Over the next 2-5 years, AI will fundamentally shape how Earth observation moves from collecting images to building planetary intelligence. At Pixxel, we operate at a planetary scale, capturing large volumes of high-resolution satellite data across space and time. The real challenge, and opportunity, is not access to imagery, but making sense of it quickly, consistently, and in ways that drive real-world decisions.Working with Earth observation at a planetary scale requires systems that can recognise patterns, change, and signal across vast amounts of data. AI makes that possible. It allows us to move beyond static images and start understanding how the planet is changing; subtly, continuously, and often before those changes are visible on the ground. This matters across use cases: spotting early signs of crop stress, identifying small methane leaks, tracking changes in water quality, or monitoring environmental degradation as it happens rather than after the fact.In the near term, AI’s role in our work is about scale and speed. Earth observation generates petabytes of complex data, spanning multiple wavelengths and time periods. AI enables continuous analysis of this data, surfacing signals and trends that would otherwise remain buried. Tasks such as change detection, anomaly identification, and classification can increasingly be automated, reducing the time between data capture and insight. Just as importantly, AI enables combining different data types, such as hyperspectral, multispectral, SAR, and others, into a more complete view of what is happening on the ground.The tasks we most want AI to handle are those that benefit from repetition and long-term pattern recognition. Monitoring large geographies over time, detecting subtle deviations from normal behaviour, and flagging areas that warrant deeper investigation are all areas where AI can operate at a much larger scale. This allows experts to focus on what matters most: interpretation, context, and decision-making.Looking ahead, one of the most exciting possibilities is for intelligence to move closer to where data is generated. As onboard compute becomes more capable, satellites will be able to analyse imagery in orbit and downlink insights rather than raw data. This could significantly reduce latency and bandwidth constraints, enabling faster responses to time-sensitive events such as environmental incidents, infrastructure failures, or natural disasters.Ultimately, what we hope AI will enable is a shift from episodic observation to continuous understanding of the planet. AI is what will turn Earth observation from a record of the past into a system that can help us anticipate change.