About

AI changed how the data team works at Carwow: roles became wider, planning horizons got shorter, and maintaining high quality became harder. Six months ago we were stuck and realized we couldn’t mandate AI adoption – we needed to change our approach. We shifted to a person-centric approach: we built tools to make using AI easy and fun, and we invested in a proper harness to keep the pace. Join this session to learn about the mistakes we made and how we fight ‘Clauded Dashboards’.

Key Takeaways:

• AI adoption is a people challenge, not a mandate: roles broadened and learning accelerated, so adoption works best when tools make experimentation easy, practical, and engaging.
• AI quality depends on data foundations: inconsistent definitions, stale data, and inaccessible systems lead to unreliable outputs and “Clauded dashboards.” A semantic layer and clear guardrails improve consistency.
• Scale through governed self-serve access: composable tooling can remove data-team bottlenecks, let teams build audiences themselves, and activate the same trusted data across channels.