---
title: "Why AI Gives Different Answers to the Same Data Question"
canonical: "https://www.owox.com/blog/articles/ai-different-answers-same-data-question"
updated: "2026-10-29"
---

# Why AI Gives Different Answers to the Same Data Question

[AI in Analytics](/blog/topics/ai) · Updated August 11, 2026 · [Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · 13 min read

![Why AI Gives Different Answers to the Same Data Question](https://cdn.owox.com/www/content-marketing-article/ai-different-answers-same-data-question/thumbnail/1-gpyfmr.png/public)

**Ask an [AI assistant](/product/ai-assistants) the same question about your company data twice and you can get two different numbers. Most explanations blame the model – temperature, randomness, prompt wording. On your own data that is rarely the real cause. The variance comes from a definition nobody wrote down: the model has to pick one of several competing meanings of “revenue”, and nothing tells it which one your company uses.**

Here is the version of this that happens in real companies.

A founder types _“what was our revenue last month?”_ into an AI assistant wired to the company warehouse. It answers $412,000. Ten minutes later, in a different thread, they ask _“how much revenue did we do in July?”_ and get $389,000.

Same data. Same month. Two answers, both delivered with total confidence, neither carrying a footnote explaining what it counted.

The instinct is to conclude the AI is unreliable and go back to asking the analyst. That instinct is half right – the answer _was_ unreliable – but the diagnosis is wrong, and the wrong diagnosis leads to the wrong fix.

## **The explanation you will find everywhere**

Search this question and you will get a consistent answer about how language models work. It is accurate as far as it goes.

### **Sampling and temperature**

Language models predict the next token as a probability distribution and then sample from it. That sampling introduces randomness by design – it is what makes the output feel natural rather than robotic. A \`temperature\` setting controls how much. Set it to zero and the model always takes the most likely token.

### **Non-determinism runs deeper than temperature**

Even at temperature zero, identical prompts can produce different outputs. Thinking Machines Lab traced the dominant cause to a lack of [batch invariance](https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/) in inference kernels – the arithmetic changes subtly depending on how requests are batched together on the server. Their work shows that with batch-invariant kernels, 1,000 identical runs produce 1,000 identical outputs.

So non-determinism is a real engineering problem, and it is a solvable one.

### **Why none of this explains your revenue number**

Here is the thing that should bother you: **a $23,000 gap between two revenue answers is not a rounding artifact of token sampling.** Sampling variance changes phrasing. It does not move a number by six percent.

Something else is happening, and it is happening in the step everyone skips over – between your question and the SQL that runs against your warehouse.

## **What actually goes wrong when AI queries your data**

When a language model translates a business question into SQL, it has to make a series of decisions that nobody told it how to make. Each one is a place where two runs can legitimately diverge.

### **It invents joins**

Your database or data warehouse has an \`orders\` table and a \`customers\` table. Are they joined on \`customer\_id\`, or on \`user\_id\`, or through an intermediate \`accounts\` table? If the relationship is not declared anywhere the model can read, it infers one from column names.

In our own runs that inference held. On 11 October 2026 we gave three fresh sessions of one model only the table schema of our demo shop and one revenue question: all three wrote the join correctly, and all three counted every order at gross, $941,162.88, where completed orders at net come to $789,011.69, $152,151.19 apart. One question, three sessions, clean demo data: it shows the mechanism, not a rate. In our other runs that day, every answer written with raw SQL access used the right tables and joins, 18 of 18. When a join does go wrong, it silently duplicates rows and inflates the total. **The query runs, returns a number, and nothing indicates a guess was made.**

### **It guesses the table grain**

Does one row in your \`subscriptions\` table represent a subscription, a billing period, or a plan change? Aggregating the wrong grain double-counts revenue in a way that looks entirely plausible.

### **It applies filters nobody asked for**

Should test transactions be excluded? Internal transfers? Refunded orders? Your analyst knows the answer because they have been burned before. The model has no such history, so on one run it might filter \`status = ‘completed’\` and on the next include everything.

### **It picks one of your definitions of revenue**

This is the largest source of variance and the least technical. If marketing counts gross bookings, finance counts net receipts, and the CRM counts closed-won value, then _“revenue”_ has three legitimate answers already sitting in your warehouse. The model picks one. There is no reason it picks the same one twice.

### **The two queries, side by side**

Strip away the chat interface and the $412,000 answer and the $389,000 answer are two SQL statements that differ by a handful of tokens. Here is the shape of the tables the model was handed:

```
orders(order_id, account_id, created_at, status, amount_gross)
refunds(refund_id, order_id, refunded_at, amount)
accounts(account_id, user_id, is_internal, plan)
```

Nothing in that schema says whether a refund belongs to the month of the original order or the month it was issued, that accounts flagged \`is\_internal\` are excluded from company revenue, or that \`status\` contains a value called \`authorized\` for charges that never settled. The model decides all three, fresh, on every run.

The first run wrote the simple thing:

```
SELECT SUM(amount_gross) FROM orders
WHERE created_at >= '2026-07-01' AND created_at < '2026-08-01';
```

The second run was more careful:

```
SELECT SUM(o.amount_gross) - COALESCE(SUM(r.amount), 0)
FROM orders o
LEFT JOIN refunds r ON r.order_id = o.order_id
JOIN accounts a ON a.account_id = o.account_id
WHERE o.created_at >= '2026-07-01' AND o.created_at < '2026-08-01'
  AND o.status = 'completed' AND a.is_internal = FALSE;
```

Both statements are valid. Both executed without error. Both returned a tidy figure with a dollar sign in front of it. The gap between them is refunds issued in July plus a handful of internal test orders – business logic, not arithmetic.

And the second query, the _careful_ one, carries a defect of its own. The \`LEFT JOIN\` to \`refunds\` fans out any order with two partial refunds into two rows, so \`SUM(o.amount\_gross)\` counts that order’s gross amount twice. **A more sophisticated query can be more wrong than a naive one, and it looks more trustworthy while it happens.**

**The AI is not hallucinating here. It is disambiguating** – repeatedly, invisibly, and without a rule to follow.

![Two answers to “What was our revenue last month?”, one without a data model and one with a governed model, differing.](https://cdn.owox.com/www/webflow/6a7c6e29fac3f023dc5b60a4_d6100560.png/public)

## The Turning Point

OWOX Data Marts

See your first report built in real time. _15 minutes._

1.  Connect your data warehouse
2.  Pick your metrics
3.  Get a live Google Sheets report

In the time it takes to write a ticket. Then imagine never writing that ticket again.

[Book a Demo](/book-a-call)

We'll use your actual use case

## **The benchmark almost nobody cites**

There is published evidence for exactly how badly this goes, and it is startling once you see the two numbers side by side.

On [Spider](https://yale-lily.github.io/spider), the Yale text-to-SQL benchmark built on clean academic schemas, top systems reach around **91% execution accuracy**. That is the number that makes AI-on-your-data look solved.

Then there is [BEAVER](https://arxiv.org/abs/2409.02038), a benchmark built from _real enterprise data warehouses_ rather than tidy teaching databases. On BEAVER, state-of-the-art systems reach **10.8% accuracy**. With oracle hints supplied on subtasks, it rises to 30.1%. How these scores are counted, and how to measure the same thing on your own data, is in [text-to-SQL accuracy: benchmarks vs your own warehouse](/blog/articles/text-to-sql-accuracy-benchmarks-production).

Roughly 91% on clean schemas. Roughly 11% on real corporate warehouses. **The gap is not the model. The gap is the schema.**

[BIRD](https://bird-bench.github.io/), which sits between the two in realism, tells a consistent story: human performance is 92.96% execution accuracy while the best model on the leaderboard reaches 81.95%.

![A bar chart of text-to-SQL accuracy on published benchmarks, with clean schemas near 92% and real warehouses near 11%.](https://cdn.owox.com/www/webflow/6a7c6e29fac3f023dc5b60a1_5eabaebb.png/public)

The uncomfortable implication: the demos you have seen were probably run against something closer to Spider than to your warehouse.

## **Determinism is not correctness**

This is the part that matters most, and it is where most advice on this topic quietly fails.

Suppose you fix everything in the first section. Temperature zero. Batch-invariant kernels. Fully reproducible inference. Now you ask the same question twice and get the same answer both times.

**You have made the answer consistent. You have not made it right.**

### **A stable wrong number is the one nobody checks**

Inconsistency is annoying, but it is also a smoke alarm. The $23,000 gap is the reason anyone looked under the hood at all. Two different answers force a conversation about what “revenue” means; one answer, repeated, forces nothing.

Make that system deterministic and the alarm goes silent while the fire keeps burning. The fanned-out \`LEFT JOIN\` over-counts by the same amount every time, so the number stops moving and starts looking like a fact – board deck in April, investor update in May, compensation plan in June. Because each figure agrees with the last, every check you would think to run confirms it.

**Reproducibility launders a guess into a fact.** By the time someone notices, the error is not in one slide. It is in the trend line, and the trend line is what people decided on.

### **Auditing a number back to its definition**

The property you want is not “the same answer twice.” It is that every figure resolves to a **named metric** (\`revenue\_net\`, not the word “revenue”), a stored SQL definition, and an author with a version history. That is the difference between a number that arrived and a number that was issued.

Here is the test. Take the revenue figure in your last board deck and walk it backwards.

1.**Which named metric is it?** If the answer is “the revenue number,” stop – there is no definition to audit, only a query somebody ran.

2.**Show the SQL.** Not the dashboard, not the export: the join path, the grain, the \`WHERE\` clause.

3.**Read the filters out loud.** Internal accounts? Refunds? Authorized-but-unsettled charges? Each one you cannot answer is a place two runs can diverge.

4.**Ask who last changed it and why.** With no change history, the definition drifts and nobody notices that last quarter and this quarter were computed differently.

A system that survives those four steps can still be wrong – but it can be _corrected_, once, at the source. A system that fails them cannot be corrected at all, only re-litigated.

Consistency is not the goal. **Traceability is** – being able to say which definition produced this number and who wrote it.

## **What actually fixes it**

If the problem is ambiguity in the schema, the fix is removing the ambiguity – not swapping models. The places that written meaning can live, a semantic layer among them, are compared in [semantic layer for AI agents: do you need one](/blog/articles/semantic-layer-for-ai-agents).

### **The evidence for this is measurable**

A [2026 paired benchmark](https://arxiv.org/abs/2604.25149) across three frontier models tested 100 natural-language questions with and without a semantic document describing the data. Without it: 45.5–50.5% accuracy. With it: 67.7–68.7%.

A 4 KB markdown document describing what the tables mean improved accuracy by **17 to 23 percentage points**. The finding that should reframe your roadmap: with the semantic document in place, **which model you used stopped mattering** in a statistically meaningful way.

You are probably not one model upgrade away from trustworthy answers. You are one [data model](/product/data-modeling) away. The short version of that answer, written to be forwarded to whoever asked, is in [can you trust AI with your business numbers?](/blog/articles/can-you-trust-ai-business-numbers).

### **The analyst writes the definition once**

A governed [data model](/product/data-modeling) is where the ambiguity gets resolved in advance. The join path is declared. The grain is stated. The filters are written down. _Revenue_ means one thing, in SQL, authored by a person who is accountable for it.

The AI then reads from that model rather than composing its own interpretation of your warehouse. The joins are given, and so is the definition, the part no model can work out from a schema. **Every number traces back to human-written SQL**, which means someone can be asked to defend it.

### **What this does not fix**

Worth being straight about the boundary. A data model does not make the AI’s prose correct, only its numbers. It does not eliminate the analyst – someone still writes and maintains the definitions. And it does not help with questions the model does not cover; those still require new work.

What it does is collapse the answer space for the questions it _does_ cover, from “several defensible interpretations” to one. How nine tools go about it is compared in [AI data analyst tools you can trust](/blog/articles/ai-data-analyst-tools-comparison).

## **The objections worth taking seriously**

Three pushbacks come up every time this argument is made. Each is cheaper than building a data model, which is exactly why each deserves a straight answer.

### **“Can’t I just prompt better?”**

Partly, and it is worth doing – telling the assistant _“revenue means completed orders, net of refunds, excluding internal accounts”_ removes three of the ambiguities above. But that instruction lives in one chat thread. Your CFO’s thread does not have it, nor does the scheduled report, the Slack query, or you in three weeks. **A prompt is a definition scoped to one conversation.** Paste the join paths in every time and you have written a data model anyway, in the least durable place available.

### **“Won’t the next model fix this?”**

This is what the paired benchmark above was built to test, and the result points the other way: with the semantic document in place, the choice of frontier model stopped mattering. **A better model reasons better about the information it has; it cannot recover information nobody wrote down.** Whether refunds belong to the order month or the refund month is not latent in your schema waiting to be inferred – it is a decision your finance team made, or failed to make.

### **“What about RAG over my schema?”**

Retrieval over table and column names helps the model find the right tables. It does not tell the model what they mean: retrieving \`orders.status\` gives you the column, not the fact that \`authorized\` means the card was held and never captured. Retrieval over your _documentation_ is a real improvement – but somebody had to write that documentation. **RAG is a delivery mechanism for definitions, not a substitute for having them.** Point it at a wiki page last edited two years ago and it delivers a stale definition, with citations attached. Where retrieval breaks on warehouse metadata, and what to do instead, is the subject of [RAG over your warehouse metadata is a trap](/blog/articles/rag-warehouse-metadata-trap).

## **How you know it is working**

One signal you can check next week, one that only surfaces a quarter in.

### **The early signal**

Ask the same question three ways – _“revenue last month,”_ _“how much did we bill in July,”_ _“July top line”_ – in three separate sessions. You want the same number three times, each answer naming the metric it used. The stronger test is disagreement: _“that is not what I get”_ should become a two-minute comparison of two named definitions, not a two-day reconciliation. **The argument moves from the number to the definition, the only place it can be settled.**

### **What failure looks like at month three**

Failure is quiet erosion, not a crash, and it has a recognisable shape:

•A question the model did not cover got a one-off query, which quietly became a recurring report

•A definition changed and old figures were never restated, so the trend line has an unlabelled seam

•Two metrics now exist for one concept – \`revenue\` and \`revenue\_v2\` – and nobody knows which the board deck uses

•The analyst who authored the definitions left, the SQL is still there, and nobody feels entitled to change it

All four start the same way: a definition was needed, and answering the question was faster than writing it down. **The health check is not whether the numbers agree. It is whether the count of ungoverned queries is going up or down.**

## **Where to start**

You do not need a governance program. Start with the three metrics your leadership team argues about most – usually revenue, [customer acquisition cost](/metrics/customer-acquisition-cost), and some version of active customer – and get one written definition for each, in SQL, in a place every tool queries.

That is the entire intervention. Everything else is elaboration.

With [OWOX Data Marts](/features/data-mart-management), the model lives in your own warehouse and the definitions are authored by your analyst. AI assistants query the governed model instead of raw tables, so the answer to _“what was our revenue last month?”_ comes from the definition your analyst wrote, the same one behind the number in the board deck. Check the label of the first figure: in our runs, asking for the “top line” led with gross revenue, labelled, with net next to it.

Ask the question twice. Getting the same answer is the low bar. **Getting an answer somebody can defend is the actual one.**

## FAQ

## Frequently Asked Questions

Do I still need a data analyst if I have AI analytics?

Yes. The analyst writes and maintains the definitions the AI reads. The change is that they write each definition once rather than re-answering the same question every week.

How do I make AI give reliable answers on my business data?

Define your core metrics once in SQL, in a governed data model that sits between the AI and your raw tables. The model declares join paths, grain, filters, and metric definitions, so the AI reads a settled interpretation instead of constructing its own.

What is the difference between consistent answers and correct answers?

Consistency means the same question returns the same number. Correctness means that number reflects the definition your business agreed on. A system can be perfectly consistent and reliably wrong — which is harder to catch, because nothing disagrees.

Will a better model fix inconsistent answers about my data?

The evidence suggests not. A 2026 paired benchmark found that adding a 4 KB semantic document raised accuracy by 17 to 23 percentage points, and with it in place the choice of frontier model became statistically irrelevant.

Is this the same thing as an AI hallucination?

Not exactly. A hallucination invents a fact. This is disambiguation — the model faces a genuinely underspecified question and picks an interpretation. The output is a real number from a real query; it just answers a slightly different question than the one you meant.

Why does AI get my company's data wrong when it does fine on demos?

Because demo databases are clean and documented while real warehouses are not. On the Spider benchmark, built from academic schemas, top systems hit about 91% accuracy. On BEAVER, built from real enterprise warehouses, state-of-the-art systems reach 10.8%.

Does setting temperature to 0 make an LLM deterministic?

Not fully. Even at temperature 0, identical prompts can yield different outputs because inference kernels are not batch-invariant — results shift depending on how requests are batched. This is solvable with batch-invariant kernels, but determinism only guarantees the same answer, not a correct one.

Why does AI give different answers to the same question?

Two separate causes. At the model level, sampling randomness and non-batch-invariant inference kernels produce output variance. When the question is about your company data, a larger cause takes over: a definition nobody wrote down. The model has to choose between competing meanings of a metric, such as gross or net revenue, and nothing tells it which one your company uses. In our own runs the tables and joins were right every time; the open choice was the definition.

## Who wrote this

![Ievgen Krasovytskyi](https://cdn.owox.com/www/webflow/68404586b341508a789a4aa5_.png/public)

[Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) Head of Marketing

Ievgen Krasovytskyi is the Head of Marketing at OWOX, leading strategy across content, SEO, product marketing, and AI-powered automation. With deep expertise in analytics infrastructure, data warehouses, and marketing technology, he builds systems that connect marketing performance to business outcomes. Ievgen writes about SaaS growth, analytics workflows, and the future of AI in marketing operations.

[LinkedIn](https://www.linkedin.com/in/ievgen-krasovytskyi-a38a1253/) · [All articles](/team/ievgen-krasovytskyi)

[AI in Analytics](/blog/topics/ai)

## Related articles

[AI in Analytics · AI Analytics Agents Replace Your Backlog, Not Your Analyst · July 2, 2026](/blog/articles/ai-analytics-agent-replace-backlog)

[AI in Analytics · AI Hallucinating SQL: What It Looks Like and How to Catch It · October 14, 2026](/blog/articles/ai-analytics-hallucination-deterministic-sql)

[AI in Analytics · Trace an AI Answer to Its SQL: From Number to Source Table · October 14, 2026](/blog/articles/ai-analytics-hallucinations-sql-traceability)

[See all articles →](/blog/articles)

## Links

Pages this page links to, on this site and on docs.owox.com. Where the page has a Markdown twin, its address follows the link.

- [AI in Analytics](https://www.owox.com/blog/topics/ai) — /blog/topics/ai.md
- [Ievgen Krasovytskyi](https://www.owox.com/team/ievgen-krasovytskyi) — /team/ievgen-krasovytskyi.md
- [AI assistant](https://www.owox.com/product/ai-assistants) — /product/ai-assistants.md
- [Book a Demo](https://www.owox.com/book-a-call) — /book-a-call.md
- [text-to-SQL accuracy: benchmarks vs your own warehouse](https://www.owox.com/blog/articles/text-to-sql-accuracy-benchmarks-production) — /blog/articles/text-to-sql-accuracy-benchmarks-production.md
- [semantic layer for AI agents: do you need one](https://www.owox.com/blog/articles/semantic-layer-for-ai-agents) — /blog/articles/semantic-layer-for-ai-agents.md
- [data model](https://www.owox.com/product/data-modeling) — /product/data-modeling.md
- [can you trust AI with your business numbers?](https://www.owox.com/blog/articles/can-you-trust-ai-business-numbers) — /blog/articles/can-you-trust-ai-business-numbers.md
- [AI data analyst tools you can trust](https://www.owox.com/blog/articles/ai-data-analyst-tools-comparison) — /blog/articles/ai-data-analyst-tools-comparison.md
- [RAG over your warehouse metadata is a trap](https://www.owox.com/blog/articles/rag-warehouse-metadata-trap) — /blog/articles/rag-warehouse-metadata-trap.md
- [customer acquisition cost](https://www.owox.com/metrics/customer-acquisition-cost) — /metrics/customer-acquisition-cost.md
- [OWOX Data Marts](https://www.owox.com/features/data-mart-management) — /features/data-mart-management.md
- [AI in Analytics · AI Analytics Agents Replace Your Backlog, Not Your Analyst · July 2, 2026](https://www.owox.com/blog/articles/ai-analytics-agent-replace-backlog) — /blog/articles/ai-analytics-agent-replace-backlog.md
- [AI in Analytics · AI Hallucinating SQL: What It Looks Like and How to Catch It · October 14, 2026](https://www.owox.com/blog/articles/ai-analytics-hallucination-deterministic-sql) — /blog/articles/ai-analytics-hallucination-deterministic-sql.md
- [AI in Analytics · Trace an AI Answer to Its SQL: From Number to Source Table · October 14, 2026](https://www.owox.com/blog/articles/ai-analytics-hallucinations-sql-traceability) — /blog/articles/ai-analytics-hallucinations-sql-traceability.md
- [See all articles →](https://www.owox.com/blog/articles) — /blog/articles.md