What if AI makes things up? Accuracy in business workflows

AI can add a nonexistent product feature to a quotation, misread a date or confidently present an unsupported explanation. Polished writing can make the error harder to notice. When designing a business tool, the question is not just how capable the model is. You must also decide which step will prevent an incorrect answer from being sent to a customer or saved in a system.

This is a real risk. NIST's Generative AI Profile discusses confidently presented false content under confabulation. For a business, the consequences can be concrete: an incorrect price, wrong stock information or an unsupported promise. The examples below are scenarios for assessing workflows, not claims about incidents at a customer.

Separate generating language from reading records

Making a customer email clearer and determining a product's current price are different tasks. The former may benefit from language assistance; the latter should use an approved price record. Retrieve the price from the record system using the relevant product code instead of asking the model to recall it. The model may organise the explanation, but preserve where the number came from.

The same distinction applies to calculations. Software with explicit rules can calculate defined discounts, quantities and totals. Having AI interpret the whole table should not replace a known calculation. Show the product code, currency, unit and date together. An apparently correct number attached to the wrong product or an old version is still an incorrect result.

Sources reduce risk but do not remove validation

Giving the tool relevant company documents or records helps. Yet the correct document might not have been found, an old version might have been selected, or the answer might go beyond the evidence. A displayed link alone is insufficient. Reviewers should be able to inspect the passage used and whether it actually supports the statement.

Where possible, retain the source name, version and effective date with the output. When two documents disagree, flag the conflict instead of asking the model to guess which is right. The responsible person should determine source priority. After updating a document, check whether old content remains in search results. Source management is more than a one-time upload.

“I don't know” must be an actionable result

Asking a model to acknowledge uncertainty is a good start, but not a sufficient control. The application should recognise missing information as a separate status. A missing required product code, unavailable source or conflicting prices can place the result in a review-needed state. That state must stop the next sending step.

Otherwise, a warning can appear on screen while the automation sends the document anyway. Connect the warning to permission to proceed. Specify who supplies missing information and how the checked record is processed again. We discuss customer-facing tools in what happens when an AI assistant answers incorrectly; here the focus is the accuracy of the whole workflow.

Choose controls according to consequences

An expression in an internal meeting summary and a quotation sent to a customer have different consequences. Controls should reflect that. Simple format checks can be automated: required fields, expected date formats and product codes present in a list. Interpretation and exceptional commitments may require an authorised reviewer.

• Identify fields that must match the source exactly.
• Run calculation and consistency rules separately from text generation.
• Place records needing review in a separate queue with a clear reason.
• Record who approved which fields before sending.
• Verify the actual saving or sending result afterwards.

Asking a second model to check can provide another signal; it does not guarantee independent verification. Both models may rely on the same incomplete document. Comparing calculations, identity matches and recorded values directly with existing records is often a more direct control. Give reviewers the relevant source and differences, not just the final text.

Test incorrect situations before release

A trial with only a clean example is insufficient. Use controlled cases such as an unreadable PDF, missing row, two products with the same name, an old price list and contradictory documents. Producing an answer every time is not the goal. Sometimes the correct result is to stop and report a specific gap. Include that distinction in acceptance criteria.

Trial records should contain the input, source used, output, check findings and final action status. These make it easier to identify affected records after an error. After a correction, rerun the failing example as well as the clean one. Use suitable test data instead of sending test messages to real customers or creating production records.

Take care: accuracy and data security are separate

A workflow that answers correctly does not mean every kind of data should be sent to it. Separate unnecessary personal information and assess where data goes, who can access it and how long it is kept. Read URİKEM's current practices in the data security section. Design validation, access and retention rules together.

Start with one repeated task and draw where errors should be stopped: stop without a source, route conflicts to review, and proceed to the authorised step only after checks pass. This does not promise zero errors. It makes errors visible, limits their effects and supports correction without accidentally repeating an action.

Frequently asked questions

Is telling the model to use only sources enough?

No. Check that the source actually supports the answer, and define how processing stops when sources are missing, outdated or contradictory.

Does checking with another AI eliminate errors?

No. A second model can also make mistakes or rely on the same incomplete source. Where possible, validate critical fields against source records and explicit rules, with authorised human review when needed.

Does an error mean starting everything again?

With separate results for each step, affected records can be identified and only the necessary part corrected. A rerun must not send something or create a record for a second time.