As artificial intelligence becomes embedded in more business operations, from logistics to land use planning, the conversation around geospatial data readiness is shifting. It’s no longer just about having data—it’s about having the right kind of data, structured and prepared for intelligent systems to extract value.

At a recent industry conference, experts from across the geospatial and AI landscape weighed in on a recurring challenge: while AI capabilities are advancing rapidly, most organisations are still grappling with foundational issues around data readiness.

“AI adds value only if data is structured and the spatial thinking is incorporated.”

This insight captures the essence of the challenge. AI is not magic—it doesn’t create value in isolation. For AI models to produce meaningful insights from geospatial data, that data needs to be structured, clean, and encoded with spatial context. Without this structure, even the most advanced AI models struggle to perform.

At Mappex, we see this challenge regularly. Organisations are eager to explore AI-powered insights, whether for environmental monitoring, infrastructure planning, or resource allocation. But when the underlying geospatial data is fragmented, inconsistent, or lacks a well-defined schema, AI applications grind to a halt.

“Everyone is experimenting with AI, but data quality and architecture remain the blockers.”

This quote reflects the current state of play. There's enthusiasm—and even urgency—to integrate AI into operations, but many data teams find themselves stuck. The architecture is not scalable. The datasets are incomplete. Metadata is missing or misaligned. Without fixing these core issues, AI adoption can become frustrating and costly.

What we often find is that the real barriers aren't in the AI tools themselves. The tech is there, and it's evolving quickly. The blockers are much more mundane—and much more important.

“Bottlenecks in structuring and cleaning data, not the AI itself.”

In practical terms, this means organisations need to spend more time on data preparation. That includes everything from normalising coordinate systems to tagging datasets with consistent ontologies. These are not glamorous tasks, but they are essential.

At Mappex, we believe that preparing geospatial data for AI is not just a technical exercise—it's a strategic one. Clean, structured data enables not only smarter models but smarter decisions.

“Great AI isn’t about tech—it’s about the data, the model, and the people.”

The most successful AI projects are those where teams invest equally in data governance, human expertise, and modelling. It’s not enough to plug data into a model and expect results. Teams need to understand where the data comes from, what it represents, and how it connects to business goals.

This is where having a well-defined data ontology becomes a serious competitive edge.

“AI-Ready Data - Companies with well-defined ontologies have a huge competitive edge. They know how data is used, where it comes from, and how it ties to business processes.”

Ontologies define the relationships between data elements in a way that machines—and people—can understand. When these structures are in place, they allow for automation, consistency, and scalability. AI models can more easily learn from and reason about the data. Analysts can trace decisions back to source. And business stakeholders can trust the outcomes.

So, what does AI-readiness look like for geospatial data?

• Structured data formats (GeoJSON, shapefiles, raster tilesets, etc.) with consistent schemas

• Metadata standards and provenance tracking

• Clean, deduplicated datasets with normalised spatial references

• Ontologies that reflect real-world relationships and use cases

• Systems that connect data to workflows, people, and decision points

At Mappex, our mission is to simplify access to high-quality GIS data for businesses, researchers and institutions. We help you go beyond “having data”—we help you use it, intelligently and effectively.

Because in the age of AI, data is not just an asset—it’s a strategy. And readiness starts now.