Quality, cost, speed: has AI changed the rules of healthcare market research?

Quality, cost, speed: has AI changed the rules of healthcare market research?

When I started out in healthcare market research, we used to tell clients they could have two out of three: quality, cost or speed.

If they wanted a project delivered quickly, without compromising quality, it would cost more. We needed more resources, more people and more human hours.

If they wanted to keep costs down while maintaining quality, they needed to allow more time. Lower respondent incentives, for example, could make recruitment slower.

And if they wanted it quickly and cheaply? Quality would almost inevitably suffer. Less experienced teams, less time for analysis and pressure to cut corners.

That was the model. For much of my career, it reflected the practical realities of delivering research.

I think it’s time we revisited it.

AI is changing the relationship between the time we spend on a project and the value we deliver.

Tasks that once required hours of manual work can now be completed much faster. Organising transcripts, comparing responses, exploring themes and developing initial reporting can all benefit from AI support.

That creates an opportunity to deliver research more quickly and at a lower cost, while maintaining quality.

But maintaining quality takes more than putting transcripts into an AI tool and accepting whatever comes back.

AI makes mistakes. It can misinterpret a comment, overlook context or present a conclusion with more confidence than the evidence warrants.

This is where being close to the research matters.

When you have been involved in fieldwork—listening to interviews, asking follow-up questions and hearing how respondents describe their experiences—you have a foundation for challenging the analysis.

Does that theme reflect what we heard? Is this a consistent finding or one memorable comment? Has an important distinction between patient groups been lost?

You can check the output against the source material and use your judgement to refine it.

AI can also help us see things we might otherwise miss: a recurring phrase across interviews, an inconsistency worth investigating or a connection that deserves a closer look. Those are leads to examine, rather than findings to accept automatically.

The researcher’s role is changing, and our judgement remains central.

We can spend less time processing information and more time questioning it, understanding it and working out what it means for the client.

The old “two out of three” rule described a world in which delivering more usually meant adding more human hours.

AI gives us a chance to change that equation.

The opportunity is to make research faster and more affordable while keeping experienced researchers hands-on at the moments that matter.