Grab the Map, We’re Going on an Adventure: What AI really means for the future of Market Research
At a Market Research Society event in London this week, a deceptively simple question was posed: ‘Will researchers need to understand research in the future?’
It is tempting to assume the answer is ‘No’. As AI becomes more capable, surely the need for deep expertise fades. But that line of thinking assumes the challenge is simply operating the technology, and that is not where the real risk lies.
AI is the engine, not the expertise
Most of us can drive a car. We can start it, steer it, follow directions, and get ourselves from A to B without too much trouble. Some can even handle the basics, like topping up the oil or changing a tyre. Very few of us are mechanics, and yet that has never stopped us driving with confidence. The same is increasingly true of AI in research. It is becoming the engine that powers our work, accelerating analysis and enabling tasks that once took days to complete to now be completed in minutes. Quite frankly, how exciting is that?
Researchers do not need to build the ‘engine’, but they do need to know how to use it. The risk, though, is assuming that knowing how to drive is enough. Because driving is not the job; the job is getting to the right destination.
Following the route is not the same as getting it right
Most of us have experienced what we fondly call a ‘SatNav special’. You follow the instructions with complete confidence, only to find yourself navigating a narrow, single-track road with hedges brushing the sides of the car, questioning how this could possibly be the fastest way. Technically, it may well be the shortest route, but it certainly feels far from that in the moment.
AI in research can behave in much the same way. It will optimise for efficiency, identify the most logical route through the data, and present an answer that feels structured, coherent, and reassuringly confident. But that does not guarantee it has taken you down the right path.
Even the destination can be wrong
There is also the question of the destination itself. We have all spotted something on a map that looks promising and, with a quick Google, seems interesting enough to justify a detour. Only when we arrive does it turn out to be underwhelming or simply not what we expected. It looked right, it sounded right, but in reality, it was not.
That is the real risk with AI-generated insight. Not that it is obviously wrong, but that it is convincingly, plausibly off.
This is not an argument against AI. The engine is extraordinary, the chassis is improving rapidly, and the SatNav is often remarkably effective. The point is not to reject the technology, but to avoid becoming distracted by it. It is all too easy to focus on how powerful the system is, rather than whether it is actually taking us where we need to go.
AI will suggest a route quickly and confidently, and often it will be a good one. But the first route is not always the best route, and sometimes it is not even the right destination.
Capability is outpacing judgement
It’s also important to acknowledge the growing gap between capability and maturity. Many research buyers are understandably eager to embrace AI, but too often they are trying to run before they can walk. Powerful tools are being deployed without the analytical grounding needed to properly interrogate the outputs.
That creates a difficult dynamic. Speed increases, confidence rises, but scrutiny falls away. In that environment, hallucinations are not always caught. They are sometimes accepted and presented as fact.
So where does that leave the role of the researcher?
Not as a mechanic, and not simply as a driver, but as the navigator and leader responsible for deciding where to go and whether the destination makes sense once you get there.
We need to grab the map, take the wheel, and properly sense-check what we are being shown. That means challenging outputs that feel too easy, questioning conclusions that appear a little too neat, and filtering not just for what is plausible, but for what is genuinely true.
Where has this come from? What assumptions sit behind it? What has been smoothed over or left out? We must be willing to challenge answers that feel a little too easy, or conclusions that land a bit too neatly. Because good research has never been about accepting the first answer; it has always been about pressure-testing it.
If anything, AI raises the bar here. When outputs arrive faster and with more confidence, the temptation is to move on just as quickly. But speed and confidence are not the same as accuracy, and they are certainly not a substitute for judgement.
So, our job as researchers is to slow that moment down. To probe the logic, explore alternative readings, and pay attention to the edges of the data, not just the centre. Without that layer of critical thinking, we are not really doing research; rather, just passing plausible things on.
The value of going off route
Perhaps most importantly, we should not lose sight of the value in going off route altogether. Some of the most memorable journeys come from ignoring the SatNav, taking an unexpected turn, or following a path that was not part of the original plan. That is often where you find the best views and the most interesting discoveries.
The same applies to research. The most valuable insights rarely emerge from the obvious path alone. They come from creative leaps, from exploring the edges of the data, and from leaning into the mess rather than smoothing it away.
This is a craft, not a shortcut
That is our role and, more importantly, it is our craft, built up over years of doing the job properly. The ability to question, to probe, to sit with something that does not quite stack up and push a little further is not something that just appears; it is learned, practised and refined over time.
Which is why the question of whether researchers will need to understand research in the future feels off. It assumes that this layer of thinking is optional, or something that can be replaced without consequence.
A more useful question might be why we would assume a world without that expertise functions well at all. Because once you take away that judgement, that context, that willingness to challenge what looks plausible on the surface, you are not left with a cleaner or more efficient version of truth. At that point, it starts to feel less like progress and more like a kind of Wild West, where outputs are plentiful, but certainty is not.
Final thought
In a world where anyone can drive, AI can recommend destinations, and systems can suggest the route, the real value does not lie in operating the machine. It lies in knowing how to validate and sense-check those inputs before deciding where to go, and in recognising when you have been taken somewhere that only appears to be right.
Otherwise, we risk becoming very good at following directions without ever really understanding the journey. And at that point, we are no longer researchers, but passengers in driverless cars, which is a very different conversation altogether.
If you’re exploring how to get the most from AI in research, The Harris Poll UK can help you navigate what comes next.