Quirk’s Chicago felt like a clear reflection of where the insights industry is right now.
AI was everywhere. That was obvious from the exhibitor mix, the session themes, and the wider conversation around the event, from synthetic personas to AI-led tools and the question of where these newer approaches actually fit.
What stood out was not that AI was present, but how people were talking about it.
The fear that AI is here to replace the consumer insights function no longer seems to be driving the conversation in the same way. The tone felt more practical. More people are starting to look at AI as something that can help speed up parts of the process, reduce repetitive work, and get insights to stakeholders faster.
That shift matters because it moves the conversation away from hype and towards use. The question is less “Will AI replace research?” and more “Where does it actually help?” That feels like a more realistic place for the industry to be – more comfortable with the idea that it can be an additive benefit rather than a threat.
One of the clearest takeaways from Chicago was that technology on its own is not enough. The methodology behind it matters just as much.
That felt especially relevant because so many platforms were making similar claims. There was a lot of overlap in how tools were being described. But speed and automation only get you so far if the inputs are weak. If the wrong respondents are going into the system, or if the methodology underneath it is shaky, then all you are doing is getting to a bad answer faster.
Not everything that looks advanced is necessarily strong. There is still a real difference between a slick tech platform and research done properly.
That is where Delineate feels different, blending tech with research methodology to help clients get their data fast, organize it quickly, and get it to stakeholders in time to act, while still grounding the work in proper research practice. What matters more is identifying growth opportunities and making sure that brand and campaign tracking actually helps people make better decisions. In other words, connecting the work to moments that matter and to decisions that need to be made.
Another thing that came through clearly was how much pressure insight teams are still under to make their work more useful. Consumer insights cannot just become a check-the-box exercise. Awareness, consideration, superiority, and the rest can easily slip into routine reporting if teams are not careful.
That is a real risk now. Budgets are under pressure, expectations are changing, and there are more tools in the market than ever. If the data arrives too late, gets repeated too mechanically, or loses any real connection to action, people may still look at the numbers, but they stop really using them. The work starts to become a process rather than a tool for decision-making.
That is why the structure around the data matters too.
A lot of bigger companies are still working through how to organize their data properly for benchmarking and comparison. It is very difficult to make sense of today’s numbers if there is no usable baseline, no easy way to look back, and no clear frame for what good or bad actually looks like. In many cases, the problem is not a lack of data, but a lack of structure around the data teams already have.
One of the more useful reminders from Chicago is that the industry is not going to change overnight, but the direction of travel feels clearer. AI is not going away, and neither is the pressure to move faster. The real challenge is using technology where it genuinely helps, while keeping the quality, structure, and research discipline needed to make the output useful.
What will matter most is using AI to create better and faster systems for getting quality data into the hands of people who need to make real business decisions.
And with IIEX North America in Washington, D.C. coming up later this month, it will be interesting to see how much of that same conversation carries through there as well.
