Data teams are increasingly using AI tools and agents to accelerate development, documentation, and analysis. While productivity gains are real, they introduce new challenges around governance, security, context, and cognitive load. This panel explores how organisations enable data teams to use AI effectively while maintaining quality, control, and sustainable ways of working.
Applying AI safely to improve data team productivity
Managing risk, access, and oversight when using AI tools
Scaling AI usage across teams without increasing fragility
At Syngenta, data products are being produced, enriched, and consumed by AI agents across the full lifecycle, from data captured in the field to insights delivered in real time. This shift is changing how data is structured, how pipelines are built, and how products are ultimately used. Elcio Abrahao explores how AI-driven workflows are being introduced alongside existing data products, including spec-driven development, agent-enabled pipelines, and real-time applications in agriculture. As this evolves, new challenges are emerging around cost, trust, and variability, particularly where non-deterministic AI outputs replace traditional, rule-based approaches.
Most organisations are data-rich but decision-poor. Despite significant investment in data platforms and analytics, many teams still rely on intuition, outdated reports, or fragmented views when making critical decisions. This panel brings together enterprise leaders to explore why data often fails to influence decisions in practice, and what needs to change across operating models, culture, and product design to close the gap between insight and action.
Many organisations discover that launching an external data product is only the beginning. Adoption is often slower than expected as buyers face integration friction, trust concerns, and competing alternatives. This presentation explores why demand is selective, how buyers evaluate external data products, and what differentiates products that gain traction from those that remain niche or experimental.
Understanding how buyers evaluate and compare external data products
Identifying barriers to adoption beyond data quality
Positioning data products around outcomes rather than datasets