Geospatial AI gains ground as enterprise projects fall short

May 22, 2026 2 min read

The MIT report found that 95% of enterprise AI initiatives are still failing to generate measurable business impact.

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Although artificial intelligence is already embedded in enterprise and government operations, many projects still fail to generate measurable results.

In this environment, geospatial AI is gaining momentum as a tool designed to connect data analysis with real-world territorial decision-making.

Artificial intelligence is already being used across analysis, automation and decision-making processes within businesses and public institutions.

Organizations increasingly rely on these technologies to identify patterns, analyze documents and optimize operational tasks through accelerated data processing.

According to the report Artificial Intelligence and the Employability of the Future, 47% of executives and 42% of workers in Colombia identify AI as a useful tool for automating processes and optimizing routine tasks.

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In addition, nearly 35% of both groups believe the technology can improve competitiveness and operational efficiency inside organizations.

However, several studies suggest that many enterprise AI initiatives still struggle to produce measurable business impact.

According to the MIT report The GenAI Divide: State of AI in Business 2025, 95% of enterprise AI projects are not generating measurable results within organizations.

Companies seek to connect AI with operational decision-making.

Industry specialists argue that one of the main challenges is transforming data analysis into operational decisions applicable to real-world environments.

Although artificial intelligence can identify patterns and explain what happened inside large datasets, many organizations still struggle to incorporate territorial and contextual variables into decision-making processes.

This limitation becomes particularly critical in industries where location, mobility and territory are essential operational variables, including retail, infrastructure, public services and urban planning.

Against this backdrop, geospatial artificial intelligence (GeoAI) is beginning to gain traction. The technology combines machine learning models with geographic information systems to analyze spatial patterns and generate recommendations based on territorial variables.

GeoAI combines spatial analysis and artificial intelligence.

According to specialists, this approach allows maps and geographic data to evolve into systems capable of detecting relationships, inferring behaviors and supporting decisions related to mobility, security, business expansion and resource management.

Deiro González, Technology Manager at Esri Colombia, noted:

“GeoAI represents a shift because it is no longer only about observing territory, but about asking it what it needs and obtaining data-driven answers.”

The company stated that large volumes of geographic data, imagery and time-series information are becoming increasingly important inputs for territorial analysis and enterprise decision-making.

According to specialists, GeoAI aims to solve one of the main bottlenecks affecting enterprise artificial intelligence: transforming analysis into operational decisions capable of generating measurable impact.

“AI functions as an assistant that helps organizations maximize the value of geographic information and generate richer outputs, including augmented reality models that support even better decisions,” González added.