Speed is easy to measure. Quality is harder. What are we optimising for?
Every AI tool I see makes the same promise: do it faster. Faster synthesis, faster reports, faster recruitment. And mostly, it delivers.
But nobody hired a research team to be fast. They hired us to help them make better decisions about real people. So the question I keep coming back to is simple: is AI making our work better, or just quicker?
Speed is easy to see
It is easy to say 'synthesis took two days, now it takes two hours'. It is much harder to say whether the insight is as deep, whether we missed the quiet participant, or whether the team trusted the findings enough to act on them.
If we only measure speed, we will only get speed.
Where faster can mean worse
- ○We skim the transcripts because the summary looks complete.
- ○We report the obvious themes and miss the outlier who mattered.
- ○We run more studies but spend less time with any of them.
Where faster can mean better
- ○We spend the saved time back with participants, not on slides.
- ○We test more ideas earlier, so fewer bad ones get built.
- ○We use AI to challenge our work, not just produce it.
A simple test
Next time AI saves you a day, write down what you did with that day. If the answer is 'more of the same', you got faster. If the answer is 'went deeper', you got better.
The question for you
What did you do with the last hour AI saved you?
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