Organisations worldwide are harnessing the power of spatial analysis—a time-tested technology —with AI to elevate their analysis and prediction capabilities.
Case in point: 40% of goods produced in the US are distributed via waterways. To keep these goods moving, waters must be deep enough. One of the world’s largest and most diverse engineering organisations monitors the country’s 25,000 miles of waterways and 400 ports. They use a combination of spatial analysis and AI—and have saved $100 million a year. How? By improving their ability to predict decreasing water depths they can deploy dredging equipment more efficiently and maintain the movement of goods.
That’s the transformative power of pairing spatial analysis with AI, what’s known as geospatial AI.
Spatial analysis relies on geographic information systems (GIS)—the mapping and data analysis technology behind many business decisions and government operations. Geospatial AI takes cutting-edge GIS and enriches it exponentially, adding the ability for greater prediction, automation, and precision.
The pairing of spatial analysis and AI means organisations can ask and answer questions at a speed and scale humans alone could never achieve. That’s because geospatial AI is a powerful means of synthesising data in the context of location. Decision-makers can prioritise what needs to happen where, and where now.
Where are critical resources and how can we operate in those places with the least impact on the environment and threatened species?
Where are assets or operational locations in danger from floods, fires, or extreme heat?
Where are our best customers and locations and where are they likely to be in the future?
It’s not magic, it’s data—and it’s automation. It lets people make more effective decisions both in real time and for the longer term. This transformational tool also excels at image recognition. With the explosion of imagery coming from satellites, drones, and aircraft—coupled with the urgency to make sense of rapid changes—geospatial AI can swiftly sift through every pixel to find answers.
The fusion of AI and spatial analysis results in three powerful capabilities:
- Automate workflows and repeat them at scale: Make manual processes faster and more accurate for operational intelligence, asset and network management, supply chain monitoring, and land management.
- Enhance predictive analysis: Uncover patterns in historical and real time data to forecast risks, find opportunities, and support an array of decisions.
- Surface new insights: Examine extensive troves of data related to demographics, economics, and geography for deeper understanding and actionable intelligence.
For leaders in business and government, geospatial AI takes agility to a whole new level and adds the ability to do almost instantaneous analysis. It then changes the questions and assumptions and runs the analysis again—just as quickly.
And the results are transformational. A transportation organization in Europe saves hundreds of hours using geospatial AI to determine when specific road sections will need repairs. Leaders at a well-known global logistics company use it to predict when a plane will need parts or maintenance. A US utility company use geospatial AI to pinpoint and prioritise water mains most in need of replacement.
Geospatial AI becomes an enterprise tool with wide-ranging value. It optimizes everything from deciding how best to manage resources to assessing implications of policy changes and market trends.
This type of AI-enhanced modern GIS technology gives leaders in business and government the ability to see the present in incredible detail. Then they can look beyond the horizon to predict outcomes and, ultimately, make smarter decisions. They can ask a new dimension of questions and get location-specific answers.
At a moment when problems seem to be increasing in complexity just as fast as the data needed to solve those problems is expanding in volume, Geospatial AI is the tool to meet the moment. It doesn’t cut through or minimise the complexity. It allows those looking for a solution to take the complexity into account.