AI SEO Statistics: Machinery Manufacturers (2026-07 edition)

Reading the 120 answers model by model shows how differently the three assistants treat the same machinery manufacturers questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 50% (ChatGPT) down to 15% (Gemini), a 35-point gap on an identical question set.

Across the 40 machinery manufacturers answers it produced, ChatGPT recommended hiring a professional in 50% of them and suggested a DIY approach first 32.5% of the time. It named a specific provider in 5% of answers (about 0.1 distinct providers per answer) and included price or cost information 5% of the time. ChatGPT asked a clarifying question before answering in 42.5% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 20%, averaging 701 words per answer. On the remaining cues it told the buyer to check reviews in 5%, pointed to case studies or a portfolio in 5%, and framed the choice around local proximity in 12.5%; a selection-criteria checklist appeared in 35% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 machinery manufacturers answers it produced, Claude recommended hiring a professional in 30% of them and suggested a DIY approach first 17.5% of the time. It named a specific provider in 15% of answers (about 0.9 distinct providers per answer) and included price or cost information 7.5% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 22.5%, averaging 330 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 7.5%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 50% of its answers and a recommendation to gather multiple quotes in 7.5%.

Across the 40 machinery manufacturers answers it produced, Gemini recommended hiring a professional in 15% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 22.5% of answers (about 1.1 distinct providers per answer) and included price or cost information 22.5% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 10%, averaging 239 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 10%; a selection-criteria checklist appeared in 37.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a machinery manufacturers buyer to a professional (50%) and Gemini the least (15%). ChatGPT produced the longest answers, at 701 words on average. Specific providers were named most often by Gemini (22.5%) — even there, roughly one answer in 4 carried a name.

On other behaviors the three models move almost in lockstep — the points of near-consensus for machinery manufacturers, where all three landed within a few points of each other:

• Tells the buyer to check reviews: 2.5%–5% across all three (a 3-point spread).
• Mentions local proximity: 10%–12.5% across all three (a 3-point spread).
• Warns about red flags or scams: 10%–15% across all three (a 5-point spread).
• Mentions case studies or portfolio: 0%–7.5% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 90% of questions) and least consistently on "asks a clarifying question" (25%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the machinery manufacturers buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.3% of answers on average. Verifying credentials or certifications appeared in 17.5%. Warning about red flags or scams appeared in 11.7%.

On structuring the decision, a selection-criteria checklist showed up in 40.8% of answers on average and a recommendation to gather multiple quotes in 5%. The single least-reproduced protective signal for machinery manufacturers is "tells the buyer to check reviews" at 3.3% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 40 questions behind every percentage on this page were drawn from real machinery manufacturers (manufacturing / industrial B2B; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact machinery manufacturers question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.