Using AI in Market Research Without Losing the Evidence

Define the research decision

State what the business needs to learn and how the answer will affect a decision. A product team choosing between two service improvements needs different evidence from a team exploring a new market.

Choose suitable research methods and participants before using AI to process the material. A model’s plausible answer is not a substitute for collecting evidence.

Prepare an approved research set

Organise interview notes, survey responses or public documents with source identifiers and dates. Remove information unnecessary for analysis and use tools approved for the material.

Check the data readiness guide for ownership and quality. Keep the original records available so summaries can be checked.

Use AI to propose structure

Ask for candidate themes, a coding outline or questions for further investigation. Require links back to record identifiers rather than accepting unattributed claims.

In an illustrative interview review, a researcher would inspect the passages behind a proposed theme and look for contradictory responses. Do not present generated quotes or synthetic personas as observed customer evidence.

Challenge the interpretation

Check whether uncommon views disappeared, leading questions shaped the responses or a theme reflects only one participant. Separate direct observations from your interpretation and hypotheses.

Google’s People + AI Guidebook provides a human-centred reference. Apply the same scrutiny to attractive AI suggestions as to a researcher’s initial assumptions.

Connect findings to the next test

Write a decision note identifying the evidence, limitations and unresolved questions. Validate a proposed change with customers or a controlled experiment.

Use design thinking with AI and the product innovation guide to turn research into a testable workflow.

Sources and further reading

• Google People + AI Guidebook