Not necessarily.
Imagine you ask ChatGPT Work Data agent:
“What is this month’s Revenue?”
It replies:
100.
But when you check your company’s daily Power BI report, it says:
92.
The first reaction might be:
“AI made another mistake.”
In data analysis, however:
Two different numbers do not necessarily mean:
One is wrong.
More likely:
They are answering different questions.
The most common first difference: different data sources
Suppose the Data agent queries:
The company Data Warehouse’s real-time updated Sales Table.
While the original Power BI uses:
A Reporting Table updated only once daily at midnight.
And a new batch of orders comes in the afternoon.
Then, the numbers will differ.
This is completely normal.
It’s not because:
AI miscalculated,
nor because Power BI is faulty,
but because:
Data update timings differ.
Even when both sources are named Sales
Company data environments are often not as tidy as expected.
You might see multiple sales tables with names like:
Sales.
Sales_Final.
Sales_Report.
Finance_Sales.
Revenue_Daily.
Orders.
Completed_Orders.
All of these sound reasonable but serve completely different purposes.
When results differ, the first question is always:
“Which dataset did you use?”
Second difference: Different time periods
For example, Power BI might display:
The full month of September.
But on September 14, the Data agent reports:
Month-to-date figures.
Both dashboards say:
“September Revenue.”
But one covers the complete month, the other only to date.
Of course, the numbers won’t match.
Common mix-up: Comparing “same days” vs. “full month”
If you ask:
“Compare this month to last month,”
There can be two approaches:
1. September 1–14 vs. August 1–14
2. September 1–14 vs. August 1–31
Neither is mathematically wrong,
but their business implications differ significantly.
You must look beyond the numeric difference and ask:
“What exactly is being compared?”
Third difference: Different filters
This is one of the most common causes of mismatched dashboards.
Power BI might default to:
Viewing only Taiwan sales.
But the Data agent queries:
Global figures.
Or Power BI excludes internal test accounts, while Data agent does not.
Or the official dashboard counts only completed orders, while Data agent includes pending orders.
Naturally, Revenue differs.
Filters may not always be visible
Users might forget they set a filter on a region three months ago and left it active.
The following day, the dashboard still shows that filter applied,
while the Data agent pulls fresh data without such filters,
leading to different results.
Don’t immediately blame AI.
The dashboard reflects a view filtered by a user, not full data.
Fourth and most important difference: Metric definitions vary
This is the hardest part of enterprise data analysis.
“Revenue” sounds straightforward,
but it can mean any of the following:
Gross Sales.
Net Revenue.
Booked Revenue.
Recognized Revenue.
Paid Revenue.
Revenue after Refunds.
Each can be a valid official figure,
but serve different purposes.
For example, an order of NT$10,000
The customer orders 10,000.
Later returns goods worth 2,000.
So what is the Revenue?
Sales teams might report:
Original order value of 10,000.
Finance might report:
Net Revenue of 8,000.
Accounting might only recognize revenue over certain periods,
making recognized revenue yet another figure.
All these can be correct.
So don’t ask: “Which Revenue is the true one?”
A better question is:
“Which Revenue definition fits the business question we’re answering?”
This is the role of the Semantic Layer,
where the company defines:
What Revenue means.
What an Active Customer is.
What Churn means.
What a Qualified Lead is.
AI then speaks the same language.
Data agent can use existing company business definitions
OpenAI designs Data agent so it does not blindly guess KPIs.
Instead, it uses:
Company’s defined metric definitions,
Custom calculations,
Data relationships,
Semantic layers,
and trusted dashboards.
If a company has formal definitions,
the best practice is to tell Data agent:
“Use the Finance-approved Net Revenue definition.”
Not just “Revenue.”
When numbers differ, the first step isn’t to ask AI to recalculate
Many say immediately:
“You’re wrong. Power BI says 92, try again.”
But that’s not always best.
You tell AI Power BI must be right, so it tries to match 92.
It’s better to ask:
“Your result is 100, official dashboard is 92. Don’t change your analysis yet. Compare data sources, time periods, filters, and metric definitions to identify the differences first.”
This is true debugging.
Don’t treat matching numbers as the only validation
Even if AI adjusts to also output 92,
It doesn’t guarantee correctness.
It may have used wrong data,
and coincidentally matched the same number.
Real validation is understanding:
Where and how the numbers come from.
Not just whether the final numbers match.
Better verification involves four layers
First:
Source:
Which dataset does each pull?
Second:
Period:
Are the date ranges exactly the same?
Third:
Filter:
Are regions, statuses, customer types, or test data filters aligned?
Fourth:
Metric Definition:
Are formulas identical?
If all are the same but numbers differ,
then investigate:
Calculations, joins, data quality, logic errors, etc.
Joins can also inflate numbers
Suppose one order corresponds to three products.
If join logic is flawed,
One order might be counted three times,
artificially increasing revenue.
Similarly, if one customer has multiple contact records,
joining may duplicate transactions.
This is a typical data analysis error, but is hard to detect just by looking at dashboards.
Transparency of evidence is critical for Data agent
OpenAI’s key design for Data agent is enabling users to ask follow-up questions like:
What evidence supports each finding?
Instead of only getting:
“Revenue is 100.”
You can ask:
Which table was used?
Which time period?
Which filters?
How is the metric defined?
Which queries support this result?
These are the foundations for trust in data results.
OpenAI’s internal Data agent emphasizes verifiability
OpenAI’s internal introduction of their Data agent states:
This kind of system will make mistakes.
Therefore, internal tools expose:
Assumptions, execution steps, underlying results,
so users can review and verify.
This design mindset is crucial.
Because AI data analysis doesn’t need to never err,
but when differences arise,
they must be traceable.
“Official Power BI dashboard” isn’t infallible either
This is important.
Many companies assume a dashboard used for years must be correct.
But dashboards are built by humans, too.
They can have:
Old filters,
Incorrect joins,
Outdated metric definitions,
Refresh failures,
Calculation logic flaws.
OpenAI’s Data agent Alpha use case micro1 reported finding errors in an old performance dashboard during rebuild.
So differences between AI and old BI are not always bad;
Sometimes they signal:
Data quality issues.
Worst practice is picking whichever number you prefer
For example:
AI says 100.
Dashboard says 92.
100 looks better, so you report 100.
That’s wrong.
Or saying:
Power BI has always been used, so no matter what, report 92.
That’s insufficient as well.
You must explain differences.
A good analysis answers:
Why is my result 100?
Why is the official dashboard 92?
Where do the 8 differences come from?
Which data, filters, definitions, or update times caused this?
If these can be explained, both numbers might be retained.
Finance and Sales often need different dashboards
Sales wants to know:
How many new deals closed today?
Finance wants to know:
How much revenue was officially recognized this month?
Their numbers should differ.
The real mistake is calling both figures simply “Revenue”
without specifying which type.
Data agents may push companies to address:
Metric naming conventions.
If your company constantly disputes one number, the problem isn’t AI
For example, monthly meetings:
Sales says 120.
Finance says 104.
Marketing says 135.
They spend half an hour arguing which is correct.
The real cause is likely:
No unified metric definition.
AI simply adds a fourth number to the confusion.
Without addressing the semantic layer,
AI only increases chaos faster.
Mature AI Data adoption is not about everyone asking their own questions
It’s about:
Everyone asking about the same business metric,
Knowing the official definition,
Knowing the source of truth,
Which dashboards are exploratory,
Which are official reports,
And which numbers are suitable for board meetings, financial reports, or customers.
This is true data governance.
Permissions can also cause different results for different people
Data agent doesn’t suddenly get access to all company data because it’s AI.
It respects original permissions on tables, rows, and columns.
So if two employees ask the same question,
but one can only see Taiwan data and the other Global,
Results differ.
This doesn’t mean one AI session is wrong,
but that their accessible data universe differs.
When sharing AI analysis, don’t send just a screenshot
If you send only a revenue chart,
Others won’t know
Which source you used,
Which date,
Which filters,
Or which metric.
Better to include at least:
Data source,
Period,
Filters,
And metric definitions.
Only then can others judge if it’s comparable.
Practical debugging prompt example
Suppose:
Data agent = 100;
Power BI = 92.
Instead of demanding:
“Make your result match Power BI,”
Say:
“Don’t assume either side is wrong. Compare sources, time periods, filters, and metric definitions, plus data freshness to identify where differences begin.”
This prompt aims for:
Explaining the difference,
not matching numbers.
If source differs
Decide which source of truth fits the business question.
If period differs
Use exactly matching date ranges to compare again.
If filters differ
Align region, status, customer segments, test data exclusions, etc.
If metric definitions differ
Don’t rush to change formulas.
Ask if this is for sales, finance, or operations use case,
And select the correct official definition accordingly.
If all four align but numbers still differ
Then begin technical debugging:
Query logic, join conditions, duplicates, nulls, refresh issues, data pipelines, calculations.
Call in data teams or experts because the issue is narrowed down.
This is one of Data agent’s biggest values
It doesn’t replace data teams,
but enables non-experts who might only ask:
“Why are these numbers different?”
to get insights like:
“The difference is Metadata: same source, same period, same filters, but revenue definitions differ.”
Data experts then focus directly on the real issue
instead of spending time clarifying trivial details.
Matching dashboard numbers don’t prove analysis correctness either
Conversely, if both the Data agent and Power BI show 100,
Can you say it’s definitely correct?
No.
They could both use the same wrong source, formula, or outdated definition.
Consistency is a check signal, not a proof of correctness.
This mirrors our July conversations:
“AI showing full source and steps is no guarantee of correctness.”
Traceability ≠ Accuracy
Without traceability, you can’t even identify errors.
Which number should be used in meetings?
This depends on the company’s:
Official business definitions,
Sources of truth,
And governance policies.
Not on which AI answer looks more confident,
Or which dashboard is prettier.
Formal financial data should follow Finance-approved definitions.
Operational exploratory use can utilize more real-time data,
But must be clearly labeled.
The answer today is straightforward
Data agent says 100.
Power BI says 92.
Do not immediately conclude:
“AI is wrong.”
First compare:
Source, Period, Filter, Metric Definition.
If needed, look at data freshness, joins, calculations, permissions.
Mature data analysis is not about all tools showing identical numbers,
but about:
Explaining why numbers differ.
So when two dashboards conflict, don’t ask:
“Who is wrong?”
Ask:
“Are they even measuring the same thing?”
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AI Quick Q&A｜2026/07/31: Does AI providing full source and analysis steps guarantee correctness?
