Partnerships come in several forms, each offering unique value depending on the goals and resources of the organizations involved. Here are a few prominent types when it comes to EO: –Focus research organizations, which involve collaboration between research institutions and private companies, and drive advancements in technology (e.g. foundation models) to help solve pressing environmental issues by combining academic expertise with commercial business processes. For example, the European Organization for Nuclear Research (CERN) recently partnered with EnduroSat, NTU Athens and AGENIUM Space to apply CERN’s existing AI capabilities to enable real-time data filtering onboard EO satellites. –A classic yet powerful model, public-private partnerships (PPPs), bring together government agencies and private companies to co-develop EO solutions. These can unlock funding or access to government-led EO initiatives, reducing the burden on internal resources while enhancing the organization’s capacity to focus on core business operations. For this model, within an EO context, governments often provide access to publicly funded data sources (such as from NASA or ESA satellites), while private entities offer technical expertise, data analytics and commercialization pathways. The Carbon Mapper coalition exemplifies this type of partnership. Backed by philanthropy, the coalition brings together NASA’s JPL, Planet, California Air Resources Board, University of Arizona, Arizona State University and RMI – along with funders, including High Tide Foundation, Bloomberg Philanthropies and the Grantham Foundation for the Protection of the Environment, among others. –Data-sharing partnerships focus on the mutual exchange of data between organizations to enhance EO capabilities. These partnerships can involve agreements where entities share satellite imagery, sensor data or analytical insights to create a more comprehensive dataset. Partnerships to acquire, validate and ground-truth EO data can be crucial for building reference or training datasets. This can be achieved by engaging with customers, citizen science campaigns or local universities. Moreover, organizations possessing unique datasets but lacking in EO or AI expertise can significantly enhance their capabilities. By forming alliances with specialized technology companies, these datasets can be transformed into powerful training data, thereby unlocking new potential and driving innovation. –Consortia allow multiple organizations – often across sectors – to join forces to tackle a common goal related to EO. These consortia may focus on specific domain areas like climate resilience, renewable energy planning or agricultural monitoring, leveraging EO data. The complexity of establishing and managing partnerships means organizations must diligently structure these collaborations for alignment on decision rights, data-sharing protocols, ownership of intellectual property (e.g. of proprietary algorithms or processing methods) and incentives (e.g. short- term output vs long-term return). Developing a change management plan The ingestion of spatial data may introduce new input into decision-making, organizational processes and frameworks and its successful integration requires an intentional approach to anticipate and address change. This not only involves training staff to analyse and visualize spatial data but also fostering a culture that embraces environmental intelligence and spatial data-driven decision-making. It is also crucial for decision-makers to understand the limitations of spatial data, such as cloud cover in optical data, which can obscure important information, and low coverage or less frequent temporal resolutions, which may affect the timeliness and completeness of insights. While teams can learn how to use EO insights without becoming EO experts, organizations will likely need to build some internal capacity over time should they look to scale. Executives should invest in capacity building through trainings, workshops and partnerships, to develop EO literacy at all levels. Measuring impact and iterating To monitor the success of EO adoption, tailored key performance indicators (KPIs) can measure the effectiveness of EO data in achieving business and sustainability goals. These success metrics should be established early in the process and scaled appropriately to the issue being solved. KPIs could include cost savings from operational efficiencies, reductions in environmental footprint and improvements in regulatory compliance. Regular reviews of EO data performance, alongside feedback from end users benefiting from the insights, will allow an organization to adjust its strategy and continuously improve EO implementation. By iterating on successes and learning from challenges, organizations can refine their EO strategies over time, achieving long-term value. Considering these factors enables executives to craft an EO strategy that is not only aligned with their organization’s goals but is also equipped to drive lasting, scalable impact. The Executive’s Playbook on Earth Observation: Strategic Insights for a Changing Planet 28

The Executive%E2%80%99s Playbook on Earth Observation