What Buyers Ask AI: Patterns in 1,012 Buyer-Style Questions Across 16 Industries

Questions this guide answers

• What questions do buyers ask ChatGPT before buying?
• What do people ask AI assistants about?
• What is AI search demand data?
• Is AI prompt volume data accurate?
• How is asking AI different from Google keyword search?
• What are the most common AI prompts by industry?

Direct answer

Prompt Pulse is a curated set of 1,012 buyer-style questions across 16 industries (verticals dated June 1–4, 2026), each labeled by buying stage, intent, persona, a relative Demand tier, and a trend direction. The questions come from public question sources and are scored with relative demand within each industry. Wording varies from buyer to buyer; the labels capture what stays stable: the intent, the persona, and the stage of the decision.

Within the dataset, the mix is lopsided toward decisions: 53.7% of questions are Decision-stage, 36.1% Consideration, and 10.3% Awareness. That describes a set built around buying, not AI usage overall. Across all consumer ChatGPT use, OpenAI's own researchers found, in conversations sampled through mid-2025, that practical guidance, seeking information, and writing account for about 77% of conversations, with product questions one part of seeking information. What the dataset does show is the shape of buying questions once someone is in a category: most ask the AI to choose, compare, or de-risk, not to define.

That shape matters for content and AEO. The questions where an answer can turn into a shortlist are specific and decision-shaped, and a brand built around "what is" explainers is answering the smallest slice of them. The live data is browsable by industry on the Prompt Pulse hub.

What the dataset captures

Start with the unit of analysis. Traditional keyword research counts the short strings people type into a search box ("crm software," "best smart lock"). Prompt Pulse works with the full natural-language question a buyer would put to an AI assistant, such as "Which CRM is considered number one for mid-market B2B sales teams?", because that sentence, not the keyword, is what ChatGPT, Perplexity, and Google's AI surfaces answer.

The current set spans 16 industries: consumer Smart Home, Fintech, the B2B SaaS stack (CRM, HR and payroll, accounting, project management, marketing and email, ecommerce, cybersecurity, and SEO and AEO), and the AI-infrastructure layer (GPUs, inference, MLOps, LLM observability, AI cloud, and AI data and storage). Every question carries five labels: a Demand tier (High, Medium, or Low), a trend (Rising, New, Stable, Cooling, or no label when there isn't enough history to call a direction), an intent, a persona, and a buying stage (Awareness, Consideration, or Decision).

• Buyer questions, by industry. A curated set of buyer-style questions for each industry, drawn from public question sources and written around needs and use cases rather than the vendors competing for them.
• The stable parts of a question. Each question is labeled by the intent behind it, the persona asking, and the buying stage. Buyers word the same need many ways; those labels are what stay constant, and they're what the findings below are built on.
• Relative demand. Tiers and trends are relative within each industry, so a High in LLM Inference is not comparable to a High in CRM.
• A set built around buying. Its stage and intent mix describe the set, not buyer behavior overall, which is why the findings below read as the shape of buying questions within a category.

Finding 1: In this set, buying questions ask AI to decide, not to define

Sort all 1,012 questions by buying stage and the distribution sits at the bottom of the funnel.

Read the scope carefully. Because the set was curated around buying, a bottom-heavy mix is partly by design. The useful signal is inside it: when you write out the questions that carry demand in a category, the "what is X" explainer is a small part of the buying conversation. Most are the questions of someone who already knows the category and is comparing options, pressure-testing concerns, and deciding.

The intent mix points the same way

Tag the same questions by intent and 52.8% are explicitly commercial (recommendation, comparison, or cost), the language of someone with a budget, not a student taking notes.

The practical reading for a marketer: if your content and your AI-search presence are built around top-of-funnel "what is" explainers, you are covering the smallest slice of the buying questions and missing the ones where a shortlist gets formed inside the answer.

Finding 2: Each industry's question set has a different shape

The industries differ in how decision-heavy their questions are and in how much of their demand is moving. The table covers every industry on the hub. Demand tiers are left out on purpose: they are relative within each industry, so counting High-tier questions across rows would compare unlike things.

Three patterns stand out. Project Management (69%) and CRM and sales tech (64%) have the most decision-heavy sets, while Ecommerce Platforms (44%) and LLM Inference and Serving (45%) have the least. Movement is concentrated: Smart Home, Fintech, and Cybersecurity hold 80 of the 146 questions flagged rising or new, and across the whole set more questions were cooling (253) than rising or new. And the AI-infrastructure verticals are thin on trend history: 235 of their 267 questions carry no trend label, so read their stage mix, not their momentum.

Finding 3: What's heating up: “can AI do this without a specialist, and can I trust it?”

146 of the 1,012 questions are flagged rising or new in this snapshot, and their intent mix differs from the set as a whole: Concern is the largest intent among them (49 of 146, or 34%, versus 18.6% overall). Read together, they show a theme that crosses unrelated industries: buyers asking whether AI and automation now let them do work that used to need a developer, an agency, or a specialist, and whether the AI version can be trusted.

It shows up in SEO ("Can a non-technical marketer realistically do their own SEO without a developer?"), in CRM ("Can I use an AI assistant to build a lightweight internal CRM using existing tools I already have?"), in payroll ("Can AI actually handle payroll end-to-end, or does it still need a human to verify everything?"), in email marketing ("Is using an AI writing tool inside an email platform actually improving open and click rates, or is it just a marketing feature?"), and in security ("Is zero trust security achievable without a large dedicated security team, or is it only practical for enterprises?"). In Smart Home, the rising questions cluster around getting started with automation and around frustrations that push people to switch, such as subscriptions and unreliable apps.

The strategic read: in category after category, the rising edge of these question sets is a skeptical do-it-yourself buyer. If your positioning assumes the buyer needs a specialist to evaluate you, or skips the "does it actually work" question, the questions heating up fastest are the ones you are least prepared to answer. Trend is a directional signal, relative within each industry, so treat the movement as a heat map, not a traffic forecast.

Finding 4: The questions are specific, and they're buyable

The Decision-stage questions aren't vague. They name the buyer, the use case, and often the spec, and they are exactly the prompts where being the AI's recommended option can win or lose a deal. A few examples from the High-demand, Decision-stage questions, as they appear on the hub:

• CRM & Sales Tech: "Which CRM is considered number one for mid-market B2B sales teams?"
• CRM & Sales Tech: "What are the hidden costs of enterprise CRM platforms I should budget for beyond the license fee?"
• Cybersecurity: "What are the best penetration testing companies for a financial services firm with strict compliance requirements?"
• AI Cloud & Cluster Infrastructure: "Which GPU cloud provider gives the best price-to-performance ratio for AI training in 2026?"
• AI Data, Storage & Memory: "Which vector database is best for a production RAG pipeline handling millions of documents?"
• Marketing & Email: "What are the best top-5 automation tools for a lean marketing team that needs to automate email, social, and lead scoring?"

Why this matters for SEO and AEO

Buyers do use AI when they buy. Forrester's State of Business Buying 2026 found 94% of business buyers use AI during their buying process, and Gartner's May 2026 survey found 45% of B2B buyers used generative AI mainly to gather information on vendors and products. Keyword research and question-level demand answer two different questions, and you now need both: a keyword tool shows the head terms people type into Google; Prompt Pulse shows the full questions an AI assistant reasons over when it assembles an answer and a shortlist.

The workflow this enables is straightforward, and it doesn't require any of the schema or markup tricks that don't move AI answers:

• Use the questions as a starting prompt set, then make it yours. Pull the Decision and Consideration questions in your vertical, add the phrasing your own buyers use in sales calls, and see our guide to building an AI search prompt set.
• Check, honestly, whether your content answers them at all. Look for real answers to the comparison, cost, and concern questions where buying happens, not just explainers for the Awareness ones.
• Look at the rising questions and write to the demand that's accelerating, not the demand that peaked last year, remembering that trend is directional.
• Map intent to format: recommendation and comparison questions want genuinely useful comparison content; concern questions want honest "where this goes wrong" answers; cost questions want real numbers.
• Then measure whether it worked. The gap between "we published content for this question" and "the AI now mentions us on this question" is the whole game, and it's measurable by rerunning the same prompts on the same engines.

See your industry and whether AI recommends you

Two next steps, depending on what you need. To see the buyer-style questions in your category, ranked by relative demand and flagged for what's heating up, browse the Prompt Pulse hub. It's free to browse.

To find out whether AI actually mentions you when buyers ask Decision-stage questions, Start Free: 10 buyer prompts on ChatGPT, with your mention rate, competitor share of voice, and the sources ChatGPT cited, free, no credit card. Multi-engine tracking and the rest of the Measure → Diagnose → Execute → Verify loop are on paid plans.

Sources

• SolCrys Prompt Pulse: live data by industry
• Chatterji et al., How People Use ChatGPT (NBER Working Paper 34255, September 2025)
• Forrester: The State of Business Buying, 2026 (Jan. 21, 2026)

FAQ

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