Specific-provider naming is rare across all three models (5-10%), so ranking in AI answers currently depends far more on being associated with selection criteria, red-flag warnings, and credential checks than on brand-name recall.
AI SEO Statistics: Professional Services (2026-07 edition)
Across 120 AI responses about professional services, models rarely name specific providers (5-10%) but consistently emphasize how to choose one, especially through selection-criteria lists and, less often, credential or red-flag warnings. ChatGPT, Claude, and Gemini diverge sharply on tone and structure — from whether they ask clarifying questions to how much cost information they share — meaning visibility strategies must target shared decision-criteria content rather than any single model's quirks.
40 questions · 120 AI responses · 3 models · measured 2026-07-02
Key statistics
Every number below is measured, anchored, and sourced.
The question bank
The questions we tested — sampled from real buyer journeys in professional services.
Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.
Show all 40 questions
By service
Not all professional services services are treated the same by AI.
We ran the same measurement on 45 distinct professional services services. The rate at which ChatGPT, Claude and Gemini push buyers toward a professional swings widely, and that gap is exactly where authority is won or lost.
| # | Service | Hire-a-pro rate | Model gap |
|---|---|---|---|
| 01 | Charity Nonprofit | 88.9% | 23.7 pts |
| 02 | Amusement Parks | 82.5% | 26.8 pts |
| 03 | Outdoor Industry | 78.3% | 25.8 pts |
| 04 | Accountant | 77.8% | 23.3 pts |
| 05 | Dog Trainers | 71.7% | 22.8 pts |
| 06 | Movie Theaters | 71.7% | 25.3 pts |
| 07 | Wedding Planner | 71.1% | 20 pts |
| 08 | Architect | 68.9% | 25.9 pts |
| 09 | Financial Advisor | 68.9% | 25.2 pts |
| 10 | Financial Planner | 68.9% | 24.8 pts |
| 11 | Insurance Agent | 68.4% | 24.9 pts |
| 12 | Accounting Firm | 66.7% | 20.4 pts |
| 13 | Bookkeeping | 66.7% | 20.7 pts |
| 14 | Female Entrepreneurs | 65.6% | 18 pts |
| 15 | Life Coaches | 63.3% | 23.3 pts |
| 16 | Associations | 62.4% | 18.5 pts |
| 17 | Interior Designer | 62.2% | 23 pts |
| 18 | Dating | 61.7% | 27.2 pts |
| 19 | Delivery Service | 60.8% | 23.2 pts |
| 20 | Aviation | 60% | 26.7 pts |
| 21 | Translators | 58.3% | 21.8 pts |
| 22 | Consultant | 57.8% | 20.4 pts |
| 23 | Event Planner | 57.8% | 23.3 pts |
| 24 | Consulting Firm | 53.3% | 22.6 pts |
| 25 | Copywriter | 53.3% | 19.3 pts |
| 26 | Marketing Agency | 53.3% | 20.4 pts |
| 27 | Recreation Entertainment | 51.7% | 23.2 pts |
| 28 | Videographer | 51.1% | 18.1 pts |
| 29 | Web Designer | 48.9% | 16.7 pts |
| 30 | Logistics Companies | 47.5% | 20.7 pts |
| 31 | Recruitment Agency | 46.7% | 17 pts |
| 32 | IT Company | 44.4% | 19.6 pts |
| 33 | Photographer | 40% | 16.7 pts |
| 34 | Charter | 39.2% | 18.3 pts |
| 35 | SEO Content Strategy for Energy Industry | 38.3% | 16.4 pts |
| 36 | Limo | 37.5% | 20.4 pts |
| 37 | Web Design Agency | 35.6% | 18.9 pts |
| 38 | Recording Studios | 34.2% | 18.2 pts |
| 39 | Best Solutions for SEO B2b | 22.5% | 13.3 pts |
| 40 | Adult Industry | 21.7% | 22.5 pts |
| 41 | SEO Political Campaigns | 21.7% | 13.6 pts |
| 42 | Bowling Alleys | 20.8% | 21.4 pts |
| 43 | Escape Rooms | 17.5% | 16.1 pts |
| 44 | Paintball Arenas | 15% | 17.2 pts |
| 45 | Adult Dating Websites | 11.7% | 24.2 pts |
Measured across ChatGPT, Claude and Gemini · 15 buyer questions per service × 3 models · Authority Specialist AI Study. Free to cite with attribution.
Model by model
16-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 professional services buyers.
| ChatGPT | Claude | Gemini | Consensus | |
|---|---|---|---|---|
| Recommends hiring a professional | 53% | 35% | 30% | 65% |
| Suggests DIY first | 20% | 13% | 3% | 83% |
| Names specific providers | 5% | 8% | 10% | 93% |
| Gives price or cost info | 18% | 15% | 30% | 73% |
| Tells to check reviews | 8% | 10% | 0% | 83% |
| Tells to verify credentials | 10% | 10% | 0% | 85% |
| Mentions case studies / portfolio | 18% | 18% | 5% | 78% |
| Mentions local proximity | 5% | 8% | 0% | 90% |
| Gives selection criteria | 33% | 45% | 33% | 43% |
| Warns about red flags | 10% | 28% | 15% | 75% |
| Asks a clarifying question | 25% | 50% | 0% | 45% |
| Recommends multiple quotes | 0% | 3% | 0% | 98% |
What this means
What this means for professional services businesses.
Model choice materially changes the user experience: ChatGPT pushes toward hiring a professional and writes long answers, Claude asks clarifying questions and warns about scams more, and Gemini is terser, cost-focused, and rarely interactive.
Guidance that firms want associated with their brand — reviews/ratings checks, credential verification, multiple quotes — is under-delivered by all models (0-27.5%), representing white space where authoritative, structured content could shift AI outputs.
The 16.3 divergence index reflects real behavioral splits (e.g., asks_clarifying_question ranges 0-50%, warns_about_red_flags ranges 10-27.5%), meaning firms should not optimize for a single model's pattern but for the traits several models share, like selection-criteria framing.
Because average providers named per response is below 1 for every model (0.2-0.7), earning even a single mention requires content that maps directly onto the specific criteria and warnings models already tend to generate.
AI visibility is measurable. We just measured it for your industry.
Open your dashboard to see how ChatGPT, Claude and Gemini describe YOUR business — mentions, recommendations, citations, gaps.
Methodology
A controlled snapshot, documented end to end.
40 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 2026-07-02, 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 →