Artificial intelligence is quickly becoming part of the GIS conversation. Across the geospatial industry, organizations are exploring GeoAI, AI assistants, automated feature extraction, predictive analysis and, increasingly, AI agents that can interact with GIS tools and data. Esri, for example, is expanding AI capabilities across ArcGIS, including assistants that can help with tasks such as data collection, metadata, coding and application development, while also developing agentic AI that can automate GIS workflows.
For local governments, it is tempting to look at these developments and immediately start asking what AI could do with their GIS. Could it identify infrastructure from imagery? Analyze patterns in service requests? Help staff find information? Predict where maintenance may be needed?
Those are legitimate questions, and the technology is moving quickly. But there is another question worth asking first.
Is your GIS ready for AI?
AI is only as useful as the information behind it
AI can process enormous amounts of information, identify patterns and automate tasks, but it still needs reliable information to work with. That makes the condition of an organization’s underlying data particularly important.
A recent paper published in Discover Cities makes this point in the context of digital twins and AI initiatives in small and midsize cities. The researchers argue that technology adoption can move faster than the governance, data quality, cybersecurity and institutional capacity needed to support it. Their proposed “Foundation First” framework places foundational data management and governance ahead of increasingly advanced analytics and AI applications.
The issue is familiar to many local governments. Information may be spread across a GIS, spreadsheets, work-order systems, permitting applications, asset databases and department-specific records. Some information may be current while other records have not been updated in years. Different departments may use different naming conventions or maintain separate versions of the same information.
AI does not eliminate those problems and, in some cases, it can make them more consequential.
If an AI system is asked to analyze inaccurate or incomplete information, it can produce an answer that sounds convincing while still being wrong. The more automated the process becomes, the more important it is to understand where the underlying data came from, how current it is and how much confidence should be placed in the result.
The GIS industry is already moving in this direction
The shift toward AI is more than speculation about what might happen someday. GeoAI is already being used for tasks such as geospatial data extraction, change detection, point-cloud classification and automated mapping. A 2026 review of GeoAI applications by national mapping agencies identified these areas among the technology’s practical uses, while also pointing to challenges involving data availability, infrastructure, expertise, explainability, bias and geoprivacy.
Standards organizations are also looking at what is needed to make AI-driven geospatial systems more reliable. The Open Geospatial Consortium’s 2026 Testbed-21 work focused on the growing volume of imagery, sensor data, video, digital twins and other geospatial information and the need for systems to share that information while preserving its meaning, quality and trustworthiness.
ISO’s geospatial standards community is similarly examining how GeoAI can be supported through standards for training data, metadata, data quality and provenance. The goal is essentially to make geospatial information more understandable and trustworthy for AI systems rather than simply making more data available to them.
The message emerging from all of this work is fairly consistent. AI may be the new technology getting the attention, but good data remains the foundation.
What makes a GIS more AI-ready?
There is no single switch that makes a GIS “AI-ready.” For a local government, readiness is more likely to come from a collection of basic practices that have value regardless of whether AI is ever added.
- Accurate data matters. Roads, parcels, addresses, utilities, facilities and other assets need to reflect current conditions. Data that has not been maintained will limit the usefulness of any analysis built on top of it.
- Accessible data matters. Information locked inside individual departments or desktop applications is difficult to connect to other systems. Web-based GIS can make spatial information easier for authorized users across an organization to access and use.
- Connected data matters. GIS becomes more valuable when location information can be associated with other operational information, such as work orders, inspections, permits, service requests or customer records. Interoperability is becoming increasingly important as organizations work with more systems and more types of data.
- Data governance matters. Someone needs to know who owns a dataset, who updates it, what standards apply and how changes are documented. Data readiness is an organizational responsibility as much as a technology issue.
- Trust matters. As automated systems become more involved in analyzing information and making recommendations, organizations need to understand the source and limitations of the information being used. That includes considering privacy, security, bias, provenance and quality.
You don’t need AI to start preparing for AI
InteractiveGIS is not yet offering AI capabilities within iGIS; however, the important point is that organizations do not have to wait for an AI feature to appear in their GIS platform before thinking about whether their data is ready for the next generation of technology. In fact, many of the steps that make GIS more prepared for AI are simply good GIS practices:
- Keeping information current.
- Reducing duplicate datasets.
- Connecting departments.
- Establishing clear ownership.
- Making spatial data accessible to the people who need it.
- Creating workflows that keep information moving back into the system instead of allowing it to become outdated.
Those improvements have value today, whether or not an organization ever introduces an AI tool.
The recent research around digital twins makes this especially clear. The authors describe a progression that begins with foundational inventory and digitization, moves toward integration and open data, and only later advances into analytical and predictive applications.
That is a useful way to think about AI and GIS. The goal isn’t to adopt the newest technology simply because it is available. The goal is to build an information environment where new technology can actually be useful when the right application comes along.
AI may change how people interact with GIS, how spatial information is analyzed and how much work can be automated. But it doesn’t change the basic value of accurate, accessible and well-managed geographic information.
For organizations thinking about what comes next, getting the foundation right may be one of the most important steps they can take.
Sources
- Esri, The Next Era of AI and ArcGIS, Summer 2026.
- Poudel & Moore, Governance and data readiness as prerequisites for digital twin adoption in small and midsize cities, Discover Cities, August 2026.
- Didouz, Sebari & Ait El Kadi, The Use of Geospatial Artificial Intelligence Technologies (GeoAI) within National Mapping Agencies: A Review, 2026.
- Open Geospatial Consortium, OGC Testbed-21 Demonstration: The Future of Geospatial Intelligence, June 2026.
- ISO/TC 211, Geospatial Intelligence Meets AI: How ISO/TC 211 Standards Will Help Shape the Future, April 2026.
