What Quirk’s London Revealed About the Future of Research

At this year’s Quirk's London, one theme surfaced repeatedly across conversations, panels and workshops. Research teams are under growing pressure to move quickly, while still maintaining confidence in the decisions their work supports.

AI-powered workflows, synthetic audiences, automated analysis and increasingly compressed timelines are reshaping how insights teams operate. But alongside the excitement around speed and scale came a more urgent conversation around trust:

• Can organizations still trust the data they’re collecting?
• Are faster workflows weakening critical thinking?
• How do teams maintain rigor while operating under constant pressure to deliver more, faster?
• And what role should technology actually play in modern research?

Across sessions spanning AI, fraud prevention, fan engagement, stakeholder management and digital twins, a consistent concern kept emerging.The future of insights depends not just on how quickly teams can deliver findings, but on whether organizations can maintain confidence in the decisions their research supports.

Here are five themes that stood out most from the event.

1. Speed Is No Longer the Competitive Advantage

For years, research technology focused primarily on acceleration: faster fieldwork, faster analysis, faster reporting, faster access to insights. Now, speed is expected.

What organizations are struggling with is how to preserve confidence within increasingly compressed workflows.

That tension sat at the center of Rep Data’s panel discussion, “From Data to Decisions: Maintaining Confidence in Modern Research,” moderated by our EVP and Head of Research Steven Snell, PhD, and featuring perspectives from across the research ecosystem, including Naira Musallam, Rep’s EVP of Product and Co-Founder of the SightX platform. James Endersby from Opinium and Marline Alsuar-Dean from Reckitt also shared their perspectives.

Our panelists repeatedly emphasized that speed itself is not inherently risky and that the real danger emerges when accelerated workflows reduce opportunities for deeper interrogation, critical thinking and nuance.They noted that teams are becoming increasingly focused on quick summaries and top-line findings at the expense of deeper analysis. As timelines shrink, researchers have less room to interrogate nuance, explore contradictions, or fully understand what the data is actually saying.

The conversation reinforced a growing industry reality that fast outputs are not automatically trustworthy outputs.

2. AI Is Expanding Research Capabilities, But Also Raising the Stakes

AI dominated discussions throughout the conference, but the tone was notably more mature than in previous years. We found that the conversation has shifted from experimentation to operationalization.

During sessions like “Insights in the Age of AI,” speakers described how AI is now deeply embedded into day-to-day workflows across ideation, survey drafting, analysis and content creation. For many teams, the question is no longer whether to use AI but how to govern it responsibly.

At the same time, multiple sessions at Quirk’s London warned about the risks of overreliance on AI-generated outputs. Many speakers cautioned that AI can create the illusion of rigor by producing polished summaries and plausible conclusions that may not actually withstand scrutiny. The industry is also seeing growing concern around what several presenters referred to as “AI slop”: shallow, believable, but ultimately low-quality outputs generated without sufficient methodological grounding.

Research expertise, critical thinking and human interpretation repeatedly emerged as the skills organizations now value most.

Across sessions, speakers repeatedly returned to the same point. AI can accelerate research workflows, but quality, trustworthiness and strategic judgment still depend on the people designing, interpreting and validating the work.

3. Data Quality Is Becoming a Business-Critical Issue

Data quality was no longer treated as an operational concern confined to sample teams or survey execution.

At Quirk’s London, it emerged as a central business issue.

That was especially evident during “Catch the Fraudster if You Can,” our fun game-show session focused on the growing sophistication of survey fraud and the financial impact poor-quality data is having across the industry. Steve along with Rep’s EVP of Growth Strategy, Julia Mittermayr, hosted a panel of “contestants” who were challenged to find fraud in question responses. Thank you to Nancy Nernon (G3 Translate), Sophie Goddard (MRWeb), Gaelle Bertrand (Philip Morris International), Lakhani Shashi (Yonder Data Solutions) and Shifra Cook (Ayda) for braving the contest!

It was revealed that low-quality or fraudulent responses may account for as much as 38–50% of survey data in some environments, contributing to billions in wasted incentives, operational inefficiency and compromised decision-making.

Our discussion, and others at the conference,  moved beyond obvious fraud detection, and emphasized that modern quality challenges now include:

• AI-generated open ends,
• professional survey takers,
• behavioral inconsistencies,
• synthetic responses,
• and increasingly believable low-quality data.

One particularly important takeaway was that high-quality data does not always look “perfect.”Authentic human responses often contain typos, emotional inconsistency or imperfect phrasing. Meanwhile, fraudulent responses can appear highly polished,  especially when AI tools are involved.

The implication for the industry is significant. Quality can no longer be evaluated solely at the surface level. It requires layered behavioral, technological and methodological validation.

4. The Industry Is Relearning the Value of Human Judgment

Across sessions focused on AI, digital twins and automation, a common realization kept resurfacing. Technology is becoming more powerful, but human judgment is becoming more valuable alongside it.

Whether discussing synthetic audiences, AI-assisted analysis or predictive modeling, presenters repeatedly stressed that technology should augment, not replace, human expertise.

Sessions on digital twins and synthetic data drew an especially important distinction between believable outputs and validated insights. Multiple speakers warned against treating synthetic systems as inherently accurate simply because they produce coherent or predictive responses.

The strongest approaches emphasized:

• representative foundational data,
• layered qualitative and quantitative inputs,
• ongoing validation,
• transparency,
• and human oversight.

Sessions like “Lost in Translation: From Consumer Understanding to Business Impact” focused on a different challenge. Research can uncover uncomfortable truths, but getting organizations to act on them is often far more difficult. Various panelists discussed how insights teams increasingly need to function not just as researchers, but as translators, storytellers and internal advocates capable of navigating organizational tension when consumer insights conflict with commercial priorities.

The message here was that  in an AI-enabled research landscape, the differentiator will not simply be access to technology, it will also be the ability to apply judgment, context, interpretation and strategic influence.

5. Research Impact Depends on Connected Ecosystems, Not Isolated Tools

One of the clearest shifts visible across the conference was the industry’s movement away from isolated point solutions toward connected research ecosystems.

Whether the topic was fan engagement measurement in Formula 1, stakeholder collaboration, AI governance or fraud prevention, it kept returning to the same issue. Insights become less effective when workflows are fragmented.

Sessions repeatedly emphasized the need to better connect:

• data quality,
• audience understanding,
• stakeholder alignment,
• execution,
• interpretation,
• and activation.

That was particularly visible in sessions exploring sponsorship measurement and fan engagement, where organizations are increasingly expected to prove ROI through integrated behavioral, social and experiential data, versus isolated metrics.And discussions around client-agency collaboration highlighted how trust, transparency and contextual understanding often produce better outcomes than purely transactional relationships.

Across the event, the direction of the industry feels like it will belong to organizations that can combine quality data, intelligent technology and human expertise within connected, trustworthy systems.

The Bigger Shift Happening Across Research

Quirk’s London made it clear that the industry is entering a new phase of maturity.

The conversation is no longer simply about how quickly research can happen. It is increasingly about whether organizations can maintain confidence, trust and meaningful business impact while operating at modern speed.Technology will continue to evolve rapidly, AI capabilities will continue to improve and synthetic systems will become more sophisticated.

The organizations that succeed will not be the ones that move fastest at all costs. They will be the ones that know how to balance speed with rigor, automation with judgment and innovation with trust.