AI SEO Statistics: Aem (2026-07 edition)

Reading the 111 answers model by model shows how differently the three assistants treat the same aem questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 48.6% (ChatGPT) down to 27% (Gemini), a 22-point gap on an identical question set.

Across the 37 aem answers it produced, ChatGPT recommended hiring a professional in 48.6% of them and suggested a DIY approach first 10.8% of the time. It named a specific provider in 10.8% of answers (about 0.1 distinct providers per answer) and included price or cost information 5.4% of the time. ChatGPT asked a clarifying question before answering in 48.6% of cases, warned about red flags or scams in 8.1%, and told the buyer to verify credentials in 16.2%, averaging 731 words per answer. On the remaining cues it told the buyer to check reviews in 5.4%, pointed to case studies or a portfolio in 24.3%, and framed the choice around local proximity in 5.4%; a selection-criteria checklist appeared in 37.8% of its answers and a recommendation to gather multiple quotes in 5.4%.

Across the 37 aem answers it produced, Claude recommended hiring a professional in 35.1% of them and suggested a DIY approach first 5.4% of the time. It named a specific provider in 24.3% of answers (about 0.6 distinct providers per answer) and included price or cost information 8.1% of the time. Claude asked a clarifying question before answering in 62.2% of cases, warned about red flags or scams in 18.9%, and told the buyer to verify credentials in 27%, averaging 338 words per answer. On the remaining cues it told the buyer to check reviews in 8.1%, pointed to case studies or a portfolio in 24.3%, and framed the choice around local proximity in 5.4%; a selection-criteria checklist appeared in 59.5% of its answers and a recommendation to gather multiple quotes in 5.4%.

Across the 37 aem answers it produced, Gemini recommended hiring a professional in 27% of them and suggested a DIY approach first 2.7% of the time. It named a specific provider in 32.4% of answers (about 0.7 distinct providers per answer) and included price or cost information 10.8% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 8.1%, and told the buyer to verify credentials in 5.4%, averaging 218 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 5.4%; a selection-criteria checklist appeared in 37.8% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route an aem buyer to a professional (48.6%) and Gemini the least (27%). ChatGPT produced the longest answers, at 731 words on average. Specific providers were named most often by Gemini (32.4%) — even there, roughly one answer in 3 carried a name.

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

• Mentions local proximity: 5.4% across all three models.
• Gives price or cost information: 5.4%–10.8% across all three (a 5-point spread).
• Recommends multiple quotes: 0%–5.4% across all three (a 5-point spread).
• Suggests a DIY approach first: 2.7%–10.8% across all three (a 8-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "suggests a DIY approach first" (identical coding in 89.2% of questions) and least consistently on "asks a clarifying question" (24.3%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the aem buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 4.5% of answers on average. Verifying credentials or certifications appeared in 16.2%. Warning about red flags or scams appeared in 11.7%.

On structuring the decision, a selection-criteria checklist showed up in 45% of answers on average and a recommendation to gather multiple quotes in 3.6%. The single least-reproduced protective signal for aem is "recommends multiple quotes" at 3.6% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 37 questions behind every percentage on this page were drawn from real aem (technology / SaaS; 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 aem 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.