AI-generated RFQs are flooding B2B inboxes. Here's how one manufacturer filtered the noise

A small UK precision moulder started receiving more RFQs than ever in 2026. The inquiries were neatly formatted, often accompanied by drawings and comparison tables, and addressed to the right company. Conversion barely moved. The sales team was spending more hours quoting and less time doing the engineering work that actually differentiated the business.

That pattern, documented in a July 2026 Harvard Business Review piece by Graham Kenny and Ganna Pogrebna, is becoming a recognizable operating problem across B2B. AI-assisted procurement tools now let buyers generate supplier shortlists, draft technically plausible RFQs, and distribute them to a dozen vendors in minutes. The buying cycle gets faster for the customer. For the supplier, it gets noisier.

The scale of AI-mediated buying

The numbers behind the shift are meaningful. A Semrush report cited in the HBR piece puts AI use in product and service research at roughly 50% of consumers. McKinsey data referenced in the same article ranks shopping-related tasks as the third most popular application of generative AI, behind only a handful of other use cases. Financial services (13%) and travel (21%) join consumer goods (39%) as categories where AI plays a decisive role in purchase decisions.

These figures cover both B2C and B2B behavior. On the B2B side, the practical effect is that buyers no longer need deep familiarity with a category to produce a credible-looking procurement document. AI handles the formatting, the language, and the logistics of simultaneous outreach.

More inquiries, not better inquiries

The HBR reporting describes the problem clearly: AI can manufacture a race to quote faster rather than better, and faster is the race a specialist least wants to win. For firms whose value lies in improving a customer's specification before work begins, responding to every inbound request on equal terms means competing on price before demonstrating expertise.

The disguised case study in the article, a company Kenny and Pogrebna call Meridian Mouldings, illustrates how that dynamic plays out in practice. When the managing director reviewed the inquiry pipeline, three signals stood out. Many RFQs had obviously been sent to multiple suppliers simultaneously, some with the wrong company name in the salutation. The technical language was generic, making jobs look simpler than they were. And critical application context was routinely missing: operating environment, stress tolerances, whether earlier versions had failed, whether the customer wanted design advice or just a price.

Preparing a responsible quote at Meridian required engineering input, not clerical work. Poorly formed RFQs were therefore pulling capacity from exactly the people the firm most needed to protect.

The qualification framework that changed the pipeline

Meridian's response, as reported in HBR, started with measurement. The team sorted recent inquiries into three buckets: ready to quote, needs clarification, and quote fishing. That exercise alone changed the default. Instead of reflexively quoting every inbound request, the team ran a short qualification screen before anyone opened a drawing.

The screen asked whether the inquiry explained the application, specified operating conditions and material requirements, included a drawing or sample, and showed evidence of a genuine buying process rather than a broad market scan. Inquiries that passed moved quickly to quotation. Promising but incomplete ones received a short technical clarification note requesting the missing detail. The questions themselves, covering application, usage stress, volume, previous failures, and timing, often opened a real commercial conversation that the original RFQ had not.

Inquiries that went quiet after clarification were treated as a result rather than a loss. According to the HBR reporting, buyers unwilling to answer basic application questions rarely valued the firm's specialist expertise in the first place, and losing them early freed time for work that actually fit the company's capabilities.

What the case reveals for procurement and sales leaders

The Meridian example points to a broader operational tension. AI procurement tools lower the effort required to initiate a supplier search, which benefits buyers in commodity or near-commodity categories. In specialist markets, that same efficiency can obscure whether a buying process is serious, whether a specification is ready, and whether the customer's problem is even well-defined.

Kenny and Pogrebna frame this as a shift in where competitive advantage resides. Managing AI-shaped interactions, including the quality of information that flows through them, is now part of the competitive task. That is not a marketing observation. It is an operational one, and it falls on sales, commercial, and engineering teams to act on it.

Meridian's final reported change was internal: the monthly sales review stopped leading with raw RFQ volume and started with conversion-oriented questions. How many inquiries were worth quoting? How many needed clarification? How many were quote fishing? Which qualified opportunities were in active technical discussion? The metric that had always signaled pipeline health, inquiry count, turned out to be a poor proxy once AI made that number easy to inflate.

What this means for your team

• Audit your inbound pipeline now: sort recent RFQs into ready-to-quote, needs-clarification, and quote-fishing categories to quantify how much AI-generated noise your team is already absorbing.
• Define a qualification screen specific to your product or service complexity, the questions a buyer must answer before engineering or technical staff invest time in a quote.
• Redesign your pipeline metrics: track qualified opportunities and clarification-response rates alongside raw inquiry volume, so rising RFQ counts don't mask flat or declining conversion.
• Evaluate whether your online presence, specifications, case studies, and technical documentation gives AI buying tools enough context to pre-qualify your firm for the right opportunities before an RFQ even arrives.