Artificial Intelligence (AI) and Machine Learning (ML) are no longer abstract concepts; they are reshaping the geospatial industry in profound ways. What began as incremental improvements in automation has evolved into a paradigm shift, where intelligent systems can process vast datasets, detect patterns, and predict outcomes with unprecedented accuracy. This transformation is redefining how we create maps, analyze spatial data, and deliver insights that power decision-making across multiple sectors.
Let me share a couple of examples from my own work environment at TomTom. As an AI-native company, we have been using this technology for many years. AI enables us to move beyond traditional workflows. Highly automated map-making reduces the time and cost of maintaining global maps while, more importantly, improving precision and freshness. In this way, we are able to create and maintain a new type of global map called the lane-level map, which enables functionalities such as lane-level navigation. A second example is predictive modeling, which allows us to anticipate traffic patterns, optimize routes accordingly, and even support urban mobility planning.
There are numerous applications utilizing AI across the entire geospatial industry. Pattern recognition in satellite imagery enables detecting environmental changes, monitoring deforestation, and even predicting the crop yields—applications that were unimaginable a decade ago.
These capabilities are not just technical milestones; they are enablers of societal progress. Smarter mobility solutions reduce congestion and emissions. Enhanced environmental monitoring supports sustainability goals. AI-driven geospatial analytics enable governments, businesses, and communities to make informed decisions more quickly and with greater confidence.
Responsible Innovation
Every technological innovation brings greater responsibility along with it. AI systems in geospatial contexts must address ethical considerations, data privacy, and transparency to ensure their responsible use. The ability to process geospatial and location data at scale raises questions about consent and security. Biases in algorithms can lead to flawed insights. Therefore, responsible AI is not optional—it is a prerequisite for trust and the provision of reliable and accurate information for informed decision-making. Industry leaders must adopt governance frameworks that ensure fairness, accountability, and explainability.
At the same time, we must ensure we don’t suffocate innovation by overregulation. Collaboration among technology providers, regulators, and end-users is critical to achieving this balance.
Pragmatic Middle Ground
The industry is divided between optimism and caution. Some envision an AI-first future where automation dominates every aspect of geospatial workflows. Others warn of an “AI bubble,” in which expectations outpace reality. The truth lies in a pragmatic middle ground: AI is a powerful tool, but its success depends on thoughtful integration and continuous validation.
I believe that companies and organisations should embrace this new technology. AI is here to stay, and not becoming AI-native could exclude companies and organisations from future growth and success.
However, I see AI as a major accelerator—not a replacement—for human expertise. I advocate for combining advanced algorithms with domain knowledge to deliver solutions that are both innovative and reliable.
AI is a catalyst for progress, but it is up to us—leaders, innovators, and practitioners—to guide its evolution with integrity and purpose. Together, we can harness the power of AI to build a smarter, more sustainable world.
