3 lessons from the first wave of AI adoption in GIS

Geographic Information Systems, more commonly known as GIS, bring together location-based data, mapping and analysis to help organisations manage field and office operations and understand places, assets and patterns. GIS is used across infrastructure planning, environmental monitoring, and urban development, where understanding what is happening and where is critical, with analysis helping to power decision-making.  

Much of the conversation around AI in GIS has focused on faster workflows, automated analysis and newer capabilities like generating an app or adding layers to a map with a single prompt. But as organisations begin putting AI into the hands of their teams, the main takeaway hasn’t been about what AI can do, but how it is changing the role of teams and GIS specialists.  

It’s now been long enough to assess some of the key learnings from the first wave of AI adoption in the sector. Drawing on early adoption patterns from my observations so far, here are three key lessons for GIS leaders planning their own AI strategies:  

1. AI is making GIS expertise more, not less, valuable 

This is a lesson that is being experienced across many sectors. But while AI can take over more formulaic or repetitive tasks, its growing use is also enhancing the value of GIS expertise and experience, as there is a greater need for people who can evaluate AI outputs and perform more complex tasks that require human judgement and contextual understanding.  

As access to geospatial intelligence expands, for instance, GIS professionals are increasingly responsible for setting standards, validating outputs and enabling colleagues to work confidently with spatial data, rather than simply producing every map or analysis themselves. In turn, AI also frees up time for them to focus on higher level tasks and therefore put their skills to better use.  

2. The biggest gains are often happening beyond the GIS team  

While experienced GIS professionals are adopting AI gradually, field workers, operational teams and business users are using it to engage with spatial data more independently, unlocking value across the wider organisation. In particular, AI is having an impact on the speed and autonomy of these users to complete tasks that previously required a specialist GIS team. AI tools mean that, for many everyday tasks, opening desktop GIS is no longer necessary. A complex selection of features driven by a spatial query, for example, takes AI less than a minute – the same task that could take an hour to build and run on desktop. 

Equally, there is a clear benchmark asymmetry emerging. While GIS experts benchmark AI against their own speed, other employees around them benchmark it against not being able to do the task at all. This means that the gains in value from AI beyond the GIS team are even greater. Non-GIS employees now have access to AI insights and are finally able to work with spatial data without booking time with the dedicated GIS team. 

3. Successful AI adoption is about organisational change  

The organisations seeing the greatest value aren’t simply deploying AI tools – they’re rethinking how GIS expertise is shared across the business, combining broader access to spatial intelligence with strong governance and human oversight. For AI adoption to succeed, the technology has to be designed with governance in mind. Every AI action should be proposed, previewed and confirmed by a human before anything is saved. Ultimately, spatial data is connected to live operations, and this should always be top of mind.  

Moreover, as tasks now happen ‘in the moment’, there’s no longer an organisational change queue. The ability to drive through new ideas, skills and processes can take place faster. But again, robust governance is crucial to ensuring that this change takes place safely and that AI is being used correctly. It’s better to take the time to set out best practice for its use than to rush change and end up with disjointed processes and training that impact rather than enhance workflows.  

Democratising GIS with AI 

AI adoption continues to happen at a frantic pace. But its use in GIS has already provided some key lessons, especially around how it has impacted the role of both GIS specialists and wider teams. When it comes to planning an AI strategy, instead of asking how AI can replace existing workflows, what organisations should be considering is how it can extend the reach of GIS expertise across the business. 

By democratising access to AI tools and spatial data, teams have greater independence and capability to complete tasks without GIS specialists. This subsequently gives the dedicated GIS team more time to focus on higher level tasks and enhances their value. But for both of these changes to occur successfully, human oversight is critical to ensure accuracy and that AI is being applied correctly.  

The first wave answered whether AI belongs in GIS. The next wave will be decided by which organisations treat it as organisational change rather than a feature, and they will be the ones that capture most of its value.