AI is good at reading large amounts of text and finding patterns, which makes it useful for customer insight — if you treat its output as a lead to check, not a finding.
Useful jobs
- Summarising feedback: reviews, support tickets, survey comments, sales call notes and social posts grouped into themes.
- Social listening: spotting rising topics, sentiment shifts and emerging complaints faster than manual reading.
- Desk research: summarising reports, competitor pages and category news into a brief.
- Campaign evaluation: turning dashboards and test results into a readable summary of what worked.
In Gartner's survey of 418 marketing leaders (July–September 2024), nearly half (47%) of marketing organizations reported a large benefit from using generative AI for evaluation and reporting in their campaigns.
Where it goes wrong
- Hallucinated sources and quotes: AI may invent a statistic or a customer quote. Every number in a deck needs a traceable source.
- Sampling bias: social data over-represents loud and online customers.
- Synthetic respondents: "AI personas" can help draft hypotheses, but they are not customers. Validate with real research before big decisions.
- Privacy: don't paste personal data into unapproved tools.
A good insight workflow
- Define the question and the decision it supports.
- Let AI cluster and summarise the raw material.
- A researcher checks a sample against the source and removes anything unsupported.
- Validate the big findings with real customers (a survey, interviews or a test).
- Record sources so the finding can be traced later.
The payoff
Faster, broader insight means more decisions are grounded in customer reality rather than opinion. The time saved should go into talking to more customers, not fewer.