How to Find Out What People Actually Ask AI About Your Industry

Ask ChatGPT how to see which prompts people in your industry are typing into AI assistants, and there’s a good chance it will tell you that data “isn’t publicly available.” That answer is wrong — and if you take it at face value, you’ll keep planning content with keyword tools while your buyers ask their real questions somewhere you can’t see.

The questions people ask AI about your industry are discoverable. Not perfectly, and not from any single source, but reliably enough to build a content strategy on. This guide covers the five methods that work, the blind spot each one has, and — the step almost everyone skips — how to validate that a prompt is worth answering before you invest in content for it.

Key Takeaways

• AI prompts are structurally invisible to keyword tools: the average ChatGPT prompt runs about 23 words versus roughly 4 for a Google query, and 65–85% of prompts can’t be matched to any keyword in a traditional database.
• Roughly 80% of ChatGPT usage falls into three buckets — practical guidance, seeking information, and writing — and the “seeking information” share doubled in a single year, making AI a direct substitute for search.
• Five discovery methods exist: direct probing, mining your own funnel, translating Google-era signals, community mining, and prompt-tracking platforms. Each has a blind spot; use at least two.
• Discovery isn’t the finish line. Validate every prompt against three questions — is there real AI demand, are current citations weak, does it map to revenue — before writing anything.
• You can see whether AI already recommends your business in seconds with a free visibility check.

Why You Can’t Just Look This Up in a Keyword Tool

The instinct is reasonable: demand research means keyword research, so open your keyword tool and search for what people ask AI. It doesn’t work, and the reason is structural, not a missing feature.

People talk to assistants differently than they type into Google. Semrush’s analysis of 17 months of clickstream data found the average ChatGPT prompt is about 23 words long, while a typical Google query is around four. A Google user types “commercial hvac maintenance cost.” The same buyer asks an assistant: “We run a 40-person logistics company with three warehouses and our HVAC contract is up for renewal — what should preventive maintenance actually cost, and what’s usually padded?”

That phrasing gap has a measurable consequence. In the same Semrush study, 65–85% of ChatGPT prompts couldn’t be matched to any keyword in their database. The majority of AI demand doesn’t have a keyword-tool equivalent at all. It isn’t low-volume — it’s uncounted.

We see this firsthand. When our team pulled keyword data for terms like “AI search attribution” and “AI prompt volume” — questions we watch real buyers ask assistants every week — the keyword database returned no entries at all. The top suggestions for “prompt volume” were about adjusting voice-prompt loudness in Ford trucks. The demand exists; the Google-era instruments just can’t see it.

So the honest starting point is this: keyword tools remain useful for what people type into Google, but for AI demand you need different sources. Five of them, in practice.

What Do People Actually Ask AI? The Data

Before the methods, it helps to know the shape of the demand you’re looking for.

The best public evidence is a 2025 study by OpenAI’s economic research team and Harvard economist David Deming, published through the National Bureau of Economic Research, analyzing 1.5 million ChatGPT conversations. About 80% of usage falls into three categories: practical guidance (how-to advice and ideation, roughly 29% of all messages), seeking information, and writing help.

The category that matters most for businesses is seeking information — searching for facts about products, services, people, and current events. It doubled from 14% to 24% of all usage in a single year, and the study’s authors describe it as “a very close substitute for web search.” Layer on the scale — OpenAI reported handling roughly 2.5 billion prompts per day as of mid-2025 — and the picture is clear: hundreds of millions of product and service questions are being asked daily in a channel most businesses have never measured.

Your industry’s slice of that demand is what the following five methods surface.

5 Ways to Discover What Your Buyers Ask AI

No single method gives you the full picture. Each one below includes its blind spot, so you can pair methods that cover for each other.

1. Ask the Assistants Directly

The fastest start: open ChatGPT, Gemini, and Perplexity and ask the questions a buyer would ask. Vary the persona (“as a CFO…”, “for a small clinic…”), the constraint (“under $500/month”, “without hiring”), and the stage (“is X worth it” vs “best X for Y”). Note which questions produce recommendation-style answers — those are the prompts where a brand can win or lose a buyer.

A repeatable probe battery looks like this — run each template across at least two assistants and log the answers:

• “Best [category] for [specific buyer type]” — the recommendation prompt where brands win or lose.
• “Is [category/product] worth it for [business size/situation]?” — the justification prompt buyers ask before budget conversations.
• “[Product A] vs [product B] for [use case] — which should I choose?” — the comparison prompt closest to a decision.
• “What should [category] cost for [scenario], and what’s usually overpriced?” — the pricing-anxiety prompt.
• “What do I need to know before buying [category]?” — the due-diligence prompt where objections form.

Then push one level deeper: ask the assistant itself, “What are the most common questions people like [your buyer] ask about [your category]?” Assistants generalize from training data, so the answers are a useful map of question territory, even though they can’t tell you frequency. Keep a simple log — prompt, assistant, date, brands mentioned, sources cited — because you’ll reuse it as your baseline when you start measuring.

Blind spot: you’re sampling your own imagination. Manual probing confirms how assistants answer, but it can’t tell you which prompts real buyers actually use or how often.

2. Mine Your Own Funnel

Your business already collects AI-era questions — they’re just filed under other names. Sales-call recordings and demo Q&As contain the exact phrasing buyers use when a human finally gets them. Support tickets show the post-purchase questions. Your site-search log shows what visitors expected to find. And a one-line addition to your lead forms — “Where did you first hear about us?” with an “AI assistant (ChatGPT, Gemini, etc.)” option — turns every conversion into a datapoint.

Buyers who found you through an assistant will often tell you the question they asked, if anyone thinks to ask them. Make it a standard discovery-call question.

Blind spot: this only surfaces questions from people who already reached you. The prompts that routed buyers to competitors never enter your funnel.

3. Translate Google-Era Signals

Your Google Search Console data, People Also Ask boxes, and autocomplete suggestions are still real demand signals — they’re just phrased in keyword dialect. The technique is translation: take a question-style GSC query and expand it into the longer, contextual form a prompt takes. “hvac maintenance cost” becomes “what should HVAC maintenance cost for a small commercial building, and what’s usually included?”

The 23-word rule is your guide: add the who, the constraint, and the situation that a searcher omits but a prompter includes.

Two practical filters make this fast. In Search Console, filter queries containing “how”, “what”, “why”, “should”, “vs”, or “best” — question-shaped queries translate most naturally into prompts. And in the People Also Ask boxes for your money keywords, note the questions Google already clusters together: assistants decompose broad prompts into similar sub-questions when they research an answer, so a PAA cluster approximates how an AI will break down your topic.

Blind spot: by definition, this only covers the demand that has a Google equivalent — and 65–85% of prompts don’t. Translation extends your map; it doesn’t complete it.

4. Mine the Communities AI Learns From

Reddit threads, niche forums, and professional Slack and Discord groups are where buyers compare notes in their own words — and those same threads are heavily cited by AI engines when they answer. Search the subreddits your buyers frequent for question posts about your category. The recurring ones are prompts, near-verbatim: people increasingly paste the same question into an assistant that they once posted to a forum.

This method does double duty: the threads tell you what’s being asked, and they’re also venues where a genuinely helpful answer builds the third-party presence AI engines trust.

Blind spot: community questions skew toward the frustrated and the unusual. Volume is invisible and sentiment is not representative.

5. Use a Prompt-Tracking Platform

The four manual methods share one gap: none of them tells you how many people ask a question, or whether AI already has a preferred answer. That’s the gap prompt-tracking platforms close. They sample real AI conversations at scale, estimate monthly demand per prompt, and show which brands and sources the assistants currently cite when answering.

This is the category GrowthNow operates in, so weigh our perspective accordingly. Our Question Discovery view lists the prompts people ask AI about your industry with an estimated monthly demand figure, your current visibility on each, and who’s being cited today — which turns discovery from a brainstorm into a ranked list.

If “prompt volume” is new vocabulary: it’s the AI-era equivalent of search volume — an estimate of how often a question is put to AI engines monthly. Treat any prompt-volume figure (ours included) as a ranking signal rather than a precise count; the honest use is comparing prompts against each other, not quoting absolute numbers.

How to Validate a Prompt Before You Write Anything

Here’s the step every guide to this topic skips: discovery gives you a list, but a list isn’t a strategy. Before a prompt earns content, it should pass three questions.

1. Is the AI demand real? Manual methods can’t answer this; a tracking platform’s demand estimate can. A prompt you find fascinating may be asked rarely; an unglamorous one (“is [category] worth it for a business my size?”) may be asked constantly.

2. Are the current citations weak? Run the prompt and look at what assistants cite. No sources, outdated sources, or a Reddit thread = open territory. A strong, recent, authoritative answer = expensive territory. Prioritize vacuums.

3. Does it map to revenue? A prompt can have demand and weak citations and still not deserve content if the asker never becomes your customer. Score prompts by how close they sit to a buying decision.

Here’s what that looks like in practice, from a real (anonymized) Question Discovery view:

Read one row end to end: the prompt has real monthly demand, the brand’s visibility on it is 0%, and the current citations are a three-year-old blog post and a forum thread. That combination — demand, absence, weak incumbents — is a green light. A prompt with demand but strong incumbent citations goes to the back of the queue. A prompt with no measurable demand doesn’t get content at all, no matter how good the article idea sounds in a meeting.

The gate also works in reverse: it catches phantom opportunities before they consume a quarter’s content budget. The most common failure mode we see is teams writing for the question they would ask — usually a sophisticated, jargon-heavy version — while buyers ask a plainer, more situational one. Validation replaces that guess with evidence: the exact phrasings that carry demand, ranked, with the competitive state attached to each.

This gate is the difference between publishing fifty articles hoping something gets cited and publishing ten that each target a confirmed vacuum. It’s also the difference in reporting: “we published 12 posts” is an activity update, while “we’re now cited on 9 of the 15 highest-demand prompts in our category” is a result.

From Discovered Prompts to Being the Answer

Discovery and validation tell you where to compete. Winning the citation is its own discipline — structuring content so AI engines can extract it, building the E-E-A-T signals engines trust, and earning third-party mentions. We’ve covered that playbook in depth in our guide to getting recommended by AI engines, so this section stays short and closes the loop instead.

The loop is: Discover → Validate → Win → Measure. Measurement is where most teams stall, because AI-referred visitors often arrive unlabeled. Two developments make it tractable: Google Analytics 4 now includes a dedicated AI-channel grouping for assistant-referred sessions, and Adobe’s Q1 2026 analysis found AI-referred traffic growing 393% year over year. Track two numbers monthly: your visibility per validated prompt (are you cited more often than last month?) and AI-referred sessions in your analytics. When both climb together, the loop is working — and the prompts you validated in step two are the ones producing customers.

Frequently Asked Questions

What are common things people ask AI?

Across 1.5 million analyzed ChatGPT conversations, about 80% of usage is practical guidance (how-to advice, ideation), seeking information (facts about products, services, and people), and writing help. For businesses, the fastest-growing category is seeking information — including “best X for Y” and “is X worth it” questions that used to be Google searches — which doubled its share in one year.

Is there a ChatGPT search console?

No. Unlike Google Search Console, no AI assistant offers a first-party dashboard showing which prompts mentioned or cited your site. Visibility into AI demand comes from indirect methods — funnel mining, community mining, signal translation — or from prompt-tracking platforms that sample AI conversations at scale and report demand and citations per prompt.

How do I get ChatGPT to recommend my business?

Assistants recommend brands they find repeatedly in trustworthy, extractable sources: structured content that answers questions directly, consistent entity information, reviews, and third-party mentions on sites AI engines cite. The full playbook is in our guide to getting recommended by AI engines — but it starts with knowing which prompts you’re trying to be the answer to, which is what this article covers.

What is the 30% rule for AI?

The 30% rule is a working heuristic for human-AI collaboration: let AI handle roughly 70% of repetitive, data-heavy work while humans keep the ~30% that requires judgment, context, and accountability. Applied to AEO content: automation can draft and structure at scale, but a human should own prompt selection, claims, and anything that carries your brand’s name.

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