What buyers actually ask AI: 984 prompts across 15 industries

Questions this guide answers

• What do people ask AI assistants about?
• What questions do buyers ask ChatGPT before buying?
• What is AI search demand data?
• How is asking AI different from Google keyword search?
• Which industries have the most AI buyer demand?
• What are the most common AI prompts by industry?

Direct answer

We analyzed 984 real buyer questions that people put to AI assistants across 15 industries — the SolCrys Prompt Pulse dataset, refreshed 2026-06-03. Each question is tagged by demand level, whether it's heating up or cooling, the buyer's intent, the persona asking, and where it sits in the buying journey.

The single clearest finding: nearly 90% of what buyers ask AI is mid-to-late-funnel (89.5% sits in Consideration or Decision). More than half — 53% — is purchase-ready Decision-stage. Only one question in ten is the "what is X?" awareness query people assume AI is mostly used for. Buyers aren't asking AI to define your category. They're asking it to pick a winner.

That matters because the answer they get is increasingly the shortlist they act on. The rest of this piece breaks down the four findings and what they mean for content and AI-search strategy. The full, live data is browsable by industry on the Prompt Pulse hub.

What we looked at

A quick definition, because the unit of analysis is the whole point. Traditional keyword research counts the short strings people type into a search box ("crm software", "best smart lock"). Prompt Pulse counts something different: the full natural-language question a buyer asks an AI assistant — "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 actually answer.

The current dataset spans 15 verticals — from consumer Smart Home through the B2B SaaS stack (CRM, HR, Accounting, Project Management, Marketing, Ecommerce, Cybersecurity, SEO/AEO) to the AI-infrastructure layer (GPUs, inference, MLOps, observability, cloud, data). Every prompt carries five tags: a demand tier (High / Medium / Low), a trend (rising, stable, or cooling), an intent (recommendation, comparison, cost, concern, how-to, informational), a persona, and a buying stage (Awareness → Consideration → Decision).

One honest caveat we keep on the data itself: these are directional AI-demand signals, relative within each vertical — not exact query counts. They're built to show shape and movement (what's in demand, what's heating up), not to be read as a precise search-volume meter.

Finding 1: Buyers ask AI to decide, not to define

Sort all 984 questions by buying stage and the distribution is lopsided toward the bottom of the funnel.

The awareness layer — "what is a CRM", "how does a smart thermostat work" — is barely a tenth of the questions. The overwhelming majority are buyers who already know the category and are now comparing options, pressure-testing concerns, and deciding.

The intent mix says the same thing louder

Tag the same questions by intent and over half are explicitly commercial — recommendation, comparison, or cost — the language of someone with a wallet open, 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're optimized for the 10% and absent from the 90% where the buying decision is actually being made inside the answer.

Finding 2: Demand is concentrated, and uneven by industry

Across the dataset, 45% of questions land in the High demand tier — these aren't long-tail curiosities, they're the questions buyers ask most. But the shape differs sharply by vertical, and that's the actionable part. The table below is every industry in the current snapshot, with how many questions it holds, how many are High-demand, how many are purchase-ready (Decision-stage), and how many are currently rising.

Two patterns stand out. Consumer and SaaS categories (Smart Home, Project Management, CRM, Accounting) carry the largest, most decision-heavy question sets — Project Management is 70% purchase-ready. And the AI-infrastructure verticals (LLM inference, observability, GPUs, cloud) are small but almost entirely High-demand: when there are only 31 questions buyers ask about LLM serving, nearly every one is a high-intent expert question. Niche, but no noise.

Finding 3: What's heating up — the "can AI do this without a specialist?" wave

122 of the 984 questions are currently trending up. Read them together and a theme jumps out across unrelated industries: buyers asking whether AI and automation now let them do work that used to require a developer, an agency, or a specialist hire.

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?"), and in email marketing ("How do I use AI tools inside my email marketing platform to write subject lines and improve open rates?"). The same questions cluster as rising in Smart Home, where the surge is around onboarding and automation setup for first-time builders.

The strategic read: in category after category, the rising edge of demand is the "do it myself with AI" buyer. If your category positioning still assumes the buyer needs a specialist to evaluate you, the questions heating up fastest are the ones you're least prepared to answer. (Trend is a directional signal — recent demand versus prior period, relative within each vertical — 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're exactly the prompts where being the AI's recommended option wins or loses a deal. A few real examples from the High-demand, Decision-stage set, verbatim:

• CRM & Sales Tech: "Which CRM is considered number one for mid-market B2B sales teams?"
• AI Accelerators & GPUs: "What is the typical GPU memory requirement for training a 7B parameter language model?"
• AI Cloud & Cluster Infrastructure: "What are the GPU instance options on a major cloud platform and how do their prices stack up in 2026?"
• AI Data, Storage & Memory: "How should I handle storage for synthetic AI training data pipelines that generate large volumes of samples continuously?"
• 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

Keyword research and prompt-level demand answer two different questions, and you now need both. A keyword tool tells you the head terms people type into Google. Prompt Pulse tells you the full questions buyers ask an AI assistant — which is the unit those assistants reason over when they assemble an answer and a shortlist.

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

• Find the Decision and Consideration prompts in your vertical — the 90% where buying happens — and check, honestly, whether your content answers them at all.
• Look at the rising questions and write to the demand that's accelerating, not the demand that peaked last year.
• Map intent to format: recommendation and comparison prompts want genuinely useful comparison content; concern prompts want honest "where this goes wrong" answers; cost prompts want real numbers.
• Then measure whether it worked — the gap between "we published content for this prompt" and "the AI now mentions us on this prompt" is the whole game, and it's measurable.

How we built it

Prompt Pulse synthesizes its question set from four public demand sources — Google's People-Also-Ask, autocomplete, keyword-expansion data, and the questions buyers actually ask in vertical communities — then deduplicates them and scores each prompt against an AI-search demand signal to produce the demand tier and the 12-month trend. It's deliberately brand-blind: the dataset is about the questions buyers ask, not about named third-party brands, so it's safe to act on as content strategy. The verticals refresh on a monthly cadence, which is why this snapshot is dated rather than evergreen.

What you see publicly is the prompt text and the labels (demand tier, trend, intent, persona, stage). The absolute volume numbers and the scoring weights stay internal, and the public signals are directional and relative within each vertical — not exact query counts. The full methodology and the live, browsable data sit on the Prompt Pulse hub.

See your industry — and whether AI recommends you

Two next steps, depending on what you need. To see the real questions your buyers are asking AI in your category — ranked by demand, with what's heating up — browse the Prompt Pulse hub. It's free and refreshed monthly.

To find out whether AI actually mentions you when buyers ask those Decision-stage questions, run the free SolCrys audit: 10 buyer prompts through ChatGPT, with your mention rate, competitor share of voice, and the sources AI cited. The pattern we see most often is a brand that's well-optimized for the 10% awareness questions and invisible on the 90% where the decision gets made — and that gap is exactly what a measure-then-fix loop is built to close.

Sources

• SolCrys Prompt Pulse — live data by industry

FAQ

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