How Mondelez is Unlocking Always-On Consumer Intelligence with AI-Powered Research

Consumer expectations are shifting faster than traditional research cycles can keep up with. For global FMCG brands like Mondelez, the challenge isn’t just gathering insights; it’s getting them into the hands of senior stakeholders quickly enough to matter, across multiple teams, categories, and formats, all while managing tighter budgets.

Join Anna Citro, Innovation & Strategy Insight Manager Europe, Mondelez, and Discuss as they share how Mondelez is transforming its approach to consumer intelligence across its chocolate and growth platform teams. This isn’t a story about replacing research; it’s about making it work harder.

In this session, you will hear directly from someone doing this work about what it looks like to move from one-off qualitative studies to a connected, AI-powered research platform, one where insights from real consumer conversations feed a virtual persona capable of answering qualitative questions on demand. Anna will walk through real use cases from her own day-to-day work, including how she used a virtual persona built from previous research sessions to answer an urgent qualitative question for senior stakeholders in minutes, not weeks.

You will also learn how Mondelez is navigating the balance between self-serve and fully-served research models, how they are integrating qual and quant within a single platform, and why the source of the insight, actual consumers talking rather than summary reports, makes all the difference.

Don’t miss this conversation about what AI-powered consumer science looks like when it’s actually being used, not just piloted.

3 key takeaways from this session:

1. The case for always-on consumer intelligence: why a connected research platform delivers more value than the sum of its individual studies, and how the knowledge compounds over time
2. Virtual personas in practice: what they can and can’t do today, and how to use them to accelerate decision-making for senior stakeholders without waiting for a full research cycle
3. Making AI research tools work for your team: how to balance speed, budget, and quality across self-serve and fully-served models, and what good looks like at the category level