Draft, pending test: written from current vendor docs. We have not run this workflow end to end yet, so nothing below is a measured result. Not indexed.

AI for market research: a step-by-step process with deep research tools and Grok

Write a research brief with the exact questions, geography and time frame, run it through ChatGPT's deep research for a sourced report, then use Grok to check what's happening on X right now. Pull every number into a table with its source, verify the ones you'll actually use, and label market-size figures as third-party estimates. AI speeds up desk research; it doesn't replace talking to customers.

Who it's for & level

Founders validating an idea or preparing a deck, and marketers sizing a new segment. You need a sourced overview in hours, not a consultancy report in weeks, and you know AI-generated market sizes can be made up.

Level
intermediate
Tools
ChatGPTGrokGoogle Sheets
Roles
Founders & small biz Marketers
Goals
Automate busywork
Tasks
Competitor research
Time to set up
2–3 h for a verified brief, most of it waiting and checking (est.)
Cost
Included in your ChatGPT and Grok/X plans, within their research-run limits. No extra data purchases in this workflow.
Time saved
1–2 days of manual desk research for a first-pass market brief (est.) (est.)
Test status
Pending test
Last verified
pending test
Model versions
n/a
Written by
Usecased editorial (pending test) (agent)
Published
2026-10-15

Prerequisites

The problem

Early market research is either a weekend of googling or a confident AI answer with a TAM number nobody can trace. Investors and teammates ask 'where's that from?', and you can't say. You need speed and a source for every number.

Steps

  1. Write the brief. Define the market precisely (who buys, what problem, where), the time frame, and 5–8 questions: size and growth, segments, main players, buying triggers, pricing norms, regulation, recent shifts.
  2. Run deep research. Start a deep research run with the brief prompt below. Answer its clarifying questions narrowly. Expect it to take minutes, not seconds.
  3. Extract an evidence table. Ask for every number and key claim in a table: claim, value, source name, URL, publication date, type (primary data / analyst estimate / press). Paste into Sheets.
  4. Add real-time signals with Grok. Ask Grok what founders, buyers and analysts on X have said in the last 30–90 days about the market's main shifts, with links. Add notable items to a 'signals' tab.
  5. Verify what you'll use. Open the source for every number that goes into a deck or decision. Check the figure, year and definition match. Replace or drop anything that doesn't.
  6. Write the brief and the open questions. Summarise from the verified table only, and list what desk research can't answer, to take into customer interviews.

Exact prompt & config

1. Deep research brief prompt
Research brief: B2B scheduling software for independent physiotherapy clinics in Spain and Portugal, 2023–2026.
Answer:
1. How many independent clinics are there (by country)? Primary sources first (national statistics, professional associations).
2. What do clinics currently use for scheduling and billing? Name products.
3. Typical pricing models and price ranges.
4. Regulations affecting patient data and e-invoicing.
5. Notable shifts since 2024.
For every number give the source, URL and publication date, and say whether it's primary data or an estimate. Say "no reliable source found" rather than estimating yourself.
2. Evidence table extraction
From your report, output a table: claim | value | source | URL | published | type (primary / analyst estimate / press / vendor) | confidence (high/med/low).
Include every number in the report. No new claims.
3. Grok signals prompt
Search X posts from the last 90 days about scheduling or practice-management software for physiotherapy clinics in Spain or Portugal (Spanish, Portuguese and English).
Group into: complaints about current tools, new entrants, regulation talk. 2 links per group with author and date. Say if there's little signal.

Sample output

Example output, written to show the expected shape. Not captured from a real run yet.

(Example of the expected shape, not a captured run)

| claim | value | source | type | confidence |
| Registered physiotherapists in Spain | [value from source] | Consejo General de Fisioterapeutas, 2025 report | primary | high |
| Market size, clinic software Iberia | [value] | analyst blog, 2024 | analyst estimate | low |
Signals (Grok): clinics complaining about e-invoicing changes (2 links) …
Open questions: willingness to switch; who decides in multi-therapist clinics.

Why an evidence table

The deliverable that survives scrutiny isn’t the prose report, it’s the table of claims with sources and types. Prose hides weak numbers; a table with ‘analyst estimate, low confidence’ next to a figure makes the weak spots obvious before someone else finds them.

The loop that keeps you in control

  1. Brief with precise definitions and questions.
  2. Research with sources required.
  3. Table every number with type and date.
  4. Verify what you’ll use.
  5. Interview customers about what’s left open.

Going further

Turn the player list into a sourced matrix with our AI competitor analysis case, and keep watching the space with our Grok research case.

How it went

Pending test. What we expect, based on the vendor docs and public reports below. This becomes a measured result after our run.

Expected result, pending our test: a useful sourced overview with several numbers that turn out to be analyst estimates, outdated or defined differently than claimed once checked. We'll record how many cited numbers held up, time spent verifying and what Grok added that deep research missed.

Worth knowing

FAQ

How can AI help with market research?

It speeds up desk research: finding sources, summarising reports and building an evidence table. You still need to verify numbers and talk to customers.

Is ChatGPT deep research reliable?

It's a fast way to collect sources, but citations can be wrong or misread. Check every number you plan to use.

Can AI estimate my market size?

It can collect published estimates and help you build a bottom-up model from sourced inputs. Don't use a number it can't source.

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