Power Query M is powerful, but it can feel verbose and opaque when you do not write it every day. That makes it a strong candidate for AI assistance, especially when you want a first draft or help debugging a stubborn step.
The trap is obvious: transformation code can look plausible while quietly changing the data in the wrong way.
Quick answer
Use AI to draft or troubleshoot M code faster, but review each transformation step against the intended data outcome. The most reliable workflow is to let AI help you write or explain the query, not to trust the final transformation blindly.
- You know the transformation you want but do not want to write the M from scratch.
- A query step is failing and you need help diagnosing it.
- You can compare the transformed output with the intended result.
Where AI helps most
AI is particularly useful when translating plain-English transformation intent into first-pass M code or explaining what an existing step is doing.
Why output review still matters
The real check is not whether the code compiles. It is whether the transformed table still matches the intended business meaning after each step.
A sensible workflow
Describe the source and target clearly, ask AI for the draft or fix, then inspect both the query steps and the resulting table before the query becomes part of production reporting.
Worked example: cleaning a monthly export
An analyst needs to remove header noise, split one combined field, standardise dates, and filter cancelled rows. AI can draft the M steps quickly, but the analyst still checks the output table against the original brief.
Common mistakes
- Trusting code because it runs.
- Failing to describe the target output clearly in the prompt.
- Skipping result review because the query preview looks tidy.
When to use something else
If the data problem can be solved with regular Excel structure, better tables may be simpler. If the problem is broader data cleaning, Power Query fundamentals still matter.
Frequently asked questions
Can AI write Power Query M code?
Yes. It is good at turning plain-English transformation intent into first-pass M, and at explaining what an existing step does. Review each step against the intended data outcome before trusting it.
Why is 'the code compiles' not enough?
The real check is whether the transformed table still matches the intended business meaning after each step, not whether the M is syntactically valid. Valid code can quietly produce the wrong table.
What is a sensible workflow?
Describe the source and target clearly, ask AI for the draft or fix, then inspect both the query steps and the resulting table before the query goes into production reporting.
Where does AI help most with M?
Drafting tricky transformations such as pivot, unpivot, custom columns, and merges, and explaining or fixing a step you did not write. It shortens the gap between intent and a working query.
Why is M hard to get right by hand?
Its functional style and case-sensitivity trip people up, and small syntax or type issues are common. That is exactly where AI drafting and explanation save time, provided you verify the output.
How do I debug a broken query with AI?
Paste the failing step and the error, and describe what the table should look like. Then check the actual result against your expectation, since the error message alone rarely tells the whole story.
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