AI, Ethics, and the Future of Insights:

Explore how organizations can embrace automation without sacrificing creativity, trust, critical thinking, and human connection, while preparing their workforce for a future where people and AI agents work side by side.

Key Takeaways:

1. Stop competing with AI. Start orchestrating it.

2. Protect human work that creates trust, creativity, and connection.

3. Design workplaces where people and AI agents thrive together.

Join us for our thesis on how to achieve Zero Distance to consumers, category and culture using Anthropology, AI and the Internet. Experience the Human Context Protocol through a live demo across trends, discourse, segmentation and foresight workflows, and hear stories from insights teams using it today.

Key Takeaways:

1. Find out how the Human Context Protocol grounds LLMs in real cultural signals for deeper insight

2. Experience Zero Distance in practice: from research question to cultural answer in minutes

3. Hear from teams who have deployed Anthropologic and how it has changed the way they work

GenAI solved our volume problem but created a value crisis: 54% of leaders find AI output unactionable. We’re scaling noise, not intelligence. To fix this, quality control must be built-in by design. I’ll walk through our Cursor operation—checking claims against backend product reality across 3,000+ transcripts—to replace "human-in-the-loop" review with scalable backend governance.

Key Takeaway:

1. The AI frontier in research is a unified context layer connecting customer claims to product reality

2. Humans must retain accountability by treating everything AI touches as a governed tech product.

3. Value begins when humans stop reviewing AI output line-by-line and start building governance.

Two researchers, one inside a frontier AI lab and one leading research delivery at an AI-native agency, compare notes on where this industry lands by 2028.  Knit's AI Trust Index shows researchers' top fear isn't replacement. It's non-researchers making high-stakes calls on AI output no expert touched. Expect a candid conversation on expertise, cognition, and the next generation.
Key Takeaways:
1. Why the governance gap, not automation, is the real near-term risk to insights quality.
2. How to keep the productive struggle that builds researcher judgment, without slowing down.
3. What a viable career path into research looks like when the grunt work is gone.

Discover how AI can uncover the hidden drivers of innovation success and identify winning ideas more accurately than traditional concept testing alone. Drawing on a meta-analysis of 100+ beverage launches, this session shows how Cambri Launch AI combines consumer feedback, real-world sales and AI modelling to predict commercial success, explain why ideas win and improve innovation decisions.

Key Takeaways:

1. Discover how AI-powered meta-analysis can reveal hidden drivers of innovation success

2. Learn how AI augments concept testing to better predict and explain commercial winners

3. See how consumer feedback, sales data and AI combine for smarter innovation decisions

AI is rapidly changing how people discover information, make decisions, shop, and work, but adoption isn’t happening evenly. In this session, Jennifer Musil, Global President of Research at The Harris Poll, will reveal the latest research on how consumers and professionals are using AI, where adoption is accelerating, and where skepticism and hesitation remain.

Then, Sam Kraus, Sr. Director of Research at The Harris Poll, will demonstrate what happens when AI enters the path to purchase: which brands are winning AI recommendations, which are losing ground, and what it takes to become the product AI recommends.

Attendees will gain a clearer view of where AI adoption is headed, how consumer decision-making is changing, and what brands need to do now to stay visible, relevant, and preferred in an AI-powered world.

US use cases dominate the AI narrative. But the real signal comes from global founders navigating infrastructure gaps, regulatory friction, and resource scarcity. Using intelligence across 14 Google Accelerator programs in APAC, EMEA, and LATAM, this session explores how regional context shapes AI adoption differently—and what insights pros miss by only reading mainstream tech press.

Key Takeaways:

1. AI isn't a monolith: Regional constraints completely reshape how founders build.

2. Emerging markets use problem-first AI to bypass broken infrastructure.

3. The mainstream tech press misses the real, durable signals of global adoption.

What if your consumer personas could answer questions, stress-test ideas, and explore trends on demand? Magnum and Vurvey built AI populations grounded in real demand occasions—and now use them across trend exploration, innovation framing, deep dives, and brand strategy. This is what always-on insight looks like in practice.

Key Takeaways:

1. Static personas become interactive AI populations teams can query across any strategic question.

2. Grounding AI populations in demand moments instead of demographics produces sharper, faster decision

3. One insight framework can power trend work, innovation, deep dives, and brand strategy at once.

Traditional surveys and 1:1 interviews play a central role in Ancestry's insights work, but they increasingly found important customer context left unexplained. In this session, Ancestry's insights team shares how they evolved their approach to capture emotion, motivation, and nuance—while still operating at enterprise scale.

Key Takeaways:

1. Where surveys and interviews break down when teams need confidence, not just direction

2. How customers' own words revealed insights that quantitative data consistently missed

3. What changed when richer customer stories became central to decision-making

Enterprise research is shifting from one-off projects to always-on programs, and most organizations are still working out how. Drawing on a two-year journey building an AI-enabled global insights program spanning 120 studies and 40+ countries, this brand-side case study covers where automation genuinely accelerates insight, where it quietly breaks, and where human judgment stays irreplaceable.

Key Takeaways:

1. How to turn project-based research into a continuous, always-on program inside a large enterprise

2. Where AI genuinely accelerates qualitative and quantitative research — and where it quietly breaks

3. Why research becomes the evaluation layer, not the production layer, once anyone can generate output

"Ask AI where next year's marketing budget should go and it answers instantly, confidently, with citations, and repeats the industry's most expensive myths. Pam Forbus (Mondelez) shows where the machine's confident answers on targeting, messaging and measurement fall apart, and why insights must lead, not research, its way to becoming the evidence the machine runs on.

Key Takeaways:

1. Fluent isn't Truth:  AI absorbs the consensus of the internet or your knowledge base, not truths.

2. Better models won't fix bad inputs: The fix is not a better model, it's our own evidence standards.

3. Insights are truth teachers, not users of AI:  Insights leaders must ground AI in what's proven."

From quantilabs experiment to validated solution: How Category Twins helps brands de-risk innovation in minutes.

Synthetic data has generated massive buzz in market research—along with a healthy dose of skepticism. So, what does it actually take to transform an AI experiment into a trusted, enterprise-grade research tool?

With Category Twins about to be fully released on the quantilope platform, join Dr. Jannik Meyners, Vice President of Data Science & AI, and Dr. Beatrice Capestany, Vice President of Solutions, for an exclusive look behind the scenes at our development journey. We’ll walk through how Category Twins evolved from our early-stage innovation hub, quantilabs, into a validated solution tested alongside 25 pilot clients.

In this session, you’ll learn:

• Where the data comes from: How we transform high-quality tracking data into interactive, reliable buyer personas.
• The innovation roadmap: How real-world client feedback refined the tool from early beta to full release.
• Practical application: How to "Interview" your Category Twins to test early-stage ideas and de-risk strategy in minutes—before spending budget in the real market.

Vibe Coding is having a moment, but don't let the word "coding" fool you. You can now create your own highly personalized tools without knowing how to write a single line of code. In this session, we'll de-mystify the world of vibe coding, share best practices for when and how to use it, and build a functioning Insights tool LIVE using nothing but conversational English, Claude, and creativity.

Key Takeaways:

1. You don't need a technical background or code-writing skills in order to vibe code useful tools

2. Your repetitive, mechanical, or time-consuming tasks are prime candidates for vibe-coded solutions

3. Context is key: Clear, well-structured, highly detailed prompts yield the best first tool versions

AI is changing how we work, yet most workshops still follow the same old-fashioned playbooks. In this session, we'll explore practical techniques for redesigning workshops around human-AI collaboration to strengthen alignment, spark more diverse ideas, and prioritize effectively. Let's make workshops faster, better, and more fun. 

Key Takeaways: 

1. AI reduces time-to-ouput, allowing us to begin workshops with high-fidelity artefacts

2. Friction must be intentionally and strategically re-introduced to maximize quality and impact

3. Alignment was a secondary benefit which must be prioritised and nurtured

Synthetic research can deliver a fast first read, but its greatest value often comes from what happens next. Using one realistic business question, we'll show how rapid follow-up studies help teams challenge early findings, test stronger alternatives, reveal important boundary conditions, and refine leading ideas. The result isn't just a faster answer, but a more informed decision.

Key Takeaways:

1. Know when a synthetic finding is exploratory, directional, or decision-ready.

2. Use controlled reruns to see what holds, what changes, and why.

3. Learn to design research programs instead of standalone studies.

AI is changing research, but what's useful is hard to separate from the hype. Eric Knoben, Head of Insights at Listen Labs and former President of Emerald Research, shares where AI earns a place in the research process and where teams keep it out. Then Ryan Kelly, Insights Lead, demos Listen and shows exactly how it works, live, so you can judge it for yourself.

Key Takeaways:

1. When to use AI-moderated interviews vs. human-led studies, and how to combine them

2. Guardrails for data quality, recruitment, and governance: what to automate and when not to

3. How insights teams operationalize AI research, with lessons to apply this quarter

Strong forecasts fail when stakeholders do not trust how they are defined, explained, or governed. This session shows how to make predictive analytics decision-ready through KPI alignment, transparent assumptions, clear narrative, and audit-ready design. Attendees will leave with a practical framework to turn forecasts into trusted business action.

Key Takeaways:

1. Why insights compound: how reusing accumulated knowledge changes the economics of research.

2. What staged, low-risk adoption looks like in practice — and what we'd do differently.

3. Early lessons on governance, change management and getting teams to actually use the system.

As AI reshapes insights, this IIEX.AI session explores how synthetic data can create value, where caution is needed, and why validation is essential to trust. Attendees will learn how to evaluate AI-enabled research approaches and apply synthetic data responsibly, with quality and transparency at the center.

Key Takeaways:

1. How can a quality validation step be applied to synthetic data?

2. What guardrails needed to ensure privacy, fidelity, utility, and decision confidence

3. How can synthetic data responsibly, with quality and transparency?

Insights and research teams keep hearing the same message: AI will do that. This talk names the pattern behind that assumption — Blue Ketchup Thinking, when a fast, confident AI answer gets mistaken for a validated one — and reframes the specific judgment calls that make research expertise impossible to automate away.

Key Takeaways: 

1. Blue Ketchup Thinking: why a fast, confident AI signal isn't a validated one

2. The Trough of Accountability: why AI adoption outruns who's answerable for it

3. The judgment calls AI can't own — and how to make them visible to leadership

AI is making more of the research process faster, cheaper, and accessible to people well beyond the traditional insights function. If generating research and producing answers are no longer scarce capabilities, where will the industry’s value come from next?

Brand-side insights professionals and leaders from major agency and technology partners will publicly have an open and honest discussion about what organizations will still need, value, and pay for in this new environment. They’ll explore which capabilities are becoming commodities, where human expertise and partnership will matter most, and how insights teams and providers must evolve to remain essential. This will not be a panel with polished answers. It'll be an open debate about what happens next and what kind of industry we want to build.