AI is now table stakes. Quality is the differentiator.
By Centico Research · 3 min read
A couple of years ago, telling a client “we use AI” felt like an advantage. Today it is an expectation. Surveys say the vast majority of research teams already use AI somewhere in their workflow — coding, summarising, drafting, analysis. When everyone has the same tools, the tool stops being the story.
So the real question has quietly changed. It is no longer “do you use AI?” It is “how do you stop AI from quietly degrading your data?”
The new baseline
Automation is brilliant at the repetitive middle of a project — reading thousands of verbatims, standardising messy fields, flagging duplicates. It is fast and tireless and consistent. But it is also confidently wrong in ways that are easy to miss: it invents a tidy theme that isn’t quite right, misses sarcasm, or treats a fraudulent response as genuine because the words look fine.
Where quality is actually won
Quality is won in the handover between machine and human. The model does the heavy first pass; an experienced person checks the frame, resolves the edge cases, and takes responsibility for what ships. That second step is not a formality — it is the entire difference between “processed quickly” and “correct.” Skip it to save a few hours and you risk the one thing a research buyer cannot forgive: data they cannot trust.
What to ask a partner
Three questions separate the careful from the careless. Who reviews the AI’s output before it reaches me? What happens to my data — is it used to train models? And can you show me where a human changed the machine’s answer? A confident, specific answer to all three is the new mark of quality.
— Centico Research
