Geospatial AI platform
ArcGIS is a comprehensive geospatial AI platform that integrates machine and deep learning models, natural language experiences, and modern, extensible frameworks to empower users with intelligent, scalable, and secure geospatial solutions.
Since its inception in the 1980s, ArcGIS has continuously embraced emerging technologies — from early command-line GIS systems to graphical interfaces, cloud computing, and mobile platforms. The integration of artificial intelligence (AI) began with foundational machine learning techniques for spatial analysis and image classification. By the late 2010s, AI was embedded into core ArcGIS workflows, enabling automation of tasks like feature extraction from imagery and predictive modeling. Over time the infusion of AI into ArcGIS has expanded to include deep learning, computer vision, and natural language - reflecting a steady evolution from tool-based enhancements to a more integrated, platform-wide approach to geospatial AI built on an robust architecture and foundational AI framework.
Esri’s aim in evolving ArcGIS as geospatial AI platform is to:
- Improve decision making though trusted, scientific applications of AI in GIS
- Deliver geospatial capabilities, reasoning, and intelligence through invocable and autonomous agents
- Improve productivity through intelligent user experiences and application assistants
- Deliver a secure, configurable, and extensible framework on which Esri, partners, and customers can build
ArcGIS as a geospatial AI platform has four main components, as shown below.
GeoAI
GeoAI is the integration of geospatial data and artificial intelligence techniques — such as machine learning, deep learning, and large language models — within ArcGIS to automate, enhance, and scale spatial analysis. It encompasses a wide range of capabilities, from object detection in imagery and 3D scenes to natural language-powered assistants and predictive modeling. While Geospatial AI serves as the broader umbrella term for AI across ArcGIS, the term GeoAI is used more precisely to describe scientific and analytical applications that leverage pre-trained models, spatially-aware algorithms, and domain-specific tools. Learn more about GeoAI.
AI assistants
AI assistants in ArcGIS are user experience enhancements that drive productivity and ease of using natural language. AI Assistants leverage one or more AI skills (simple task-oriented capabilities) and AI agents (complex workflow orchestrators) that are managed through the AI framework and can be extended or customized by developers. These assistants are designed to work across ArcGIS products and applications, enabling users to query, analyze, and automate workflows through conversational experiences. They maintain context awareness, respect organizational data boundaries, and support both embedded and standalone deployment. Learn more about AI assistants in ArcGIS.
AI agents
AI agents in ArcGIS are components that operate within the broader geospatial AI platform to orchestrate and execute complex workflows using spatial and non-spatial data. These agents are designed to reason over structured and unstructured inputs, invoke geoprocessing tools, and interact with ArcGIS services and APIs to complete multi-step workflows with minimal user intervention. As the latest evolution of AI in ArcGIS, AI agents are designed with the same foundational principles as the rest of ArcGIS - prioritizing interoperability with cloud-native infrastructure, leveraging organizational data securely, and aligning with the ArcGIS well-architected framework and associated deployment patterns.
AI framework
The AI framework in ArcGIS provides a modular, extensible foundation for integrating AI capabilities across the ArcGIS ecosystem. It enables both Esri product teams as well as partners, customers, and developers to register, manage, and deploy AI skills — discrete units of functionality that can be invoked by assistants, agents, or applications — using a consistent API and runtime environment. It supports both hosted and bring-your-own-model scenarios, making it adaptable to a wide range of use cases, from conversational assistants to automated geoprocessing. The AI framework serves as the foundation for ArcGIS as a geospatial AI platform.
Continue to learn about the architecture of ArcGIS.
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