AI in GIS: A Strategic Guide for Managers and Decision-Makers

Every GIS manager is being asked about AI. Few have been given a clear, vendor-neutral framework for answering. This half-day course bridges that gap — built specifically for decision-makers rather than analysts, and focused on strategy, governance, and the choices you will actually face over the next twelve to eighteen months.

You’ll start by cutting through the hype and building the vocabulary that the rest of the course depends on. You’ll learn the critical distinction between GeoAI — machine learning and deep learning for spatial tasks like object detection, classification, and prediction — and Generative AI and AI Assistants, the natural language interfaces now appearing throughout GIS software. You’ll learn the two emerging paradigms for how AI interacts with spatial data, understand which one is producing real production results today, and get an honest assessment of what works reliably right now versus where the limitations remain — including the surprising places where large language models still struggle with basic spatial reasoning. You’ll also look ahead to Autonomous GIS, where AI agents plan, execute, and verify multi-step spatial workflows.

In Module 2, you’ll get a structured survey of AI capabilities across the major GIS platforms, so you know what is actually in the box you already own. The deepest coverage is given to Esri, since most attendees are Esri shops — including the ArcGIS Pro Assistant, ArcGIS Online assistants, the GeoAI toolbox, more than 100 pretrained deep learning models, foundation models like SAM and Prithvi, third-party AI service connections, and Esri’s Trusted AI framework. You’ll also survey AI capabilities across Google, QGIS, and Microsoft platforms, cloud-native options like CARTO, and get an honest assessment of how general-purpose tools like ChatGPT, Claude, and Gemini are being used for GIS work.

Module 3 shifts from “what exists” to “what does it take to adopt.” You’ll work through the four pillars of AI adoption: licensing and cost, including what’s already included in your existing Esri licenses versus what requires new investment; infrastructure and hardware, including which capabilities run on existing hardware versus which need GPU investment; data governance and privacy, including where your data goes when staff use AI assistants and what administrative controls are available; and workforce readiness, including the different training paths needed for analysts, power users, and managers. You’ll learn to distinguish quick wins that boost productivity immediately from longer-term strategic investments.

Module 4 brings everything together in a facilitated workshop. Working through a guided framework in your participant workbook, you’ll assess your organization’s current AI readiness, identify your highest-value AI use cases, evaluate what’s achievable today versus what requires new investment, and outline a phased adoption plan with near-term, medium-term, and long-term direction. You’ll leave the course with a draft AI roadmap document you can refine and share with leadership to guide your team’s AI adoption strategy.