Original research · 2026-07 edition

AI SEO Statistics: B2b Tech (2026-07 edition)

34 questions · 102 AI responses · 3 models · measured 2026-07-06

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The question bank

The questions we tested — sampled from real buyer journeys in b2b tech.

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

What are the key features I should look for in a project management tool for a remote team of 50?
Is it cheaper to hire a freelance developer to build a custom inventory system or just pay for a SaaS subscription?
How do I know if a software vendor's security certifications like SOC2 are actually legit?
What is the average implementation time for a new ERP system in a mid-sized manufacturing company?
Our current CRM is too clunky; what are some lightweight alternatives that still have robust reporting?
What questions should I ask during a demo to see if the software can actually handle our high transaction volume?
Is a per-seat pricing model better or worse than usage-based pricing for a growing startup?
What are the red flags to watch out for when reading a Master Service Agreement for a tech vendor?
Show all 34 questions
Can I integrate a third-party marketing automation tool with a custom-built legacy database?
How much should we budget for yearly maintenance and support fees beyond the initial license cost?
What is the difference between a managed service provider and a SaaS platform when it comes to cybersecurity?
We need to move our data to the cloud in under 30 days; is that even realistic for a company with 10TB of records?
Why do some B2B software companies hide their pricing and force you to talk to a salesperson?
How can I verify a software vendor's uptime claims if they do not have a public status page?
What are the hidden costs of switching from an on-premise solution to a cloud-based one?
Should we look for an all-in-one suite or a best of breed stack for our HR and payroll needs?
What kind of technical support response times are standard for enterprise-level software contracts?
How do I evaluate the ROI of a new AI-driven customer service chatbot before we commit to a year-long contract?
Are there any specific compliance risks I should be aware of when using a third-party payment processor in Canada?
What is the best way to run a pilot program or POC with a new tech vendor without disrupting our daily operations?
If a SaaS company goes out of business, what happens to all the data we have stored on their platform?
How do I convince my CFO that we need to upgrade our outdated accounting software this quarter?
What are the pros and cons of using a low-code platform vs hiring a full-stack dev team for a customer portal?
Why is our current software implementation taking twice as long as the vendor promised in the sales pitch?
What metrics should I track to see if our team is actually adopting the new collaboration tool we just bought?
Can I negotiate the price of a standard SaaS subscription if I am willing to sign a three-year deal?
What is the difference between an API-first platform and one that just has an available API?
How do I find a local consultant who specializes in implementing enterprise CRM systems for non-profit organizations?
What are the common pitfalls when migrating from one email marketing platform to another?
Is it better to buy software from a massive established corporation or a smaller, more agile startup?
How does white labeling work if I want to resell a SaaS product as part of my own agency's service?
What should be included in a Service Level Agreement to ensure we are not left hanging during a system outage?
Are there any open-source alternatives to major enterprise resource planning software that are actually reliable?
How do I determine if a software's API documentation is comprehensive enough for our developers?

Model by model

18-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 b2b tech buyers.

Behavior rates across 34 b2b tech buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional29%12%6%77%
Suggests DIY first32%9%12%77%
Names specific providers15%38%35%44%
Gives price or cost info15%12%12%88%
Tells to check reviews9%3%0%91%
Tells to verify credentials15%9%3%82%
Mentions case studies / portfolio9%0%0%91%
Mentions local proximity0%0%0%100%
Gives selection criteria38%53%27%41%
Warns about red flags6%18%6%77%
Asks a clarifying question41%56%0%24%
Recommends multiple quotes3%9%0%88%

By model

How each assistant handled B2b Tech questions.

Reading the 102 answers model by model shows how differently the three assistants treat the same b2b tech questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 29.4% (ChatGPT) down to 5.9% (Gemini), a 24-point gap on an identical question set.

Across the 34 b2b tech answers it produced, ChatGPT recommended hiring a professional in 29.4% of them and suggested a DIY approach first 32.4% of the time. It named a specific provider in 14.7% of answers (about 0.7 distinct providers per answer) and included price or cost information 14.7% of the time. ChatGPT asked a clarifying question before answering in 41.2% of cases, warned about red flags or scams in 5.9%, and told the buyer to verify credentials in 14.7%, averaging 740 words per answer. On the remaining cues it told the buyer to check reviews in 8.8%, pointed to case studies or a portfolio in 8.8%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 38.2% of its answers and a recommendation to gather multiple quotes in 2.9%.

Across the 34 b2b tech answers it produced, Claude recommended hiring a professional in 11.8% of them and suggested a DIY approach first 8.8% of the time. It named a specific provider in 38.2% of answers (about 1.4 distinct providers per answer) and included price or cost information 11.8% of the time. Claude asked a clarifying question before answering in 55.9% of cases, warned about red flags or scams in 17.6%, and told the buyer to verify credentials in 8.8%, averaging 334 words per answer. On the remaining cues it told the buyer to check reviews in 2.9%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 52.9% of its answers and a recommendation to gather multiple quotes in 8.8%.

Across the 34 b2b tech answers it produced, Gemini recommended hiring a professional in 5.9% of them and suggested a DIY approach first 11.8% of the time. It named a specific provider in 35.3% of answers (about 1.2 distinct providers per answer) and included price or cost information 11.8% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 5.9%, and told the buyer to verify credentials in 2.9%, averaging 254 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 26.5% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a b2b tech buyer to a professional (29.4%) and Gemini the least (5.9%). ChatGPT produced the longest answers, at 740 words on average. Specific providers were named most often by Claude (38.2%) — even there, roughly one answer in 3 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 17.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a b2b tech buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 55.9% (Claude) — a 56-point spread.
  • Gives selection criteria: from 26.5% (Gemini) to 52.9% (Claude) — a 26-point spread.
  • Suggests a DIY approach first: from 8.8% (Claude) to 32.4% (ChatGPT) — a 24-point spread.
  • Recommends hiring a professional: from 5.9% (Gemini) to 29.4% (ChatGPT) — a 24-point spread.
  • Names a specific provider: from 14.7% (ChatGPT) to 38.2% (Claude) — a 24-point spread.

The widest single gap — asks a clarifying question, 56 points — means a b2b tech 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 b2b tech market.

Where they agree

The points of near-consensus in B2b Tech.

On other behaviors the three models move almost in lockstep — the points of near-consensus for b2b tech, where all three landed within a few points of each other:

  • Mentions local proximity: 0% across all three models.
  • Gives price or cost information: 11.8%–14.7% across all three (a 3-point spread).
  • Tells the buyer to check reviews: 0%–8.8% across all three (a 9-point spread).
  • Mentions case studies or portfolio: 0%–8.8% across all three (a 9-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (23.5%).

Every behavior, measured

All twelve coded behaviors for B2b Tech, averaged across the three models.

The behaviors AI models reproduce most often for b2b tech are gives selection criteria (39.2% on average), asks a clarifying question (32.4%) and names a specific provider (29.4%); the rarest are mentions local proximity (0%), mentions case studies or portfolio (2.9%) and recommends multiple quotes (3.9%). Each figure below is the share of a model's 34 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: 39.2% on average (ChatGPT 38.2%, Claude 52.9%, Gemini 26.5%) — a 26-point spread.
  • Asks a clarifying question: 32.4% on average (ChatGPT 41.2%, Claude 55.9%, Gemini 0%) — a 56-point spread.
  • Names a specific provider: 29.4% on average (ChatGPT 14.7%, Claude 38.2%, Gemini 35.3%) — a 24-point spread.
  • Suggests a DIY approach first: 17.7% on average (ChatGPT 32.4%, Claude 8.8%, Gemini 11.8%) — a 24-point spread.
  • Recommends hiring a professional: 15.7% on average (ChatGPT 29.4%, Claude 11.8%, Gemini 5.9%) — a 24-point spread.
  • Gives price or cost information: 12.8% on average (ChatGPT 14.7%, Claude 11.8%, Gemini 11.8%) — a 3-point spread.
  • Warns about red flags or scams: 9.8% on average (ChatGPT 5.9%, Claude 17.6%, Gemini 5.9%) — a 12-point spread.
  • Tells the buyer to verify credentials: 8.8% on average (ChatGPT 14.7%, Claude 8.8%, Gemini 2.9%) — a 12-point spread.
  • Tells the buyer to check reviews: 3.9% on average (ChatGPT 8.8%, Claude 2.9%, Gemini 0%) — a 9-point spread.
  • Recommends multiple quotes: 3.9% on average (ChatGPT 2.9%, Claude 8.8%, Gemini 0%) — a 9-point spread.
  • Mentions case studies or portfolio: 2.9% on average (ChatGPT 8.8%, Claude 0%, Gemini 0%) — a 9-point spread.
  • Mentions local proximity: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the b2b tech buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the b2b tech buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 3.9% of answers on average. Verifying credentials or certifications appeared in 8.8%. Warning about red flags or scams appeared in 9.8%.

On structuring the decision, a selection-criteria checklist showed up in 39.2% of answers on average and a recommendation to gather multiple quotes in 3.9%. The single least-reproduced protective signal for b2b tech is "tells the buyer to check reviews" at 3.9% 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 B2b Tech providers?

For service providers the decisive question is whether these systems name anyone at all. Across 102 b2b tech answers, a specific provider was named in 29.4% of responses on average — roughly 1.1 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for b2b tech: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 34 B2b Tech questions cover.

The 34 questions behind every percentage on this page were drawn from real b2b tech (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 b2b tech 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 34 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific b2b tech 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.

34 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 2026-07-06, 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 →