Watch the video of this presentation

Synopsis

How did they analyse 20,140 questionnaires for the first UK Readership Study in 1928 without computers? By hiring Chartered Accountants skilled in counting! Since then, market research has worked to bring order to the “chaos” of survey data, using advances in science and technology to improve efficiency—automating sampling and weighting, simplifying data collection (CATI, CAPI, online), and reducing workloads through passive measurement and automation. However, research data is not absolute; changes in method can alter results, sometimes negatively. Throughout, the industry has acted professionally to manage and explain these shifts. Now, with AI as the latest disruption, how should we respond? What opportunities and risks does it present, and what can history teach us?

Appendix: Links to the AMSR Archive

• MRS Conference Papers `Attitude Scaling’ 2nd Annual Conference 1958 – Page 2 – Conference Papers from the Market Research Society – The AMSR Online Archive​
• MRS Conference Papers 16th Annual Conference 1973 (part 2, pp 191-351) – Page 30 – Conference Papers from the Market Research Society – The AMSR Online Archive​
• Collection of data by telephone (CDT) its viability in consumer research, G. P. Hyett and G. M. Allan; The case for telephone research, David Miln and David Stewart-Hunter  MRS Conference Papers 19th Annual Conference 1976 – Page 3 – Conference Papers from the Market Research Society – The AMSR Online Archive​
• Comparing Telephone and Face to Face Interviews: a report on a methodological research survey. – Page 1 – Market Research Development Fund – The AMSR Online Archive​
• 26 questions to help research buyers of online samples – Page 21 – Guides and rule books – The AMSR Online Archive​
• Technical Change: can the market research industry cope? Proceedings of a Technical and Development Conference, July 1987 – Page 5 – Market Research Development Fund – The AMSR Online Archive

Watch the video

Synopsis

As the application of insight within organisations become faster, more data rich and more decentralised, insight is no longer confined to a single function or team. Instead, brands are evolving into continuous generators and users of insight; with value increasingly defined not by the production of research, but by the confidence it creates in decision making.

In this keynote, Paul Griffiths will explore how client side organisations are re-wiring their research and insight workflows as friction is removed from tasks such as analysis, synthesis and signal detection. The session will examine what this means for internal insight teams as their role shifts from producing outputs to enabling better decisions across the organisation. It will also surface a growing tension between how brands describe their transformation and what is actually happening in practice; highlighting gaps between stated ambition, lived behaviour and the realities insight teams face when managing stakeholders who expect speed, certainty and clarity.

These changes have profound implications for the agencies that support them. Agencies are being challenged to rethink how they anticipate client needs, where they insert themselves into the decision process, and how they articulate a value proposition centred on decision confidence rather than delivery alone. Traditional project structures and pricing models are under pressure, while new commercial and operating models are beginning to emerge.

Designed for technologists working across the market research industry, this session connects organisational change to agency adaptation. From workflow design and commercial models to the role technology plays in shaping trust, judgement and outcomes.

Rather than offering definitive answers, the session will test emerging hypotheses, surface unresolved tensions and invite the audience into a shared exploration of what it truly means to be fit for change when competitive advantage is increasingly determined by the quality of decisions organisations are able to make.

Watch the video

Synopsis

The future of this industry will not be decided by tools alone. It will be shaped by how people respond to change.

Periods of rapid change rarely feel energising when you are living through them. Uncertainty can be exhausting.

Fear of change leads our minds to start asking difficult questions:

• Am I still relevant?
• Do I know enough to keep up?
• What if I get this wrong?

In many professions, these feelings show up as hesitation or resistance to new tools or new ways of working.

Beneath the surface, something more human is happening. Faced with too much uncertainty, our nervous system moves into protection mode.

Instead of exploring, we conserve. Instead of experimenting, we hold on to what we know. It can look like reluctance to change. Usually it is uncertainty fatigue.

In the research and survey technology profession, this tension is particularly visible. The industry is built on rigour, consistency and methodological integrity. Those instincts have safeguarded the credibility of insight for decades.

Yet today the landscape is shifting quickly. AI, automation and new platforms are transforming how work gets done.

The challenge is not simply learning new tools. The deeper challenge is how human beings respond to uncertainty and change.

This session explores this human dimension:

• Why capable professionals sometimes freeze in moments of rapid change.
• Why fear of getting it wrong can quietly slow progress.
• How each of us can move from protection to curiosity again and help others do the same.

Through reflection and interactive discussion, participants will explore how their personal relationship with change shapes how teams and organisations respond to it.

Participants will leave with practical ways to:

• recognise the natural human responses to uncertainty
• shift from fear and fatigue toward curiosity and experimentation
• support colleagues navigating the emotional side of change
• create environments where people feel safe learning and adapting

Because while technologies will continue to evolve, the real driver of progress remains deeply human.

Watch the video of this presentation

Watch the video

Synopsis

Our experience shows that AI in research works best when it is developed at the cutting edge of delivery and in a way that is informed by project needs. When AI is built at a distance, it is more likely to drift into general capability that is less useful in practice. Rolling out AI close to delivery requires integrated teams that include both technical and research expertise.

This session draws on practical experience of building “symbiotic” teams where technical specialists work side-by-side with researchers and methodologists. We discuss how methodological judgement is built into AI-enabled delivery: agreeing what “good” looks like up front, using clear QA checkpoints through the work, and pairing technical staff with senior expertise. We also explore the dynamics that make or break symbiosis: status, trust, competing definitions of value, confidence, and the balance between speed and rigour.

We end with a caution and a longer-term question. Symbiosis works today largely because experienced researchers can guide technical delivery and anchor standards. But if AI reduces the volume of junior, hands-on work through which researchers traditionally learn the craft, the pipeline that produces those experts may weaken. The industry therefore needs team models that do two things at once: deliver high-quality AI-enabled work now, and actively reproduce the methodological capability that quality will depend on in the future.

Watch the video

Synopsis

If you think AI is a tooling problem, you’ve already lost. Most organisations treat AI adoption like a product rollout: ship the tool, run the training, measure usage. In qualitative research, that approach fails—because the real barrier isn’t capability, it’s culture.

Hear how Kantar led the cultural change needed to move traditional qualitative researchers into an AI‑first world without breaking trust, quality, or professional identity. The provocative claim: AI isn’t the hard part. Humans are. And if you treat adoption as a rational decision, you will miss the functional, social and emotional forces that determine whether people try AI once—or build it into how they work every day.

See a practical framework for diagnosing and designing change across three layers: Functional – workflow truth, not workflow fantasy Social – respecting the craft cultureEmotional – psychological safety as infrastructure

Delegates will leave with practical ways to make change work in real teams—across mixed reactions, different speeds of openness, and the tension between global scalability and local reality—without becoming punitive, prescriptive, or lost in “AI hype.

Watch the video

Synopsis

Join us to feel the tension and experience the emotion as we announce our very first inductees into the Research Technologist Hall of Fame.

Over the last few months our panel of industry experts have been nominating and deliberating on who should be our inaugural members of this prestigious research technology community. A tough job to choose only a small number of learned professionals from so many amazing research technologist who have driven our industry forward over many decades.

The Research Technologist Hall of Fame serves as a prestigious and enduring platform to celebrate the legacy and impact of research technology professionals. This is an opportunity to recognise and honour individuals who have made outstanding contributions to the advancement and application of technology within the market research and insights industry.

ASC is very proud to be partnering with Ipsos our sponsor for the Research Technologist Hall of Fame.

Watch the video

Stnopsis

Watch the video

Synopsis

Survey teams face increasing pressure to deliver deeper insights while maintaining methodological rigor, operational stability, and client trust. One potential shift is moving from static online surveys to AI-moderated interviews (AIMIs), sometimes called conversational surveys. The main question isn’t whether this approach is innovative, but whether it is reliable and operationally sound.

AIMIs combine traditional survey architecture, including closed and scaled questions, with AI-moderated probing. Respondents answer via text or voice, and the system generates contextual follow-ups while preserving questionnaire logic, routing, and comparability.

This session presents evidence from two independent Research on Research studies comparing static online surveys with AIMIs using identical questionnaires and balanced samples, by the University of Mannheim and Human Highway research.

In a randomized study conducted by the University of Mannheim (n=200), the AI-moderated format generated 39% more words, 51% more unique words, and 36% more distinct themes, while reducing gibberish responses from 10% to 0%. Readability remained stable.

A second study (Human Highway, n=1,003) found that AI-moderated interviews generated longer responses, wider conceptual variety, increased semantic cohesion, and more in-depth reasoning, with voice input further boosting depth. Thematic structure stayed consistent across modes.

The session will focus on practical implications, including how AIMIs integrate into existing survey workflows, how they enhance data quality at the point of collection, and how they can be piloted without disrupting delivery.
Delegates will leave with evidence-based guidance on when and how to implement AIMIs while upholding research standards.

Watch the video

Synopsis

Our industry has a long history of falling in love with the “next big thing.” From online research to big data, from social media listening to synthetic samples, each wave arrives with bold promises—often accompanied by a surge of optimism that reality struggles to match.

This session explores how and why that gap emerges. Looking across past innovations, it shows how early expectations can diverge sharply from what is ultimately delivered. Synthetic data is used as a live case study: an innovation currently moving through its own hype cycle, with moments of genuine breakthrough alongside areas where performance falls short.

The session examines how researchers can remain open to new possibilities while staying grounded in evidence. It also reflects on a broader question….how do we think about the future more effectively? What mindset helps us distinguish between transformative change and overpromise? How do companies more effectively plan for the future?

Watch the video

Synopsis

AI has reshaped how packaging gets made — productivity is up, creative output has multiplied, and expectations have followed. The bottleneck is no longer generating designs; it’s knowing which ones will perform. That calls for prediction, not opinion.

In this session, Remi Denoyer shows how Behaviorally built Pack.AI to do exactly that. Unlike generalist LLMs that offer impressions without domain grounding, Pack.AI is a multimodal model trained on thousands of real shopper observations from PackFlash® studies. It uses transfer learning from vision networks to read pack semantics — structure, colour, layout — and fuses that with brand and category context to produce predictions anchored in behavioural data and correlated with sales outcomes across billions of data points.

Remi will walk through how the model understands brand equity on shelf, what it takes to stay predictive rather than just generative, and how the tool fits into a consultant’s everyday workflow — helping insight teams screen more design routes, faster, without lowering their standards. The session speaks directly to this year’s Fit for Change theme: it’s a practical look at how behavioural AI lets practitioners get work done without breaking things and stay effective as individuals, even as the tools and expectations around them keep shifting.

View here four podcast interviews with participants on the day.