❌ Create business logic from vague questions
❌ Reason across multiple reports
❌ Query data outside the current semantic model
“Is Copilot connected to the data model?”
Yes — but only to the active semantic model of the report.
It does not:
See other reports
Traverse datasets
Join unrelated models
Perform cross-report reasoning
Each report = isolated context.
This explains why:
“We have two reports with related data and Copilot can’t answer questions between them”
That is by design, not a bug.
Why ChatGPT seems “better”
ChatGPT:
Is general reasoning AI
Can make assumptions
Can invent examples
Is not bound by governance or model integrity
Copilot:
Is a governed enterprise assistant
Prioritizes correctness and safety over creativity
Refuses to act when context is incomplete
So ChatGPT feels “smarter” — but Copilot is being intentionally conservative.
How to get better results from Copilot
To make Copilot useful:
1⃣ Create clear base measures
Total Passengers := SUM ( FactPassengers[PassengerCount] )
2⃣ Use a proper Date table with Month/Year
3⃣ Ask specific, model-aware questions, for example:
“Create a Month-over-Month % change measure based on [Total Passengers] using the Date table.”
Now Copilot can help.
Summary
✔ Copilot is not bad AI
✔ It is not a general-purpose assistant
✔ It does not infer missing logic
✔ It operates strictly inside the semantic model
✔ Cross-report questions are not supported
Once you align expectations, Copilot becomes a very good productivity assistant — just not a replacement for a BI developer’s reasoning.
If this explanation helped clarify the limitations and behavior, please give Kudos 👍 and mark this reply as the Accepted Answer ✔ so others evaluating Copilot can set the right expectations as well.
Thanks everyone for the response.
I'll reply you because i understand all that points BUT...
I don't think the same.
My prompt is very complete with the info that i need and the AI needs.
The measures are very clear and i ask for specific answer.
I have the measure with the real name, wrike what i need very clarity and copilot can not resolve it.
ChatGPT resolve it easy, is no hard guys.. I understand the limitations but this is very useless for me. "Intelligent" is nothing for to much AIs not alone for copilot, but this is embarrasing i ask for a aritmetic result is easy for a simple calculator..
When i ask for cross report means some like this:
If one said "passengers" 100.000 in nov.
And the other said "passengers" 80.000 "NS" and 20.000 "SN" in nov.
¿How many passengers of the 100.000 are NS and how SN?
A easy summary for 2 reports...Thank you for the follow-up — and I genuinely understand your frustration. You’re not wrong that, from a human perspective, this is a very simple arithmetic problem, and yes, ChatGPT can solve it easily.
However, the key point is that Copilot in Power BI is not failing at math — it is refusing to cross semantic boundaries it is not allowed to cross.
What you are describing is a cross-report reconciliation problem, not a calculation problem.
When you say:
“Report A says 100,000 passengers in November
Report B splits November into 80,000 NS and 20,000 SN
How many of the 100,000 are NS vs SN?”For a human (or ChatGPT), the assumption is obvious: they refer to the same population.
For Copilot:
These are two separate semantic models
There is no guaranteed relationship
There is no shared grain or lineage
There is no enforced business rule that NS + SN = total passengers
Copilot is designed to not assume equivalence, even when it seems obvious.
That’s why it asks for:
The exact measure
The exact table
The exact relationship
All data inside the same model
It’s not intelligence vs stupidity — it’s governance vs inference.
Why ChatGPT “works” here
ChatGPT:
Assumes the numbers are compatible
Ignores data lineage
Doesn’t care if the logic would be invalid in a governed BI system
Copilot:
Must avoid generating potentially wrong business answers
Cannot reconcile two reports unless they are backed by the same dataset
Cannot “peek” into another report or model
The real limitation (and you’re right to call it out)
Where I agree with you 100%:
Copilot does not currently help enough with reconciliation-style reasoning, even when:Measures exist
Names match
Logic is obvious to a human
That does limit its usefulness today for analysts doing validation, reconciliation, or sanity checks across reports.
So yes — for this use case, Copilot is not helpful. That’s a fair and valid conclusion.
The practical takeaway
Copilot works best when:
All numbers live in one semantic model
Relationships are explicit
Measures are already defined
For:
Cross-report summaries
Reconciliation
“Explain the difference between report A and report B”
Today, external reasoning tools (like ChatGPT) are still better.
You’re not misunderstanding Copilot — you’re simply hitting a real product boundary. Calling that out is fair, and it’s exactly the kind of feedback Microsoft needs to hear.
If this explanation helped clarify why this happens (even if you still disagree with the design), please consider giving Kudos 👍 and marking as the Accepted Answer ✔ so others don’t hit the same frustration without context.
Hi Alexx22 ,
Thank you for reaching out to the Microsoft Community Forum.
Could you please try the proposed solution shared by SavioFerraz ? Let us know if you’re still facing the same issue we’ll be happy to assist you further.
Regards,
Dinesh
