Why AI Still Can’t Visualize Location Data 

Everyone is adding AI to their analytics, and a chatbot can answer a question. But ask it to put your data on a map and the cracks show. It will generate something that looks right and is geographically wrong, with no base layer and no legend. Maps are where AI quietly falls apart. This session is a practitioner walkthrough of why location visualization is hard for AI, what customers actually ask for, and how much already works inside your product today. We’ll cover:

• Why Maps Break AI: The two failure modes that show up every time: plausible but wrong, and no context layer. The durable gap is cartographic judgment, projection, classification, symbology, not code generation.

• What Customers Actually Ask For: Which color a pin shows and what happens when you click it, a line drawn from point A to point B, a basemap that matches the app. The demand is for control, not AI.

• What You Can Already Do Today: Marker, region, and world maps, custom basemaps, imported boundaries, travel lines, and smart zoom, shown in an embedded analytics demo.

• Where It’s Heading: The next wave of location features: geo and radius filtering, smarter zoom, and custom boundaries joined to your data.

The point isn’t that AI is useless on a map. It’s that the decisions that make a map trustworthy are still yours to make, and the products that build that judgment in are the ones that win.