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Where AI helps in market research — and where human judgement must stay

Hoog Research Team · Research & Advisory · 6 min read

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Executive summary

AI tools can dramatically accelerate discovery, extraction and the analysis of qualitative data. They can also produce confident, plausible and wrong outputs. The research firms that benefit most treat AI as an accelerator inside a disciplined process — with humans accountable for design, verification and interpretation.

Key findings

  1. 01AI adds most value in high-volume, repeatable tasks: source discovery, extraction, transcription and first-pass coding.
  2. 02Every AI-generated claim needs a traceable source that a human has checked.
  3. 03Research design, sampling and interpretation remain human responsibilities.
  4. 04Clients should ask research partners how AI is used and how outputs are validated.

Where AI accelerates research

The strongest use cases are those where the volume of material exceeds what a team can read carefully, and where outputs can be checked against sources.

  • Research discovery: finding relevant reports, filings and articles faster.
  • Data extraction: pulling entities, figures and claims into structured tables with source links.
  • Competitive monitoring: scanning news, product pages and job postings for changes.
  • Interview processing: transcription, summarisation and first-pass thematic coding.

Where humans must stay in control

Defining the business question, choosing the right respondents, judging source quality and deciding what findings mean for a specific company are judgement tasks. AI can support them, but cannot be accountable for them.

The risk is not only factual error. It is also false precision — a clean-looking number or theme that hides weak evidence.

A practical validation standard

At Hoog, AI-assisted outputs follow a simple rule: nothing reaches a client without a human reviewer verifying the source, checking the logic and confirming the interpretation.

Business implications

  • Ask any research provider to explain their AI validation process.
  • Expect faster timelines on discovery-heavy work — not lower standards.
  • Agree data-handling rules for AI tools before sharing confidential material.

Sources

  1. ICC/ESOMAR International Code on Market, Opinion and Social Research and Data Analytics — ESOMAR

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