Original research · 2026-07 edition

AI SEO Statistics: Consultant (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — sampled from real buyer journeys in consultant.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

Our team's productivity has stalled and we can't figure out why; is this something a business consultant handles or do we need new software?
Can I fix my company's supply chain issues myself using online courses or should I hire an operations consultant?
What specific certifications or past experience should I look for when hiring a strategy consultant for a small tech startup?
How much does a management consultant typically charge for a 3-month project to overhaul HR policies?
Is it better to pay a consultant a flat project fee or an hourly rate for long-term advisory work?
What's the difference between hiring a boutique consulting firm and an independent freelance consultant for market research?
Do I need a consultant who can come to my office in person, or is remote consulting just as effective for leadership training?
What are some warning signs during an initial call that a professional consultant might not be the right fit for my corporate culture?
Show all 15 questions
I need a crisis management consultant immediately because of a PR issue; how fast can someone typically start an engagement?
How do I measure the actual ROI of hiring a consultant for business process improvement?
What does the first month of working with a growth consultant actually look like in terms of day-to-day interactions?
Should I hire a general business consultant or someone who specializes specifically in my industry like healthcare or construction?
What kind of final report or tangible documentation should I expect at the end of a professional consulting engagement?
How do I introduce a new consultant to my employees without making them feel like their jobs are being audited or at risk?
I only have a $5,000 budget for professional advice; what's the most impactful consulting service I can get to help my retail business grow?

Model by model

20-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about consultant buyers.

Behavior rates across 15 consultant buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional67%53%53%87%
Suggests DIY first7%13%7%87%
Names specific providers0%7%20%73%
Gives price or cost info20%0%27%60%
Tells to check reviews20%0%0%80%
Tells to verify credentials20%0%0%80%
Mentions case studies / portfolio47%13%0%53%
Mentions local proximity13%13%0%87%
Gives selection criteria53%47%33%33%
Warns about red flags13%20%7%80%
Asks a clarifying question73%67%0%13%
Recommends multiple quotes0%0%0%100%

By model

How each assistant handled Consultant questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same consultant questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 66.7% (ChatGPT) down to 53.3% (Claude), a 13-point gap on an identical question set.

Across the 15 consultant answers it produced, ChatGPT recommended hiring a professional in 66.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 0% of answers (about 0 distinct providers per answer) and included price or cost information 20% of the time. ChatGPT asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 20%, averaging 706 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 46.7%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 consultant answers it produced, Claude recommended hiring a professional in 53.3% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 0% of the time. Claude asked a clarifying question before answering in 66.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 0%, averaging 328 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 13.3%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 46.7% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 consultant answers it produced, Gemini recommended hiring a professional in 53.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 20% of answers (about 0.6 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 0%, averaging 280 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 0%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a consultant buyer to a professional (66.7%) and Claude the least (53.3%). ChatGPT produced the longest answers, at 706 words on average. Specific providers were named most often by Gemini (20%) — even there, roughly one answer in 5 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 20.4 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a consultant buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (ChatGPT) — a 73-point spread.
  • Mentions case studies or portfolio: from 0% (Gemini) to 46.7% (ChatGPT) — a 47-point spread.
  • Gives price or cost information: from 0% (Claude) to 26.7% (Gemini) — a 27-point spread.
  • Names a specific provider: from 0% (ChatGPT) to 20% (Gemini) — a 20-point spread.
  • Tells the buyer to check reviews: from 0% (Claude) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 73 points — means a consultant buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the consultant market.

Where they agree

The points of near-consensus in Consultant.

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

  • Recommends multiple quotes: 0% across all three models.
  • Suggests a DIY approach first: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions local proximity: 0%–13.3% across all three (a 13-point spread).
  • Warns about red flags or scams: 6.7%–20% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (13.3%).

Every behavior, measured

All twelve coded behaviors for Consultant, averaged across the three models.

The behaviors AI models reproduce most often for consultant are recommends hiring a professional (57.8% on average), asks a clarifying question (46.7%) and gives selection criteria (44.4%); the rarest are recommends multiple quotes (0%), tells the buyer to verify credentials (6.7%) and tells the buyer to check reviews (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Recommends hiring a professional: 57.8% on average (ChatGPT 66.7%, Claude 53.3%, Gemini 53.3%) — a 13-point spread.
  • Asks a clarifying question: 46.7% on average (ChatGPT 73.3%, Claude 66.7%, Gemini 0%) — a 73-point spread.
  • Gives selection criteria: 44.4% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 33.3%) — a 20-point spread.
  • Mentions case studies or portfolio: 20% on average (ChatGPT 46.7%, Claude 13.3%, Gemini 0%) — a 47-point spread.
  • Gives price or cost information: 15.6% on average (ChatGPT 20%, Claude 0%, Gemini 26.7%) — a 27-point spread.
  • Warns about red flags or scams: 13.3% on average (ChatGPT 13.3%, Claude 20%, Gemini 6.7%) — a 13-point spread.
  • Suggests a DIY approach first: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Names a specific provider: 8.9% on average (ChatGPT 0%, Claude 6.7%, Gemini 20%) — a 20-point spread.
  • Mentions local proximity: 8.9% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Tells the buyer to check reviews: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 20%, Claude 0%, Gemini 0%) — a 20-point spread.
  • Recommends multiple quotes: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the consultant buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the consultant buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 6.7% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 13.3%.

On structuring the decision, a selection-criteria checklist showed up in 44.4% of answers on average and a recommendation to gather multiple quotes in 0%. The single least-reproduced protective signal for consultant is "recommends multiple quotes" at 0% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Consultant providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 consultant answers, a specific provider was named in 8.9% of responses on average — roughly 0.3 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for consultant: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Consultant questions cover.

The 15 questions behind every percentage on this page were drawn from real consultant (professional services; 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 consultant 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.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific consultant question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →