AI and the end of not knowing?

What does AI’s growing role in research tell us about how uncomfortable we suddenly are with not knowing?

Across the Insights Association Corporate Researchers Conference (CRC) program, an interesting theme is surfacing. One of certainty.

We’re seeing it in always-on intelligence, synthetic data, living segmentations and AI moderation. All reflecting systems that answer questions in real time. And each responding to a very real operational problem.

But together, they point to something more culturally significant: organizations are becoming less willing to wait, wonder or admit that people remain partly unknown.
Traditional research begins with an admission that often sits awkwardly within business culture. Put simply: “We don’t know”. And that creates room to step outside the organization’s world and encounter realities that may be contradictory, unclear or difficult to understand.

AI offers a vastly different relationship with knowledge. An answer is always within reach, sometimes without asking another person. And when everything else feels uncertain, having an answer can feel safer than contending with what we don’t know.

But can we build models that help organizations move through that uncertainty without flattening the uncertainty itself?

The pressure to know now

Research used to make room between question and answer. But increasingly, that gap is treated as inefficiency.

Businesses are moving through political instability and technological upheaval while still under pressure to act now. Under these conditions, uncertainty no longer feels like a natural part of making decisions. Instead, it’s begun to feel like vulnerability. A liability. Something a functioning organization should be able to solve away.

AI research systems soothe this institutional nervous system. Another answer is always available. Another signal to consult. Another customer to simulate. The organization never has to sit for long in the discomfort of not knowing.

The fantasy of the cooperative consumer

People are inconvenient sources of knowledge. They take time. They misunderstand the question. They contradict themselves. They describe their lives in language the business sometimes cannot hear.

Synthetic respondents solve much of this friction. Used well, they bring a consumer perspective into decisions where people are usually absent, helping teams test assumptions at a speed or scale that live research cannot support.

But their very cooperativeness is also the risk. Built from what has been captured about people, they do not pause to question, refuse to answer, or change as they evolve. They turn the messy, changing person into someone the system can act upon. A system-ready self.

And everything moves more smoothly. But what happens when we start to mistake that smoothness for truth? When an organization becomes more dependent on its model of reality than on reality itself?

The paradox of knowing more

AI gives organizations an abundance of apparent knowledge about people. More signals. More answers. More certainty. Yet all this knowing can leave us with a narrower worldview.
Synthetic systems are built from what has already been captured. They can recombine existing knowledge with startling fluency.
But culture is rarely so legible. It mutates through jokes, workarounds and contradictions. It takes shape in what people cannot yet articulate, between what someone says and what the world allows them to do.

A system built from existing evidence can generate highly plausible futures. But plausibility is not the same thing as emergence. The danger with AI is that it repeats the existing world so fluently that this very repetition begins to look like foresight. And research, therefore, gets stuck answering yesterday’s questions about tomorrow’s people.

Accessing the emergent

This is where cultural intelligence becomes essential. AI-powered research can tell us about the world already captured. But cultural research gets us closer to the world still taking shape.

Culture shapes behavior before people can explain what they are responding to. What is marginal today can become tomorrow’s common sense. And by the time a change can be modeled, the cultural tension that made it significant may be gone.

To access the emergent, we have to look beyond the obvious places. Into adjacent categories, shifting rituals, visual language and futures people are preparing for now. Semiotics, cultural insight and foresight techniques (both anticipatory and speculative) help us read these movements while they’re still in motion.

This work protects the conditions under which a business can still be surprised. Not surprise as novelty or entertainment, but surprise as a cue to pause and reflect. A moment to stay with uncertainty, pay attention to what does not fit and look closer rather than forcing it into what we already know.

And that matters commercially because surprise is where distinctiveness begins. It might show us a need that competitors have missed. A tension the category has ignored. A future people are making room for. Meaning comes from having a point of view on the future a brand wants to help create.

AI can offer certainty about what is likely, but cultural understanding builds conviction about what is worth doing.

Tomorrow’s twins

This is why, for the team of semioticians, cultural strategists and data analysts at Space Doctors, digital twins presented a curious alternative starting point. Our partners at Human8 build digital twins from first-party data shared by real, consenting participants and tested against the people they represented, bringing a consumer perspective into decisions where people would usually be absent.

But how do you stop even a well-grounded twin from holding someone still in the present?

That question became Future Twins. Working together, we brought each participant’s own imagined future into five culturally distinct worlds for 2030, shaped by Coca-Cola’s category evidence, Space Doctors’ cultural strategy and wider foresight. The value was in seeing how people might change as the culture around them changed too. Each future gave us somewhere new to think from, without claiming to know which one would arrive.

Those shifts helped shape innovation platforms and refine a Powerade concept for LA 2028. The work with Human8 and Coca-Cola won Gold at the 2026 ESOMAR Awards for both Excellence in Global Research and the Tony Cowling Foundation Innovation Award.

When culture moves

At CRC, much of the conversation will circle speed, scale and AI’s reach. But can we build research systems that still recognize things they don’t know to look for?

That is the difference between certainty and conviction. Certainty can come quickly. Conviction has to survive contact with people, culture and a changing future.

Conviction lets a team choose and a brand mean something. It comes from seeing what others missed and deciding what future the business is prepared to help make real.

AI can help us anticipate more answers, but staying close to culture is how we notice when those answers no longer fit. Conviction is what lets us decide what to do next.

Get in touch with Kira to find out more about this topic.