How Do I Verify AI-Generated SQL, Charts, and Conclusions Before Using Them?

Can I trust AI to analyze a CSV or spreadsheet?

You can use AI to analyze a CSV or spreadsheet, but you should trust the verification trail rather than the fluent answer. Start with a small set of numbers that already have an accepted answer: a monthly total, customer count, inventory balance, or figure from a controlled report. Ask the tool to reproduce that result and compare it with the trusted source before asking it to explain a trend.

That first reconciliation is useful because it exposes the quiet choices inside an analysis. Is the total based on rows, unique customers, or invoices? Does a blank value mean zero, unknown, or not applicable? Is a date being grouped by day, month, or fiscal period? These are ordinary data questions, but they can change a conclusion without producing an error message.

Build a simple audit record as you work. Save the original file or a governed reference to it, the prompt, the generated SQL or formula, the definitions supplied to the tool, the result, and the reviewer’s notes. The National Institute of Standards and Technology AI Risk Management Framework calls for documentation of test sets, metrics, tools, performance measures, and validation evidence under deployment-like conditions. In plain English, the evidence should be sufficient for someone else to check the work without guessing what happened.

For business reporting, keep the question narrow before widening it. “Calculate revenue under this documented definition for this date range” is reviewable. “Tell us what is happening in the business” invites the tool to make interpretive leaps. AI can help prepare the work; it should not quietly replace the definitions behind it.

How do I check whether AI-generated SQL is correct?

Check AI-generated SQL by validating its business meaning, its table relationships, and its returned values; successful execution is only the beginning. Read the query in chunks: selected tables, joins, filters, grouping, calculations, and ordering. Then state, in plain language, what each chunk is supposed to do. If that plain-language explanation does not match the original question, the SQL is not ready.

Start with data grain, the level represented by one row. A customer table may have one row per customer, while an orders table may have many rows per customer. Joining them can repeat customer attributes across every order. That may be correct for an order-level calculation and wrong for a customer count. Check the join keys, join type, and whether a one-to-many relationship could multiply amounts or counts.

Next, inspect every filter and calculation. Confirm whether a condition belongs in a WHERE clause or an aggregate condition, whether dates include the intended boundary, and whether the metric needs a distinct count. Look especially hard at null handling. A query may execute while excluding blank values, grouping them together, or converting them in a way that contradicts the business definition.

There is a reason for this caution. The NL2SQL-BUGs benchmark contains 2,018 expert-annotated natural-language, schema, and SQL instances, including 999 semantically incorrect examples. It reports average large-language-model accuracy of 75.16% for detecting semantic SQL errors, with particular difficulty around subqueries, join-type mismatches, functions, and deeper database knowledge. A second AI review may be useful, but it is not an independent control.

Use a second route to the answer. Run the AI query on a small, inspectable sample and manually calculate a few records. Write a simpler comparison query that returns counts and sums at each stage. The 2026 NL2SQLBench paper in the Proceedings of the VLDB Endowment separates the work into schema selection, candidate generation, and query revision; that is also a practical review sequence. Verify that the right tables were chosen before debating whether the final syntax is elegant.

What mistakes does AI make when analyzing business data?

AI can make mistakes in scope, definitions, joins, missing-value treatment, calculations, charts, and conclusions, often without a runtime error. The dangerous version is not a broken query. It is a plausible result built from the wrong table, the wrong unit of analysis, or an unstated assumption.

Common examples are easy to recognize once they are named. A chart can show growth because a partial month is compared with a complete month. A customer metric can rise because duplicate records were introduced by a join. A percentage can look alarming because the denominator changed. A conclusion can overstate a pattern when the source data contains a mix of unknown and zero values. None of these problems require the AI to invent a number; a misapplied definition is enough.

Require the analysis to expose its workings. A useful deliverable includes the exact question, source tables or files, table grain, joins, filters, metric definitions, SQL or formulas, chart settings, results, exceptions, and reviewer sign-off. This is less glamorous than receiving a one-paragraph conclusion. It is also how a conclusion becomes contestable in the useful sense: a reviewer can trace it, challenge it, and correct it.

Privacy belongs in the same workflow. NIST’s 2024 generative-AI risk profile identifies risks including leakage, unauthorized use, disclosure, de-anonymization, and inference of sensitive data, and recommends recording data provenance, known issues, human-oversight roles, model versions, and access modes. The NIST Generative AI Profile also recommends documenting the origin and history of generated data. Do not send a company spreadsheet to a tool merely because it accepts uploads.

Where personal data is involved, governance needs more than a checkbox. The UK Information Commissioner’s Office says that, in the vast majority of cases, AI use involving personal data is likely to create high risk to individuals’ rights and freedoms and therefore requires a case-by-case Data Protection Impact Assessment. It also says outsourced AI tools require independent due diligence and regular review. A human reviewer should have the evidence and authority to change the outcome; a rubber stamp is not meaningful review.

The practical finish is straightforward: keep the AI output, but keep the evidence that could prove it wrong. That process does not make every answer correct. It makes errors visible before they become decisions.