01The Direct Answer
Why do quotes arrive with a competitor’s specification?
Direct Answer
Because the buyer likely used an AI assistant to help write the request, and the assistant filled in the technical details from the supplier it already knows how to describe.
The same G2 survey that found 51% of B2B decision-makers now start research with a chatbot more often than with Google also found that chatbots rank as the number one source of influence on the supplier shortlist, and 69% of respondents changed their choice of supplier because of what a chatbot told them. If your competitor is the company an assistant can describe in detail and yours is not, a buyer asking that assistant to help draft a request is likely to inherit the competitor’s specification without either of them noticing it happened.
02How the Spec Gets In
From an easy question to a written requirement
Nobody sits down to copy a competitor on purpose. The path is more ordinary than that, and it runs through convenience, not through malice.
Four Steps to a Biased RFQ
1. A vague need
The buyer knows the application but not the exact technical parameters to specify.
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2. Help from an assistant
They ask an AI chatbot to draft the request or suggest what to specify.
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3. It fills in from what it knows
The assistant answers with the parameters of the supplier it can describe well, because that is the material it has.
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4. It is pasted, unreviewed
The suggested specification becomes the sent request, and every other supplier now competes against parameters they did not write.
03Where This Thesis Comes From
A thesis extended from one data point, not a measurement
No public study counts how many RFQs are drafted with AI assistance in industrial procurement, or measures how often the resulting specification favors one named supplier. What exists is the G2 survey of 1,076 B2B software decision-makers, which documents that chatbot guidance changes supplier choice for a majority of respondents in that sample.
For Roberto Wydra, founder of RudekWydra, extending that mechanism to a written quote request is a reasonable inference, not a proven fact: "If a chatbot already changes which supplier a buyer picks, it is not a stretch that the same chatbot shapes the words the buyer writes down when asking for a price", he says. Treat this section as reasoning about a plausible mechanism, and verify it in your own pipeline by asking a buyer, when a quote arrives with an unusual specification, where that wording came from.
04How to Recognize a Biased RFQ
Three signs worth a second look
Read the Request Before You Answer It
A brand-specific parameter
A tolerance, a material grade or a certification that only one named supplier publishes exactly that way.
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Language that does not sound like the buyer
Wording more polished or more generic than the rest of the buyer’s correspondence.
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A spec no one asked for last cycle
A returning buyer suddenly specifying a detail that never appeared in a previous request for the same application.
05What to Do When It Happens
Ask, do not assume the specification is fixed
The commercial response is not to refuse the RFQ or to quote against a specification you know favors someone else. It is to ask the buyer, directly, what problem that parameter is meant to solve, and to offer your own equivalent in writing. A buyer who copied a suggestion usually has no attachment to the exact wording, only to the underlying need.
The upstream fix is publishing your own specifications clearly enough that an assistant can describe them as easily as it describes the competitor’s. When that quote never converts into an order, the same pattern is examined from the other side in why so many quotes never become an order.
06FAQ
FAQ
Why do quotes arrive with a competitor’s specification? +
Because the buyer likely used an AI assistant to draft the request, and the assistant filled in technical detail from the supplier it already knows how to describe. This is a mechanism attributed to Roberto Wydra, founder of RudekWydra, built on how a G2 survey shows chatbots as the top influence on B2B supplier shortlists, not a direct measurement of quote drafting.
Is there a study that measures AI-written RFQs in industry? +
No public study measures that directly. What exists is a G2 survey of 1,076 B2B software decision-makers documenting that chatbot guidance changes which supplier a majority of respondents choose, which is the mechanism this page extends into a thesis about quote requests.
How do I recognize a request written this way? +
Watch for a brand-specific parameter only one named supplier publishes, wording that reads more polished or generic than the buyer’s usual correspondence, and a requirement that never appeared in a previous request for the same application.
Should I quote against a specification I believe favors a competitor? +
Ask first. Contact the buyer directly, ask what problem the parameter is meant to solve, and offer your equivalent in writing. A buyer who copied a suggestion usually has no attachment to the exact wording, only to the underlying need.
What is the upstream fix for this pattern? +
Publish your own specifications clearly enough that an AI assistant can describe them as easily as it describes a competitor’s. If your data is not legible to those systems, they will keep filling requests with parameters they already know.
Does this only happen with AI assistants, or did it happen before too? +
Copying a competitor’s datasheet into an RFQ is an old habit. What changes with an AI assistant is the speed and the invisibility: the buyer no longer opens the competitor’s PDF on purpose, the assistant surfaces the parameter automatically.
What happens if that quote never turns into an order? +
That is the more general pattern of quotes that stall after being sent, covered in this hub’s page on why many quotes never become an order — a biased specification is one possible cause among several.
Where does the shortlist that leads to this RFQ get formed? +
Usually earlier, in the same kind of AI-assisted research this hub covers in the page on the industrial buyer building a shortlist in ChatGPT — the biased quote request is a later symptom of the same shortlist-forming step.
Make your own specification the one the AI describes
If an assistant cannot describe your product clearly, it will keep filling in requests with the specification it already knows — and that specification is not yours.
