Once You Test GenAI on Your Own Qual Data, the Conversation Changes

After two decades in qualitative research, I’ve learned something important: people don’t adopt a new method because it sounds promising.
They adopt it because it delivers results on their own data.

This is exactly what I observe when researchers start experimenting with AI for qualitative analysis.
Not theoretical interest.
Not curiosity.
A concrete shift that happens the moment they upload their interviews and see what comes out.

Recent academic work confirms this dynamic.
The TAM-based study by Chatzichristos (2025) shows that perceived usefulness is the strongest predictor of AI adoption. Once researchers see a real benefit on their tasks — transcription, coding, theme extraction — their intention to use AI increases sharply.

And this benefit appears faster than many expect.

What I see when people run their first dataset

Most teams start with a small study — 6, 10, maybe 20 interviews.
They don’t change their process. They simply test.

The outcomes are consistent:

1. Coverage
AI retrieves all core themes identified manually. In several projects, including a 20-interview study on Swiss health insurance, the automated analysis covered 100% of the manually identified topics.

2. Speed
Time spent on analysis drops by 60–70%. Not because the researcher disappears, but because the machine handles volume and repetition.

3. Consistency
Large datasets are treated systematically. No fatigue, no drift, no loss after transcript number 12.

4. Scalability
Multi-language projects (German + French in my case) can be analyzed in a unified structure — something that is usually cumbersome manually.

This is not “innovation theatre.”
It’s operational efficiency applied to qualitative research.

Why starting with one study is enough

Market researchers often assume they need to redesign everything to use AI.
They don’t.

Testing a single dataset is enough to understand the structural impact:

• faster throughput

• improved reliability

• capacity to process more material

• more time available for interpretation and recommendations

• easier collaboration with non-research stakeholders

• This is aligned with what organizational AI research points out: adoption accelerates once benefits become tangible in the workflow.
  

The threshold is low.
The return is high.

Where AI stops — and where the researcher remains essential

AI does not see context, business strategy, brand positioning or market constraints.
It does not replace the final interpretation.

What it does replace is:

• hours spent reading transcripts

• manual coding

• repetitive synthesis work

• checking for internal consistency across large volumes

The researcher’s value — constructing meaning, connecting insight to strategy — stays intact.
In practice, it becomes more central.

Why data IQ built Insight-lab

We didn’t build Insight-lab to “automate qual.”
We built it to make the analytical phase significantly more efficient, so that researchers can focus their time where it actually matters: interpretation, recommendations, strategic alignment.

The tool is designed for one thing: processing qualitative data at scale, with speed and reliability, while keeping the researcher in control.

What I advise every team is simple:

Start with one study you know well.
Run the analysis.
Compare.
Challenge the output.
Decide based on evidence — not hype.

Because once you’ve tested GenAI on your data, the conversation changes.