How to Use AI to Research Competitors and Product Ideas

Product research used to mean scrolling competitor pages one at a time, copying details into a spreadsheet by hand. AI tools don’t replace the Google Trends validation step we already rely on, but they cut a real chunk of the manual work down to minutes.

What AI Is Actually Good At Here

• Summarizing a competitor’s product page or ad copy into its core selling points, fast
• Comparing multiple competitor descriptions side by side and pulling out what they all emphasize (which tells you what customers likely care about)
• Turning a rough product idea into a list of realistic variations or adjacent products worth checking demand for

What It’s Not Good At

AI has no live access to real sales numbers, and it can’t tell you a product is actually selling well — it can only work with what you paste in or what it can find publicly. Treat every output as a starting point to verify, not a conclusion.

A Practical Workflow

1. Paste 3-5 competitor product pages or ad captures and ask for the common angles they use — problem being solved, price positioning, main objection they address
2. Ask for gaps, not just summaries — “what does none of these competitors mention that customers might still care about?” often surfaces a genuine angle
3. Cross-check demand with Google Trends before committing budget — AI’s read on “this seems popular” isn’t demand data, it’s a pattern read from text

Using It on Ad Copy, Not Just Products

The same approach works on competitor ad copy specifically — paste a few ads running in the same niche and ask what angle keeps repeating across all of them. If everyone’s leading with price, leading with something else (delivery speed, a guarantee, a specific use case) is often the easier way to stand out. This pairs naturally with our AI ad copy writing process once you’ve picked an angle.

Paste Reviews, Not Just Product Pages

A competitor’s product page shows what they claim about the product. Their actual customer reviews show what’s really true about it — which features disappointed people, what question kept coming up in the comments, what a competitor’s own customers wish were different. Pasting a batch of a competitor’s negative and neutral reviews (not just the five-star ones) and asking what complaints repeat most often routinely surfaces the exact gap a new product or a differentiated angle can fill, in a way the polished product page itself never reveals.

Turning a Summary Into an Actual Angle

Getting a clean summary of what competitors emphasize is only step one — the real work is turning that into a specific angle rather than stopping at “here’s what they all say.” If every competitor leads with price, the useful next question isn’t “should I also lead with price,” it’s “what would a customer who’s already price-shopped everywhere actually still want to know” — often something like real delivery speed, return policy clarity, or proof the product works for a specific use case competitors gloss over. Asking the AI tool directly for that second-level question, rather than stopping at the first summary, is what turns research into something usable in actual ad copy.

This Isn’t a One-Time Pass

A competitive landscape shifts — new entrants show up, an existing competitor changes their angle once something stops converting for them. Treating this research as something done once before launch, then never revisited, means missing exactly the moment a competitor’s shift creates a new gap or closes one that used to work. Rerunning this same process every few weeks once a product is live, especially before increasing ad budget meaningfully, catches shifts early enough to actually act on them.

Paste Actual Text, Not Just a Link

Most AI tools can’t reliably browse a live competitor page in real time the way a person can, and asking one to “look at” a URL often produces a guess based on general knowledge of that brand rather than what’s currently on the page. Copying the actual visible text (or a screenshot, for tools that support image input) directly into the prompt is the difference between a real analysis of what’s there right now and a plausible-sounding guess about what’s probably there.

A Mistake Worth Avoiding

Asking an AI tool something like “is this a good product to sell” and treating the answer as market research. It has no access to your specific market’s demand, competition level, or margins — it will confidently answer anyway. Use it to organize and compare information you provide, not to generate a verdict from nothing. A useful gut check before trusting any AI-generated market read: if the same tool were given the opposite product, would it still sound just as confident? If yes, the confidence itself isn’t signal — it’s just how these tools default to sounding regardless of what’s actually being asked.

FAQ

Can AI tell me whether a product is actually selling well?

No. It has no live access to real sales numbers, and it can’t tell you a product is selling — it can only work with what you paste in or what it can find publicly. Treat every output as a starting point to verify, not a conclusion.

What’s the practical workflow that actually works?

Paste 3–5 competitor product pages or ad captures and ask for the common angles, then ask for gaps — “what does none of these competitors mention that customers might still care about?” — then cross-check demand with Google Trends before committing budget.

Why paste reviews instead of just product pages?

A product page shows what the competitor claims. Their actual customer reviews show what’s really true: which features disappointed people, what question kept coming up, what their own customers wish were different. That’s where the gap a new angle can fill usually hides.

Should I ask an AI tool “is this a good product to sell”?

No. It has no access to your market’s demand, competition level, or margins, and it will confidently answer anyway. A useful gut check: if the same tool given the opposite product would sound just as confident, the confidence itself isn’t signal.