Conference Agenda

• A clear evolution path from “insights owner” to “enterprise commercial leader: ” what to stop doing (service model), what to start doing (decision architecture, growth levers), and how to earn decision rights with the C-suite.
• A practical AI operating model for insights (blessing when governed, curse when unmanaged): where AI genuinely accelerates discovery and synthesis, where it amplifies bias and false certainty, and the guardrails that keep humans accountable for judgment.
• How insights work itself is changing: from research outputs to decision products: Moving from reports to “always-on learning systems,” translating signals into choices, and building insight assets that plug directly into brand, revenue, and commerce workflows.
• A readiness blueprint for the next 12 months: The skills to hire and train (prompting is not a strategy), the data and tool stack basics, the rituals that create speed, and a simple measurement system for proving business impact beyond satisfaction scores.

• Direct, unmoderated conversations between marketers and consumers to foster authentic empathy and multicultural understanding.
• Meta facilitation and AI-powered storytelling, capturing image and audio narratives at scale to build a living library of real consumer voices.
• In-the-moment qual at the shelf, uncovering the tension between intention and behaviour where decisions truly happen.
• Elevating qual´s role in shaping business decisions that resonate with real people

• What AI gets wrong (and right): real-world testing results that show where AI accelerates insight, and where human oversight is critical to avoid risk and misinterpretation
• Why trust is a human job: how empathy, ethics, and contextual judgment safeguard credibility and decision confidence in AI-powered research
• From data to decisions: the specific ways human interpretation turns AI outputs into coherent stories leaders can actually act on
• How to operationalize the balance: practical frameworks for blending AI scale with human judgment across insight generation, validation, and storytelling

• Decoding Brand DNA: Uncovering core values, differentiators, and competitive strengths.
• Mapping Shopper Realities: Analyzing purchase behaviors, key occasions, and decision drivers.
• Identifying Growth Levers: Spotlighting opportunities, barriers, and brand assets for acceleration.
• Activating with Clarity: Delivering actionable guidelines for optimized offers and impactful communication.

In this session you will learn from a real CPG case study how:

• Legacy qual methods are constrained by blind spots and inefficiencies
• One-size-fits-all methods cap insight and business impact
• Finch Brands uses the CIRCLE model to enable connected, adaptive learning
• How qualitative value can compound over time

• Thinking about futures in a practical/agile way
• Reducing ¨doomsday¨ noise in organizational change drivers
• Convert qualitative learnings into actionable decision-maker tools
• Gaining participation and buy-in in futuring

• Designing a multi-year foresight study based on the “Natural Foresight Framework”
• Using hands-on exercises to apply foresight tools to real institutional challenges, creating narratives of possible futures and testing provocative scenarios
• Navigating misinformation and enhancing information literacy while keeping human interaction at the center
• Building legitimacy and influence by engaging stakeholders directly, turning futures thinking into a shared practice rather than a top-down strategy

• Navigating reorgs without losing strategic impact, maintaining continuity and keeping insight anchored to business priorities
• Doing more with less: building a high-leverage insight function
• Democratising insight safely and effectively across the organisation
• Partnering differently: enabling decision makers, not just delivering research
• Sustaining credibility and influence in uncertain times

• Evaluating the credibility of AI-driven inputs, from synthetic respondents to AI-generated consumer content
• Assessing the impact of ChatGPT in qualitative open-ends, balancing clarity versus authenticity
• Testing AI-assisted techniques such as automated probing and theme detection to see where they enhance—or miss—human nuance
• Reframing the qualitative researcher’s role in an AI-enabled landscape by pairing technological capability with critical judgment

• Reframing traditional product development and research approaches to prioritize adaptability over linearity
• Applying qualitative insights to reshape product relevance, connecting existing products with evolving user needs
• Evaluating the benefits and limitations of retrofitting research to current tech capabilities
• Leveraging tips and lessons from Adobe’s case study to enhance your own strategy

• Meet the R+D Accelerator, a large company's answer to start-up success
• Understand capabilities that make a difference
• Develop internal partnerships that bring the consumer to life while delivering speed
• Leverage these partnerships to influence business decisions

• Debating what should (and shouldn’t) be automated – which aspects of qualitative research benefit from AI augmentation and where is human touch irreplaceable?
• Defending depth in a world obsessed with speed without getting left behind
• Shifting the value of research from investigation to interpretation, with AI handling data collection and surface analysis
• Moving from resistance to leadership and taking a proactive role in guiding responsible AI use

• Recruiting creatively to access hard-to-reach and anonymous participants when traditional pipelines fall short
• Building trust in stigmatized and privacy-sensitive communities
• Fostering safety through open, non-judgmental research environments
• Translating sensitively to turn delicate findings into bold product decisions – all while respecting privacy

• Beyond Words: How Video Intelligence Is Expanding the Qualitative Lens
• Decoding Real Consumer Behavior: What Social Video Reveals That Surveys Can’t
• From Microtrends to Meaning: Using Video Intelligence to Spot What’s Next
• Why They Believe: Using Video Intelligence to Craft Stronger Claims & Concepts

• Translating experience into action: turn lived insights into shippable standards for inclusivity, ethics, and authenticity so teams can act with confidence
• Building guardrails from stories: convert findings into operational safeguards—revocable consent, redress flows, bias checks, and restoration metrics
• Designing for global resilience: use a simple resilience canvas to adapt work and products to diverse languages, bandwidths, and norms without losing rigor or authenticity
• Applying and embedding: working through real-world cases to create a two-week upskilling plan that embeds inclusive, responsible, and authenticity-driven practices across AI teams

• Examine how AI disrupted and rebuilt the content industry — and uncover the parallels with the growing commoditization of research
• Understand where the research industry sits in this cycle of acceleration, quality decline, and renewal — and recognise what leading teams are doing differently
• Explore the biggest unresolved challenges in AI-enabled research, identify who’s currently setting the pace, and anticipate what’s likely to emerge in the next few years

• Consumers are curious about agentic AI but awareness and understanding are still low.​
• Younger, higher-income Gen Z and millennials are emerging as key early adopters.​
• People see strong benefits in efficiency and learning but also have major concerns about oversight, privacy, and job loss.​
• Trust-building features like human review, clear opt-ins, transparency, and “panic button” controls are critical for adoption.

• Blending Humanity and Technology: why tomorrow´s qual researcher must integrate deep human insights with fluency in digital tools and platforms
• Data Empathy: building the ability to interpret and connect diverse data formats (text, video, behavioural) while maintaining emotional intelligence and cultural sensitivity
• Human-Centered Storytelling in a Digital Age: transforming AI-driven outputs (dashboards, summaries, visualizations) into narratives that resonate
• Future-Proofing Through Lifelong Learning: embracing continuous growth in technology, ethics, and collaboration to stay ahead in an evolving insights ecosystem