Product teams are under constant pressure to answer big, messy questions: who their real competitors are, what customers truly care about, how market trends are shifting, and where opportunities or risks are emerging.
Too often, they turn to AI hoping for a shortcut and instead get confidently wrong answers, salesy content, or outdated “insights.”
The goal isn’t to stop using AI for market intelligence; it’s to use it in a way that’s grounded, verifiable, and worthy of strategic decisions.
That is what we plan to address in today’s newsletter.
Common traps when using AI for market research
AI is particularly dangerous when it sounds right but is actually wrong. In market research, this usually shows up in a few patterns:
Invented “facts”
The model fabricates exact numbers, dates, or competitor features that were never stated in any trusted source. This is especially likely when you ask for very specific details like precise market sizes or competitor pricing without providing data.Uncritical repetition of sales material
If you let AI pull “what’s out there on the web” without constraints, it often leans on marketing sites, press releases, and vendor blogs. You then get a competitor’s vision of reality rather than an independent view.Outdated or decontextualized information
AI systems trained on older data can present past market realities as if they are current. In fast-moving spaces, this can make your strategy lag by years.Vague, generic insights
When your prompts are broad, AI falls back to generic product truths: “customers value price, quality, and ease of use.” Technically correct, but useless for decisions.Blurred source types
Analyst opinions, customer reviews, scientific reports, and marketing claims all get blended into one seamless narrative, making it hard to judge reliability.
If you treat AI’s answers as “truth,” these traps distort your understanding of the market and create misplaced confidence in your roadmap and bets.
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A better mental model: AI as a grounded research assistant
To get real value, treat AI as a research assistant sitting on top of trusted sources, not as an oracle that magically knows the market.
A good mental model:
You decide which data sources are acceptable. Think analyst reports, regulatory filings, earnings call transcripts, third-party research, customer reviews, your own CRM or support tickets.
AI’s job is to read, synthesize, and compare those sources, not invent new facts.
Every important claim should be traceable back to a document, dataset, or transcript you could inspect if needed.
This is the essence of “grounding”: the answer must be anchored in specific underlying documents, not just the model’s internal patterns.
How to ask AI the right kind of questions
The quality of your questions determines whether you get grounded insights or fluffy hallucinations. A few practical patterns:
Anchor questions in explicit sources
Instead of:
“Who are the main competitors in B2B payments in Europe”
Ask:
“Using recent analyst reports and major fintech industry publications from the last 12 months, identify the top 5 B2B payment competitors in Europe and state which sources mentioned each one.”Ask for evidence and traceability
Add requirements like:“List the sources you used and what each source actually said.”
“If you are not sure, say you’re not sure rather than guessing.”
“Separate your synthesis from direct quotes or data.”
Use time bounds
Markets move quickly. Add time framing:“Over the past 12 months…”
“Since January 2024…”
“In the latest available financial year…”
Separate fact from interpretation
Structure prompts to create layers:“First, list the factual events or data points (product launches, funding, price changes, regulatory events).”
“Then, provide your interpretation of the implications for our product strategy, clearly labeled as interpretation.”
Be specific about customer signals
Instead of:
“What do SMB merchants care about in checkout solutions”
Ask:
“Based on actual customer reviews, NPS comments, and support tickets, what themes appear most frequently for SMB merchants using online checkout solutions, and approximately how often does each theme appear relative to others”Explicitly forbid guessing
Add a standing rule in your prompts:“Do not fabricate data, numbers, or quotes. If the information is not present or not clear in the sources, say so.”
These patterns force the AI to operate like a disciplined researcher rather than a storyteller.
Keeping AI grounded in trustworthy market information
To ensure accuracy, you need both good sources and good workflow habits:
Curate your “source universe”
Decide upfront what counts as trustworthy for market intelligence. For example:Financial and regulatory filings
Broker and independent analyst reports
Industry research firms and think-tank publications
Earnings call and conference transcripts
Customer reviews from established platforms
Your internal data (support tickets, sales notes, win–loss analyses)
Guard against marketing-heavy sources
You do not have to exclude vendor and competitor marketing content completely, but treat it as:Input for positioning and messaging, not neutral truth
Evidence of how they want to be perceived, not necessarily how the market sees them
When asking AI to summarize competitors, add constraints like:“Use marketing sites only to extract how the company positions itself, then cross-check claims against independent sources.”
Verify before you decide
For any insight that could influence roadmap or investment:Ask the AI to list the exact sources behind each key claim.
Spot-check a sample of those sources yourself.
Look for consistency across multiple independent references rather than relying on a single document.
Codify a “no single-source” rule
If a claim comes from one blog post or one vendor deck, treat it as a hypothesis.
Ask AI:“Find at least three independent sources that support or contradict this claim. If you cannot, label it as weakly supported.”
Use AI mainly for synthesis and pattern-spotting
Use AI where it shines:Reading hundreds of pages across multiple reports and surfacing recurring themes.
Comparing how different analysts or customers talk about the same company.
Spotting trend language that is emerging across many documents.
Avoid asking it for exact current numbers unless it is reading from a specific, recent dataset.