For the last decade, many organisations have treated geospatial data as a visualisation tool— simply putting dots on a map. But the landscape has shifted violently. If you are still relying on desktop-bound GIS and legacy formats to manage a moving fleet, you aren’t just behind the curve; you are leaving critical ROI on the asphalt.

Join experts from CARTO and Snowflake for a candid session on the “Art of the Possible” in modern spatial analytics. The speakers will dismantle the traditional approach to GIS and introduce a 5-Level Geospatial Maturity Model designed to audit your current capabilities.

You’ll explore how the transition to Agentic GIS allows systems to not just “show” a vehicle’s location, but to autonomously optimise routes, predict delays, and re-balance fleets in real-time.

This session will cover:

• The Evolution Trap: Why traditional GIS workflows fail the demands of real-time, dynamic routing and Cloud Native AI.
• The Maturity Curve: A self-assessment framework to move from Level 1 (Ad-hoc Mapping) to Level 5 (Agentic GIS)—where AI agents proactively manage spatial workflows.
• Dynamic Fleet Optimisation: How to leverage Spatial Indexes (H3, S2) and Cloud Data Platforms to query millions of moving coordinates and calculate optimal routes in seconds, not hours.
• Real-World Evolution: A look at how leading logistics and delivery organisations migrated from legacy batch processing to Agentic, event-driven spatial stacks.

What you’ll learn:

•  The Path to Autonomy: How to transition from reactive mapping to Agentic GIS that handles complex routing logic independently.
• Speed & Scale: Utilising cloud-native architecture to solve “The Travelling Salesman Problem” across massive datasets without performance bottlenecks.
• Agentic Workflows: How to use SQL and Python to build AI agents that monitor spatial triggers and execute fleet adjustments automatically.
• Actionable Steps: How to launch your own “Geospatial Audit” to benchmark your routing and logistics maturity against industry standards.