Original research · 2026-07 edition

AI SEO Statistics: Consulting Firm (2026-07 edition)

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

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The question bank

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

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

Our sales have plateaued for three quarters despite increasing our ad spend, should we bring in a growth consultant or just hire a new marketing agency?
Is it worth hiring a consultant to help with digital transformation or can my internal IT team handle the transition to a cloud-based workflow?
What specific KPIs should I look for when reviewing a management consultant's past case studies to ensure they actually deliver ROI?
What is the typical fee structure for a small business strategy consultant—do they usually charge a flat project rate or take a percentage of revenue growth?
How do boutique consulting firms differ from the big global firms in terms of hands-on implementation versus just giving a slide deck?
Does it matter if a consultant is local to my area if we are a fully remote company looking for organizational restructuring?
What are some red flags to watch out for during an initial discovery call with a high-end business consultant?
We need to prepare for an acquisition in the next 90 days, what kind of consultant specializes in cleaning up operations and financials for a quick exit?
Show all 15 questions
I'm a founder who is overwhelmed by day-to-day ops; how do I find someone to help me build a scalable leadership structure?
What questions should I ask a consultant's references to find out if they were actually easy to work with and not just smart on paper?
I have a $20,000 budget for operational improvements; is that enough to get a decent consultant for a three-month engagement?
What's the difference between a business coach and a management consultant when it comes to fixing team culture issues?
Is it normal for a consulting firm to ask for a large deposit upfront before they've even started the discovery phase?
My manufacturing costs are rising but I can't figure out where the waste is; what type of specialist consultant do I need for supply chain optimization?
Can I just use a template for a strategic plan or do I really need to pay a professional to facilitate the planning session for my board?

Model by model

23-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 consulting firm buyers.

Behavior rates across 15 consulting firm buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional60%47%53%80%
Suggests DIY first13%27%7%67%
Names specific providers0%20%13%73%
Gives price or cost info20%27%20%73%
Tells to check reviews27%20%0%67%
Tells to verify credentials13%7%0%87%
Mentions case studies / portfolio53%27%7%40%
Mentions local proximity20%7%7%87%
Gives selection criteria60%53%60%47%
Warns about red flags20%20%20%73%
Asks a clarifying question53%47%0%27%
Recommends multiple quotes27%0%0%73%

By model

How each assistant handled Consulting Firm questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same consulting firm questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 60% (ChatGPT) down to 46.7% (Claude), a 13-point gap on an identical question set.

Across the 15 consulting firm answers it produced, ChatGPT recommended hiring a professional in 60% of them and suggested a DIY approach first 13.3% 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 53.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 624 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 53.3%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 26.7%.

Across the 15 consulting firm answers it produced, Claude recommended hiring a professional in 46.7% of them and suggested a DIY approach first 26.7% 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. Claude asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 347 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 26.7%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 consulting firm 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 13.3% of answers (about 0.8 distinct providers per answer) and included price or cost information 20% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 0%, averaging 253 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 6.7%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a consulting firm buyer to a professional (60%) and Claude the least (46.7%). ChatGPT produced the longest answers, at 624 words on average. Specific providers were named most often by Claude (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 22.6 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a consulting firm buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 53.3% (ChatGPT) — a 53-point spread.
  • Mentions case studies or portfolio: from 6.7% (Gemini) to 53.3% (ChatGPT) — a 47-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 26.7% (ChatGPT) — a 27-point spread.
  • Recommends multiple quotes: from 0% (Claude) to 26.7% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 6.7% (Gemini) to 26.7% (Claude) — a 20-point spread.

The widest single gap — asks a clarifying question, 53 points — means a consulting firm 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 consulting firm market.

Where they agree

The points of near-consensus in Consulting Firm.

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

  • Warns about red flags or scams: 20% across all three models.
  • Gives price or cost information: 20%–26.7% across all three (a 7-point spread).
  • Gives selection criteria: 53.3%–60% across all three (a 7-point spread).
  • Recommends hiring a professional: 46.7%–60% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to verify credentials" (identical coding in 86.7% of questions) and least consistently on "asks a clarifying question" (26.7%).

Every behavior, measured

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

The behaviors AI models reproduce most often for consulting firm are gives selection criteria (57.8% on average), recommends hiring a professional (53.3%) and asks a clarifying question (33.3%); the rarest are tells the buyer to verify credentials (6.7%), recommends multiple quotes (8.9%) and mentions local proximity (11.1%). 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:

  • Gives selection criteria: 57.8% on average (ChatGPT 60%, Claude 53.3%, Gemini 60%) — a 7-point spread.
  • Recommends hiring a professional: 53.3% on average (ChatGPT 60%, Claude 46.7%, Gemini 53.3%) — a 13-point spread.
  • Asks a clarifying question: 33.3% on average (ChatGPT 53.3%, Claude 46.7%, Gemini 0%) — a 53-point spread.
  • Mentions case studies or portfolio: 28.9% on average (ChatGPT 53.3%, Claude 26.7%, Gemini 6.7%) — a 47-point spread.
  • Gives price or cost information: 22.2% on average (ChatGPT 20%, Claude 26.7%, Gemini 20%) — a 7-point spread.
  • Warns about red flags or scams: 20% on average (ChatGPT 20%, Claude 20%, Gemini 20%).
  • Suggests a DIY approach first: 15.6% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 6.7%) — a 20-point spread.
  • Tells the buyer to check reviews: 15.6% on average (ChatGPT 26.7%, Claude 20%, Gemini 0%) — a 27-point spread.
  • Names a specific provider: 11.1% on average (ChatGPT 0%, Claude 20%, Gemini 13.3%) — a 20-point spread.
  • Mentions local proximity: 11.1% on average (ChatGPT 20%, Claude 6.7%, Gemini 6.7%) — a 13-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 26.7%, Claude 0%, Gemini 0%) — a 27-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.

Trust signals

How well the models protect the consulting firm buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the consulting firm buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 15.6% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 20%.

On structuring the decision, a selection-criteria checklist showed up in 57.8% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for consulting firm is "tells the buyer to verify credentials" at 6.7% 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 Consulting Firm providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 consulting firm answers, a specific provider was named in 11.1% of responses on average — roughly 0.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for consulting firm: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Consulting Firm questions cover.

The 15 questions behind every percentage on this page were drawn from real consulting firm (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 consulting firm 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 consulting firm 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 →