Is there an AI that does proper market research, not just summaries?

What does AI market research actually cover?

89% of market researchers already use AI tools, regularly or in trials, according to a Qualtrics survey of more than 3,000 researchers in 14 countries published in October 2024. Adoption is settled. What is not settled is which job the tool was hired to do, because three different jobs travel under the same search term.

The first job is collecting new data: AI-moderated interviews and surveys, and synthetic panels that simulate respondents. The second is finding and summarizing what is already published: assistant deep research modes, answer engines, and AI search inside paid data libraries. The third is AI for market analysis proper: deciding which figures measure the same thing, which genuinely disagree, and which number belongs on the slide.

Most disappointment with AI market research comes from buying the second job and expecting the third. Strategy teams and agency strategists describe the result in almost identical terms: a summary of the internet, when the brief called for a structured argument with a number behind it.

Even the investors funding the category say there is no shared yardstick: a June 2025 a16z analysis noted that no established benchmarks or evaluation frameworks exist, so companies end up defining their own. Caspr's follows.

Why is citing sources not enough?

Citation is the floor, and not every tool clears it. In March 2025 the Tow Center for Digital Journalism at Columbia tested eight AI search tools on 1,600 queries, asking each to name the headline, publisher, date and URL behind a news excerpt. More than 60% of the answers were wrong, most delivered with full confidence; the premium versions erred more often because they declined less. This means a link proves that a source exists, not that it says what the answer claims.

Clearing the floor is still not the test. A citation says where a number came from, not whether it is the right number for the question. Estimates for the same market routinely differ because they count different things: in UK ready meals, four research firms put the 2024–2025 market between $5.75 billion and $8.70 billion; in Saudi industrial valves, published 2025 figures run from $725 million to about $2.09 billion. Every one of those figures can be cited correctly.

A tool that returns any one of them with a clean link has passed the citation test and failed the analysis. The reader inherits the hardest hour of desk research, working out how to reconcile two market size estimates, and usually discovers it in the meeting, when the client has seen the other number.

So the question to put to any AI market research tool is not whether it cites. It is what the tool does when one source says $5.75 billion and another says $8.70 billion.

AI market research tools compared: what is each format built to do?

Lists of the best market research tools usually rank brands. The fair comparison is by format: the deep research modes from OpenAI, Google and Anthropic share one design, Statista and AlphaSense another, and a ranking across them scores tools built for different jobs. The table describes each tool only in the terms its maker publishes, and says so where a maker is silent on sources that disagree.

Read down the last column. Three of the five formats, six named products from OpenAI to AlphaSense, are built to find and present published evidence, and not one of their makers says what happens when that evidence disagrees. Reconciling is simply not the job those formats were built for.

Deep research modes deserve the closest look, because breadth is what they sell: Google says its version browses up to hundreds of websites. Breadth, though, is an input to the problem, not its answer: the more sources a tool reads, the more figures it finds that disagree, and a long report can still hand the reader four market sizes and no basis for choosing.

AI search inside a data library solves a different problem. Statista says its Research AI answers only from Statista data, never the open web. The trade is scope: a figure published outside the library cannot be set against the library's own.

What should you look for in an AI market research tool?

Six tests, each answerable from the first report a tool produces. None needs the benchmark that a16z says the category still lacks.

The disagreement test is the fastest. Ask a tool for the size of the UK ready meals market, where four published figures already run from $5.75 billion to $8.70 billion. A tool that returns one number, however well cited, has shown how it will behave on every market where the disagreement is not yet known.

Basis matters as much in valuation as in sizing. Caspr's Study of vertical SaaS valuations treats EV/TTM revenue, EV/NTM revenue and EV/ARR as complementary lenses, not interchangeable ones; a tool that quotes a multiple without its basis hands the reader the comparison error laid out in NTM against TTM revenue multiples.

The declared-gap test applies the Tow Center finding to market data: the premium search tools erred more often because they declined less. A tool that says it found no regional data, and how it estimated instead, beats one that returns a precise regional figure with no visible method.

Caspr's position: buy the reconciliation, not the search

The best AI for market research is the one that does the step a buyer cannot hand to a search box: deciding whether UK ready meals is a $5.75 billion or an $8.70 billion market, and why. It is also the step most AI tools for market research leave with the reader: of the five formats in the table above, only one claims it.

Deep research is sold as a feature of assistant subscriptions: OpenAI released its version first to Pro users, Anthropic to its Max, Team and Enterprise customers, and Perplexity made its own free with a daily cap. Finding sources is close to free; what costs money is the senior time spent turning four market sizes into one. Set against what market research costs when commissioned, the benchmark for an AI tool is the price of a defensible document, not of a login.

A chat thread is a format for questions. A board report, an investment memo and a client deck are formats for arguments: a claim, the number behind it, its source, and what it means for the decision. A tool built for the first format leaves the reader to rebuild the second.

Caspr is built to the second standard. A single-line prompt returns a structured, cited report as a PDF or editable PPTX in under 15 minutes, priced per analysis; a Market Research Study is $80. The worked example below shows what a Study does with four published figures that do not agree.

The position has limits. If the job is new data, such as what buyers think of a concept, no desk research tool is the answer; that needs fieldwork. Synthetic panels are AI-simulated audience response, useful for screening ideas early and never a substitute for real respondents. And for a single fact with one obvious source, an answer engine is the faster tool.

What does this mean for your next decision?

The test is the same for every reader. What changes is the room the number has to survive.

Whichever tool is chosen, run it first on a market whose disagreement is already known, such as Saudi industrial valves, where published 2025 figures run from $725 million to about $2.09 billion. How it handles that spread predicts how it will handle an unfamiliar one.

When a figure is challenged, the answer has to exist before the question does: the publisher, what the figure counts, and why it beat the alternatives. Having all three ready is how a team can defend a market size estimate without leaving the meeting to check. A tool that delivers it is doing analysis. One that does not is a faster way to find the problem.

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