Original research · 2026-07 edition

AI SEO Statistics: Life Science (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 life science.

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

What are the best software options for tracking clinical trial patient enrollment in real-time?
Is it cheaper to build a custom database for our genomic sequencing data or pay for a specialized life sciences SaaS platform?
What specific questions should I ask a vendor to ensure their platform is fully 21 CFR Part 11 compliant?
What is the typical implementation fee for a mid-sized LIMS for a startup biotech lab?
How do I choose between an Electronic Lab Notebook and a full Laboratory Information Management System for a small research team?
Are there cloud-based R&D platforms that handle both GDPR and HIPAA requirements for international clinical trials?
What are the warning signs that a life science software provider doesn't actually understand FDA validation requirements?
We have an IND filing in six months; how long does it usually take to migrate legacy data into a new regulatory document management system?
Show all 15 questions
How can I automate the data cleaning process for multi-site clinical trial results without hiring more data managers?
Do most life science SaaS companies offer a sandbox environment for testing validation protocols before we sign a contract?
Is it better to go with a per-user seat license or a usage-based pricing model for bioinformatics software?
What are the pros and cons of using a general-purpose project management tool versus a purpose-built biotech R&D platform?
Our lab is moving from paper to digital; what's the best way to vet a vendor's data migration services for 10 years of physical records?
Why do some life science tech companies hide their pricing until after a demo, and is that a red flag for hidden costs?
What kind of post-launch support and training should be included in a contract for a new electronic data capture system?

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 life science buyers.

Behavior rates across 15 life science buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional27%13%13%87%
Suggests DIY first7%7%7%100%
Names specific providers13%40%47%60%
Gives price or cost info13%20%33%67%
Tells to check reviews0%0%0%100%
Tells to verify credentials13%27%13%73%
Mentions case studies / portfolio13%0%0%87%
Mentions local proximity0%7%0%93%
Gives selection criteria40%67%53%20%
Warns about red flags7%13%7%87%
Asks a clarifying question33%27%0%53%
Recommends multiple quotes20%7%0%80%

By model

How each assistant handled Life Science questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same life science questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 26.7% (ChatGPT) down to 13.3% (Claude), a 13-point gap on an identical question set.

Across the 15 life science answers it produced, ChatGPT recommended hiring a professional in 26.7% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 13.3% of answers (about 0.5 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 33.3% of cases, warned about red flags or scams in 6.7%, and told the buyer to verify credentials in 13.3%, averaging 811 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 0%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 20%.

Across the 15 life science answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 40% of answers (about 2.6 distinct providers per answer) and included price or cost information 20% of the time. Claude asked a clarifying question before answering in 26.7% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 26.7%, averaging 347 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 6.7%; a selection-criteria checklist appeared in 66.7% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 life science answers it produced, Gemini recommended hiring a professional in 13.3% of them and suggested a DIY approach first 6.7% of the time. It named a specific provider in 46.7% of answers (about 1.7 distinct providers per answer) and included price or cost information 33.3% 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 13.3%, averaging 230 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 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a life science buyer to a professional (26.7%) and Claude the least (13.3%). ChatGPT produced the longest answers, at 811 words on average. Specific providers were named most often by Gemini (46.7%) — even there, roughly one answer in 2 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 16.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a life science buyer happens to ask matters most:

  • Names a specific provider: from 13.3% (ChatGPT) to 46.7% (Gemini) — a 33-point spread.
  • Asks a clarifying question: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Gives selection criteria: from 40% (ChatGPT) to 66.7% (Claude) — a 27-point spread.
  • Gives price or cost information: from 13.3% (ChatGPT) to 33.3% (Gemini) — a 20-point spread.
  • Recommends multiple quotes: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — names a specific provider, 33 points — means a life science 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 life science market.

Where they agree

The points of near-consensus in Life Science.

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

  • Suggests a DIY approach first: 6.7% across all three models.
  • Tells the buyer to check reviews: 0% across all three models.
  • Warns about red flags or scams: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions local proximity: 0%–6.7% across all three (a 7-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 100% of questions) and least consistently on "gives selection criteria" (20%).

Every behavior, measured

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

The behaviors AI models reproduce most often for life science are gives selection criteria (53.3% on average), names a specific provider (33.3%) and gives price or cost information (22.2%); the rarest are tells the buyer to check reviews (0%), mentions local proximity (2.2%) and mentions case studies or portfolio (4.4%). 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: 53.3% on average (ChatGPT 40%, Claude 66.7%, Gemini 53.3%) — a 27-point spread.
  • Names a specific provider: 33.3% on average (ChatGPT 13.3%, Claude 40%, Gemini 46.7%) — a 33-point spread.
  • Gives price or cost information: 22.2% on average (ChatGPT 13.3%, Claude 20%, Gemini 33.3%) — a 20-point spread.
  • Asks a clarifying question: 20% on average (ChatGPT 33.3%, Claude 26.7%, Gemini 0%) — a 33-point spread.
  • Recommends hiring a professional: 17.8% on average (ChatGPT 26.7%, Claude 13.3%, Gemini 13.3%) — a 13-point spread.
  • Tells the buyer to verify credentials: 17.8% on average (ChatGPT 13.3%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Warns about red flags or scams: 8.9% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 6.7%) — a 7-point spread.
  • Recommends multiple quotes: 8.9% on average (ChatGPT 20%, Claude 6.7%, Gemini 0%) — a 20-point spread.
  • Suggests a DIY approach first: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Mentions case studies or portfolio: 4.4% on average (ChatGPT 13.3%, Claude 0%, Gemini 0%) — a 13-point spread.
  • Mentions local proximity: 2.2% on average (ChatGPT 0%, Claude 6.7%, Gemini 0%) — a 7-point spread.
  • Tells the buyer to check reviews: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the life science buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the life science buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 0% of answers on average. Verifying credentials or certifications appeared in 17.8%. Warning about red flags or scams appeared in 8.9%.

On structuring the decision, a selection-criteria checklist showed up in 53.3% of answers on average and a recommendation to gather multiple quotes in 8.9%. The single least-reproduced protective signal for life science is "tells the buyer to check reviews" 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 Life Science providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 life science answers, a specific provider was named in 33.3% of responses on average — roughly 1.6 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for life science: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Life Science questions cover.

The 15 questions behind every percentage on this page were drawn from real life science (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 life science 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 life science 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 →