Original research · 2026-07 edition

AI SEO Statistics: Telecom (2026-07 edition)

40 questions · 120 AI responses · 3 models · measured 2026-07-06

See how AI talks about YOUR businessFull data table

The question bank

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

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

Why does my office phone system keep lagging during peak hours?
Is it cheaper to use a softphone app or buy physical desk phones for a team of 10?
What's the difference between UCaaS and a regular VoIP service for a law firm?
How do I port my business landline number to a cloud-based provider without downtime?
What are the must-have features for a call center software if we have 50 agents?
Can I set up an IVR menu myself or do I need to hire a telecom consultant?
What kind of internet bandwidth do I need to support 25 simultaneous VoIP calls?
Are there any hidden fees I should look for in a business internet and phone bundle?
Show all 40 questions
How do I know if my current PBX system is end-of-life and needs replacing?
What are the red flags when reviewing a 3-year telecom service agreement?
We are moving to a new office next month; how far in advance should I order fiber installation?
Is 5G fixed wireless a reliable backup for a retail store's point-of-sale system?
How much should I expect to pay per user for a premium hosted phone system with CRM integration?
What's the best way to record and archive customer service calls for compliance?
My remote employees are complaining about call quality; how can I troubleshoot their home setups?
Can a small business get a professional auto-attendant without paying for an enterprise tier?
What is the process for setting up a multi-site phone system that works across three different states?
Is SD-WAN worth the investment for a mid-sized company with high bandwidth needs?
How do I switch from a traditional PRI to SIP trunking to save money?
What questions should I ask a telecom broker to ensure they aren't just pushing one brand?
Are there any reliable VoIP providers that offer 24/7 live support for emergencies?
How can I integrate my phone system with my existing help desk software?
What are the pros and cons of using a virtual receptionist service vs. an automated system?
How do I verify the uptime claims of a cloud communications provider?
We need a toll-free number for a marketing campaign starting in 48 hours; who can set that up fastest?
What's the difference between a managed service provider and a direct telecom carrier?
Is it better to lease or buy VoIP hardware for a startup with limited capital?
How does international calling work on a cloud system if we have clients in Europe and Asia?
What security features should I look for to prevent toll fraud on my business line?
Can I use my existing analog fax machine with a new digital phone system?
How do I compare the total cost of ownership between on-premise and cloud-based telecom?
What happens to my business phone system if our office internet goes down?
Are there specific telecom regulations I need to worry about for a healthcare practice?
Why is my business being charged for regulatory recovery fees on my monthly bill?
How can I get a better deal on my business mobile fleet plan for 100+ devices?
What is the standard implementation timeline for a new enterprise-level VoIP rollout?
Do I need a dedicated router for my office VoIP traffic to ensure high quality?
How can I track which marketing channels are driving the most phone calls?
What are the signs that a telecom provider is oversubscribing their network?
Is it possible to have a single phone number ring on both a desk phone and a mobile app simultaneously?

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 telecom buyers.

Behavior rates across 40 telecom buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional38%20%13%60%
Suggests DIY first38%30%30%68%
Names specific providers23%30%33%70%
Gives price or cost info10%13%20%75%
Tells to check reviews3%3%0%95%
Tells to verify credentials10%5%0%85%
Mentions case studies / portfolio3%3%3%95%
Mentions local proximity8%5%3%88%
Gives selection criteria40%55%33%40%
Warns about red flags8%10%10%93%
Asks a clarifying question43%55%0%35%
Recommends multiple quotes13%13%3%83%

By model

How each assistant handled Telecom questions.

Reading the 120 answers model by model shows how differently the three assistants treat the same telecom questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 37.5% (ChatGPT) down to 12.5% (Gemini), a 25-point gap on an identical question set.

Across the 40 telecom answers it produced, ChatGPT recommended hiring a professional in 37.5% of them and suggested a DIY approach first 37.5% of the time. It named a specific provider in 22.5% of answers (about 1.3 distinct providers per answer) and included price or cost information 10% of the time. ChatGPT asked a clarifying question before answering in 42.5% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 10%, averaging 675 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 7.5%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 telecom answers it produced, Claude recommended hiring a professional in 20% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 30% of answers (about 1.7 distinct providers per answer) and included price or cost information 12.5% of the time. Claude asked a clarifying question before answering in 55% of cases, warned about red flags or scams in 10%, and told the buyer to verify credentials in 5%, averaging 311 words per answer. On the remaining cues it told the buyer to check reviews in 2.5%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 5%; a selection-criteria checklist appeared in 55% of its answers and a recommendation to gather multiple quotes in 12.5%.

Across the 40 telecom answers it produced, Gemini recommended hiring a professional in 12.5% of them and suggested a DIY approach first 30% of the time. It named a specific provider in 32.5% of answers (about 1.4 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 10%, and told the buyer to verify credentials in 0%, averaging 257 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 2.5%; a selection-criteria checklist appeared in 32.5% of its answers and a recommendation to gather multiple quotes in 2.5%.

Taken together, ChatGPT is the assistant most likely to route a telecom buyer to a professional (37.5%) and Gemini the least (12.5%). ChatGPT produced the longest answers, at 675 words on average. Specific providers were named most often by Gemini (32.5%) — 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 17.5 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a telecom buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 55% (Claude) — a 55-point spread.
  • Recommends hiring a professional: from 12.5% (Gemini) to 37.5% (ChatGPT) — a 25-point spread.
  • Gives selection criteria: from 32.5% (Gemini) to 55% (Claude) — a 23-point spread.
  • Names a specific provider: from 22.5% (ChatGPT) to 32.5% (Gemini) — a 10-point spread.
  • Gives price or cost information: from 10% (ChatGPT) to 20% (Gemini) — a 10-point spread.

The widest single gap — asks a clarifying question, 55 points — means a telecom 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 telecom market.

Where they agree

The points of near-consensus in Telecom.

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

  • Mentions case studies or portfolio: 2.5% across all three models.
  • Tells the buyer to check reviews: 0%–2.5% across all three (a 3-point spread).
  • Warns about red flags or scams: 7.5%–10% across all three (a 3-point spread).
  • Mentions local proximity: 2.5%–7.5% across all three (a 5-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "tells the buyer to check reviews" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (35%).

Every behavior, measured

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

The behaviors AI models reproduce most often for telecom are gives selection criteria (42.5% on average), suggests a DIY approach first (32.5%) and asks a clarifying question (32.5%); the rarest are tells the buyer to check reviews (1.7%), mentions case studies or portfolio (2.5%) and mentions local proximity (5%). Each figure below is the share of a model's 40 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: 42.5% on average (ChatGPT 40%, Claude 55%, Gemini 32.5%) — a 23-point spread.
  • Suggests a DIY approach first: 32.5% on average (ChatGPT 37.5%, Claude 30%, Gemini 30%) — a 8-point spread.
  • Asks a clarifying question: 32.5% on average (ChatGPT 42.5%, Claude 55%, Gemini 0%) — a 55-point spread.
  • Names a specific provider: 28.3% on average (ChatGPT 22.5%, Claude 30%, Gemini 32.5%) — a 10-point spread.
  • Recommends hiring a professional: 23.3% on average (ChatGPT 37.5%, Claude 20%, Gemini 12.5%) — a 25-point spread.
  • Gives price or cost information: 14.2% on average (ChatGPT 10%, Claude 12.5%, Gemini 20%) — a 10-point spread.
  • Warns about red flags or scams: 9.2% on average (ChatGPT 7.5%, Claude 10%, Gemini 10%) — a 3-point spread.
  • Recommends multiple quotes: 9.2% on average (ChatGPT 12.5%, Claude 12.5%, Gemini 2.5%) — a 10-point spread.
  • Tells the buyer to verify credentials: 5% on average (ChatGPT 10%, Claude 5%, Gemini 0%) — a 10-point spread.
  • Mentions local proximity: 5% on average (ChatGPT 7.5%, Claude 5%, Gemini 2.5%) — a 5-point spread.
  • Mentions case studies or portfolio: 2.5% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 2.5%).
  • Tells the buyer to check reviews: 1.7% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 0%) — a 3-point spread.

Trust signals

How well the models protect the telecom buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the telecom buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 1.7% of answers on average. Verifying credentials or certifications appeared in 5%. Warning about red flags or scams appeared in 9.2%.

On structuring the decision, a selection-criteria checklist showed up in 42.5% of answers on average and a recommendation to gather multiple quotes in 9.2%. The single least-reproduced protective signal for telecom is "tells the buyer to check reviews" at 1.7% 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 Telecom providers?

For service providers the decisive question is whether these systems name anyone at all. Across 120 telecom answers, a specific provider was named in 28.3% of responses on average — roughly 1.5 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for telecom: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 40 Telecom questions cover.

The 40 questions behind every percentage on this page were drawn from real telecom (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 telecom 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 40 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 telecom 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.

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 2026-07-06, 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 →