February 2026 Summaries

CARTO and Snowflake are collaborating to enhance spatial analytics and AI capabilities within the energy and utilities sector by integrating these technologies directly into Snowflake's data cloud. This partnership aims to unify IoT and business data to improve operational efficiency, resilience, and sustainability in a governed environment, enabling energy organizations to modernize infrastructure and achieve lower-carbon operations. By leveraging CARTO’s spatial intelligence and Snowflake’s cloud-native solutions, energy companies can perform real-time location-based data analysis and gain actionable insights without the need for additional pipelines, thereby enhancing decision-making processes across their networks. This approach not only supports the modernization of the energy sector by integrating new data sources but also aids in achieving ambitious sustainability goals by providing a scalable platform for AI-driven solutions.

CARTO and Nagarro have partnered to help enterprises transition AI projects from pilot stages to production by integrating Location Intelligence into established workflows and systems. This collaboration aims to overcome common obstacles such as workforce skills, operational readiness, and compliance that prevent AI pilots from scaling successfully. Despite growing interest in AI, many projects remain stalled, creating a "pilot graveyard" due to challenges in achieving operational readiness and governance. The partnership leverages Nagarro's AI engineering expertise and CARTO's cloud-native platform to provide enterprises with AI-ready tech stacks and production solutions that incorporate spatial insights, enhancing decision-making in crucial areas like supply chain coordination and customer engagement. This approach is designed to facilitate the safe and effective scaling of AI initiatives, delivering measurable business value by embedding geographic context into everyday decision-making processes.

Geospatial foundation models, under the broader umbrella of GeoAI, are gaining traction in geospatial analytics, driven by advances in large-scale representation learning and models like Google's Population Dynamics Foundation Model. Despite promising developments, challenges remain in scaling data access, designing robust architectures, and integrating models into analytical workflows. The recent Geospatial Foundation Models Workshop in Barcelona, organized by CARTO and the Barcelona Supercomputing Center, brought together industry and academic experts to discuss these issues, highlighting the diversity of approaches and the importance of bridging the gap between experimental results and practical applications. Participants emphasized the need for scalable retrieval strategies, interpretability, and the development of benchmarks to assess models' readiness for deployment. CARTO's efforts focus on integrating third-party embeddings, developing analytical tools, and collaborating with the Barcelona Supercomputing Center to advance population dynamics models. The workshop underscored the necessity of sustained collaboration to transition from exploratory phases to mature applications in geospatial foundation models.

CARTO has integrated its AI-driven geospatial analytics platform, Agentic GIS, with Oracle Generative AI, allowing users to perform scalable and secure geospatial AI analysis within Oracle Cloud. This integration enhances the capabilities of spatial analysis by transforming traditional workflows into more accessible, automated processes that utilize AI agents, which can execute complex geospatial tasks based on natural language inputs. The integration ensures data governance and security, as all operations are confined within the Oracle environment, leveraging Oracle's existing AI models and infrastructure. This collaboration aims to democratize advanced geospatial insights, making them available across organizations without the need for additional environments or external data transfers. Additionally, Oracle's Agent Factory platform complements this by offering a no-code solution to build and deploy intelligent agents that can utilize these geospatial workflows, further expanding the reach and efficiency of spatial analysis. This development is part of a broader trend within spatial analytics to move beyond static representations to more dynamic and interactive systems that can provide deeper insights and support faster decision-making processes.

AI, cloud-native tools, and evolving skills are significantly transforming the spatial analytics industry as it approaches 2026, with insights drawn from over 200 geospatial experts. AI, while not replacing spatial analytics, serves as a force multiplier by automating repetitive tasks, allowing experts to focus on critical aspects like question formulation and decision-making, although organizational adoption is uneven. Cloud-native spatial analytics has become essential, with 68.5% of organizations conducting their analyses in the cloud, yet tool fragmentation hinders integration and efficiency. Geospatial expertise is increasingly valuable, with challenges in hiring prompting organizations to empower non-experts through self-service tools and AI-driven systems to extend expert knowledge. These trends suggest a future where spatial analytics is deeply embedded in strategic decision-making, necessitating a focus on operationalizing analytics with robust data security and integration.